CN112883557B - Low-carbon comprehensive energy market simulation method based on presumed variation model - Google Patents
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Abstract
The invention discloses a low-carbon comprehensive energy market simulation method based on a presumed variation model, which comprises the steps of firstly providing a power market and natural gas market game model based on the presumed variation model, wherein the model assumes that competition among energy producers is competition of output, decision-making behaviors of a single producer are influenced by strategies of other producers, and elastic coefficients of loads are considered; then, a carbon trading market mechanism based on the carbon quota trading of the power producer is provided; meanwhile, the invention considers that the gas turbine unit participates in the electric power market, the natural gas market and the carbon trading market at the same time. In the proposed market framework, each market participant constitutes a relationship of a non-cooperative game; and finally, solving the optimality condition of the optimization problem of each market participant by a direct method, and further identifying the equilibrium point of the comprehensive energy market game.
Description
Technical Field
The invention relates to the field of comprehensive energy systems and power markets, in particular to a low-carbon comprehensive energy market simulation method based on a presumed variation model.
Background
Under the drive of factors such as gradual decommissioning of the coal-fired unit, high-proportion penetration of new energy, low carbonization of an energy system, reduction of natural gas price and the like, the power generation proportion of the gas turbine unit is expected to be gradually improved, so that the power system and the natural gas system are expected to show a deep coupling trend. While the potential application of the electro-gas technology is expected to further deepen the coupling. In this context, the high-ratio synchronization of the gas turbine units leads to the fact that the coordinated operation between the power system and the natural gas system becomes particularly important.
It is worth noting that the current construction of the electric-gas interconnection comprehensive energy system has the problems of insufficient price competitiveness of a gas turbine set, information barrier between sub-energy systems, incomplete market mechanism and the like. Meanwhile, the national development and improvement committee releases a new central pricing catalogue in 16 days 3 and 2020, and electric power and natural gas projects in the original catalogue are respectively modified into power transmission and distribution and oil and gas pipeline transportation according to natural monopoly links, namely, marketization pricing of the electric power and the natural gas is gradually realized. Therefore, a complete market trading system is urgently needed to be designed, a fair and reasonable competitive environment is provided for market participants, and multiple markets are traded by cooperating electric power, natural gas and carbon. Especially under the strategic guidelines of the current comprehensive energy system, the electric power market, the carbon trading system and the like in China, the solution of the problems has important significance for the construction of a new generation of low-carbon energy system in China.
Compared with the traditional coal-fired unit, the gas turbine unit has the advantages of low carbon emission and strong flexibility, but the advantages cannot be fully embodied in the current mechanism; on the other hand, the current subsidy mechanism for the new energy unit and the gas turbine unit is too subjective, and unfairness to other participants is inevitable.
Disclosure of Invention
The invention provides a low-carbon comprehensive energy market simulation method based on a presumed variation model, which aims to overcome the defects of the prior art, takes the gas turbine set into consideration to participate in electric power, natural gas and carbon trading markets simultaneously, and realizes the synergistic clearing of the low-carbon comprehensive energy market; and the simulation result of the comprehensive energy market is expected to provide reference for the formulation of market policies.
The invention adopts the following technical scheme for solving the technical problems:
the invention provides a low-carbon comprehensive energy market simulation method based on a presumed variation model, which comprises the following steps of:
step 1, generating a series of typical scenes in a year based on historical section data of a power system and a natural gas system;
step 2, generating an electric power consumer model and a natural gas consumer model in each typical scene;
step 3, constructing an optimal decision model of an electric power producer, an optimal decision model of a natural gas producer, an optimal decision model of a power transmission system operator and an optimal decision model of a gas transmission system operator based on the conjecture variation model based on the typical scenes and the electric power consumer model and the natural gas consumer model generated in the step 2;
step 4, constructing a carbon transaction model for considering carbon quota transaction aiming at each power producer;
step 5, calculating the optimality condition of the optimal decision model of the power producer, the optimality condition of the optimal decision model of the natural gas producer, the optimality condition of the optimal decision model of the power transmission system operator and the optimality condition of the carbon transaction model in step 4;
simultaneously solving an electric power consumer model, a natural gas consumer model, the optimality condition of an electric power producer optimal decision model, the optimality condition of a natural gas producer optimal decision model, the optimality condition of an optimization decision model of a power transmission system operator, the optimality condition of the optimization decision model of the power transmission system operator and the optimality condition of the carbon transaction model in the step 4 to obtain a game equilibrium solution;
step 6, outputting a simulation result of the low-carbon comprehensive energy market according to the game equilibrium solution obtained in the step 5;
in the step 2, the step of the method is carried out,
the power consumer model is:
wherein the content of the first and second substances,
is the upper limit value of the power of the electrical load d,
is the actual power value, k, of the electrical load d dt Is the elastic coefficient of the electrical load, λ i(d)t For an electrical loadd the electricity price of the node where l is the power producer,
as a set of all the power producers,
for the amount of transactions between the power producer l and the electrical load d,
for the amount of transactions between the transmission system operator and the electrical load d,
is the inverse demand function of the electrical load d;
the natural gas consumer model is:
wherein the content of the first and second substances,
is the upper limit value of the air load e,
is the actual flow value, k, of the air load e et Is the elastic coefficient of the air load, u m(e)t Is the gas price of the node where the gas load e is located, l' refers to the natural gas producer,
is a collection of all the producers of natural gas,
between the natural gas producer l' and the gas load eThe amount of the transaction of (a) is,
for the amount of transactions between the gas transmission system operator and the gas load e,
as a function of the inverse demand of the air load e.
As a further optimization scheme of the low-carbon comprehensive energy market simulation method based on the presumed variation model, in the step 3,
the optimal decision model of the power producer l is as follows:
wherein σ t Is a weight of the scene t and,
for the output of the set v in the scene t,
is the transaction amount between the power producer except the power producer l and the electric load d, w i(d)t The power transmission cost, sigma, of the ith node where the electric load d is located in the scene t t Is a weight of the scene t and,
a collection of coal-fired units owned by an electricity producer l,
a gas turbine group owned by the power producer l,
is a set of electrical loads d that are,
as a set of scenes t, w i(v)t For the transmission cost of the i-th node where the unit v is located,
in order to reduce the power generation cost of the coal-fired unit,
is the operation and maintenance cost, eta, of the gas turbine set v For the energy conversion efficiency of the gas turbine unit, u m(v)t For the natural gas price at the node m where the unit v is located,
in order to be the price of carbon,
is the unit power generation carbon emission of the unit v,
the amount of carbon balance is l,
is the installed capacity of the unit v,
is a dual variable of formula (A-6),
and
dual variables of the left side inequality and the right side inequality of the formula (A-7) respectively;
the optimal decision model of the natural gas producer l' is as follows:
wherein epsilon is a natural gas load set,
for the trading volume of the natural gas producer l' with the natural gas load e,
is the gas production of source w at scene t,
for the trading volume of natural gas producers other than the natural gas producer l' with the gas load e,
for the trade volume, q, of the natural gas producer l' with the unit v m(e)t Gas transmission cost q of a node where a gas load e is located in a scene t m(v)t Gas transmission cost q of a set v in a scene t m(w)t Gas transmission cost, omega, of the node where the gas source w is located in the scene t g Is a set of generator sets,
is a set of air sources,
the cost of the gas production of the gas source w,
is the gas production capacity of the gas source w,
is a dual variable of formula (A-9),
and
are dual variables of the left side inequality and the right side inequality of the formula (A-10).
As a further optimization scheme of the low-carbon comprehensive energy market simulation method based on the presumed variation model, the method comprises the following steps of:
the carbon transaction model that accounts for carbon quota transactions is:
wherein ^ denotes a complementary relaxation constraint, and in the complementary relaxation conditional expression (A-11), if and only if the cumulative carbon emission of the power generator reaches the carbon quota, that is, the carbon emission of the power generator reaches the carbon quota
The carbon quota emission constraint is a key constraint at this time, and the carbon price is a positive number.
As a further optimization scheme of the low-carbon comprehensive energy market simulation method based on the presumed variation model, the optimality conditions of the power producer in the step 5 are as follows:
wherein:
to estimate the competition coefficient of the power producer l in the deterioration model.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
the conjecture variation model takes the market decision-making behaviors of the power/natural gas producer into account, and the decision-making behaviors of a single producer are influenced by the strategies of other producers and the price elasticity of the electric load and the gas load is considered; the invention considers that the gas turbine unit simultaneously participates in the electric power, natural gas and carbon trading markets, and realizes the synergistic clearing of the low-carbon comprehensive energy market; and the simulation result of the comprehensive energy market is expected to provide reference for the formulation of market policies.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a schematic diagram of an integrated energy market framework.
Fig. 3 is a graph comparing node electricity prices with carbon prices for 4 cases.
Detailed Description
The present invention is further illustrated by the following figures and specific examples, which are to be understood as illustrative only and not as limiting the scope of the invention, which is to be given the full breadth of the appended claims and any and all equivalent modifications thereof which may occur to those skilled in the art upon reading the present specification.
The invention provides a low-carbon comprehensive energy market simulation method based on a presumed variation model, which aims at the condition that a gas turbine unit participates in the trading of an electric power market, a natural gas market and a carbon market at the same time, and provides an electric power-natural gas-carbon trading market collaborative clearing mechanism, namely, the low-carbon comprehensive energy market clearing is executed according to decision quotations of market participants under the condition of meeting the multi-period steady-state operation constraint of an electric power system, the multi-period slow dynamic operation constraint of a natural gas system and the carbon emission quota constraint of an electric power producer. The invention aims to realize the simulation of the comprehensive energy market by the conjecture variation model and realize the low-carbon cooperative operation of the power system and the natural gas system. Fig. 2 is a schematic diagram of an integrated energy market framework, fig. 1 is a flow chart of the method of the invention, and the method is as follows:
1 low-carbon comprehensive energy market model
1.1 electric Power market model based on presumed variation model
The electricity market model consists of models of electricity consumers, electricity producers, and operators of transmission systems.
First, the model of the power consumer is:
Equation (B-1) describes the elasticity of the electrical load, and equation (B-2) describes the trade between the electrical load and the power producer and the operator of the transmission system.
Secondly, the optimization decision model of the power producer is as follows:
in the formula, σ t As a weight of the scene t,
for the output of the set v at the scene t,
is the transaction amount between the power producer except the power producer l and the electric load d, w i(d)t The power transmission cost, sigma, of the ith node where the electric load d is located in the scene t t As a weight of the scene t,
a collection of coal-fired units owned by an electricity producer l,
a gas turbine group owned by the power producer l,
is a set of electrical loads d that are,
for a set of scenes t, w i(v)t For the transmission cost of the i-th node where the unit v is located,
in order to reduce the power generation cost of the coal-fired unit,
is the operation and maintenance cost, eta, of the gas turbine set v For the energy conversion efficiency of the gas turbine unit, u m(v)t For the natural gas price at the node m where the unit v is located,
in order to be the price of carbon,
is the unit power generation carbon emission of the unit v,
the amount of carbon balance is l,
in order to be the installed capacity of the unit v,
is a dual variable of formula (B-4),
and
are dual variables of the left inequality and the right inequality of the formula (B-5).
Equation (B-3) is an objective function of the power producer, equation (B-4) is that the sum of producer l and all load trades is equal to the sum of the power generation of producer l with the generator, and equation (B-5) describes the power generation capacity constraint of the generator set.
Finally, the optimization decision model of the transmission system operator is as follows:
in the formula:
for the power purchase quantity, theta, at node i by the transmission system operator at time t it Is the phase angle of node i at time t, b ij At the susceptance of the line ij,
for the upper limit of the transmission capacity of the line ij,
the node is a set of power system nodes and is a set of nodes connected with the power node i.
Is a dual variable of formula (B-7),
is a dual variable of formula (B-8),
and
is a dual variable of formula (B-9).
Equation (B-6) is an objective function of a transmission system operator to maximize transmission of the transmission network, equation (B-8) is a constraint of a power node balance equation, and equation (B-9) is a constraint of transmission capacity of the transmission line.
1.2 Natural gas market model based on conjecture variation model
The natural gas market model is also composed of models of natural gas consumers, natural gas producers, and operators of gas transmission systems.
First, the model for natural gas consumers is:
in the formula:
is the upper limit value of the air load e,
actual flow rate value, k, of air load e et Is the elastic coefficient of the air load, u m(e)t Is the gas price of the node where the gas load e is located.
For the trade between the natural gas producer l and the gas load e,
is the amount of the transaction between the gas transmission system operator and the gas load e.
As an inverse demand function of the air load e. Wherein the content of the first and second substances,
is the upper limit value of the air load e,
actual flow rate value, k, of air load e et Is the elastic coefficient of the air load, u m(e)t Is the gas price of the node where the gas load e is located, l' refers to the natural gas producer,
as a set of all the power producers,
for the amount of trade between the natural gas producer l' and the gas load e,
for the amount of transactions between the gas transmission system operator and the gas load e,
as a function of the inverse demand of the air load e.
Equation (B-10) describes the flexibility of the gas load, and equation (B-11) describes the trade between the gas load and the natural gas producer and the gas transmission system operator.
The optimization decision model of the natural gas producer is as follows:
wherein epsilon is a natural gas load set,
for the trading volume of the natural gas producer l' with the natural gas load e,
is the gas production of gas source w at scene t,
for the trading volume of natural gas producers other than the natural gas producer l' with the gas load e,
for the trade volume, q, of the natural gas producer l' with the unit v m(e)t Gas transmission cost q of the node of the gas load e in the scene t m(v)t Gas transmission cost q of a set v in a scene t m(w)t Gas transmission cost, omega, of the node where the gas source w is located in the scene t g Is a set of generator sets,
is a set of air sources,
the cost of the gas production of the gas source w,
is the gas production capacity of the gas source w,
are dual variables of the formula ((B-13),
and
dual variables of the left-hand inequality and the right-hand inequality of the formula (B-14), respectively
Equation (B-12) is the objective function for the natural gas producer, equation (B-13) is the sum of producer l and all gas load trades equal to the sum of gas production rates that producer l owns the gas source, and equation (B-14) describes the gas production capacity constraint for the gas source.
Finally, the optimization decision model of the gas transmission system operator is:
in the formula: m and n are natural gas nodes,
is a set of natural gas nodes, and is,
is a collection of natural gas pipelines and is characterized in that,
is a set of pressurizing stations;
for the gas purchase quantity at node m at time t, F, of the gas transmission system operator mnt The flow rate of the pipe mn at the time t,
is the flow rate of the pressurizing station k at time t, [ pi ] mt Which is the square of the pressure value at node m at time t,
is the square of the pressure value at the inlet of the pressurizing station k,
the square of the pressure value at the outlet of the pressurizing station k,
for the conversion efficiency of the compression station, W mn Is the gas transmission constant of the pipe mn,
and
the upper limit and the lower limit of the pressure square value of the node m,
and with
The compression ratio of the compression station k is the maximum value and the minimum value,
the upper limit value of the flow of the pressurizing station;
is a dual variable of formula (B-16),
is a dual variable of formula (B-17),
is a dual variable of formula (B-18),
is a dual variable of formula (B-19),
and with
Is a dual variable of formula (B-20),
and
is a dual variable of formula (B-21),
and
is a dual variable of formula (B-22).
Equation (B-15) maximizes the objective function for gas transmission network transmissions for the gas transmission system operator. And the formula (B-17) is a node flow balance constraint. Equation (B-18) describes the relationship of the line flow to the head and tail end node pressure in a second order cone. Equation (B-19) assumes that the flow direction of the pipe is known. Equation (B-20) describes the nodal pressure constraint of the natural gas system. The expressions (B-20) and (B-21) are compression ratio constraint and flow constraint of the pressurizing station respectively.
1.3 carbon trading market model based on carbon quota
In the carbon trading mechanism, a policy maker distributes a fixed carbon quota to each power producer, and when the carbon emission of the power producer is lower than the carbon quota, the carbon quota can be sold from a carbon trading market; when the power producer carbon emissions are above the carbon quota, the carbon quota may be purchased from a carbon trading market. The carbon trading market's model of coming out is:
wherein, ^ t refers to complementary relaxation constraint. In the complementary relaxation condition equation (A-11), if and only if the cumulative carbon emission of the power generator reaches the carbon quota, that is
The carbon quota emission constraint is a key constraint at this time, and the carbon price is a positive number.
2 solving of market equilibrium points
And for the optimization decision models of the market subjects, replacing the optimization decision models with the optimality conditions of the optimization decision models. And simultaneously solving each optimality condition to obtain the balance point of the Nash game.
The optimality conditions of the power producer decision models (B-3) - (B-5) are:
The optimality conditions of the optimization decision models (B-6) - (B-9) of the power transmission system operator are as follows:
the formulas (B-29) - (B-32) are dual constraints of the original problems (B-6) - (B-9), and the formula (B-33) is a strong dual equation.
The optimality conditions for the natural gas producer decision models (B-10) - (B-12) are:
wherein:
to predict the competition coefficient for natural gas producer l' in the deterioration model. The equations (B-34) - (B-37) are dual constraints of the original problems (B-10) - (B-12), and the equation (B-38) is a strong dual equation.
The optimality conditions of the optimization decision models (B-15) - (B-22) of the gas transmission system operator are as follows:
the formulas (B-39) - (B-46) are dual constraints of the original problems (B-15) - (B-22), and the formula (B-47) is a strong dual equation.
Finally, the complementary relaxation conditions (B-23) set out by the carbon trading market can be equivalently replaced by the following constraints:
and combining the optimality conditions (B-24) - (B-50) of the optimization decision models, the power consumer models (B-1) - (B-2) and the natural gas consumer models (B-10) - (B-11) to construct the following nonlinear optimization models to solve the Nash equilibrium solution:
3 example analysis
The test example of the invention consists of an IEEE-57 node power system and an actual 134 node natural gas system; the natural gas system comprises 3 gas sources, 132 pipelines, 45 load nodes and 1 pressurizing station; the generators connected with the power buses 1, 2 and 3 are gas turbine sets and are connected with the natural gas nodes 2, 8 and 15. The present invention takes into account 4 energy producers, including producer 1 (having the generator sets to which power buses 1-3 are connected), producer 2 (having the generator sets of power buses 6, 8, 9, and 12), producer 3 (having the gas sources of nodes 1 and 20), and producer 4 (having the gas source of node 80). Carbon credits for producers 1 and 2 were 1.7Mton and 2.4Mton, respectively.
The invention designs 4 comparison cases, wherein the carbon quota values are 100% carbon quota, 90% carbon quota, 85% carbon quota and 80% carbon quota respectively. The results of the comparative analysis of the 4 cases are shown in fig. 3, and it can be seen from the results that as the carbon quota is reduced, the electricity price and the carbon price are gradually increased, so that a reasonable carbon quota policy needs to be formulated in the policy level for reducing the carbon emission in the power industry, and although the carbon emission can be directly reduced by too low carbon quota, the problem of too large burden on consumers is easily caused. The comprehensive energy market simulation result provides decision basis for market policy makers.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (4)
1. A low-carbon comprehensive energy market simulation method based on a presumed variation model is characterized by comprising the following steps:
step 1, generating a series of typical scenes in a year based on historical section data of a power system and a natural gas system;
step 2, generating an electric power consumer model and a natural gas consumer model in each typical scene;
step 3, constructing an optimal decision model of an electric power producer, an optimal decision model of a natural gas producer, an optimal decision model of a power transmission system operator and an optimal decision model of a gas transmission system operator based on the conjecture variation model based on the typical scenes and the electric power consumer model and the natural gas consumer model generated in the step 2;
step 4, constructing a carbon transaction model for considering carbon quota transaction aiming at each power producer;
step 5, calculating the optimality condition of the optimal decision model of the power producer, the optimality condition of the optimal decision model of the natural gas producer, the optimality condition of the optimal decision model of the power transmission system operator and the optimality condition of the carbon transaction model in the step 4;
simultaneously solving an electric power consumer model, a natural gas consumer model, the optimality condition of an electric power producer optimal decision model, the optimality condition of a natural gas producer optimal decision model, the optimality condition of an optimization decision model of a power transmission system operator, the optimality condition of the optimization decision model of the power transmission system operator and the optimality condition of the carbon transaction model in the step 4 to obtain a game equilibrium solution;
step 6, outputting a simulation result of the low-carbon comprehensive energy market according to the game equilibrium solution obtained in the step 5;
in the step (2), the first step is that,
the power consumer model is:
wherein the content of the first and second substances,
is the upper limit value of the power of the electrical load d,
is the actual power value, k, of the electrical load d dt Is the elastic coefficient, λ, of the electrical load i(d)t Is the electricity price of the node where the electric load d is located, i refers to the power producer,
as a set of all the power producers,
for the amount of transactions between the power producer l and the electrical load d,
for the amount of transactions between the transmission system operator and the electrical load d,
is the inverse demand function of the electrical load d;
the natural gas consumer model is:
wherein, the first and the second end of the pipe are connected with each other,
is the upper limit value of the air load e,
is the actual flow value, k, of the air load e et Is the elastic coefficient of the air load, u m(e)t Is the gas price of the node where the gas load e is located, l' refers to the natural gas producer,
is a collection of all the producers of natural gas,
for the trade between the natural gas producer l' and the gas load e,
for the amount of transactions between the gas transmission system operator and the gas load e,
as an inverse demand function of the air load e.
2. The method for simulating the low-carbon comprehensive energy market based on the presumed variation model is characterized in that, in the step 3,
the optimal decision model of the power producer l is as follows:
wherein σ t Is a weight of the scene t and,
for the output of the set v at the scene t,
is the transaction amount between the power producer except the power producer l and the electric load d, w i(d)t The power transmission cost, sigma, of the ith node where the electric load d is located in the scene t t As a weight of the scene t,
a collection of coal-fired units owned by an electricity producer l,
for the gas turbine group owned by the power producer l,
is a set of electrical loads d that are,
as a set of scenes t, w i(v)t For the transmission cost of the i-th node where the unit v is located,
in order to reduce the power generation cost of the coal-fired unit,
is the operation and maintenance cost, eta, of the gas turbine set v For the energy conversion efficiency of the gas turbine unit, u m(v)t For the natural gas price at the node m where the unit v is located,
in order to be the price of carbon,
the carbon emission per unit generated power of the unit v,
the amount of carbon balance is l,
in order to be the installed capacity of the unit v,
is a dual variable of formula (A-6),
and
dual variables of the left side inequality and the right side inequality of the formula (A-7) respectively;
the optimal decision model for the natural gas producer l' is:
wherein epsilon is a natural gas load set,
for the trading volume of the natural gas producer l' with the natural gas load e,
is the gas production of source w at scene t,
the trading volume of a natural gas producer other than the natural gas producer l' with the gas load e,
for the trade volume, q, of the natural gas producer l' with the unit v m(e)t Gas transmission cost q of a node where a gas load e is located in a scene t m(v)t Gas transmission cost q of a set v in a scene t m(w)t Gas transmission cost, omega, of the node where the gas source w is located in the scene t g Is a set of power generating units,
is a set of air sources,
the cost of the gas production of the gas source w,
is the gas production capacity of the gas source w,
is a dual variable of formula (A-9),
and
are dual variables of the left side inequality and the right side inequality of the formula (A-10).
3. The low-carbon comprehensive energy market simulation method based on the presumed variation model as claimed in claim 2, wherein in the step 4:
the carbon transaction model that accounts for carbon quota transactions is:
wherein, t denotes the complementary relaxation constraint, in the complementary relaxation conditional expression (a-11), if and only if the cumulative carbon emission of the power generator reaches the carbon quota, i.e. if and only if
At this time, the carbon quota emission constraint is a key constraint, and the carbon price is a positive number.
4. The low-carbon comprehensive energy market simulation method based on the presumed variation model is characterized in that the optimality conditions of the power producer in the step 5 are as follows:
wherein:
to estimate the competition coefficient of the power producer l in the deterioration model.
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| CN111080099A (en) * | 2019-12-04 | 2020-04-28 | 上海交通大学 | Comprehensive energy system scheduling evaluation method and system based on carbon emission management |
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