CN115564191A - Power distribution network planning method considering green certificate transaction and carbon transaction - Google Patents

Power distribution network planning method considering green certificate transaction and carbon transaction Download PDF

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CN115564191A
CN115564191A CN202211137124.5A CN202211137124A CN115564191A CN 115564191 A CN115564191 A CN 115564191A CN 202211137124 A CN202211137124 A CN 202211137124A CN 115564191 A CN115564191 A CN 115564191A
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赵远凉
史虎军
杨强
谭斌
仇伟杰
马鑫
郭明
石启宏
杨廷榜
韦锋
张开勇
蔡永翔
肖小兵
张锐锋
何晔
黄宁钰
申炜
周宗国
苏剑锋
卢森微
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Abstract

The invention discloses a power distribution network planning method considering green certificate transaction and carbon transaction, which comprises the following steps: constructing a novel power distribution network planning and simulated operation double-layer planning model taking new energy as a main body based on green certificate trading and carbon trading mechanisms; the double-layer planning model comprises an investment decision layer and a simulation operation layer which are mutually transformed; converting the double-layer model into a single-layer nonlinear programming model by adopting a single-layer conversion method; and linearizing the nonlinear part in the single-layer nonlinear model by using a Big-M method to form a single-layer mixed integer linear programming problem, and solving the problem. On the basis of a three-section green certificate transaction model and a two-section carbon transaction model, average unit power supply quantity CO of a power distribution network is introduced 2 Emission intensity combines green certificate transaction and carbon transaction to the influence of novel distribution network planning, and the whole new forms of energy installation of the novel distribution network that obtains accounts for than, unit power supply carbon emission intensity, distribution network gross income all have great promotion.

Description

Power distribution network planning method considering green certificate transaction and carbon transaction
Technical Field
The invention relates to the technical field of power distribution networks, in particular to a power distribution network planning method considering green certificate transaction and carbon transaction.
Background
Global warming has become one of the major problems facing human society, driving CO 2 Emission reduction has become a consensus in major countries around the world. China promises to achieve carbon peak reaching 2030 years ago, and achieves the aim of carbon neutralization, called 'double carbon' for short, 2060 years ago. The largest CO of the power industry 2 One of the emissions sources, to achieve the "double carbon" goal, the ninth meeting of the central financial commission in 3 months, 2021, proposed the construction of a new energy-based power system. Under the kyoto protocol, the carbon market is taken as an important way for energy conservation and emission reduction, and has made certain achievements internationally promoting carbon emission reduction. China also strives to develop own carbon markets, 7 trial carbon markets start to operate in 2013, 12 in 2017, a national unified carbon trading system is established, and the national carbon market is formally on-line trades in 2021 in 7, wherein the national carbon markets comprise green certificate trades, carbon trading mechanisms and the like, and the green certificate trades and the carbon trading mechanisms respectively constrain an electric power system from the two aspects of new energy power generation and carbon emission. Novel distribution network planning needs to adapt to green certificate, carbon market development as the important link of novel electric power system construction, reduces carbon emission, increases the return on investment, so the influence of green certificate transaction and carbon transaction is keenly needed to be considered in novel distribution network planning.
According to the invention, the distribution network meeting the requirements of the test case is optimally designed by researching the influence of different carbon transaction prices and natural gas prices, but when the natural gas price is too low, the influence of the carbon transaction price will lose efficacy and can not reach CO 2 And (5) emission reduction effect. In the future, distributed power generation of a novel power distribution network participates in a green certificate market and a carbon trading market, so that when the novel power distribution network is planned and operated in a simulated mode, green certificate trading and carbon trading are considered at the same time, and the method has important research significance.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the technical problem solved by the invention is as follows: low planning economic benefit and high carbon emission intensity.
In order to solve the technical problems, the invention provides the following technical scheme: constructing a novel power distribution network planning and simulated operation double-layer planning model taking new energy as a main body based on green certificate transaction and carbon transaction mechanisms; the double-layer planning model comprises an investment decision layer and a simulation operation layer, wherein the investment decision layer and the simulation operation layer are mutually transformed; converting the double-layer model into a single-layer nonlinear programming model by adopting a single-layer conversion method;
and linearizing the nonlinear part in the single-layer nonlinear model by using a Big-M method to form a single-layer mixed integer linear programming problem, and solving the problem.
As a preferred scheme of the power distribution network planning method considering green certificate trading and carbon trading, the method comprises the following steps: the calculation of the two-layer planning model includes,
calculating the comprehensive investment decision cost and the simulation operation cost of the novel power distribution network by using the investment decision objective function and the simulation operation objective function;
Figure BDA0003851860020000021
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003851860020000022
a symbol representing an investment decision objective function,
Figure BDA0003851860020000023
representSimulating the sign of the running objective function, X Inv Representing investment decision variables, X Ope Representing simulated operating variables, C Inv Represents the comprehensive investment decision cost of the novel power distribution network C Ope And the comprehensive simulation operation cost of the novel power distribution network is represented, G (-) and H (-) represent investment decision constraints, and G (-) and H (-) represent simulation operation constraints.
As a preferred scheme of the power distribution network planning method considering green certificate trading and carbon trading, the method comprises the following steps: novel comprehensive investment decision-making cost C of power distribution network Inv The calculation of (a) includes that,
Figure BDA0003851860020000024
Figure BDA0003851860020000025
wherein G is WT 、G PV 、G HT 、G MT Respectively representing the investment candidate node sets G of wind power, photovoltaic, water turbine and gas turbine BAT Representing a candidate node set of energy storage investment, sigma representing an annual investment equivalent coefficient, sigma WT 、σ PV 、σ HT 、σ MT Expressing the annual investment equivalent coefficient, sigma, of wind, photovoltaic, hydro turbines, gas turbines BAT Representing the equivalent coefficient of the investment of the energy storage year, a representing the discount rate y representing the service life of the equipment, c WT 、c PV 、c HT 、c MT Expressing the unit investment price, n, of wind, photovoltaic, hydro turbines, gas turbines j,WT 、n j,PV 、n j,HT 、n j,MT Respectively representing the number of wind power, photovoltaic and water power turbines and gas turbines of the j node, c BAT Represents the unit investment price of energy storage, n j,BAT Representing the amount of energy stored at the jth node.
As a preferred scheme of the power distribution network planning method considering green certificate trading and carbon trading, the method comprises the following steps: novel comprehensive simulation operation of power distribution networkCost C Ope Including energy costs, gas turbine operating costs C MT Compensation cost C associated with IBDR IBDR Loss cost C LOSS Penalty cost for load fluctuation C LoadGap Carbon transaction cost C ED And green certificate revenue C G Wherein the energy cost comprises the cost of electricity purchase
Figure BDA0003851860020000031
And income of selling electricity
Figure BDA0003851860020000032
Novel distribution network comprehensive simulation operation cost C Ope The calculation of (a) includes that,
Figure BDA0003851860020000033
Figure BDA0003851860020000034
Figure BDA0003851860020000035
Figure BDA0003851860020000036
Figure BDA0003851860020000037
Figure BDA0003851860020000038
Figure BDA0003851860020000039
Figure BDA00038518600200000310
where, T represents the set of all time periods,
Figure BDA00038518600200000311
the price of the electricity purchased is shown,
Figure BDA00038518600200000312
indicating the price of electricity sold, P t buy Indicating purchase power, P t sale Indicating power sold, λ IBDR Representing the IBDR compensation unit price, E representing the set of all branches in the distribution network, c Loss The unit price of the loss penalty of the network is expressed,
Figure BDA00038518600200000313
representing the square I of the current in branch ij at time t 2 ij,t ，r ij Denotes the branch resistance, G MT Representing a set of candidate investment nodes, G, of a gas turbine Load Representing a set of load nodes, G BAT A set of energy storage candidate investment nodes is represented,
Figure BDA00038518600200000314
representing the fuel cost of gas turbine j during time t,
Figure BDA00038518600200000315
representing the startup cost of gas turbine j during time t,
Figure BDA00038518600200000316
represents the cost per start, k, of the gas turbine j j Represents the maintenance cost per unit of electric energy of the gas turbine j,
Figure BDA00038518600200000317
gas turbine power generation, U, representing node j j (t) represents the starting and stopping state of the unit j in the period t, the value 0 represents the shutdown, 1 represents the startup, c LoadGap A load fluctuation penalty is indicated and is,
Figure BDA00038518600200000318
represents the load demand, P, after participation in DR of the jth node at time t j,t,BAT,Cha Represents the energy storage charge amount of the jth node at the moment t, P j,t,BAT,Dis Represents the energy storage and discharge quantity, P, of the jth node at the moment t Ave Representing the mean load of the distribution network.
As a preferred scheme of the power distribution network planning method considering green certificate trading and carbon trading, the method comprises the following steps: the novel power distribution network planning constraint comprises an investment decision layer constraint and a simulation operation layer constraint;
the investment decision level constraints include,
wind power, photovoltaic, water turbine, gas turbine and energy storage which can be accessed by each node are limited, and the investment constraints of wind, light, water and gas and energy storage are as follows:
Figure BDA0003851860020000041
wherein N is j,WT 、N j,PV 、N j,HT 、N j,MT Respectively representing the upper limit of the investment quantity of the wind power, the photovoltaic and the water power turbine and the gas turbine of the jth node, wherein the investment capacities of the wind power, the photovoltaic and the water power turbine and the gas turbine are integral multiples of the capacity of a single machine, and N is j,BAT And the upper limit of the energy storage investment quantity of the jth node is shown, and the energy storage investment capacity is also an integral multiple of the single machine capacity.
As a preferred scheme of the power distribution network planning method considering green certificate trading and carbon trading, the method comprises the following steps: the simulation operation layer constraints comprise a distribution network second-order cone relaxation power flow constraint, a distribution network safety constraint, a power generation equipment capacity constraint, a power generation power constraint, a gas turbine constraint, an energy storage operation constraint, a large power grid electricity purchasing and selling constraint, a distribution network energy balance constraint, a green certificate quota and green certificate selling price constraint, a carbon emission intensity constraint and a green certificate transaction and carbon transaction amount constraint;
the acquisition of the power distribution network second-order cone relaxation power flow constraint comprises the following steps,
the method comprises the following steps of (1) constraining power distribution network power flow by adopting distflow branch power flow, and relaxing an original power flow model by adopting a second-order cone relaxation (SOCR) technology;
for branches ij and jk, setting Λ (j) as a starting point set taking node j as an end point, Ω (j) as an end point set taking node j as an end point, and G E For the set of all the nodes of the distribution network,
Figure BDA0003851860020000042
Figure BDA0003851860020000043
Figure BDA0003851860020000044
wherein i, j ∈ G E ，
Figure BDA0003851860020000045
And Q ij,t The active power and the reactive power of the branch circuit ij participating in DR at the time t are shown, and the reactive power is not influenced if the branch circuit is supposed to participate in DR,
Figure BDA0003851860020000046
represents the active power P after branch jk participates in DR at time t j,t,WT 、P j,t,PV 、P j,t,HT Representing the generated power of the wind, photovoltaic and water turbine of the node j at the moment t, G WT 、G PV 、G HT Representing a set of investment candidate nodes, x, for wind, photovoltaic, hydro turbines ij The reactance of the branch ij is represented,
Figure BDA0003851860020000047
and
Figure BDA0003851860020000048
represents the time t nodes i andthe voltage square of j;
the obtaining of the power distribution network security constraints includes,
Figure BDA0003851860020000049
wherein, U j,max 、U j,min Respectively representing the upper limit and the lower limit, I, of the voltage amplitude of the grid node j ij,max Represents the upper limit of the current amplitude of branch ij, I ij,t Representing the current of the branch ij at the time t;
the obtaining of the power plant capacity constraint includes,
Figure BDA0003851860020000051
wherein the content of the first and second substances,
Figure BDA0003851860020000052
and
Figure BDA0003851860020000053
representing the minimum and maximum capacities of the wind, photovoltaic and hydro-pneumatic installation n,
Figure BDA0003851860020000054
represents the capacity of device n;
the obtaining of the generated power constraint may include,
Figure BDA0003851860020000055
wherein the content of the first and second substances,
Figure BDA0003851860020000056
which represents the power generated by the power generator,
Figure BDA0003851860020000057
which represents the minimum output power of the device,
Figure BDA0003851860020000058
represents the maximum output power of the device;
setting up
Figure BDA00038518600200000521
Figure BDA00038518600200000510
Wherein, delta n Representing a binary variable determining the operating state of the device n,
Figure BDA00038518600200000511
representing the minimum output power coefficient, P, of the device n t E-n Representing the n output, ξ of the plant n Representing the linearized auxiliary variable.
As a preferred scheme of the power distribution network planning method considering green certificate trading and carbon trading, the method comprises the following steps: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
the obtaining of the gas turbine engine constraints includes,
Figure BDA00038518600200000512
Figure BDA00038518600200000513
Figure BDA00038518600200000514
Figure BDA00038518600200000515
wherein the content of the first and second substances,
Figure BDA00038518600200000516
respectively representing the minimum output and the maximum output of the gas turbine j,
Figure BDA00038518600200000517
respectively showing the downward climbing speed and the upward climbing speed of the gas turbine,
Figure BDA00038518600200000518
respectively representing the continuous startup state time and shutdown state time of the unit j in the period T, T j U ,
Figure BDA00038518600200000519
Respectively representing the minimum continuous startup time and shutdown time of the unit j;
the obtaining of the energy storage operating constraint may include,
Figure BDA00038518600200000520
wherein, P t,Cha 、P t,Dis Respectively representing stored energy charging power, discharging power, delta Cha 、δ Dis Respectively representing the binary variable of energy storage charging and the binary variable of discharging,
Figure BDA0003851860020000061
represents the maximum charge and discharge power, E min 、E max Respectively representing the minimum capacity and the maximum capacity of energy storage;
the acquisition of the power purchasing and selling constraint of the large power grid comprises the following steps,
Figure BDA0003851860020000062
wherein, G max Representing the maximum capacity of the distribution network to participate in energy exchange,
Figure BDA0003851860020000063
representing the purchase and sale capacity of the large power grid;
the acquisition of the power distribution network energy balance constraint includes,
the distribution network should satisfy energy balance, which can be expressed as:
Figure BDA0003851860020000064
δ buy +δ sale ＝{0,1}
the acquisition of the green license quota and the green license selling price constraint comprises,
Figure BDA0003851860020000065
A≥30％
wherein A represents a new energy quota coefficient, namely the part of the electricity supply of a certain year regulated by the government passes through the new energy supply, the new distribution network of the invention requires a quota coefficient higher than 30 percent, and E m Represents the power supply in the mth year, MW h, k g Representing a quantization factor, i.e. the number of new energy quotas is quantized to the number of green certificates, k g =1, home/MW · h, E mn Represents the generating capacity of the new energy n generator in the mth year, S re It is indicated that the generator belongs to a new energy source,
Figure BDA0003851860020000066
representing the number of green certificates that can be acquired per renewable energy source sent by the n generators,
Figure BDA0003851860020000067
and
Figure BDA0003851860020000068
respectively representing the number of green certificates bought and sold in the mth year;
Figure BDA0003851860020000069
Figure BDA00038518600200000610
wherein the content of the first and second substances,
Figure BDA00038518600200000611
the lower limit of the green license price is shown,
Figure BDA00038518600200000612
represents the upper limit of the green license price, s l Indicating the electricity price of the first green certificate on the internet, s c Indicating the price of electricity of local thermal power on-line marker post l Represents the first new energy conversion rate, h l Indicates the first new energy financial subsidy payment period, d l And the fact that the amount of the new energy financial subsidy of the first category is delayed for the payment period is shown.
As a preferred embodiment of the power distribution network planning method considering green certificate trading and carbon trading, the method comprises the following steps: also comprises the following steps of (1) preparing,
the acquisition of the carbon emission intensity constraint may include,
Figure BDA0003851860020000071
Figure BDA0003851860020000072
wherein the content of the first and second substances,
Figure BDA0003851860020000073
the total power consumption of the distribution network in the mth year is shown,
Figure BDA0003851860020000074
represents the average unit power supply quantity CO of the power distribution network in the mth year 2 Emission intensity, E q,m CO indicating mth year of distribution network 2 A total emission allowance limit value that is,
Figure BDA0003851860020000075
and
Figure BDA0003851860020000076
respectively representing CO bought and sold in the mth year of the distribution network 2 Discharge capacity;
the acquisition of the green transaction and carbon transaction amount constraints includes,
Figure BDA0003851860020000077
wherein the content of the first and second substances,
Figure BDA0003851860020000078
and
Figure BDA0003851860020000079
representing the number of green certificates bought and sold in the m-th year respectively,
Figure BDA00038518600200000710
and
Figure BDA00038518600200000711
respectively representing CO bought and sold in the mth year of the distribution network 2 The amount of discharge.
As a preferred scheme of the power distribution network planning method considering green certificate trading and carbon trading, the method comprises the following steps: the mutual transformation of the investment decision layer and the simulation operation layer comprises,
and the investment decision layer transmits planning information to the simulation operation layer, and the operation result of the simulation operation layer influences the investment decision layer.
As a preferred embodiment of the power distribution network planning method considering green certificate trading and carbon trading, the method comprises the following steps: the step of converting the two-layer model into a single-layer non-linear programming model comprises,
and converting the simulation operation layer model into a constraint condition of the investment decision layer model by adopting a single-layer conversion method through constructing a Lagrange function of the simulation operation layer model, mutually converting the KT condition based on the K of the simulation operation layer model and the simulation operation layer, and converting the double-layer model into a single-layer nonlinear programming model.
The invention has the advantages of: on the basis of a three-section green certificate transaction model and a two-section carbon transaction model, average unit power supply quantity CO of a power distribution network is introduced 2 Emission intensity combines green certificate transaction and carbon transaction to the influence of novel distribution network planning, adds the effect of demand response simultaneously, compares the influence of considering three alone, can see from the planning result: the whole new forms of energy of novel distribution network installation accounts for than, and unit power supply carbon emission intensity, distribution network gross income all have great promotion. The carbon emission intensity of unit power supply basically achieves the aim of 'carbon neutralization' of the distribution network, and can simultaneously obtain profits by selling redundant green certificate quotas and carbon quotas.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a diagram of objective functions and decision contents of a planning model of a power distribution network planning method considering green certification trading and carbon trading according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating processing and solving of a model of a power distribution network planning method considering green license transaction and carbon transaction according to an embodiment of the present invention;
fig. 3 is a system diagram of an IEEE33 node of a model of a power distribution network planning method considering green license transaction and carbon transaction according to an embodiment of the present invention;
fig. 4 is a diagram of a typical daily simulation operation result of a model of a power distribution network planning method considering green certification trading and carbon trading according to an embodiment of the present invention;
fig. 5 is a diagram showing details of a simulation operation result of a model of a power distribution network planning method considering green license transaction and carbon transaction according to an embodiment of the present invention;
fig. 6 is a diagram of simulation operation results of ES and IBDR of a power distribution network planning method considering green license transaction and carbon transaction according to an embodiment of the present invention;
fig. 7 is a diagram illustrating an influence change of a green certificate transaction and a carbon transaction price change on a power distribution network planning method in a model in which the green certificate transaction and the carbon transaction are considered according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention more comprehensible, embodiments accompanying figures of the present invention are described in detail below, and it is apparent that the described embodiments are a part, not all or all of the embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not necessarily enlarged to scale, and are merely exemplary, which should not limit the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
Referring to fig. 1 to 2, in an embodiment of the present invention, a method for planning a power distribution network in consideration of a green certificate transaction and a carbon transaction is provided, including:
s1: a novel power distribution network planning and simulation operation double-layer planning model taking new energy as a main body is constructed based on green certificate transaction and carbon transaction mechanisms. It should be noted that:
the calculation of the two-layer planning model includes,
calculating the comprehensive investment decision cost and the simulation operation cost of the novel power distribution network by using the investment decision objective function and the simulation operation objective function;
Figure BDA0003851860020000091
wherein the content of the first and second substances,
Figure BDA0003851860020000092
a symbol representing an investment decision objective function,
Figure BDA0003851860020000093
representing symbols of simulated operating objective function, X Inv Representing investment decision variables, X Ope Representing simulated operating variables, C Inv Represents the comprehensive investment decision cost of the novel power distribution network C Ope And the comprehensive simulation operation cost of the novel power distribution network is represented, G (-) and H (-) represent investment decision constraints, and G (-) and H (-) represent simulation operation constraints.
Novel comprehensive investment decision cost C of power distribution network Inv The calculation of (a) includes that,
Figure BDA0003851860020000101
Figure BDA0003851860020000102
wherein G is WT 、G PV 、G HT 、G MT Respectively representing the investment candidate node sets G of wind power, photovoltaic, water turbine and gas turbine BAT Representing a candidate node set of energy storage investment, sigma representing an annual investment equivalent coefficient, sigma WT 、σ PV 、σ HT 、σ MT Expressing the annual investment equivalent coefficient, sigma, of wind power, photovoltaic, water power turbines, gas turbines BAT Representing the equivalent coefficient of the investment of the energy storage year, a representing the discount rate y representing the service life of the equipment, c WT 、c PV 、c HT 、c MT Representing the unit investment price, n, of wind, photovoltaic, hydro turbines, gas turbines j,WT 、n j,PV 、n j,HT 、n j,MT Respectively representing the number of wind power, photovoltaic and water power turbines and gas turbines of the j node, c BAT Representing the unit investment price of energy storage, n j,BAT Representing the amount of energy stored at the jth node.
Novel comprehensive simulation operation cost C of power distribution network Ope Including energy costs, gas turbine operating costs C MT Compensation cost C associated with IBDR IBDR Loss cost C LOSS Penalty cost for load fluctuation C LoadGap Carbon transaction cost C ED And green certificate revenue C G Wherein the energy cost comprises the cost of electricity purchase
Figure BDA0003851860020000103
And income of selling electricity
Figure BDA0003851860020000104
Novel comprehensive simulation operation cost C of power distribution network Ope The calculation of (a) includes that,
Figure BDA0003851860020000105
Figure BDA0003851860020000106
Figure BDA0003851860020000107
Figure BDA0003851860020000108
Figure BDA0003851860020000109
Figure BDA00038518600200001010
Figure BDA00038518600200001011
Figure BDA00038518600200001012
where T represents the set of all time periods,
Figure BDA00038518600200001013
the price of the electricity purchased is shown,
Figure BDA00038518600200001014
indicating the price of electricity sold, P t buy Indicating purchase power, P t sale Indicating the power sold, λ IBDR Representing the IBDR compensation unit price, E representing the set of all branches in the distribution network, c Loss The unit price of the loss penalty of the network is expressed,
Figure BDA00038518600200001015
representing the square I of the current in branch ij at time t 2 ij,t ，r ij Denotes the branch resistance, G MT Representing a set of candidate investment nodes, G, of a gas turbine Load Representing a set of load nodes, G BAT Representing a set of energy storage candidate investment nodes,
Figure BDA0003851860020000111
representing the fuel cost of gas turbine j during time t,
Figure BDA0003851860020000112
representing the startup cost of gas turbine j during time t,
Figure BDA0003851860020000113
represents the cost per start, k, of the gas turbine j j Represents the maintenance cost per unit of electric energy of the gas turbine j,
Figure BDA0003851860020000114
gas turbine power generation, U, representing node j j (t) represents the starting and stopping state of the unit j in the t period, the value of the starting and stopping state represents 0, 1 represents starting, and c represents LoadGap A load fluctuation penalty is indicated and is,
Figure BDA0003851860020000115
represents the load demand, P, of the j-th node at time t after participation in DR j,t,BAT,Cha Indicates the time tEnergy storage charge amount of jth node, P j,t,BAT,Dis Represents the energy storage and discharge amount, P, of the jth node at the moment t Ave Representing the mean load of the distribution network.
The novel power distribution network planning constraint comprises an investment decision layer constraint and a simulation operation layer constraint;
the investment decision level constraints include,
wind power, photovoltaic, water turbine, gas turbine and energy storage which can be accessed by each node are limited, and the investment constraints of wind, light, water and gas and energy storage are as follows:
Figure BDA0003851860020000116
wherein N is j,WT 、N j,PV 、N j,HT 、N j,MT Respectively representing the upper limit of the investment quantity of the wind power, the photovoltaic and the water power turbine and the gas turbine of the jth node, wherein the investment capacities of the wind power, the photovoltaic and the water power turbine and the gas turbine are integral multiples of the capacity of a single machine, and N is j,BAT And the upper limit of the energy storage investment quantity of the jth node is shown, and the energy storage investment capacity is also an integral multiple of the single machine capacity.
The simulation operation layer constraints comprise a power distribution network second-order cone relaxation power flow constraint, a power distribution network safety constraint, a power generation equipment capacity constraint, a power generation power constraint, a gas turbine constraint, an energy storage operation constraint, a large power grid electricity purchasing and selling constraint, a power distribution network energy balance constraint, a green certificate quota and green certificate selling price constraint, a carbon emission intensity constraint and a green certificate transaction and carbon transaction amount constraint;
the acquisition of the relaxed power flow constraint of the second-order cone of the power distribution network comprises the following steps,
the method comprises the following steps of (1) constraining power distribution network power flow by adopting distflow branch power flow, and relaxing an original power flow model by adopting a second-order cone relaxation (SOCR) technology;
for branches ij and jk, set Λ (j) as a starting point set with node j as an end point, Ω (j) as an end point set with node j as an end point, G E For the set of all the nodes of the distribution network,
Figure BDA0003851860020000117
Figure BDA0003851860020000121
Figure BDA0003851860020000122
wherein i, j ∈ G E ，
Figure BDA0003851860020000123
And Q ij,t The active power and the reactive power of the branch circuit ij participating in DR at the time t are shown, and the reactive power is not influenced if the branch circuit is supposed to participate in DR,
Figure BDA0003851860020000124
represents the active power P after branch jk participates in DR at time t j,t,WT 、P j,t,PV 、P j,t,HT Representing the generated power of the wind, photovoltaic and water turbine of the node j at the moment t, G WT 、G PV 、G HT Representing a set of investment candidate nodes, x, for wind, photovoltaic, hydro turbines ij The reactance of the branch ij is represented,
Figure BDA0003851860020000125
and
Figure BDA0003851860020000126
represents the square of the voltage at nodes i and j at time t;
the acquisition of the safety constraints of the power distribution network includes,
Figure BDA0003851860020000127
wherein, U j,max 、U j,min Respectively representing the upper limit and the lower limit, I, of the voltage amplitude of the grid node j ij,max Represents the upper limit of the current amplitude of branch ij, I ij, Representing the current of the branch ij at the moment t;
the acquisition of the power plant capacity constraint includes,
Figure BDA0003851860020000128
wherein the content of the first and second substances,
Figure BDA0003851860020000129
and
Figure BDA00038518600200001210
representing the minimum and maximum capacities of the wind, photovoltaic and hydro-pneumatic installation n,
Figure BDA00038518600200001211
represents the capacity of device n;
the acquisition of the generated power constraint includes,
Figure BDA00038518600200001212
wherein the content of the first and second substances,
Figure BDA00038518600200001213
which represents the power of the electricity generated,
Figure BDA00038518600200001214
which represents the minimum output power of the device,
Figure BDA00038518600200001215
represents the maximum output power of the device;
setting up
Figure BDA00038518600200001216
Figure BDA00038518600200001217
Wherein, delta n Indication deviceGiven the binary variable of the operating state of the plant n,
Figure BDA00038518600200001218
representing the minimum output power coefficient, P, of the device n t E-n Representing the n output, xi of the device n Representing the linearized auxiliary variable.
Comprises the steps of (a) preparing a substrate,
the acquisition of the gas turbine constraints includes,
Figure BDA00038518600200001219
Figure BDA00038518600200001220
Figure BDA0003851860020000131
Figure BDA0003851860020000132
wherein the content of the first and second substances,
Figure BDA0003851860020000133
respectively representing the minimum output and the maximum output of the gas turbine j,
Figure BDA0003851860020000134
respectively representing the downward climbing speed and the upward climbing speed of the gas turbine,
Figure BDA0003851860020000135
respectively representing the continuous startup state time and shutdown state time of the unit j in the period T, T j U ,、
Figure BDA0003851860020000136
Respectively representing the minimum continuous startup time and shutdown time of the unit j;
the acquisition of the energy storage operation constraints includes,
Figure BDA0003851860020000137
wherein, P t,Cha 、P t,Dis Respectively representing the stored energy charging power, the discharging power, delta Cha 、δ Dis Respectively representing the binary variable of energy storage charging and the binary variable of discharging,
Figure BDA0003851860020000138
represents the maximum charge and discharge power, E min 、E max Respectively representing the minimum capacity and the maximum capacity of energy storage;
the acquisition of the power purchasing and selling constraint of the large power grid comprises the following steps,
Figure BDA0003851860020000139
wherein, G max Represents the maximum capacity of the distribution network to participate in energy exchange,
Figure BDA00038518600200001310
representing the purchase and sale capacity of the large power grid;
the acquisition of the energy balance constraints of the distribution network includes,
the power distribution network should satisfy energy balance, which can be expressed as:
Figure BDA00038518600200001311
δ buy +δ sale ＝{0,1}
acquisition of the green license quota and green license selling price constraints includes,
Figure BDA00038518600200001312
A≥30％
wherein, A represents a new energy quota coefficient, namely, in the supplied electric quantity of a certain year regulated by the government, through the new energy supply part, the new distribution network of the invention requires the quota coefficient to be higher than 30%, E m Represents the supply power of the m-th year, MW h, k g Representing a quantization factor, i.e. the number of new energy quotas is quantized to the number of green certificates, k g =1, home/MW · h, E mn Represents the generating capacity of the new energy n generator in the mth year, S re It is indicated that the generator belongs to a new energy source,
Figure BDA00038518600200001313
representing the number of green certificates that can be acquired per renewable energy source sent by the n generators,
Figure BDA00038518600200001314
and
Figure BDA00038518600200001315
respectively representing the number of green certificates bought and sold in the mth year;
Figure BDA0003851860020000141
Figure BDA0003851860020000142
wherein the content of the first and second substances,
Figure BDA0003851860020000143
the lower limit of the green certificate price is shown,
Figure BDA0003851860020000144
represents the upper limit of the green license price, s l Showing the first green certificate's price of electricity on the Internet c Indicating the price of electricity of local thermal power on-line marker post l Represents the first new energy conversion rate, h l Indicates the first new energy financial subsidy payment period, d l Showing that the first new energy financial subsidy is delayed in amountAnd (4) period.
Also comprises the following steps of (1) preparing,
the acquisition of the carbon emission intensity constraint includes,
Figure BDA0003851860020000145
Figure BDA0003851860020000146
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003851860020000147
the total power consumption of the distribution network in the mth year is shown,
Figure BDA0003851860020000148
represents the average unit power supply quantity CO of the power distribution network in the mth year 2 Emission intensity, E q,m CO indicating mth year of distribution network 2 A total emission allowance limit value that is,
Figure BDA0003851860020000149
and
Figure BDA00038518600200001410
respectively representing CO bought and sold in the mth year of the distribution network 2 Discharging amount;
acquisition of the green transaction and carbon transaction amount constraints includes,
Figure BDA00038518600200001411
wherein the content of the first and second substances,
Figure BDA00038518600200001412
and
Figure BDA00038518600200001413
representing the number of green certificates bought and sold in the m-th year respectively,
Figure BDA00038518600200001414
and
Figure BDA00038518600200001415
respectively representing CO bought and sold in the mth year of the distribution network 2 The amount of discharge.
S2: the double-layer planning model comprises an investment decision layer and a simulation operation layer, wherein the investment decision layer and the simulation operation layer are mutually converted. It should be noted that:
the interconversion of the investment decision layer and the simulation run layer includes,
the investment decision layer transmits planning information to the simulation operation layer, and the operation result of the simulation operation layer influences the investment decision layer.
S3: and converting the double-layer model into a single-layer nonlinear programming model by adopting a single-layer conversion method. It should be noted that:
the step of converting the two-layer model into the single-layer non-linear programming model comprises,
and converting the simulation operation layer model into a constraint condition of the investment decision layer model by constructing a Lagrange function of the simulation operation layer model by adopting a single-layer conversion method and converting the simulation operation layer model into a single-layer nonlinear programming model based on a KT (KT) condition of mutual conversion of a K investment decision layer and a simulation operation layer of the simulation operation layer model.
S4: and (3) linearizing the nonlinear part in the single-layer nonlinear model by using a Big-M method to form a single-layer mixed integer linear programming problem, and solving the problem.
According to the invention, on the basis of a three-section green certificate transaction model and a two-section carbon transaction model, the CO2 emission intensity of the average unit power supply quantity of the power distribution network is introduced, the influence of green certificate transaction and carbon transaction on the planning of the novel power distribution network is organically combined, and meanwhile, the effect of demand response is added, compared with the effect of independently considering the three, the influence of the three is shown from the planning result: the whole new forms of energy installation of novel distribution network accounts for than, and unit power supply carbon emission intensity, distribution network gross income all have great promotion. The carbon emission intensity of unit power supply basically achieves the aim of 'carbon neutralization' of the distribution network, and can obtain profits by selling redundant green certificate quotas and carbon quotas at the same time;
it can be seen that the novel power distribution network participates in green certificate trading, and the green certificate trading has a higher influence on power distribution network planning than carbon trading, mainly because the photovoltaic green certificate price is higher, the influence on power distribution network distributed photovoltaic planning capacity is larger, and the carbon trading mainly has a larger influence on power distribution network distributed gas turbine planning capacity. The example results show that the influence sensitivity of the green certificate trading price on the planning of the novel power distribution network is higher than that of the carbon trading price. The proportion of the new distribution network preferred to participate in green and carbon trading can then be further studied to obtain higher revenue and lower carbon emissions.
Example 2
Referring to the second embodiment of the present invention, different from the first embodiment, a verification test of a power distribution network planning method considering green certification transaction and carbon transaction is provided, in order to verify and explain the technical effects adopted in the method, the test results are compared by means of scientific demonstration to verify the real effects of the method.
The invention carries out planning calculation based on an IEEE33 node system modified in the southwest region, and the planning results of the novel power distribution network considering green certificate transaction and carbon transaction are shown in tables 1 and 2, so that the positions and capacities of various planned distributed power supplies can be seen; the investment cost of the fan and the water turbine is lower than that of the photovoltaic, so that the investment of the fan and the water turbine reaches the upper limit of planning of 2.5MW 3 and 10MW, the operation cost of the gas turbine is higher, the installed capacity planning only has the photovoltaic of nodes of 7MW,24 and 31 which is closer to the stored energy, and the photovoltaic output fluctuation can be reduced through the stored energy, so the planning capacity is 2.5MW, the distance between the photovoltaic of node 10 and the water turbine of node 6 is closer, the output of the water turbine is larger, and the photovoltaic planning capacity of node 10 is smaller than 1.1MW. The planned renewable energy installation ratio of the novel power distribution network reaches 77.12 percent, which is basically close to the 2060 'carbon neutralization' renewable energy installation ratio requirement 80 percent expected by experts.
Table 1: and a novel power distribution network planning result table considering green certificate transaction and carbon transaction.
Device Quantity (position) volume/MW
Energy storage 6(5) 1.08
Energy storage 5(16) 0.9
Energy storage 6(25) 1.08
Energy storage 4(33) 0.9
Wind power generation 25(3) 2.5
Wind power generation 25(17) 2.5
Wind power generation 25(21) 2.5
Photovoltaic system 11(10) 1.1
Photovoltaic system 25(24) 2.5
Photovoltaic system 25(31) 2.5
Water turbine 10(6) 10
Gas turbine 7(2) 7
Table 2: and (4) considering a power distribution network planning and simulation operation cost result table of GPCT and CET.
Cost item Investment (Wanyuan) Operation (Wanyuan)
Energy storage 1344
Wind power generation 11250
Photovoltaic system 15250
Water turbine 15000
Gas turbine 2100
IBDR 0.1261
Natural gas 2.2129
Load fluctuations 5.0027
Loss of network 1.1318
Green syndrome (wind) -1.6289
Green certificate (light) -2.3635
Carbon trading -0.0852
Main network power purchase -4.8850
Running cost 1d -0.4891
Investment cost 10y 44944
Running cost 10y -1785.22
Total cost 10y 43158.79
Electricity sales income 10y 71440.36
Gross profit 10y 28281.57
The simulation results are shown in fig. 4, 5 and 6, and compared with the load after the PBDR action, the peak-to-valley difference of the original load is 14.1195MW, the peak-to-valley difference of the load after the PBDR action is 12.0884MW, and the peak-to-valley difference is reduced by 14.39%. The peak-to-valley difference after ES and IBDR adjustment became 9.3911MW, which was reduced by 33.49% compared to the original load peak-to-valley difference. The ES is charged when the electricity rate is low (load is low) at 1. The same effect can be achieved by the same IBDR, which has no investment cost but needs operation cost.
The output of the water turbine is stable, and the power generation cost is low, so the water turbine is planned according to the maximum capacity and can be used for generating power for a long time. The wind power is large at local night and small in daytime, the photovoltaic power generation is performed in the following way of 6-00. During the night time 15.
The output of the non-water renewable energy of the power distribution network accounts for 48.94%, the output of the water-containing renewable energy accounts for 90.39%, the output of the MT accounts for 9.61%, and the total carbon emission of the novel power distribution network is 24.04 g/kW.h, which is close to zero carbon emission.
Taking traditional power distribution network planning without considering DR and green certificate trading and carbon trading as reference, considering DR and GPCT, CET and other factors as comparative examples, as shown in tables 3 and 4.
Table 3: and (5) comparing the planning schemes.
Scheme(s) DR Green certificate transaction and carbon transaction Income (Wanyuan)
1 Is free of Is free of 13638
2 Is free of Is provided with 23183
3 Is provided with Is free of 18834
4 Is provided with Is provided with 28282
Table 4: plan scheme comparison tables are based on DR.
Scheme(s) Green certificate transaction Carbon trading Income (Wanyuan)
5 Is provided with Is free of 27983
6 Is composed of Is provided with 19287
7 Is composed of Is composed of 18834
8 Is provided with Is provided with 28282
From the investment and simulation operation results in table 3, the traditional planning yield of the scheme 1, which does not take DR into account, green certification transaction (GPCT) and carbon transaction (CET) into account, is 13638 ten thousand yuan, but cannot meet the new energy consumption quota of a novel power distribution network and the average unit power supply amount CO of the power distribution network 2 Emission intensity requirements; the DR is considered in the scheme 3, and compared with the scheme 1, the overall benefit is improved by 38.10% without considering the planning of GPCT and CET; the DR is not considered in the scheme 2, and compared with the scheme 1, the overall benefit is improved by 69.99% in the planning of GPCT and CET; compared with the traditional planning of the scheme 1, the planning of the scheme 4 considering DR and GPCT and CET has the advantages of lowest planning total cost, highest total income, 28282 ten thousand yuan of income, 107.38 percent improvement of the overall income, 90.39 percent of output of water-containing renewable energy and average CO 2 The emission intensity is 24.04 g/kW.h, and the new energy consumption quota and the average unit power supply quantity CO of the novel power distribution network can be simultaneously met 2 Emission intensity requirements, distribution networks profit by selling excess green credits and carbon credits.
As can be seen from table 4, in the new distribution network, the important component of the revenue is the income of green certificate transaction and carbon transaction; the green certificate trading accounts for a higher ratio than the carbon trading, the green certificate trading income 27983 ten thousand yuan is considered in the scheme 5, the carbon trading income 19287 ten thousand yuan is considered in the scheme 6, and the former has a larger influence on the planning of the power distribution network than the latter because the quota of the carbon emission right of the power distribution network mainly based on new energy is small and the carbon trading amount is low.
The impact of green transaction (GPCT) and carbon transaction (CET) price changes on the plan is shown in fig. 7. As can be seen from fig. 7, the investment cost of the fan and the water turbine is low, the maximum capacity planning is performed in various planning processes, the photovoltaic cost is high, the photovoltaic green certificate transaction price is also high, the photovoltaic planning capacity is greatly influenced by the photovoltaic green certificate transaction price, and the photovoltaic planning capacity and the photovoltaic green certificate transaction price are in positive correlation; when the prices of GPCT and CET are from 0 to 0.9 times, the PV planning capacity is increased, and the total income is correspondingly increased; when the prices of GPCT and CET are from 0.9 time to 1 time, MT capacity planning starts to be reduced, the reason is that PV installation is increased due to the increase of green certificate transaction prices, the total income is increased faster than the income provided by MT, and the green certificate transaction has larger influence sensitivity on power distribution network planning than carbon transaction. When the prices of GPCT and CET are from 1.1 times to 1.2 times, the PV planning capacity is further increased, the income of green certificate trading is increased, the total income is increased, and MT and HT planning are further reduced; when the prices of GPCT and CET are from 1.2 times to 1.3 times, PV reaches the limit of planning capacity, cannot be increased continuously and reaches the upper limit of planning.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.

Claims (10)

1. A power distribution network planning method considering green certificate transaction and carbon transaction is characterized by comprising the following steps:
constructing a novel power distribution network planning and simulated operation double-layer planning model taking new energy as a main body based on green certificate transaction and carbon transaction mechanisms;
the double-layer planning model comprises an investment decision layer and a simulation operation layer, and the investment decision layer and the simulation operation layer are mutually transformed;
converting the double-layer model into a single-layer nonlinear programming model by adopting a single-layer conversion method;
and linearizing the nonlinear part in the single-layer nonlinear model by using a Big-M method to form a single-layer mixed integer linear programming problem, and solving the problem.
2. The method for planning a power distribution network in consideration of green license trading and carbon trading of claim 1, wherein: the calculation of the two-layer planning model includes,
calculating the comprehensive investment decision cost and the simulation operation cost of the novel power distribution network by using the investment decision objective function and the simulation operation objective function;
Figure FDA0003851860010000011
wherein the content of the first and second substances,
Figure FDA0003851860010000012
a symbol representing an investment decision objective function,
Figure FDA0003851860010000013
representing symbols of simulated operating objective function, X Inv Representing investment decision variables, X Ope Representing simulated operating variables, C Inv Represents the comprehensive investment decision cost, C, of the novel power distribution network Ope And the comprehensive simulation operation cost of the novel power distribution network is represented, G (-) and H (-) represent investment decision constraints, and G (-) and H (-) represent simulation operation constraints.
3. The method of claim 2 for planning a power distribution network in consideration of a green license transaction and a carbon transaction, wherein: novel comprehensive investment decision-making cost C of power distribution network Inv The calculation of (a) includes that,
Figure FDA0003851860010000014
Figure FDA0003851860010000015
wherein G is WT 、G PV 、G HT 、G MT Respectively represent investment candidate node sets of wind power, photovoltaic, water turbine and gas turbine, G BAT Representing a candidate node set of energy storage investment, sigma representing an annual investment equivalent coefficient, sigma WT 、σ PV 、σ HT 、σ MT Expressing the annual investment equivalent coefficient, sigma, of wind, photovoltaic, hydro turbines, gas turbines BAT Representing the equivalent coefficient of the investment of the energy storage year, a representing the discount rate y representing the service life of the equipment, c WT 、c PV 、c HT 、c MT Representing the unit investment price, n, of wind, photovoltaic, hydro turbines, gas turbines j,WT 、n j,PV 、n j,HT 、n j,MT Respectively representing the number of wind power, photovoltaic and water power turbines and gas turbines of the j node, c BAT Representing the unit investment price of energy storage, n j,BAT Representing the amount of energy stored at the jth node.
4. The method of claim 3 for planning a power distribution network in consideration of a green license transaction and a carbon transaction, wherein: novel distribution network comprehensive simulation operation cost C Ope Including energy costs, gas turbine operating costs C MT Compensation cost C associated with IBDR IBDR Loss cost C LOSS Penalty cost for load fluctuation C LoadGap Carbon transaction cost C ED And green certificate revenue C G Wherein the energy cost comprises the cost of electricity purchase
Figure FDA0003851860010000021
And income of selling electricity
Figure FDA0003851860010000022
Novel distribution network comprehensive simulation operation cost C Ope The calculation of (a) includes that,
Figure FDA0003851860010000023
Figure FDA0003851860010000024
Figure FDA0003851860010000025
Figure FDA0003851860010000026
where, T represents the set of all time periods,
Figure FDA0003851860010000027
indicating electricity purchaseThe price of the mixture is higher than the standard value,
Figure FDA0003851860010000028
indicating the price of electricity sold, P t buy Indicating the power purchase, P t sale Indicating the power sold, λ IBDR Representing the IBDR compensation unit price, E representing the set of all branches in the distribution network, c Loss The unit price of the loss penalty of the network is expressed,
Figure FDA0003851860010000029
representing the square I of the current of branch ij at time t 2 ij,t ，r ij Denotes the branch resistance, G MT Representing a set of candidate investment nodes, G, of a gas turbine Load Representing a set of load nodes, G BAT Representing a set of energy storage candidate investment nodes,
Figure FDA00038518600100000210
representing the fuel cost of gas turbine j during time t,
Figure FDA00038518600100000211
representing the startup cost of gas turbine j during time t,
Figure FDA00038518600100000212
represents the cost, k, of each start of the gas turbine j j Represents the maintenance cost per unit of electric energy of the gas turbine j,
Figure FDA00038518600100000213
gas turbine power generation, U, representing node j j (t) represents the start-stop state of the unit j in the period of t, c LoadGap A load fluctuation penalty is indicated and is,
Figure FDA00038518600100000214
represents the load demand, P, after participation in DR of the jth node at time t j,t,BAT,Cha Represents the energy storage charge amount of the jth node at the moment t, P j,t,BAT,Dis Represents the j-th section at time tEnergy storage discharge capacity of point, P Ave Representing the mean load of the distribution network.
5. The method for planning a power distribution network in consideration of green license trading and carbon trading of claim 4, wherein: the novel power distribution network planning constraint comprises an investment decision layer constraint and a simulation operation layer constraint;
the investment decision level constraints include,
wind power, photovoltaic, water turbine, gas turbine and energy storage which can be accessed by each node are limited, and the investment constraints of wind, light, water and gas and energy storage are as follows:
Figure FDA0003851860010000031
wherein, N j,WT 、N j,PV 、N j,HT 、N j,MT Respectively representing the upper limit of the investment quantity of the wind power, the photovoltaic and the water power turbine and the gas turbine of the jth node, wherein the investment capacities of the wind power, the photovoltaic and the water power turbine and the gas turbine are integral multiples of the capacity of a single machine, and N is j,BAT And the upper limit of the energy storage investment quantity of the jth node is shown, and the energy storage investment capacity is also an integral multiple of the single machine capacity.
6. The method for planning a power distribution network in consideration of a green certificate transaction and a carbon transaction as set forth in any one of claims 1 to 5, wherein: the simulated operation layer constraints comprise a power distribution network second-order cone relaxation power flow constraint, a power distribution network safety constraint, a power generation equipment capacity constraint, a power generation power constraint, a gas turbine constraint, an energy storage operation constraint, a large power grid electricity purchasing and selling constraint, a power distribution network energy balance constraint, a green certificate quota and green certificate selling price constraint, a carbon emission intensity constraint and a green certificate transaction and carbon transaction amount constraint;
the acquisition of the power distribution network second-order cone relaxation power flow constraint comprises the following steps,
the method comprises the following steps that the flow of a distribution network is constrained by adopting distflow branch flow, and an original flow model is relaxed by adopting a second-order cone relaxation technology;
for branches ij and jk, letLet Λ (j) be a starting point set with node j as an end point, Ω (j) be an end point set with node j as a starting point, and G E For the set of all the nodes of the distribution network,
Figure FDA0003851860010000032
Figure FDA0003851860010000033
Figure FDA0003851860010000034
wherein i, j ∈ G E ，
Figure FDA0003851860010000035
And Q ij,t The active power and the reactive power of the branch circuit ij participating in DR at the time t are shown, and the reactive power is not influenced if the branch circuit is supposed to participate in DR,
Figure FDA0003851860010000036
the active power P of the branch jk participating in DR at the moment t is shown j,t,WT 、P j,t,PV 、P j,t,HT Representing the generated power of the wind, photovoltaic and water turbine of the node j at the moment t, G WT 、G PV 、G HT Representing a set of investment candidate nodes, x, for wind, photovoltaic, hydro turbines ij The reactance of the branch ij is represented,
Figure FDA0003851860010000037
and
Figure FDA0003851860010000038
represents the square of the voltages at nodes i and j at time t;
the obtaining of the safety constraints of the power distribution network comprises,
Figure FDA0003851860010000041
wherein, U j,max 、U j,min Respectively represents the upper limit and the lower limit of the voltage amplitude of the grid node j, I ij,max Represents the upper limit of the current amplitude of branch ij, I ij,t Representing the current of the branch ij at the time t;
the obtaining of the power plant capacity constraint includes,
Figure FDA0003851860010000042
wherein the content of the first and second substances,
Figure FDA0003851860010000043
and
Figure FDA0003851860010000044
representing the minimum and maximum capacities of the wind, photovoltaic and hydro-pneumatic installation n,
Figure FDA0003851860010000045
represents the capacity of device n;
the obtaining of the generated power constraint may include,
Figure FDA0003851860010000046
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003851860010000047
which represents the power generated by the power generator,
Figure FDA0003851860010000048
which represents the minimum output power of the device,
Figure FDA0003851860010000049
represents the maximum output power of the device;
setting up
Figure FDA00038518600100000410
Figure FDA00038518600100000411
Wherein, delta n Representing a binary variable determining the operating state of the device n,
Figure FDA00038518600100000412
representing the minimum output power coefficient, P, of the device n t E-n Representing the n output, ξ of the plant n Representing the linearized auxiliary variable.
7. The method for planning a power distribution network in consideration of green license trading and carbon trading of claim 6, wherein: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
the obtaining of the gas turbine engine constraints includes,
Figure FDA00038518600100000413
Figure FDA00038518600100000414
Figure FDA00038518600100000415
Figure FDA00038518600100000416
wherein the content of the first and second substances,
Figure FDA00038518600100000417
respectively representing the minimum output and the maximum output of the gas turbine j,
Figure FDA00038518600100000418
respectively showing the downward climbing speed and the upward climbing speed of the gas turbine,
Figure FDA00038518600100000419
respectively represents the continuous startup state time and shutdown state time of the unit j in the period t,
Figure FDA00038518600100000420
respectively representing the minimum continuous startup time and shutdown time of the unit j;
the obtaining of the stored energy operating constraints may include,
Figure FDA0003851860010000051
wherein, P t,Cha 、P t,Dis Respectively representing the stored energy charging power, the discharging power, delta Cha 、δ Dis Respectively representing the binary variable of energy storage charging and the binary variable of discharging,
Figure FDA0003851860010000052
represents the maximum charge and discharge power, E min 、E max Respectively representing the minimum capacity and the maximum capacity of energy storage;
the acquisition of the power purchasing and selling constraint of the large power grid comprises the following steps,
Figure FDA0003851860010000053
wherein G is max Represents the maximum capacity of the distribution network to participate in energy exchange,
Figure FDA0003851860010000054
representing the purchasing and selling capacity of the large power grid;
the acquisition of the power distribution network energy balance constraint includes,
the power distribution network should satisfy energy balance, which can be expressed as:
Figure FDA0003851860010000055
δ buy +δ sale ＝{0,1}
the acquisition of the green license quota and the green license selling price constraint comprises,
Figure FDA0003851860010000056
A≥30％
wherein A represents a new energy quota coefficient, E m Represents the supply power of the m-th year, k g Representing the quantized coefficient, E mn Shows the generating capacity of the new energy n generator in the mth year, S re It is indicated that the generator belongs to a new energy source,
Figure FDA0003851860010000057
representing the number of green certificates that can be obtained by the unit renewable energy source sent by the n generators,
Figure FDA0003851860010000058
and
Figure FDA0003851860010000059
representing the number of green certificates bought and sold in the mth year, respectively;
Figure FDA00038518600100000510
Figure FDA00038518600100000511
wherein the content of the first and second substances,
Figure FDA00038518600100000512
the lower limit of the green license price is shown,
Figure FDA00038518600100000513
represents the upper limit of the green license price, s l Showing the first green certificate's price of electricity on the Internet c Indicating the price of electricity of local thermal power on-line marker post l Represents the first new energy conversion rate, h l Indicates the first new energy financial subsidy payment period, d l And the fact that the amount of the new energy financial subsidy of the first category is delayed for the payment period is shown.
8. The method of claim 7 for planning a power distribution network in consideration of a green license transaction and a carbon transaction, wherein: also comprises the following steps of (1) preparing,
the acquisition of the carbon emission intensity constraint includes,
Figure FDA0003851860010000061
Figure FDA0003851860010000062
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003851860010000063
represents the total power consumption of the distribution network in the mth year,
Figure FDA0003851860010000064
represents the average unit power supply quantity CO of the power distribution network in the mth year 2 Emission intensity, E q,m Indicating the CO of the mth year of the distribution network 2 A total emission allowance limit value that is,
Figure FDA0003851860010000065
and
Figure FDA0003851860010000066
respectively representing CO bought and sold in the mth year of the distribution network 2 Discharging amount;
the acquisition of the green transaction and carbon transaction amount constraints includes,
Figure FDA0003851860010000067
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003851860010000068
and
Figure FDA0003851860010000069
representing the number of green certificates bought and sold in the m-th year respectively,
Figure FDA00038518600100000610
and
Figure FDA00038518600100000611
respectively representing CO bought and sold in the mth year of the distribution network 2 The amount of discharge.
9. The method for planning a power distribution network in consideration of green license trading and carbon trading of claim 8, wherein: the mutual transformation of the investment decision layer and the simulation operation layer comprises,
and the investment decision layer transmits planning information to the simulation operation layer, and the operation result of the simulation operation layer influences the investment decision layer.
10. The method for planning a power distribution network in consideration of a green license transaction and a carbon transaction of claim 9, wherein: the step of converting the two-layer model into a single-layer non-linear programming model comprises,
and converting the simulation operation layer model into a constraint condition of the investment decision layer model by adopting a single-layer conversion method through constructing a Lagrange function of the simulation operation layer model, mutually converting the investment decision layer and the simulation operation layer into a KT condition based on K of the simulation operation layer model, and converting the double-layer model into a single-layer nonlinear programming model.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116128553A (en) * 2023-04-19 2023-05-16 南京师范大学 Comprehensive energy scheduling method and system based on green license and carbon transaction interaction

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116128553A (en) * 2023-04-19 2023-05-16 南京师范大学 Comprehensive energy scheduling method and system based on green license and carbon transaction interaction

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