CN113327124B - Low-carbon P2P energy transaction method in multi-energy-carbon emission combined market - Google Patents
Low-carbon P2P energy transaction method in multi-energy-carbon emission combined marketInfo
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- 229910052799 carbon Inorganic materials 0.000 title claims abstract description 130
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Abstract
The invention discloses a low-carbon P2P energy transaction 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 various lines and nodes in a network; the invention provides a multi-energy collection (MEC) model taking a multi-energy system with a community-level micro-grid as a center as a unit, and the total carbon emission is reduced through transactions among the community-level micro-grids.
Description
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 jointly.
Background
In recent years, the rapid development of distributed energy sources at the user side has promoted the transition of energy consumption modes. The high-speed development of Photovoltaic (PV) and wind power generation technologies provides opportunities for the grid 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 sources by sharing the residual energy of local power generation, and the invention introduces the concept of an energy producer and consumer to describe the end user with energy sharing capability, and the energy producer and consumer have the right to obtain economic benefits by sharing energy with other producers and consumers, but if the energy producer and consumer cannot independently determine the transaction amount and the energy price, the transaction income can be tiny. Therefore, establishing a well-established P2P (peer-to-peer) energy trading platform has become a current research hotspot for autonomous decisions of the consumers.
Most of the existing multi-energy markets consider the characteristic of multi-energy complementation, multi-energy scheduling is carried out with the aim of optimizing economy, and few multi-energy markets consider the difference between the carbon emission of energy consumption of various energy units, so that a P2P energy utilization trading method combining the multi-energy 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 the multi-energy-carbon emission combined market, which can promote the normal running of trading and simultaneously ensure the low carbon and environmental protection of an energy system.
The aim of the invention can be achieved by the following technical scheme: a low-carbon P2P energy 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 combined market;
(5) And carrying out iterative operation on the combined market to obtain a P2P market transaction optimal solution considering the carbon emission cost.
As a further aspect of the present invention, the multi-energy aggregation model in step (1) includes that photovoltaic power generation, energy storage and energy consumption of the gas generator form a multi-energy aggregate, and for the energy producers and consumers in the community-level micro-grid, each producer and consumer target to minimize the power generation cost and maximize the power generation benefit, so that the producer and consumer model in the micro-grid can be established as follows:
Wherein: For decision variables of the ith multi-energy collection (MEC), p j,qj represents the net output power and the purchase power of the energy producer and the consumer in the jth micro-grid respectively, alpha j,βj represents the input and output electric energy of the jth energy producer and consumer in the micro-grid respectively, g j is the load g j,load of natural gas introduced by the producer j to supply the local natural gas load g j,turn of natural gas to electric energy and the load g j,turn of natural gas to electric energy, for each multi-energy collection, the power transmission network and the natural gas network need to meet the corresponding technical constraint And
In the internal model of the multi-energy collection, the energy balance of each energy producer can be modeled as follows:
Wherein: u i,j is the generated energy of the ith power generation source of the energy producer and consumer j, so that the sum of each power generation source is the total power generation amount of the corresponding producer and consumer In this embodiment the utility function is modeled using a quadratic function. The weight coefficient gamma com,γimp,γexp,γgas respectively represents the willingness of the energy producer j to trade energy and consume natural gas in and out of the MEC.
In the external model of the multi-energy collection, each microgrid manager may model as follows:
Wherein: gamma imp,γexp,γgas represents the cost coefficients of the manager of the multi-energy collection micro-grid to buy and sell electric energy and natural gas to other multi-energy collections, Compared with all the power purchased q imp,t, the power purchased power is larger to represent the load peak value of power purchased power, and gamma peak is the cost coefficient of the load peak value to realize load peak clipping of the whole multi-energy set.
Integrating the multi-energy collection micro-grid can be modeled as
As a further scheme of the invention, in the step (2), the user load data comprises annual load data of the user, and the data acquisition interval is 15 minutes at minimum;
The real-time electricity price adopts the national unified peak Gu Ping three-hour electricity price;
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;
the carbon emission intensity of each energy source is ton of carbon dioxide emission corresponding to consumption of electric energy per kWh or natural gas per m 3.
As a further aspect of the present invention, the carbon emission amount calculation in the step (3) includes 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:
Wherein: The carbon emission intensity of the branch l and the node b are respectively, the node b is a power input node directly connected to the branch l, the carbon emission rate R l of each branch l is in direct proportion to the active power P l flowing through the corresponding branch, and the proportionality coefficient is the carbon emission intensity Carbon emission intensity of node bStrength of carbon emission of generator directly connected to the nodeAnd branch carbon emission intensityDirectly related to the weighted average of (a), the weighting coefficients being generator output G h and branch power P l, respectively;
virtual carbon stream calculation for natural gas network:
Wherein: Is the carbon emission intensity of the air network node b, For the power load output by the natural gas compressor m,Carbon emission rate for natural gas compressor m; for the natural gas load output by the natural gas compressor m, C heat is the natural gas heating value, and l is the branch index connected with the compressor m; r l is the carbon emission rate on line i, The carbon emission intensity for the pipe branch l is defined as the carbon emission intensity with the injection nodeConsistent; A constant indicating the unit gas combustion discharge amount; the carbon emission rate of the natural gas load is defined as the carbon strength with the natural gas load f d and the corresponding node Consistent;
performing heteroscedastic prediction on an exponential generalized autoregressive condition of the carbon price:
rd＝β0+θrd-1+σdvd
Wherein: r d is the carbon emission price on day d, which is determined by the carbon emission price on day d-1; (alpha 0,α1,β0,β1,β2) respectively representing innovative parameters, persistent parameters and asymmetric parameters of the exponential generalized autoregressive conditional heteroscedastic model; θ is a constant less than 1, { v d } is a white noise sequence with a mean of 0 and standard deviation of 1.
As a further aspect of the present invention, the joint market in the step (4) includes an upper market for electric-gas multi-energy trading and a lower market for carbon emission trading;
the total emission E peer,t of the energy producer at the time t can be calculated by the carbon emission intensity of the corresponding node b The total emission E gene,t of the generator node can be calculated by the carbon emission intensity of the corresponding node b:
in the upper-layer market, the multi-energy aggregate market that is consolidated at the full time scale can be modeled as:
Wherein: cost function of generator Can be modeled as:
the utility function of the producer may be modeled as a piecewise function as follows:
In this model, the objective function mainly comprises two parts, To maximize the social benefits of the multi-energy collection,In order to minimize the cost of carbon emissions,To correspond to the upper carbon emission limit of the generator,AndThe output climbing power of the generator b is limited to be smaller than the upper limit range and the lower limit range of the user load and the generator respectivelyWhile the power drop is limited to less than
In the underlying market, the energy producer can be modeled as follows:
In this model, the objective function is primarily to reduce the total cost per energy producer, including the cost of the underlying market adjustment purchase ζ i,j,t(ui,j,t-Δui,j,t) energy and carbon emission penalty Besides, the model also maximizes the utility function psi i,j,t(ui,j,t-Δui,j,t) of the user and the revenue ζ i,j,tui,j,t of selling electricity;
The present model sets an upper energy consumption limit Δu i,j,t, into which an electrical load ratio χ t is introduced in order to track the electrical load supplied by the natural gas generator during the iteration.
The beneficial effects are that: quantifying carbon emissions into economic benefits through the emissions market by alternating iterations between the multi-energy-carbon emissions market, to reduce total carbon emissions while ensuring that the multi-energy transaction proceeds smoothly; the carbon emission, expenditure and income of energy production and consumption are analyzed from the angle of energy production and consumption, the transaction scheme in the P2P multi-energy system is optimized from the angle of the total system, theoretical guidance is provided for saving energy and reducing carbon emission, and the application of P2P transaction in the multi-energy system is effectively promoted;
The method ensures the economic benefit of the user after energy storage configuration, improves the efficiency and accuracy of the optimization algorithm, and has use value and popularization.
Drawings
FIG. 1 is a schematic diagram of a multi-energy collection proposed by the present invention;
FIG. 2 is a schematic diagram of a dual-layer structure of the P2P market according to the present invention;
FIG. 3 is a schematic diagram of a corresponding federated marketplace of the present invention;
Detailed Description
The technical scheme of the invention is further described below with reference to the accompanying drawings and examples.
Referring to fig. 1, a network schematic diagram formed by the multi-energy collection micro-grid of the present invention is shown, the structure can apply a low-carbon P2P energy trading method in a multi-energy-carbon emission combined market to perform optimal configuration of resources, and the method comprises the following steps:
(1) 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 an optimization model;
further, the user load data comprises annual load data of the user, and the data acquisition interval is 15 minutes at minimum;
further, the real-time electricity price adopts the national unified peak Gu Ping 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 carbon emissions intensity of each energy source is ton of carbon dioxide emissions per kWh of electrical energy consumed or per m 3 of natural gas.
(2) The multi-energy aggregation (MEC) model is as follows:
(21) The invention combines the photovoltaic power generation technology, the energy storage technology and the gas generator together to form a multi-energy aggregate. For energy producers and consumers inside the community-level micro-grid, each producer and consumer target is to minimize the power generation cost and maximize the power generation benefit, so the producer and consumer model inside the micro-grid can be built as follows:
Wherein: For decision variables of the ith multi-energy set (MEC), p j,qj represents the net output power and the purchase power of the energy producer and the consumer in the jth micro-grid respectively, α j,βj represents the input and output electric energy of the jth energy producer and the consumer in the micro-grid respectively, and g j is the load g j,load of natural gas and the load g j,turn of natural gas to electric energy which are supplied by the producer j. For each multi-energy collection, the power transmission network and the natural gas network need to meet the corresponding technical constraints And
(22) In the internal model of the multi-energy collection, the energy balance of each energy producer can be modeled as follows:
Wherein: u i,j is the generated energy of the ith power generation source of the energy producer and consumer j, so that the sum of each power generation source is the total power generation amount of the corresponding producer and consumer In the invention, the utility function is modeled by applying a quadratic function. The weight coefficient gamma com,γimp,γexp,γgas respectively represents the willingness of the energy producer j to trade energy and consume natural gas in and out of the MEC.
(23) In the external model of the multi-energy collection, each microgrid manager may model as follows:
Wherein: gamma imp,γexp,γgas represents the cost coefficients of the manager of the multi-energy collection micro-grid to buy and sell electric energy and natural gas to other multi-energy collections, Compared with all the power purchased q imp,t, the power purchased power is larger to represent the load peak value of power purchased power, and gamma peak is the cost coefficient of the load peak value to realize load peak clipping of the whole multi-energy set.
(24) The whole multi-energy integrated micro-grid can be modeled as
(3) Calculating the carbon emission;
(31) Virtual carbon flow calculation of the power network:
Wherein: The carbon emission intensity of the branch l and the node b are respectively, the node b is a power input node directly connected to the branch l, the carbon emission rate R l of each branch l is in direct proportion to the active power P l flowing through the corresponding branch, and the proportionality coefficient is the carbon emission intensity Carbon emission intensity of node bStrength of carbon emission of generator directly connected to the nodeAnd branch carbon emission intensityDirectly related to the weighted average of (a), the weighting coefficients are generator output G h and branch power P l, respectively.
(32) Virtual carbon stream calculation for natural gas network:
Wherein: Is the carbon emission intensity of the air network node b, For the power load output by the natural gas compressor m,Carbon emission rate for natural gas compressor m; for the natural gas load output by the natural gas compressor m, C heat is the natural gas heating value, and l is the branch index connected with the compressor m; r l is the carbon emission rate on line i, The carbon emission intensity for the pipe branch l is defined as the carbon emission intensity with the injection nodeConsistent; A constant indicating the unit gas combustion discharge amount; the carbon emission rate of the natural gas load is defined as the carbon strength with the natural gas load f d and the corresponding node And consistent.
(33) Exponential generalized autoregressive conditional heteroscedastic prediction of carbon price:
rd＝β0+θrd-1+σdvd
Wherein: r d is the carbon emission price on day d, which is determined by the carbon emission price on day d-1; (alpha 0,α1,β0,β1,β2) respectively representing innovative parameters, persistent parameters and asymmetric parameters of the exponential generalized autoregressive conditional heteroscedastic model; θ is a constant less than 1, { v d } is a white noise sequence with a mean of 0 and standard deviation of 1.
(4) Modeling a multi-energy-carbon emission combined market;
(41) the total emission E peer,t of the energy producer at the time t can be calculated by the carbon emission intensity of the corresponding node b The total emission E gene,t of the generator node can be calculated by the carbon emission intensity of the corresponding node b:
(42) In the upper-layer market, the multi-energy aggregate market that is consolidated at the full time scale can be modeled as:
Wherein: cost function of generator Can be modeled as:
the utility function of the producer may be modeled as a piecewise function as follows:
In this model, the objective function mainly comprises two parts, To maximize the social benefits of the multi-energy collection,To minimize carbon emission costs.To correspond to the upper carbon emission limit of the generator,AndThe output climbing power of the generator b is limited to be smaller than the upper limit range and the lower limit range of the user load and the generator respectivelyWhile the power drop is limited to less than
(43) In the underlying market, the energy producer can be modeled as follows:
In this model, the objective function is primarily to reduce the total cost per energy producer, including the cost of the underlying market adjustment purchase of ζ i,j,t(ui,j,t-Δui,j,t energy and the carbon emission penalty term In addition, the present model maximizes the user's utility function ψ i,j,t(ui,j,t-Δui,j,t) and the revenue ζ i,j,tui,j,t of selling electricity. The present model sets an upper energy consumption limit Δu i,j,t, and in order to track the electrical load supplied by the natural gas generator during the iteration, an electrical load ratio χ t is introduced into the model.
(5) After the iteration of the double-layer joint market is completed, the optimal P2P market trading result considering the carbon emission cost can be obtained. After the upper market deals, the lower market needs to calculate the carbon emissions for the corresponding deals and then redistribute the energy usage plan based on the carbon emissions market. The energy consumption plan of the lower layer is transferred to the upper layer market for iteration, and finally, the unified trading scheme is converged.
The double-layer structure of the combined market established by the invention is shown in figure 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, a smart meter and the like; the financial transmission layer covers the financial information system, combines information such as market running state and the like, is used for assisting the physical layer in carrying out transactions, and the two-layer structure perfectly shows hardware equipment and software flow of the whole P2P transaction.
The two-level combined market proposed by the invention is shown in fig. 3, the upper red circle represents the electric-gas multi-energy market, and the lower one represents the carbon emission trading market, and the two-level market corresponds to the maximization of total income and the carbon emission income of each energy producer and consumer respectively.
The invention is suitable for the multi-energy distribution network and each community-level micro-network with higher renewable energy permeability, analyzes the carbon emission, the expenditure and the income of the production energy from the perspective of energy producers and consumers, optimizes the transaction scheme in the P2P multi-energy system from the perspective of the total system, provides theoretical guidance for saving energy and reducing the carbon emission, and effectively promotes the application of P2P transaction in the multi-energy system.
It will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made to 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, substitutions and modifications which come within the spirit and principle of the invention are therefore intended to be embraced therein.
Claims (4)
1. A low carbon P2P energy trading method in a multi-energy-carbon emission combined market, the method comprising the steps of:
(1) Establishing a multi-energy set model;
(2) Acquiring user load, real-time electricity price and electricity-gas network parameters, and transmitting the collected data into the model in the step (1) according to carbon emission intensity data of each energy source;
(3) Calculating the carbon emission by applying a virtual carbon flow model;
(4) Modeling a multi-energy-carbon emission combined market;
(5) Performing iterative operation on the combined market to obtain a P2P market transaction optimal solution considering carbon emission cost;
The carbon emission amount 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:
Wherein: The carbon emission intensity of the branch l and the node b are respectively, the node b is a power input node directly connected to the branch l, the carbon emission rate R l of each branch l is in direct proportion to the active power P l flowing through the corresponding branch, and the proportionality coefficient is the carbon emission intensity Carbon emission intensity of node bStrength of carbon emission of generator directly connected to the nodeAnd branch carbon emission intensityDirectly related to the weighted average of (a), the weighting coefficients being generator output G h and branch power P l, respectively;
virtual carbon stream calculation for natural gas network:
Wherein: Is the carbon emission intensity of the air network node b, For the power load output by the natural gas compressor m,Carbon emission rate for natural gas compressor m; for the natural gas load output by the natural gas compressor m, C heat is the natural gas heating value, and l is the branch index connected with the compressor m; r l is the carbon emission rate on line i, The carbon emission intensity for the pipe branch l is defined as the carbon emission intensity with the injection nodeConsistent; A constant indicating the unit gas combustion discharge amount; the carbon emission rate of the natural gas load is defined as the carbon strength with the natural gas load f d and the corresponding node Consistent;
performing heteroscedastic prediction on an exponential generalized autoregressive condition of the carbon price:
rd＝β0+θrd-1+σdνd
wherein: r d is the carbon emission price on day d, which is determined by the carbon emission price on day d-1; (alpha 0,α1,β0,β1,β2) respectively representing innovative parameters, persistent parameters and asymmetric parameters of the exponential generalized autoregressive conditional heteroscedastic model; θ is a constant less than 1, v d is a white noise sequence with a mean value of 0 and a standard deviation of 1.
2. The method of claim 1, wherein the multi-energy collection model in step (1) comprises photovoltaic power generation, energy storage and gas generator energy consumption to form a multi-energy collection, and for the energy producers and consumers in the community-level microgrid, each producer and consumer is aimed at minimizing the power generation cost and maximizing the power generation benefit, so that the producer and consumer model in the microgrid is established as follows:
Wherein: For decision variables of the ith multi-energy collection (MEC), p j,qj represents the net output power and the purchase power of the energy producer and the consumer in the jth micro-grid respectively, alpha j,βj represents the input and output electric energy of the jth energy producer and consumer in the micro-grid respectively, g j is the load g j,load of natural gas introduced by the producer j to supply the local natural gas load g j,turn of natural gas to electric energy and the load g j,turn of natural gas to electric energy, for each multi-energy collection, the power transmission network and the natural gas network need to meet the corresponding technical constraint And
In the internal model of the multi-energy set, the energy balance of each energy producer is modeled as follows:
Wherein: u i,j is the generated energy of the ith power generation source of the energy producer and consumer j, so that the sum of each power generation source is the total power generation amount of the corresponding producer and consumer Modeling by using a quadratic function as a utility function; the weight coefficient gamma com,γimp,γexp,γgas respectively represents the willingness of the energy producer j to trade and consume natural gas in and out of the MEC;
in the external model of the multi-energy collection, each microgrid manager is modeled as follows:
Wherein: gamma imp,γexp,γgas represents the cost coefficients of the manager of the multi-energy collection micro-grid to buy and sell electric energy and natural gas to other multi-energy collections, Compared with all the power purchasing power q imp,t, the power purchasing power q imp,t is larger to represent the load peak value of power purchasing, and gamma peak is the cost coefficient of the load peak value to realize load peak clipping of the whole multi-energy set;
integrating the multi-energy collection micro-grid can be modeled as
3. The method of low carbon P2P energy trading on a multi-energy-carbon emission combined market according to claim 1, wherein in step (2), the user load data comprises annual load data of the user, and the data collection interval is at least 15 minutes; the real-time electricity price adopts the national unified peak Gu Ping three-hour electricity price;
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;
the carbon emission intensity of each energy source is ton of carbon dioxide emission corresponding to consumption of electric energy per kWh or natural gas per m 3.
4. The method of low carbon P2P energy trading in a multi-energy-carbon emission combined market according to claim 1, wherein the combined market in step (4) includes an upper market for electric-gas multi-energy trading and a lower market for carbon emission trading;
total emission E peer,t of energy producer at time t is calculated by carbon emission intensity of corresponding node b The total emission E gene,t of the generator node is calculated by the carbon emission intensity of the corresponding node b:
In the upper market, the combined multi-energy aggregate market at full time scale is modeled as:
Wherein: cost function of generator Modeling is as follows:
the utility function of the producer and the consumer is modeled as a piecewise function as follows:
In this model, the objective function mainly comprises two parts, To maximize the social benefits of the multi-energy collection,In order to minimize the cost of carbon emissions,To correspond to the upper carbon emission limit of the generator,AndThe output climbing power of the generator b is limited to be smaller than the upper limit range and the lower limit range of the user load and the generator respectivelyWhile the power drop is limited to less than
In the underlying market, modeling from the energy producer and consumer perspective is as follows:
In this model, the objective function is primarily to reduce the total cost per energy producer, including the cost of the underlying market adjustment purchase ζ i,j,t(ui,j,t-Δui,j,t) energy and carbon emission penalty Besides, the model also maximizes the utility function psi i,j,t(ui,j,t-Δui,j,t) of the user and the revenue ζ i,j,tui,j,t of selling electricity;
The present model sets an upper energy consumption limit Δu i,j,t, into which an electrical load ratio χ t is introduced in order to track the electrical load supplied by the natural gas generator during the iteration.
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