CN113327124A - Low-carbon P2P energy consumption trading method in multi-energy-carbon emission combined market - Google Patents

Low-carbon P2P energy consumption trading method in multi-energy-carbon emission combined market Download PDF

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CN113327124A
CN113327124A CN202110391254.0A CN202110391254A CN113327124A CN 113327124 A CN113327124 A CN 113327124A CN 202110391254 A CN202110391254 A CN 202110391254A CN 113327124 A CN113327124 A CN 113327124A
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徐青山
夏元兴
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Southeast University
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Abstract

The invention discloses a low-carbon P2P energy consumption trading method in a multi-energy-carbon emission combined market, which comprises the steps of establishing a multi-energy set model; collecting user load data participating in the market, carbon emission intensity of various energy sources and parameters of each line and node in the network; the method has the advantages that the total carbon emission amount is reduced on the premise of ensuring the economic benefits of energy producers and consumers, the P2P energy utilization trading of the energy producers and consumers in the network is optimized by adopting the method, the flexibility and the practicability are realized, the carbon emission market is considered, the total carbon emission amount of the whole market can be further reduced, and the popularization is easy.

Description

Low-carbon P2P energy consumption trading method in multi-energy-carbon emission combined market
Technical Field
The invention belongs to the technical field of low-carbon P2P energy consumption trading, and particularly relates to a P2P energy consumption trading method considering a multi-energy market and a carbon emission market in a combined manner.
Background
In recent years, the rapid development of distributed energy at the user end has promoted the change of energy consumption modes. The rapid development of Photovoltaic (PV) and wind power generation technologies provides the opportunity for electrical grids to ameliorate local network problems (such as voltage fluctuations) in a flexible manner. On the other hand, the energy end user can also reduce the power generation cost of renewable energy by sharing the residual energy generated locally, the invention introduces the concept of energy producers and consumers to describe the end user with energy sharing capability, the energy producer and consumer has the right to obtain economic benefit by sharing energy with other producers and consumers, but if the energy producer and consumer can not independently determine the transaction amount and the energy price, the transaction income can be slight. Therefore, establishing a P2P (peer-to-peer) energy trading platform with a perfect mechanism has become a current research hotspot for the voluntary decision of the demanding eaters.
Most of the existing multifunctional markets consider the characteristic of multifunctional complementation, multifunctional scheduling is carried out with the aim of optimal economy, and few multifunctional markets consider the difference between carbon emissions of energy consumption of various energy units, so that a P2P energy utilization trading method jointly considering the multifunctional markets and the carbon emission markets is provided.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a low-carbon P2P energy-consumption trading method in a multi-energy-carbon emission combined market, which can promote the normal operation of trading and ensure the low carbon and environmental protection of an energy system.
The purpose of the invention can be realized by the following technical scheme: a low-carbon P2P energy utilization trading method in a multi-energy-carbon emission combined market comprises the following steps:
(1) establishing a multi-energy set model;
(2) acquiring data such as user load, real-time electricity price, electricity-gas network parameters, carbon emission intensity of each energy source and the like, and transmitting the collected data into the model in the step (1);
(3) calculating the carbon emission by applying a virtual carbon flow model;
(4) modeling a multi-energy-carbon emission joint market;
(5) and carrying out iterative operation on the joint market to obtain the P2P market trading optimal solution considering the carbon emission cost.
As a further scheme of the present invention, the multi-energy aggregation model in step (1) includes photovoltaic power generation, energy storage and gas generator energy consumption to form a multi-energy aggregation, and for energy producers and consumers inside the community-level microgrid, each producer and consumer aims to minimize power generation cost and maximize power generation benefit, so that the producer and consumer model inside the microgrid can be established as follows:
Figure BDA0003016833030000021
Figure BDA0003016833030000023
Figure BDA0003016833030000024
Figure BDA0003016833030000022
Figure BDA0003016833030000031
Figure BDA0003016833030000032
Figure BDA0003016833030000033
Figure BDA0003016833030000034
Figure BDA0003016833030000035
Figure BDA00030168330300000312
in the formula:
Figure BDA0003016833030000036
decision variable, p, for the ith multi-energy set (MEC)j,qjRespectively representing net output power and purchase power, alpha, of energy producers and consumers in the jth microgridj,βjRespectively representing the input and output electric energy of the jth energy producer and consumer in the microgrid, gjNatural gas introduced for producer j to supply local natural gas load gj,loadLoad g for converting natural gas into electric energyj,turnFor each multi-energy set, the transmission network and the natural gas network need to meet corresponding technical constraints
Figure BDA0003016833030000037
And
Figure BDA0003016833030000038
in the internal model of the multi-energy set, the energy balance for each energy producer and consumer can be modeled as follows:
Figure BDA0003016833030000039
Figure BDA00030168330300000310
Figure BDA00030168330300000313
in the formula: u. ofi,jThe power generation amount of the ith power generation source of the energy producer/consumer j, so that the sum of all the power generation sources is the total power generation amount of the corresponding energy producer/consumer
Figure BDA00030168330300000311
Utility function in the present embodimentA quadratic function is applied for modeling. Weight coefficient gammacom,γimp,γexp,γgasThe willingness of energy producers and consumers j to trade and consume natural gas for energy use inside and outside the MEC is characterized respectively.
In the external model of the multi-energy set, each microgrid manager may be modeled as follows:
Figure BDA0003016833030000041
Figure BDA0003016833030000048
Figure BDA0003016833030000047
in the formula: gamma rayimp,γexp,γgasRespectively representing the cost coefficients of the managers of the multi-energy set micro-grid for buying and selling electric energy and natural gas to other multi-energy sets,
Figure BDA0003016833030000049
than all the electric power purchased qimp,tAre all large enough to characterize the peak load, γ, of the electricity purchasepeakIs the cost factor of the load peak to achieve load peak clipping for the entire multi-energy set.
The integration of the multi-energy integration micro-grid can be modeled as
Figure BDA0003016833030000042
As a further scheme of the present invention, in step (2), the user load data includes load data of the user all the year around, and the data collection interval is minimum 15 minutes;
the real-time electricity price adopts the state uniform peak-valley average three-time electricity price;
the electric-gas network parameters are the resistance reactance of each branch of the corresponding network and the parameters of the gas network pipeline;
the carbon emission intensity of each energy source is that each kWh of electric energy or each m of electric energy is consumed3Carbon dioxide emissions in tons for natural gas.
As a further scheme of the invention, the carbon emission calculation in the step (3) comprises virtual carbon flow calculation of an electric power network and virtual carbon flow calculation of a natural gas network;
virtual carbon flow calculation of the power network:
Figure BDA0003016833030000043
Figure BDA0003016833030000044
Figure BDA0003016833030000045
in the formula:
Figure BDA0003016833030000046
the carbon emission intensity of branch l and node b, node b is the power input node directly connected to branch l, and the carbon emission rate R of each branch llActive power P flowing through corresponding branchlProportional ratio, proportional coefficient being carbon emission intensity
Figure BDA0003016833030000051
Carbon emission intensity of node b
Figure BDA0003016833030000052
Carbon emission intensity of generator directly connected with the node
Figure BDA0003016833030000053
And branch carbon emission intensity
Figure BDA0003016833030000054
Are directly related to the weighted average of (1), the weighting coefficients are respectivelyFor generator GhSum branch power Pl；
Virtual carbon flow calculation for natural gas networks:
Figure BDA0003016833030000055
Figure BDA0003016833030000056
Figure BDA0003016833030000057
Figure BDA0003016833030000058
Figure BDA0003016833030000059
Figure BDA00030168330300000510
in the formula:
Figure BDA00030168330300000511
to be the carbon emission intensity of the air network node b,
Figure BDA00030168330300000512
is the electrical load output by the natural gas compressor m,
Figure BDA00030168330300000513
is the carbon discharge rate of the natural gas compressor m;
Figure BDA00030168330300000514
natural gas load of the natural gas compressor m output, CheatIs naturalThe gas heat value, l is the index of the branch connected with the compressor m; rlTo be the carbon emission rate on the line l,
Figure BDA00030168330300000515
carbon emission intensity of the pipeline branch l is defined as carbon emission intensity of the injection node
Figure BDA00030168330300000516
The consistency is achieved;
Figure BDA00030168330300000517
a constant representing a unit gas combustion emission amount; the carbon emission rate of the natural gas load is defined as the carbon emission rate of the natural gas load fdAnd carbon strength of corresponding node
Figure BDA00030168330300000518
The consistency is achieved;
and (3) carrying out variance prediction on the carbon price exponential generalized autoregressive conditions:
rd＝β0+θrd-1+σdvd
Figure BDA00030168330300000519
in the formula: r isdThe carbon emission price of day d is determined by the carbon emission price of day d-1; (alpha0,α1,β0,β1,β2) Respectively is an innovation parameter, a lasting parameter and an asymmetric parameter of the exponential generalized autoregressive conditional variance model; theta is a constant less than 1, { vdIt is a white noise sequence with mean 0 and standard deviation 1.
As a further aspect of the present invention, the unified market in the step (4) includes an upper market for the electro-gas multi-energy transaction and a lower market for the carbon emission transaction;
total emission E of energy producers and consumers at time tpeer,tIntensity of carbon emission from the corresponding node b
Figure BDA0003016833030000061
Calculated, and total emission E of the generator nodesgene,tThen it can be calculated from the carbon emission intensity of the corresponding node b:
Figure BDA0003016833030000062
Figure BDA0003016833030000063
in the upper-level market, the combined multi-energy aggregated market at full time scale can be modeled as:
Figure BDA0003016833030000064
Figure BDA0003016833030000065
Figure BDA0003016833030000066
Figure BDA0003016833030000067
Figure BDA0003016833030000068
Figure BDA0003016833030000069
in the formula: cost function of generator
Figure BDA00030168330300000610
Can be modeled as:
Figure BDA00030168330300000611
the utility function of the parity can be modeled as a piecewise function as follows:
Figure BDA00030168330300000612
in the model, the objective function mainly comprises two parts,
Figure BDA00030168330300000613
to maximize the collective social benefit of the multipotent collection,
Figure BDA0003016833030000071
in order to minimize the cost of carbon emissions,
Figure BDA0003016833030000072
in order to correspond to the upper carbon emission limit of the generator,
Figure BDA0003016833030000073
and
Figure BDA0003016833030000074
the output climbing power of the generator b is limited to be less than the upper limit range and the lower limit range of the user load and the generator respectively
Figure BDA0003016833030000075
While the power drop is limited to less than
Figure BDA0003016833030000076
In the underlying market, the following can be modeled from the perspective of energy producers and consumers:
Figure BDA0003016833030000077
Figure BDA0003016833030000078
Figure BDA0003016833030000079
Figure BDA00030168330300000710
in this model, the objective function is primarily to reduce the total cost per energy producer and consumer, including the underlying market adjustment purchase ζi,j,t(ui,j,t-Δui,j,t) Cost of energy and carbon emission penalty
Figure BDA00030168330300000711
In addition to this, the model also maximizes the utility function ψ of the useri,j,t(ui,j,t-Δui,j,t) And the income zeta of selling electricityi,j,tui,j,t；
The model is provided with an energy consumption upper limit delta ui,j,tIn order to track the electrical load supplied by the natural gas generator in an iterative process, an electrical load ratio χ is introduced in the modelt。
Has the advantages that: through alternate iteration among the multi-energy-carbon emission markets, the carbon emission is quantified into economic benefits through the emission markets, so that the total carbon emission is reduced under the condition of ensuring smooth multi-energy trading; carbon emission, expenditure and income of energy production are analyzed from the perspective of energy producers and consumers, a trading scheme in the P2P multifunctional system is optimized from the perspective of a total system, theoretical guidance is provided for saving energy and reducing carbon emission, and application of P2P trading in the multifunctional system is effectively promoted;
the method and the device have the advantages that the economic benefit of the user after energy storage configuration is guaranteed, meanwhile, the efficiency and the accuracy of the optimization algorithm are improved, and the method and the device have use value and popularization.
Drawings
FIG. 1 is a schematic diagram of the multi-energy collection proposed by the present invention;
FIG. 2 is a schematic diagram of a P2P market with a double-layer structure;
FIG. 3 is a schematic diagram of a corresponding federated marketplace of the present invention;
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Referring to fig. 1, a schematic diagram of a network formed by a multi-energy integrated microgrid according to the present invention is shown, wherein the network can be used for optimizing and configuring resources by using a low-carbon P2P energy trading method in a multi-energy-carbon emission combined market, and the method comprises the following steps:
(1) acquiring data such as user load, real-time electricity price, electricity-gas network parameters and carbon emission intensity of each energy source, and transmitting the collected data into an optimization model;
further, the user load data comprises the load data of the user all year round, and the data acquisition interval is minimum 15 minutes;
further, the real-time electricity price adopts the national uniform peak-valley average three-time electricity price;
further, the parameters of the electric-gas network are the resistance reactance of each branch of the corresponding network and the parameters of the gas network pipeline;
further, the intensity of carbon emission from each energy source is such that it consumes electric power per kWh or per m3Carbon dioxide emissions in tons for natural gas.
(2) The multi-energy aggregation (MEC) model is as follows:
(21) different community-level micro-grids play a role of market participation in the market, and the photovoltaic power generation technology, the energy storage technology and the gas generator are combined together to form a multi-energy aggregate. For energy producers and consumers in the community-level microgrid, each producer and consumer aims to minimize the power generation cost and maximize the power generation benefit, so that a producer and consumer model in the microgrid can be established as follows:
Figure BDA0003016833030000091
Figure BDA00030168330300000911
Figure BDA00030168330300000912
Figure BDA0003016833030000092
Figure BDA0003016833030000093
Figure BDA0003016833030000094
Figure BDA0003016833030000095
Figure BDA0003016833030000096
Figure BDA0003016833030000097
Figure BDA00030168330300000913
in the formula:
Figure BDA0003016833030000098
decision variable, p, for the ith multi-energy set (MEC)j,qjRespectively representing net output power and purchase power, alpha, of energy producers and consumers in the jth microgridj,βjRespectively representing the input and output electric energy of the jth energy producer and consumer in the microgrid, gjNatural gas introduced for producer j to supply local natural gas load gj,loadLoad g for converting natural gas into electric energyj,turn. For each multi-energy set, the transmission network and the natural gas network need to meet corresponding technical constraints
Figure BDA0003016833030000099
And
Figure BDA00030168330300000910
(22) in the internal model of the multi-energy set, the energy balance for each energy producer and consumer can be modeled as follows:
Figure BDA0003016833030000101
Figure BDA0003016833030000102
Figure BDA0003016833030000109
in the formula: u. ofi,jThe power generation amount of the ith power generation source of the energy producer/consumer j, so that the sum of all the power generation sources is the total power generation amount of the corresponding energy producer/consumer
Figure BDA0003016833030000103
The utility function in the invention is modeled by applying a quadratic function. Weight coefficient gammacom,γimp,γexp,γgasThe willingness of energy producers and consumers j to trade and consume natural gas for energy use inside and outside the MEC is characterized respectively.
(23) In the external model of the multi-energy set, each microgrid manager may be modeled as follows:
Figure BDA0003016833030000104
Figure BDA00030168330300001011
Figure BDA00030168330300001010
in the formula: gamma rayimp,γexp,γgasRespectively representing the cost coefficients of the managers of the multi-energy set micro-grid for buying and selling electric energy and natural gas to other multi-energy sets,
Figure BDA00030168330300001012
than all the electric power purchased qimp,tAre all large enough to characterize the peak load, γ, of the electricity purchasepeakIs the cost factor of the load peak to achieve load peak clipping for the entire multi-energy set.
(24) The whole multi-energy set micro-grid can be modeled as
Figure BDA0003016833030000105
(3) Calculating carbon emission;
(31) virtual carbon flow calculation of the power network:
Figure BDA0003016833030000106
Figure BDA0003016833030000107
Figure BDA0003016833030000108
in the formula:
Figure BDA0003016833030000111
the carbon emission intensity of branch l and node b, node b is the power input node directly connected to branch l, and the carbon emission rate R of each branch llActive power P flowing through corresponding branchlProportional ratio, proportional coefficient being carbon emission intensity
Figure BDA0003016833030000112
Carbon emission intensity of node b
Figure BDA0003016833030000113
Carbon emission intensity of generator directly connected with the node
Figure BDA0003016833030000114
And branch carbon emission intensity
Figure BDA0003016833030000115
Are directly related, the weighting coefficients are the generator output GhSum branch power Pl。
(32) Virtual carbon flow calculation for natural gas networks:
Figure BDA0003016833030000116
Figure BDA0003016833030000117
Figure BDA0003016833030000118
Figure BDA00030168330300001120
Figure BDA0003016833030000119
Figure BDA00030168330300001110
in the formula:
Figure BDA00030168330300001111
to be the carbon emission intensity of the air network node b,
Figure BDA00030168330300001112
is the electrical load output by the natural gas compressor m,
Figure BDA00030168330300001113
is the carbon discharge rate of the natural gas compressor m;
Figure BDA00030168330300001114
natural gas load of the natural gas compressor m output, CheatIs the natural gas calorific value, l is the branch index connecting the compressor m; rlTo be the carbon emission rate on the line l,
Figure BDA00030168330300001115
carbon emission intensity of the pipeline branch l is defined as carbon emission intensity of the injection node
Figure BDA00030168330300001116
The consistency is achieved;
Figure BDA00030168330300001117
a constant representing a unit gas combustion emission amount; the carbon emission rate of the natural gas load is defined as the carbon emission rate of the natural gas load fdAnd carbon strength of corresponding node
Figure BDA00030168330300001118
And (5) the consistency is achieved.
(33) Exponential generalized autoregressive conditional variance prediction of carbon prices:
rd＝β0+θrd-1+σdvd
Figure BDA00030168330300001119
in the formula: r isdThe carbon emission price of day d is determined by the carbon emission price of day d-1; (alpha0,α1,β0,β1,β2) Respectively is an innovation parameter, a lasting parameter and an asymmetric parameter of the exponential generalized autoregressive conditional variance model; theta is a constant less than 1, { vdIt is a white noise sequence with mean 0 and standard deviation 1.
(4) Modeling a multi-energy-carbon emission joint market;
(41) total emission E of energy producers and consumers at time tpeer,tIntensity of carbon emission from the corresponding node b
Figure BDA0003016833030000121
Calculated, and total emission E of the generator nodesgene,tThen it can be calculated from the carbon emission intensity of the corresponding node b:
Figure BDA0003016833030000122
Figure BDA0003016833030000123
(42) in the upper-level market, the combined multi-energy aggregated market at full time scale can be modeled as:
Figure BDA0003016833030000124
Figure BDA0003016833030000125
Figure BDA0003016833030000126
Figure BDA0003016833030000127
Figure BDA0003016833030000128
Figure BDA0003016833030000129
in the formula: cost function of generator
Figure BDA00030168330300001210
Can be modeled as:
Figure BDA00030168330300001211
the utility function of the parity can be modeled as a piecewise function as follows:
Figure BDA00030168330300001212
in the model, the objective function mainly comprises two parts,
Figure BDA00030168330300001213
to maximize the collective social benefit of the multipotent collection,
Figure BDA0003016833030000131
to minimize carbon emission costs.
Figure BDA0003016833030000132
To carbon emissions of corresponding generatorsThe limit is that the temperature of the molten steel is limited,
Figure BDA0003016833030000133
and
Figure BDA0003016833030000134
the output climbing power of the generator b is limited to be less than the upper limit range and the lower limit range of the user load and the generator respectively
Figure BDA0003016833030000135
While the power drop is limited to less than
Figure BDA0003016833030000136
(43) In the underlying market, the following can be modeled from the perspective of energy producers and consumers:
Figure BDA0003016833030000137
Figure BDA0003016833030000138
Figure BDA0003016833030000139
Figure BDA00030168330300001310
in this model, the objective function is primarily to reduce the total cost per energy producer and consumer, including the underlying market adjustment purchase ζi,j,t(ui,j,t-Δui,j,tCost of energy and carbon emission penalty
Figure BDA00030168330300001311
In addition to this, the model also maximizes the utility function ψ of the useri,j,t(ui,j,t-Δui,j,t) And earnings for selling electricityζi,j,tui,j,t. The model is provided with an energy consumption upper limit delta ui,j,tIn order to track the electrical load supplied by the natural gas generator in an iterative process, the electrical load ratio χtIs introduced into the model.
(5) After the double-layer joint market iteration is completed, the optimal P2P market trading result considering the carbon emission cost can be obtained. After the upper market trades, the lower market needs to calculate the carbon emissions for the corresponding trade, and then redistribute the energy plan based on the carbon emissions market. The lower-layer energy utilization plan is transmitted to the upper-layer market to continue iteration, and finally the unified trading scheme is converged.
The double-layer structure of the united market established by the invention is shown in fig. 2, wherein in fig. 2, the upper layer represents a physical power transmission layer, the lower layer represents a financial transaction layer, and the physical power transmission layer covers actual facilities such as a real power network, an intelligent electric meter and the like; the financial power transmission layer covers information such as a financial information system and a joint market running state to assist the physical layer to carry out transactions, and the two-layer structure perfectly represents hardware equipment and software processes of the whole P2P transaction.
The two-stage united market proposed by the invention is shown in fig. 3, the upper red circle represents an electricity-gas multifunctional market, and the lower layer represents a carbon emission trading market, and the two-stage market respectively corresponds to the maximum total income and the carbon emission income of each energy producer and consumer.
The invention is suitable for a multifunctional distribution network with higher renewable energy permeability and each community-level microgrid, analyzes carbon emission and expenditure and income of energy production from the perspective of energy producers and consumers, optimizes a transaction scheme in the P2P multifunctional system from the perspective of a total system, provides theoretical guidance for saving energy and reducing carbon emission, and effectively promotes the application of P2P transaction in the multifunctional system.
It will be appreciated by those skilled in the art that various changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the spirit and scope of the invention, and any equivalents thereto, such as those skilled in the art, are intended to be embraced therein.

Claims (5)

1. A low-carbon P2P energy use trading method in a multi-energy-carbon emission combined market is characterized by comprising the following steps:
(1) establishing a multi-energy set model;
(2) acquiring data such as user load, real-time electricity price, electricity-gas network parameters, carbon emission intensity of each energy source and the like, and transmitting the collected data into the model in the step (1);
(3) calculating the carbon emission by applying a virtual carbon flow model;
(4) modeling a multi-energy-carbon emission joint market;
(5) and carrying out iterative operation on the joint market to obtain the P2P market trading optimal solution considering the carbon emission cost.
2. The energy trading method for the low-carbon P2P on the multi-energy-carbon emission combined market according to claim 1, wherein the multi-energy aggregation model in step (1) comprises photovoltaic power generation, energy storage and gas generator energy consumption to form a multi-energy aggregation, and for energy producers and consumers inside the community-level microgrid, each producer and consumer aims to minimize the power generation cost and maximize the power generation benefit, so that the producer and consumer model inside the microgrid can be established as follows:
Figure FDA0003016833020000011
Figure FDA0003016833020000012
Figure FDA0003016833020000013
Figure FDA0003016833020000014
Figure FDA0003016833020000021
Figure FDA0003016833020000022
Figure FDA0003016833020000023
Figure FDA0003016833020000024
Figure FDA0003016833020000025
Figure FDA0003016833020000026
in the formula: gamma-shapedi＝{pj,qj,αj,βj,gjIs the decision variable of the ith multi-energy set (MEC), pj,qjRespectively representing net output power and purchase power, alpha, of energy producers and consumers in the jth microgridj,βjRespectively representing the input and output electric energy of the jth energy producer and consumer in the microgrid, gjNatural gas introduced for producer j to supply local natural gas load gj,loadLoad g for converting natural gas into electric energyj,turnFor each oneMultiple energy integration, power transmission network and natural gas network need to meet corresponding technical constraints
Figure FDA0003016833020000027
And
Figure FDA0003016833020000028
in the internal model of the multi-energy set, the energy balance for each energy producer and consumer can be modeled as follows:
Figure FDA0003016833020000029
Figure FDA00030168330200000210
Figure FDA00030168330200000211
in the formula: u. ofi,jThe power generation amount of the ith power generation source of the energy producer/consumer j, so that the sum of all the power generation sources is the total power generation amount of the corresponding energy producer/consumer
Figure FDA00030168330200000212
The utility function in this embodiment applies a quadratic function for modeling. Weight coefficient gammacom,γimp,γexp,γgasThe willingness of energy producers and consumers j to trade and consume natural gas for energy use inside and outside the MEC is characterized respectively.
In the external model of the multi-energy set, each microgrid manager may be modeled as follows:
Figure FDA0003016833020000031
Figure FDA0003016833020000032
Figure FDA0003016833020000033
in the formula: gamma rayimp,γexp,γgasRespectively representing the cost coefficients of the managers of the multi-energy set micro-grid for buying and selling electric energy and natural gas to other multi-energy sets,
Figure FDA0003016833020000034
than all the electric power purchased qimp,tAre all large enough to characterize the peak load, γ, of the electricity purchasepeakIs the cost factor of the load peak to achieve load peak clipping for the entire multi-energy set.
The integration of the multi-energy integration micro-grid can be modeled as
Figure FDA0003016833020000035
3. The low-carbon P2P energy trading method according to claim 1, wherein in step (2), the user load data includes year-round load data of the user, and the data collection interval is minimum 15 min; the real-time electricity price adopts the state uniform peak-valley average three-time electricity price;
the electric-gas network parameters are the resistance reactance of each branch of the corresponding network and the parameters of the gas network pipeline;
the carbon emission intensity of each energy source is that each kWh of electric energy or each m of electric energy is consumed3Carbon dioxide emissions in tons for natural gas.
4. The low carbon P2P energy trading method in the multi-energy-carbon emission combined market according to claim 1, wherein the carbon emission calculation in step (3) comprises a virtual carbon flow calculation of an electric power network and a virtual carbon flow calculation of a natural gas network;
virtual carbon flow calculation of the power network:
Figure FDA0003016833020000036
Figure FDA0003016833020000037
Figure FDA0003016833020000038
in the formula:
Figure FDA0003016833020000039
the carbon emission intensity of branch l and node b, node b is the power input node directly connected to branch l, and the carbon emission rate R of each branch llActive power P flowing through corresponding branchlProportional ratio, proportional coefficient being carbon emission intensity
Figure FDA0003016833020000041
Carbon emission intensity of node b
Figure FDA0003016833020000042
Carbon emission intensity of generator directly connected with the node
Figure FDA0003016833020000043
And branch carbon emission intensity
Figure FDA0003016833020000044
Are directly related, the weighting coefficients are the generator output GhSum branch power Pl；
Virtual carbon flow calculation for natural gas networks:
Figure FDA0003016833020000045
Figure FDA0003016833020000046
Figure FDA0003016833020000047
Figure FDA0003016833020000048
Figure FDA0003016833020000049
Figure FDA00030168330200000410
in the formula:
Figure FDA00030168330200000411
to be the carbon emission intensity of the air network node b,
Figure FDA00030168330200000412
is the electrical load output by the natural gas compressor m,
Figure FDA00030168330200000413
is the carbon discharge rate of the natural gas compressor m;
Figure FDA00030168330200000414
natural gas load of the natural gas compressor m output, CheatIs the heat value of natural gas, l is connectedBranch index of compressor m; rlTo be the carbon emission rate on the line l,
Figure FDA00030168330200000415
carbon emission intensity of the pipeline branch l is defined as carbon emission intensity of the injection node
Figure FDA00030168330200000416
The consistency is achieved;
Figure FDA00030168330200000417
a constant representing a unit gas combustion emission amount; the carbon emission rate of the natural gas load is defined as the carbon emission rate of the natural gas load fdAnd carbon strength of corresponding node
Figure FDA00030168330200000418
The consistency is achieved;
and (3) carrying out variance prediction on the carbon price exponential generalized autoregressive conditions:
rd＝β0+θrd-1+σdVd
Figure FDA00030168330200000419
in the formula: r isdThe carbon emission price of day d is determined by the carbon emission price of day d-1; (alpha0,α1,β0,β1,β2) Respectively is an innovation parameter, a lasting parameter and an asymmetric parameter of the exponential generalized autoregressive conditional variance model; theta is a constant less than 1, { vdIt is a white noise sequence with mean 0 and standard deviation 1.
5. The low-carbon P2P energy trading method for the multi-energy-and-carbon-emission combined market according to claim 1, wherein the combined market in the step (4) comprises an upper market for electricity-gas multi-energy trading and a lower market for carbon emission trading;
total emission E of energy producers and consumers at time tpeer,tIntensity of carbon emission from the corresponding node b
Figure FDA0003016833020000051
Calculated, and total emission E of the generator nodesgene,tThen it can be calculated from the carbon emission intensity of the corresponding node b:
Figure FDA0003016833020000052
Figure FDA0003016833020000053
in the upper-level market, the combined multi-energy aggregated market at full time scale can be modeled as:
Figure FDA0003016833020000054
Figure FDA0003016833020000055
Figure FDA0003016833020000056
Figure FDA0003016833020000057
Figure FDA0003016833020000058
Figure FDA0003016833020000059
in the formula: cost function of generator
Figure FDA00030168330200000510
Can be modeled as:
Figure FDA00030168330200000511
the utility function of the parity can be modeled as a piecewise function as follows:
Figure FDA00030168330200000512
in the model, the objective function mainly comprises two parts,
Figure FDA0003016833020000061
to maximize the collective social benefit of the multipotent collection,
Figure FDA0003016833020000062
in order to minimize the cost of carbon emissions,
Figure FDA0003016833020000063
in order to correspond to the upper carbon emission limit of the generator,
Figure FDA0003016833020000064
and
Figure FDA0003016833020000065
the output climbing power of the generator b is limited to be less than the upper limit range and the lower limit range of the user load and the generator respectively
Figure FDA0003016833020000066
While the power drop is limited to less than
Figure FDA0003016833020000067
In the underlying market, the following can be modeled from the perspective of energy producers and consumers:
Figure FDA0003016833020000068
Figure FDA0003016833020000069
Figure FDA00030168330200000610
Figure FDA00030168330200000611
in this model, the objective function is primarily to reduce the total cost per energy producer and consumer, including the underlying market adjustment purchase ζi,j,t(ui,j,t-Δui,j,t) Cost of energy and carbon emission penalty
Figure FDA00030168330200000612
In addition to this, the model also maximizes the utility function ψ of the useri,j,t(ui,j,t-Δui,j,t) And the income zeta of selling electricityi,j,tui,j,t；
The model is provided with an energy consumption upper limit delta ui,j,tIn order to track the electrical load supplied by the natural gas generator in an iterative process, an electrical load ratio χ is introduced in the modelt。
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