CN115423282A - Electricity-hydrogen-storage integrated energy network multi-objective optimization scheduling model based on reward and punishment stepped carbon transaction - Google Patents

Electricity-hydrogen-storage integrated energy network multi-objective optimization scheduling model based on reward and punishment stepped carbon transaction Download PDF

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CN115423282A
CN115423282A CN202211037608.2A CN202211037608A CN115423282A CN 115423282 A CN115423282 A CN 115423282A CN 202211037608 A CN202211037608 A CN 202211037608A CN 115423282 A CN115423282 A CN 115423282A
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李正曦
苏小玲
杨立滨
陈来军
李春来
曹博文
张海宁
司杨
安娜
韩文元
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State Grid Qinghai Electric Power Co Clean Energy Development Research Institute
Qinghai University
State Grid Qinghai Electric Power Co Ltd
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Qinghai University
State Grid Qinghai Electric Power Co Ltd
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Abstract

The invention discloses a multi-objective optimization scheduling model of an electricity-hydrogen-storage integrated energy network based on reward and punishment stepped carbon transaction, wherein the electricity-hydrogen-storage integrated energy network mainly comprises wind power, a photovoltaic unit, an energy storage battery, a hydrogen energy utilization module and a large power grid, and the operation mode of the electricity-hydrogen-storage integrated energy network can be divided into the following steps: (1) Converting the surplus power in the load valley period into hydrogen energy to supply energy to the hydrogen load or enter a hydrogen storage system; (2) the hydrogen storage system simultaneously supplies energy to the hydrogen load and the electric load; (3) During the peak load period, when the wind-solar power generation and the hydrogen fuel cell can not meet the power demand, the comprehensive energy network purchases power from the power grid or hydrogen from the hydrogen network; the invention has the advantages that: the network loss, the environmental cost and the operation cost of the electricity-hydrogen-storage comprehensive energy network are optimized, the carbon emission of the electricity-hydrogen-storage comprehensive energy network is reduced, and the new energy consumption is promoted.

Description

Electricity-hydrogen-storage integrated energy network multi-objective optimization scheduling model based on reward and punishment stepped carbon transaction
Technical Field
The invention relates to the technical field of multi-objective optimization scheduling of an electricity-hydrogen-storage integrated energy network, in particular to the technical field of multi-objective optimization scheduling of the electricity-hydrogen-storage integrated energy network based on carbon trading emission.
Background
Energy is an important material basis for the development of economic society. With the development of economic society of China, the energy demand is also rapidly increasing. Under the target development background of 'carbon neutralization', new zero-carbon energy enters an electric power system in a large scale and in a high proportion, and the energy safety in China is converted from the energy supply risk to the technical level risk.
Successful implementation of energy transformation means that supply safety, economy and environmental protection are combined with innovative and intelligent climate protection, power technology innovation is enhanced, utilization and high efficiency of clean energy are promoted, large-scale and long-distance delivery of clean energy is realized, and a new mode of friendly access and comprehensive utilization of new energy is explored. In a new global scientific and technological revolution and industrial change, advanced technologies such as the internet, the internet of things, big data, artificial intelligence and the like are integrated with the energy industry, and new technologies, new modes and new states of the energy industry are promoted. The comprehensive energy network is an energy utilization system characterized by deeply combining a new energy technology and an information technology, and is a distributed and open shared network based on renewable energy.
At present, a model of the comprehensive energy network optimization scheduling mainly simplifies a plurality of targets such as the minimum power generation cost and the minimum environmental impact cost into a single target for optimization, and mainly considers the multi-target optimization scheduling in the aspects of electricity-gas conversion, system operation economy, energy utilization rate and the like under a fixed weight, and the electricity-hydrogen-storage comprehensive energy network optimization scheduling model considering the dynamic adjustment of carbon emission cost and weight coefficients is less, and conflicts often exist among the targets, so that the targets are generally difficult to balance and are optimal at the same time.
Disclosure of Invention
In order to solve the defects in the background art, the invention provides a multi-objective optimization operation model of the electricity-hydrogen-storage integrated energy network based on reward and punishment stepped carbon transaction, a reward and punishment stepped carbon transaction mechanism is added on the basis of the traditional optimization scheduling model of the integrated energy network, and the operation cost of the integrated energy network is subsidized by selling the income obtained by the surplus carbon emission right; meanwhile, a combination type dynamic weight coefficient combining the advantages of monotonicity of a proportion type dynamic weight coefficient and quick change of an index type dynamic weight coefficient is added, the running cost and the environmental cost are balanced, the dynamic optimal running of the comprehensive energy network is realized, and the system is better in consideration of economy and environmental protection.
The technical solution adopted by the invention to solve the technical problems is as follows:
a multi-objective optimization scheduling model of an electricity-hydrogen-storage integrated energy network based on reward and punishment ladder type carbon transaction,
(1) Comprehensive energy network architecture establishment
The electricity-hydrogen-storage comprehensive energy network mainly comprises wind power, a photovoltaic unit, an energy storage battery, a hydrogen energy utilization module and a large power grid, and the operation mode can be divided into: (1) Converting surplus power in the load valley period into hydrogen energy to supply energy for hydrogen loads or enter a hydrogen storage system; (2) the hydrogen storage system simultaneously supplies energy to the hydrogen load and the electric load; (3) During the peak load period, when the wind-solar power generation and the hydrogen fuel cell can not meet the power demand, the comprehensive energy network purchases power from the power grid or hydrogen from the hydrogen network;
(2) Multi-objective optimization model process of electricity-hydrogen-storage integrated energy network
An electricity-hydrogen-storage comprehensive energy network multi-objective optimization model based on reward and punishment stepped carbon transaction aims to optimize network loss, environmental cost and operating cost of the comprehensive energy network by site selection of an access point, transaction of carbon emission weight, coordination optimization among electricity-hydrogen energy systems and balance of dynamic weight on the operating cost and the environmental cost of the comprehensive energy network;
(3) Carbon transaction mechanism establishment
The method comprises the steps that initial carbon emission quota distribution is carried out in a non-compensation distribution mode, a non-compensation carbon emission quota of a system is determined by a reference line method, the initial carbon emission quota of carbon emission generated by electricity-hydrogen-storage integrated energy network from a superior power grid for electricity purchase comes from a coal-fired generator set, in order to further control the total carbon emission amount and stimulate the enthusiasm of energy conservation and emission reduction of energy supply enterprises, a reward coefficient gamma concept is provided, namely when the total carbon emission amount of the energy supply enterprises is lower than the free distributed carbon emission quota, a certain reward subsidy is given by a government; a reward and punishment stepped carbon transaction cost calculation model is established on the basis of the carbon transaction cost calculation model, a plurality of emission intervals are stipulated, when the carbon emission is smaller than the freely distributed carbon emission amount, the carbon transaction cost is negative, the fact that an energy supply enterprise can sell redundant carbon emission quota in a carbon transaction market can be shown, a certain technical subsidy can be obtained, and the carbon transaction price corresponding to the interval with the smaller carbon emission amount is higher; when the carbon emission is greater than the carbon emission amount distributed for free, the carbon transaction cost is positive, the carbon emission right needs to be purchased in a carbon transaction market by an energy supply enterprise, the carbon transaction price corresponding to the interval with the larger carbon emission amount is higher, and the actual carbon emission amount is subjected to piecewise linearization processing when the model is solved;
in the carbon transaction cost calculation model, the relationship between the carbon transaction price and the carbon transaction amount defines a plurality of carbon transaction amount intervals: the positive interval represents that the carbon emission rights of energy supply enterprises are insufficient and the carbon emission rights of the energy supply enterprises need to be purchased from a carbon trading market; the negative interval indicates that the energy supply enterprise has the residual carbon emission right and can sell and obtain the income;
(4) Use of dynamic weighting coefficients
Firstly, a proportional dynamic weight coefficient is adopted, a proportional function has monotonicity, and the change trend of an input variable can be followed, so that the weight is changed along with the positive correlation of the variable, according to the sum of the state x (j) at the moment and the state x (j-1) at the last moment, if the current parameter is greater than the last moment, the weight is increased, and if the current parameter is less than the last moment, the weight is reduced, and the formula is as follows:
Figure BDA0003817291970000031
in the growth stage, the growth trend of the proportional function is relatively slow, the proportional function is not sensitive to the change of the parameters, the change speed of the exponential function is fast, the change trends are the same, and an exponential dynamic weight coefficient of e can be adopted, and the formula is as follows:
a 2 ＝e y(j) (2)
the exponential function is over fast increased, exponential explosion is easy to generate by directly inputting parameters, the number of the input parameters needs to be adjusted to obtain a new input parameter y (j), and the trend of the weight is not changed when the parameters are increased or decreased, wherein the formula is as follows:
y(j)＝x(j)-x(j-1) (3)
selecting a when the amplitude is relatively slow 2 It is more sensitive, has large variation range, is easy to make the weight be in the boundary value of the value range for a long time, is difficult to reflect the specific variation of the weight, and simultaneously makesFor the case of excess weighting adjustment, for a 1 And a 2 In other words, the selection of the weight can be determined by combining the advantages of the two, and a is selected when the load curve is increased or decreased by a larger amplitude through the change of the total load curve 2 The weight can be changed rapidly, and the change trend is relieved; when the load curve increases and decreases in a relatively slow manner, a is selected 1 And adjusting to form a combined dynamic weight coefficient.
The carbon emission weight quota is calculated as:
Figure BDA0003817291970000041
in the formula: e c The carbon emission quota for purchasing electricity for the superior power grid; taking chi as the carbon emission right quota of unit electric quantity, and taking 0.728 t/(MW & h); p b,t And (4) purchasing power from the superior power grid for the unit time period t.
The actual carbon emission calculation formula is as follows:
Figure BDA0003817291970000042
in the formula: e g Actual carbon emission for purchasing power for a superior power grid; a is 1 、b 1 、c 1 And calculating the coefficient for the carbon emission of the energy conversion equipment of the coal-fired unit.
In order to reduce the carbon emission of the electricity-hydrogen-storage integrated energy network, a reward coefficient gamma is introduced into a carbon transaction price, and a reward and punishment step-type carbon transaction cost model is constructed and calculated as follows:
Figure BDA0003817291970000043
C co2 ＝C c (E c -E g ) (7)
in the formula: c c A carbon trade unit price; c is the basic unit carbon number; beta is the incremental magnitude of the step price; v is the carbon emission interval lengthDegree; c co2 Is the carbon transaction cost.
Establishing an objective function: when the basic load of the comprehensive energy network is higher, the output of a fan and a photovoltaic and the energy supply of a hydrogen fuel cell are smaller, the comprehensive energy network needs to purchase electricity from an external network, so that the environmental cost is increased, the weight of the minimum objective function of the environmental cost is increased at the moment, the environmental protection performance of the comprehensive energy network is improved, the real-time weight before the environmental cost takes the real-time electricity purchasing quantity required by the comprehensive energy network as a parameter x (t), a dynamic weight optimization model is established, the weight lambda is automatically adjusted for the environmental cost increase, the operation cost weight is taken as a reference item, the operation cost weight changes along with the change of the environmental cost weight, and the comprehensive optimization scheduling objective function based on the dynamic weight is as follows:
Figure BDA0003817291970000051
Figure BDA0003817291970000052
Figure BDA0003817291970000053
in the formula: f is the comprehensive cost of the comprehensive energy network; eta is the operation cost weight coefficient; λ is an environmental cost weight coefficient;
Figure BDA0003817291970000054
for design parameters, take 1.1 and 0.9.
An objective function of operating cost of
minF 1 ＝F WT +F PV +F W +F G +C co2 (11)
In the formula: f WT 、F PV Respectively the operation and maintenance costs of the wind power generator set and the photovoltaic generator set; f W 、F G Respectively punishment cost for wind abandoning and light abandoning,
1) Operating cost of new energy
Figure BDA0003817291970000055
In the formula: t is a scheduling period; n is a radical of PV 、N WT The number of photovoltaic and wind power generator sets; sgn (ui, t) represents the start-stop state of the ith unit at the time t; a is i 、b i 、c i The operation cost coefficient of the ith unit; p i,t The output value of the unit i at the moment t,
2) Wind and light abandon punishment cost
Figure BDA0003817291970000061
In the formula: c W 、C PV Punishment coefficients of wind abandonment and light abandonment are respectively; p' Wt 、P′ PVt Respectively obtaining output predicted values of the wind power field and the photovoltaic field in a t period; p Wt 、P PVt And the actual output values of the wind power and the photovoltaic at the t time interval are respectively.
In order to minimize the carbon emission of the comprehensive energy grid, namely, the electricity purchasing quantity from the superior power grid, the environmental cost objective function is set as
minF 2 ＝E g C c (14)
In the formula: f 2 The environmental cost of the comprehensive energy network is reduced.
Establishment of constraints
1) Hydrogen energy storage system capacity constraints
Figure BDA0003817291970000062
In the formula:
Figure BDA0003817291970000063
the upper limit and the lower limit of the capacity of the hydrogen energy storage system;
2) Hydrogen production system operating constraints
Figure BDA0003817291970000064
In the formula:
Figure BDA0003817291970000065
the upper limit of the climbing power for producing hydrogen by electrolyzing water;
3) Hydrogen net constraint condition
Hydrogen net node gas pressure constraint:
Figure BDA0003817291970000066
in the formula: h i,t Is the air pressure of the node i at the time t;
Figure BDA0003817291970000067
the upper limit and the lower limit of node air pressure are respectively set;
hydrogen network flow constraint:
Figure BDA0003817291970000068
Figure BDA0003817291970000069
in the formula:
Figure BDA0003817291970000071
is the hydrogen pipeline flow;
Figure BDA0003817291970000072
pressures upstream and downstream of the conduit, respectively; k is ij Is the combined coefficient of the pipeline and the hydrogen fluid; s ij,t Is the pipeline gas flow direction at the moment t;
4) Grid constraint conditions
And power balance constraint of the power grid nodes:
Figure BDA0003817291970000073
in the formula: p (i, t) and Q (i, t) are respectively the injected active power and reactive power of a node i at the moment t; g ij 、B ij Respectively a real part and an imaginary part of i rows and j columns in the node admittance matrix; u (i, t) is the voltage amplitude of the node i at the time t; theta ij (t) is the phase angle difference between the two ends of the branch ij at the moment t; nbus is the number of network nodes;
and (3) power grid node voltage amplitude constraint:
U min (i)≤U(i,t)≤U max (i) (21)
in the formula: u shape max (i)、U min (i) The upper limit and the lower limit of the voltage amplitude allowed by the node i are respectively set;
5) Other constraints
And (3) new energy output constraint:
Figure BDA0003817291970000074
in the formula:
Figure BDA0003817291970000075
respectively taking the maximum output values of wind power and photovoltaic power;
wind and light unit climbing restraint:
Figure BDA0003817291970000076
in the formula:
Figure BDA0003817291970000077
the up-down climbing rate of the wind turbine generator;
Figure BDA0003817291970000078
the up-and-down climbing speed of the photovoltaic unit.
The invention adopts the technical proposal to achieve the following beneficial effects:
1. the invention provides a multi-objective optimization operation model of the electricity-hydrogen-storage comprehensive energy network, optimizes the network loss, the environmental cost and the operation cost of the electricity-hydrogen-storage comprehensive energy network, reduces the carbon emission of the electricity-hydrogen-storage comprehensive energy network, and promotes the consumption of new energy.
2. According to the method, the operation cost and the environment cost of the integrated energy network are balanced through site selection, carbon emission right transaction, coordination optimization and dynamic weight among the electricity and hydrogen energy systems, the network loss, the operation cost and the environment cost of the electricity-hydrogen-storage integrated energy network are optimized, the defect that a step-type carbon transaction mechanism and a dynamic weight coefficient are not considered in the conventional model is overcome, and the economic performance and the environmental protection performance of the integrated energy network can be better considered.
3. The invention can realize the dynamic adjustment of the weight coefficient of each optimization target and balance the relation among a plurality of optimization targets on the basis of meeting the minimum carbon emission of the comprehensive energy network.
4. The feasibility, effectiveness and correctness of the method are verified by comparing the network loss, the environmental cost, the operation cost and the carbon transaction cost before and after the optimization of the comprehensive energy network.
5. The invention provides a novel electricity-hydrogen-storage integrated energy network multi-target optimization operation model based on reward and punishment stepped carbon transaction by considering a reward and punishment stepped carbon transaction mechanism and a dynamic weight coefficient. The operation cost and the environmental cost of the comprehensive energy network are balanced by selecting the access point address of new energy and electricity-hydrogen conversion equipment, trading the carbon emission right, and coordinating optimization and dynamic weight among electricity-hydrogen energy systems, so that the network loss, the operation cost and the environmental cost of the electricity-hydrogen-storage comprehensive energy network are further reduced, the defect that the existing model does not consider reward and punishment stepped carbon trading mechanism and dynamic weight coefficient is overcome, and the economical efficiency and the environmental protection performance are considered during the operation of the comprehensive energy network.
Drawings
FIG. 1 is a schematic diagram of an electric-hydrogen-storage integrated energy grid architecture according to the present invention;
FIG. 2 is a flow chart of a multi-objective optimization model of the electricity-hydrogen-storage integrated energy network in the invention;
FIG. 3 is a graph showing a relationship between a carbon transaction price and a carbon transaction amount in the present invention;
FIG. 4 is a diagram of an integrated energy grid according to the present invention;
FIG. 5 is a comparison graph before and after optimization of the system loss according to the present invention;
FIG. 6 is a time-of-use electricity price diagram of the power grid of the present invention;
FIG. 7 is a graph of four photovoltaic output scenarios of the present invention;
FIG. 8 is a diagram of four fan output scenarios of the present invention.
Detailed Description
A multi-objective optimization scheduling model of an electricity-hydrogen-storage integrated energy network based on reward and punishment ladder type carbon transaction,
(1) Comprehensive energy network architecture establishment
The electricity-hydrogen-storage comprehensive energy network mainly comprises wind power, a photovoltaic unit, an energy storage battery, a hydrogen energy utilization module and a large power grid, and the operation mode of the electricity-hydrogen-storage comprehensive energy network can be divided into the following steps: (1) Converting the surplus power in the load valley period into hydrogen energy to supply energy to the hydrogen load or enter a hydrogen storage system; (2) the hydrogen storage system simultaneously supplies energy to the hydrogen load and the electric load; (3) During the peak load period, when the wind-solar power generation and the hydrogen fuel cell can not meet the power demand, the comprehensive energy network purchases power from the power grid or hydrogen from the hydrogen network, and the structure of the electricity-hydrogen-storage comprehensive energy network is shown in figure 1;
(2) Multi-objective optimization model process of electricity-hydrogen-storage integrated energy network
An electricity-hydrogen-storage comprehensive energy network multi-objective optimization model based on reward and punishment stepped carbon transaction aims at optimizing network loss, environmental cost and operating cost of the comprehensive energy network by balancing access point site selection, carbon emission weight transaction, coordination optimization among electricity-hydrogen energy systems and dynamic weight with the operating cost and the environmental cost of the comprehensive energy network, and the flow of the multi-objective optimization model is shown in figure 2;
(3) Carbon transaction mechanism establishment
The method comprises the steps that initial carbon emission quota distribution is carried out in a non-compensation distribution mode, a non-compensation carbon emission quota of a system is determined by a reference line method, the initial carbon emission quota of carbon emission generated by electricity-hydrogen-storage integrated energy network from a superior power grid for electricity purchase comes from a coal-fired generator set, in order to further control the total carbon emission amount and stimulate the enthusiasm of energy conservation and emission reduction of energy supply enterprises, a reward coefficient gamma concept is provided, namely when the total carbon emission amount of the energy supply enterprises is lower than the free distributed carbon emission quota, a certain reward subsidy is given by a government; a reward and punishment stepped carbon transaction cost calculation model is established on the basis of the carbon transaction cost calculation model, a plurality of emission intervals are stipulated, when the carbon emission is smaller than the freely distributed carbon emission amount, the carbon transaction cost is negative, the fact that an energy supply enterprise can sell redundant carbon emission quota in a carbon transaction market can be shown, a certain technical subsidy can be obtained, and the carbon transaction price corresponding to the interval with the smaller carbon emission amount is higher; when the carbon emission is greater than the carbon emission amount distributed for free, the carbon transaction cost is positive, the carbon emission right needs to be purchased in a carbon transaction market by an energy supply enterprise, the carbon transaction price corresponding to the interval with the larger carbon emission amount is higher, and the actual carbon emission amount is subjected to piecewise linearization processing when the model is solved;
in the carbon transaction cost calculation model, the relationship between the carbon transaction price and the carbon transaction amount is visualized as fig. 3, and a plurality of carbon transaction amount intervals are specified: the positive interval represents that the carbon emission rights of energy supply enterprises are insufficient and the carbon emission rights of the energy supply enterprises need to be purchased from a carbon trading market; the negative interval indicates that the energy supply enterprise has the residual carbon emission right and can sell and obtain the income;
(4) Use of dynamic weight coefficients
When a plurality of objective functions are optimized simultaneously, on one hand, each objective function needs to be lower than the expected value of the objective function as much as possible; on the other hand, the weight of the objective function is changed in real time to obtain the objective function combination with different weights, so that the optimal solution obtained by each objective is more beneficial to the overall optimization, and finally, the objective functions tend to be optimal. Compared with the conventional multi-objective optimization, the method can only generate a single solution or generate a solution set and then perform optimization, in the dynamic weighting method, the weights are self-judged and chosen according to different conditions in the multi-objective optimization process, and extra sequencing and selection are not needed while the solution set is increased. The multi-objective collaborative optimization method based on the real-time weight has the advantages of simple calculation process, less parameters needing to be designed, high efficiency and the like. For a complex multi-objective optimization problem, the method can be regarded as an optional solution method. Therefore, in order to improve the comprehensive performance, simplify the calculation, avoid preference selection and prevent neglecting other objective functions while meeting one objective function, a strategy capable of adjusting the weight in real time according to different conditions is provided.
Firstly, a proportional dynamic weight coefficient is adopted, a proportional function has monotonicity, and the change trend of an input variable can be followed, so that the weight is changed along with the positive correlation of the variable, according to the sum of the state x (j) at the moment and the state x (j-1) at the last moment, if the current parameter is greater than the last moment, the weight is increased, and if the current parameter is less than the last moment, the weight is reduced, and the formula is as follows:
Figure BDA0003817291970000101
in the growth stage, the growth trend of the proportional function is relatively slow, the proportional function is not sensitive to the change of the parameters, the change speed of the exponential function is fast, the change trends are the same, and an exponential dynamic weight coefficient of e can be adopted, and the formula is as follows:
a 2 ＝e y(j) (2)
the exponential function is over fast increased, exponential explosion is easy to generate by directly inputting parameters, the number of the input parameters needs to be adjusted to obtain a new input parameter y (j), and the trend of the weight is not changed when the parameters are increased or decreased, wherein the formula is as follows:
y(j)＝x(j)-x(j-1) (3)
selecting a when the amplitude is relatively slow 2 It is more sensitive, has large variation range, is easy to make the weight be in the boundary value of the value range for a long time, is difficult to reflect the specific variation of the weight, and causes the condition of excessive weight adjustment, and for a 1 And a 2 In other words, the selection of the weight can be determined by combining the advantages of the two, and a is selected when the load curve is increased or decreased by a larger amplitude through the change of the total load curve 2 The weight can be changed rapidly, and the change trend is relieved; when the load curve increases and decreases slowly, a is selected 1 And adjusting to form a combined dynamic weight coefficient.
(5) Photovoltaic output uncertainty analysis
The radiation intensity of illumination has direct and great influence on the photovoltaic output power, generally, the photovoltaic voltage output can provide stable electric energy for users within a certain power interval, but in actual production, the photovoltaic cells have stable power output due to the randomness and intermittency of illumination, and the output power is also influenced by the temperature, so that the photovoltaic power generation has strong uncertainty. Research also shows that other factors such as atmospheric quality, altitude and the like also have influence on photovoltaic power generation, and the larger the atmospheric quality is, the more adverse is the stability of photovoltaic output; the higher the altitude, the higher the photovoltaic output stability.
(6) Uncertain analysis of wind power output
The randomness refers to the condition that the output of a fan is random due to unstable wind speed, and the randomness of the generated power brings great challenges to the safe and stable operation of a power grid. After the wind power is connected into a power grid, the peak-valley difference of the original power utilization of the load can be increased, the safe and economic operation of the system is not facilitated, and a wind abandon method is usually selected. And a better system should utilize renewable energy to generate electricity to minimize load peak-to-valley differences and improve absorption. However, in practical engineering, with the continuous increase of the scale of wind power generation, slight changes in wind speed may cause great influence on the output of the wind farm, thereby causing irreversible damage to the power system, so in order to solve the problem of uncertainty of wind power output, other devices such as energy storage and the like are usually connected to adjust the load peak-valley difference of the system by using the charging and discharging characteristics of the devices, and the risk caused by randomness and intermittence of the wind power output is reduced.
The carbon emission weight quota is calculated as:
Figure BDA0003817291970000121
in the formula: e c The carbon emission quota for purchasing electricity for the superior power grid; taking chi as the carbon emission right quota of unit electric quantity, and taking 0.728 t/(MW & h); p b,t Purchasing from a superior power grid for a unit time t integrated energy gridElectrical power.
The actual carbon emission calculation formula is as follows:
Figure BDA0003817291970000122
in the formula: e g Actual carbon emission for purchasing power for a superior power grid; a is 1 、b 1 、c 1 And calculating the coefficient for the carbon emission of the energy conversion equipment of the coal-fired unit.
In order to reduce the carbon emission of the electricity-hydrogen-storage integrated energy network, a reward coefficient gamma is introduced into a carbon transaction price, and a reward and punishment step-type carbon transaction cost model is constructed and calculated as follows:
Figure BDA0003817291970000123
C co2 ＝C c (E c -E g ) (7)
in the formula: c c A carbon trade unit price; c is the basic unit carbon number; beta is the incremental magnitude of the step price; v is the length of the carbon emission interval; c co2 Is the carbon transaction cost.
Establishing an objective function: the method comprises the steps of taking the minimum running cost and the minimum carbon emission of the comprehensive energy network as a target, dynamically adjusting a hydrogen energy storage system and electricity purchasing quantity according to new energy output, load requirements, a hydrogen network running state and the electricity price of a superior power network, trading the carbon emission right according to the carbon emission quantity generated by electricity purchasing, and effectively promoting carbon emission reduction, when the basic load of the comprehensive energy network is high, the output of a fan and a photovoltaic and the energy supply of a hydrogen fuel cell are small, the comprehensive energy network needs to purchase electricity from an external network, so that the environmental cost is increased, at the moment, the weight of a minimum target function of the environmental cost is increased, the environmental protection performance of the comprehensive energy network is improved, a dynamic weight optimization model is established by taking the real-time electricity purchasing quantity required by the comprehensive energy network as a parameter x (t) in real-time before the environmental cost, the self-adjusting weight lambda is increased for the environmental cost, the running cost weight is taken as a reference item, the running cost weight changes along with the change of the environmental cost weight, and the comprehensive optimization scheduling target function based on the dynamic weight is as follows:
Figure BDA0003817291970000131
Figure BDA0003817291970000132
Figure BDA0003817291970000133
in the formula: f is the comprehensive cost of the comprehensive energy network; eta is an operation cost weight coefficient; λ is an environmental cost weight coefficient;
Figure BDA0003817291970000134
for design parameters, take 1.1 and 0.9.
An objective function of operating cost of
minF 1 ＝F WT +F PV +F W +F G +C co2 (11)
In the formula: f WT 、F PV Respectively the operation and maintenance costs of the wind power generator set and the photovoltaic generator set; f W 、F G Respectively punishment cost for wind abandoning and light abandoning,
1) Operating cost of new energy
Figure BDA0003817291970000135
In the formula: t is a scheduling period; n is a radical of PV 、N WT The number of photovoltaic and wind power generator sets; sgn (ui, t) represents the start-stop state of the ith unit at the time t; a is i 、b i 、c i The operation cost coefficient of the ith unit; p is i,t The output value of the unit i at the moment t,
2) Wind and light abandon punishment cost
Figure BDA0003817291970000141
In the formula: c W 、C PV Respectively is a wind abandoning punishment coefficient and a light abandoning punishment coefficient; p' Wt 、P′ PVt Respectively obtaining output predicted values of the wind power field and the photovoltaic field in a t period; p Wt 、P PVt And the actual output values of the wind power and the photovoltaic at the t time interval are respectively.
In order to minimize the carbon emission of the comprehensive energy grid, namely, the electricity purchasing quantity from the superior power grid, the environmental cost objective function is set as
minF 2 ＝E g C c (14)
In the formula: f 2 The environmental cost of the comprehensive energy network is reduced.
Establishment of constraints
1) Hydrogen energy storage system capacity constraints
Figure BDA0003817291970000142
In the formula:
Figure BDA0003817291970000143
the upper limit and the lower limit of the capacity of the hydrogen energy storage system;
2) Hydrogen production system operating constraints
Figure BDA0003817291970000144
In the formula:
Figure BDA0003817291970000145
the upper limit of the climbing power for producing hydrogen by electrolyzing water;
3) Hydrogen net constraint condition
Hydrogen net node gas pressure constraint:
Figure BDA0003817291970000146
in the formula: h i,t Is the air pressure at node i at time t;
Figure BDA0003817291970000147
respectively an upper limit and a lower limit of node air pressure;
hydrogen network flow constraint:
Figure BDA0003817291970000151
Figure BDA0003817291970000152
in the formula:
Figure BDA0003817291970000153
is the hydrogen pipeline flow;
Figure BDA0003817291970000154
pressures upstream and downstream of the conduit, respectively; k is ij Is the combined coefficient of the pipeline and the hydrogen fluid; s ij,t Is the pipeline gas flow direction at the moment t;
4) Grid constraint conditions
And power balance constraint of the power grid nodes:
Figure BDA0003817291970000155
in the formula: p (i, t) and Q (i, t) are respectively the injected active power and reactive power of a node i at the moment t; g ij 、B ij Respectively a real part and an imaginary part of i rows and j columns in the node admittance matrix; u (i, t) is the voltage amplitude of the node i at the time t; theta ij (t) is the phase angle difference between the two ends of the branch ij at the moment t; nbus is the number of network nodes;
and (3) power grid node voltage amplitude constraint:
U min (i)≤U(i,t)≤U max (i) (21)
in the formula: u shape max (i)、U min (i) The upper limit and the lower limit of the voltage amplitude allowed by the node i are respectively set;
5) Other constraints
And (3) new energy output constraint:
Figure BDA0003817291970000156
in the formula:
Figure BDA0003817291970000157
respectively the maximum output values of wind power and photovoltaic power;
wind and light unit climbing restraint:
Figure BDA0003817291970000161
in the formula:
Figure BDA0003817291970000162
the up-down climbing rate of the wind turbine generator;
Figure BDA0003817291970000163
the up-and-down climbing speed of the photovoltaic unit.
The electricity-hydrogen-storage comprehensive energy network multi-objective optimization model test:
in order to verify the correctness of the comprehensive energy network model, the optimized dispatching simulation analysis is performed by taking a power grid 33 node and a hydrogen grid 5 node as examples. And carrying out simulation analysis on the established comprehensive energy network based on an MATLAB software platform. The structure of the integrated energy grid is shown in fig. 4. As shown in FIG. 3, a wind turbine generator is connected to the grid at the 25 th node, a photovoltaic generator is connected to the grid at the 27 th node, a hydrogen fuel cell is connected to a hydrogen grid node 2 at a grid node 3, and a hydrogen production device by electrolyzing water is connected to a hydrogen grid node 5 at a grid node 18.
System loss optimization comparison
Before optimization, a hydrogen network 4 node is connected with a power grid 8 node, a hydrogen network 5 node is connected with a power grid 22 node, and the obtained power loss is 315.9857kW. Through optimization, the node with the minimum network loss is selected to be the node of the hydrogen network 2 connected with the node of the power grid 3, the node of the hydrogen network 5 connected with the node of the power grid 18, and finally the network loss is 290.4746kW, and a comparison graph is shown in FIG. 5. As can be seen from fig. 5, the grid loss of the electricity-hydrogen-storage integrated energy network can be effectively reduced by site selection of the access points of the wind turbine, the photovoltaic system and the hydrogen energy storage system, and the grid loss after optimization is reduced by 8.07% compared with that before optimization.
Optimized comparison of system running cost, environment cost and carbon trading cost
The operation cost of the invention only considers the sum of the electricity purchasing cost and the hydrogen purchasing cost, and the lowest price of each time interval is selected by comparing the prices of the electricity and the hydrogen in each time interval, thereby achieving the minimum operation cost. According to the invention, the load of each time interval is subtracted from the electric quantity generated by wind and light output, when the residual electric quantity is greater than 0, hydrogen is generated through the electrolytic cell and stored through the hydrogen storage pipe, the efficiency of the electrolytic cell is 80%, and the hydrogen storage efficiency is 91%. When the residual electric quantity is less than 0, hydrogen is firstly discharged from the hydrogen storage pipe and is converted into electric power through the fuel cell to compensate the load, if the load is overlarge, the electricity or hydrogen is needed to be purchased from the power grid, the time-of-use electricity price of the power grid is shown in fig. 6, the hydrogen price is 33.26 yuan/kg, and the efficiency of the fuel cell is 55%. The invention takes the carbon emission caused by power purchasing of the power grid as the environmental cost, and CO caused by once-through power 2 The emission is 0.96kg, the carbon trading base unit price is 0.35 yuan/kg, the length of the carbon emission interval is 500kg, the reward coefficient gamma is 0.2, and the carbon trading price increase amplitude beta is 0.25. Taking 4 typical wind-light output of the Chongli region as an example, four wind-light output scenes are shown in FIGS. 7 and 8, and the costs after optimization of the comprehensive energy network with fixed weight coefficients and dynamic weight coefficients under 4 different wind-light output scenes are compared and analyzed, as shown in Table 1.
TABLE 1 cost of fixed and dynamic weights under different wind and light force scenarios
Figure BDA0003817291970000171
As can be seen from table 1, when the wind/solar output is large or the wind/solar output is stable, the carbon emission right is used most economically and reasonably, the integrated energy network obtains a profit by selling redundant carbon emission right quota, and the operation cost of the system can be further reduced, compared with the operation cost without considering the carbon transaction cost (that is, the carbon transaction cost is 0), the operation cost of the scenario one is reduced by 17.14%, the operation cost of the scenario two is reduced by 13.63%, the operation cost of the scenario three is reduced by 33.4%, and the operation cost of the scenario four is reduced by 2.4%; the reward and punishment type carbon transaction mechanism is proved to be added to effectively reduce the operation cost of the system and improve the economy of the electricity-hydrogen-storage comprehensive energy network. In addition, compared with the comprehensive cost under the fixed weight, the comprehensive cost of the dynamic weight is reduced by 11.9% under the scene one, 2.7% under the scene two, 5.4% under the scene three and 5.9% under the scene four. The optimization results after the dynamic weight coefficients are added are all superior to the fixed weight coefficients, and the effectiveness of adding the dynamic weight is proved.
Wind-photovoltaic output prediction error analysis
The influence of the wind-solar output prediction error on the comprehensive cost of the electricity-hydrogen-storage comprehensive energy network is analyzed by comparing the actual wind-solar output with the predicted wind-solar output under the 4 scenes, and the result is shown in table 2.
TABLE 2 cost optimization results for different wind and light output prediction errors
Figure BDA0003817291970000181
As can be seen from table 2, the wind-solar output prediction error causes the optimized comprehensive cost of the comprehensive energy network to increase, and the larger the absolute value of the average error is, the more the optimized comprehensive cost increases. This is because when the wind-solar output prediction has a positive deviation, the penalty electricity price of the system for purchasing electricity from the external grid increases the operation cost of the integrated energy grid; when the wind-solar output prediction has negative deviation, the cost of wind abandoning and light abandoning of the comprehensive energy network is increased, so that the operation cost is increased.

Claims (8)

1. A multi-objective optimization scheduling model of an electricity-hydrogen-storage integrated energy network based on reward and punishment stepped carbon transaction is characterized in that:
(1) Comprehensive energy network architecture establishment
The electricity-hydrogen-storage comprehensive energy network mainly comprises wind power, a photovoltaic unit, an energy storage battery, a hydrogen energy utilization module and a large power grid, and the operation mode can be divided into: (1) Converting the surplus power in the load valley period into hydrogen energy to supply energy to the hydrogen load or enter a hydrogen storage system; (2) the hydrogen storage system supplies energy to the hydrogen load and the electric load simultaneously; (3) During the peak load period, when the wind-solar power generation and the hydrogen fuel cell can not meet the power demand, the comprehensive energy network purchases power from the power grid or hydrogen from the hydrogen network;
(2) Multi-objective optimization model process of electricity-hydrogen-storage integrated energy network
An electricity-hydrogen-storage comprehensive energy network multi-objective optimization model based on reward and punishment stepped carbon transaction aims to optimize network loss, environmental cost and operating cost of the comprehensive energy network by site selection of an access point, transaction of carbon emission weight, coordination optimization among electricity-hydrogen energy systems and balance of dynamic weight on the operating cost and the environmental cost of the comprehensive energy network;
(3) Carbon transaction mechanism establishment
The method comprises the steps that initial carbon emission quota distribution is carried out in a non-compensation distribution mode, a reference line method is adopted to determine the non-compensation carbon emission quota of a system, the initial carbon emission quota of carbon emission generated by electricity-hydrogen-storage integrated energy network from power purchase of a superior power grid comes from a coal-fired power generating set, in order to further control the total carbon emission amount and stimulate the energy conservation and emission reduction enthusiasm of energy supply enterprises, a reward coefficient gamma concept is put forward, namely when the total carbon emission amount of the energy supply enterprises is lower than the free distributed carbon emission quota, a government gives a certain reward subsidy; a reward and punishment stepped carbon transaction cost calculation model is established on the basis of the carbon transaction cost calculation model, a plurality of emission intervals are stipulated, when the carbon emission is smaller than the freely distributed carbon emission amount, the carbon transaction cost is negative, the fact that an energy supply enterprise can sell redundant carbon emission quota in a carbon transaction market can be shown, a certain technical subsidy can be obtained, and the carbon transaction price corresponding to the interval with the smaller carbon emission amount is higher; when the carbon emission is greater than the carbon emission amount distributed for free, the carbon transaction cost is positive, the carbon emission right needs to be purchased in a carbon transaction market by an energy supply enterprise, the carbon transaction price corresponding to the interval with the larger carbon emission amount is higher, and the actual carbon emission amount is subjected to piecewise linearization processing when the model is solved;
in the carbon transaction cost calculation model, the relationship between the carbon transaction price and the carbon transaction amount defines a plurality of carbon transaction amount intervals: the positive interval represents that the carbon emission rights of energy supply enterprises are insufficient and the carbon emission rights of the energy supply enterprises need to be purchased from a carbon trading market; the negative interval indicates that the energy supply enterprise has the residual carbon emission right and can sell the carbon emission right to obtain income;
(4) Use of dynamic weighting coefficients
Firstly, a proportional dynamic weight coefficient is adopted, a proportional function has monotonicity, and the change trend of an input variable can be followed, so that the weight changes along with positive correlation of the variable, and according to the sum of the state x (j) at the moment and the state x (j-1) at the last moment, if the current parameter is greater than the last moment, the weight is increased, and if the current parameter is less than the last moment, the weight is reduced, and the formula is as follows:
Figure FDA0003817291960000021
in the growth stage, the growth trend of the proportional function is relatively slow, the proportional function is not sensitive to the change of the parameters, the change speed of the exponential function is fast, the change trends are the same, and an exponential dynamic weight coefficient of e can be adopted, and the formula is as follows:
a 2 ＝e y(j) (2)
the exponential function is over fast increased, exponential explosion is easy to generate by directly inputting parameters, the number of the input parameters needs to be adjusted to obtain a new input parameter y (j), and the trend of the weight is not changed when the parameters are increased or decreased, wherein the formula is as follows:
y(j)＝x(j)-x(j-1) (3)
selecting a when the amplitude is relatively slow 2 It is more sensitive, has large variation range, is easy to make the weight be in the boundary value of the value range for a long time, is difficult to reflect the specific variation of the weight, and causes the condition of excessive weight adjustment, and for a 1 And a 2 In other words, the two advantages can be usedCombining to determine the selection of the weight, and selecting a when the increase or decrease of the load curve is larger through the change of the total load curve 2 The weight can be changed rapidly, and the change trend is relieved; when the load curve increases and decreases slowly, a is selected 1 And adjusting to form a combined dynamic weight coefficient.
2. The reward and punishment stepped carbon transaction-based multi-objective optimization scheduling model of the electricity-hydrogen-storage integrated energy network as claimed in claim 1, wherein: the carbon emission weight quota is calculated as:
Figure FDA0003817291960000031
in the formula: e c The carbon emission quota for purchasing electricity for the superior power grid; taking chi as the carbon emission right quota of unit electric quantity, and taking 0.728 t/(MW & h); p b,t And (4) the power purchased by the comprehensive energy network from the superior power grid in unit time t.
3. The power-hydrogen-storage integrated energy network multi-objective optimization scheduling model based on reward and punishment stepped carbon transaction as claimed in claim 1, wherein: the actual carbon emission calculation formula is as follows:
Figure FDA0003817291960000032
in the formula: e g Actual carbon emission for purchasing power for a superior power grid; a is 1 、b 1 、c 1 And calculating the coefficient for the carbon emission of the energy conversion equipment of the coal-fired unit.
4. The power-hydrogen-storage integrated energy network multi-objective optimization scheduling model based on reward and punishment stepped carbon transaction as claimed in claim 1, wherein: in order to reduce the carbon emission of the electricity-hydrogen-storage integrated energy network, a reward coefficient gamma is introduced into a carbon transaction price, and a reward and punishment step-type carbon transaction cost model is constructed and calculated as follows:
Figure FDA0003817291960000033
C co2 ＝C c (E c -E g ) (7)
in the formula: c c A carbon trade unit price; c is the basic unit carbon number; beta is the incremental magnitude of the step price; v is the length of the carbon emission interval; c co2 Is the carbon transaction cost.
5. The power-hydrogen-storage integrated energy network multi-objective optimization scheduling model based on reward and punishment ladder type carbon transaction as claimed in claim 1, wherein the establishment of the objective function is as follows: when the basic load of the comprehensive energy network is high, the output of a fan and a photovoltaic and the energy supply of a hydrogen fuel cell are small, the comprehensive energy network needs to purchase electricity from an external network, so that the environmental cost is increased, the weight of the minimum objective function of the environmental cost is increased at the moment, the environmental protection performance of the comprehensive energy network is improved, the real-time weight before the environmental cost takes the real-time electricity purchasing quantity required by the comprehensive energy network as a parameter x (t), a dynamic weight optimization model is established, the weight lambda is automatically adjusted according to the increase of the environmental cost, the operation cost weight is taken as a reference item, the operation cost weight changes along with the change of the environmental cost weight, and the comprehensive optimization scheduling objective function based on the dynamic weight is as follows:
Figure FDA0003817291960000041
Figure FDA0003817291960000042
Figure FDA0003817291960000043
in the formula: f is the comprehensive cost of the comprehensive energy network; eta is an operation cost weight coefficient; λ is an environmental cost weight coefficient;
Figure FDA0003817291960000045
for design parameters, take 1.1 and 0.9.
6. The power-hydrogen-storage integrated energy network multi-objective optimization scheduling model based on reward and punishment stepped carbon transaction as claimed in claim 5, wherein: an objective function of operating cost of
minF 1 ＝F WT +F PV +F W +F G +C co2 (11)
In the formula: f WT 、F PV Respectively the operation and maintenance costs of the wind power generator set and the photovoltaic generator set; f W 、F G Punishment costs for wind abandonment and light abandonment respectively,
1) Operating cost of new energy
Figure FDA0003817291960000044
In the formula: t is a scheduling period; n is a radical of PV 、N WT The number of photovoltaic and wind power generator sets; sgn (ui, t) represents the start-stop state of the ith unit at the time t; a is i 、b i 、c i The operation cost coefficient of the ith unit; p i,t The output value of the unit i at the moment t,
2) Wind and light abandoning punishment cost
Figure FDA0003817291960000051
In the formula: c W 、C PV Respectively is a wind abandoning punishment coefficient and a light abandoning punishment coefficient; p W ′ t 、P P ′ Vt Respectively obtaining output predicted values of the wind power field and the photovoltaic field in a t period; p Wt 、P PVt And the actual output values of the wind power and the photovoltaic at the t time interval are respectively.
7. The power-hydrogen-storage integrated energy network multi-objective optimization scheduling model based on reward and punishment stepped carbon transaction as claimed in claim 5, wherein: in order to minimize the carbon emission of the comprehensive energy grid, namely, the electricity purchasing quantity from the superior power grid, the environmental cost objective function is set as
minF 2 ＝E g C c (14)
In the formula: f 2 The environmental cost of the comprehensive energy network is reduced.
8. The power-hydrogen-storage integrated energy network multi-objective optimization scheduling model based on reward and punishment stepped carbon transaction as claimed in claim 1, wherein: establishment of constraints
1) Hydrogen energy storage system capacity constraints
Figure FDA0003817291960000052
In the formula:
Figure FDA0003817291960000053
the upper limit and the lower limit of the capacity of the hydrogen energy storage system;
2) Hydrogen production system operating constraints
Figure FDA0003817291960000054
In the formula:
Figure FDA0003817291960000055
the upper limit of the climbing power for producing hydrogen by electrolyzing water;
3) Hydrogen net constraint condition
Hydrogen net node gas pressure constraint:
Figure FDA0003817291960000056
in the formula: h i,t Is the air pressure of the node i at the time t;
Figure FDA0003817291960000061
the upper limit and the lower limit of node air pressure are respectively set;
hydrogen network flow constraint:
Figure FDA0003817291960000062
Figure FDA0003817291960000063
in the formula:
Figure FDA0003817291960000064
is the hydrogen pipeline flow;
Figure FDA0003817291960000065
pressures upstream and downstream of the conduit, respectively; k ij Is the combined coefficient of the pipeline and the hydrogen fluid; s ij,t Is the pipeline gas flow direction at the moment t;
4) Grid constraint conditions
And power balance constraint of the power grid nodes:
Figure FDA0003817291960000066
in the formula: p (i, t) and Q (i, t) are respectively the injected active power and reactive power of a node i at the moment t; g ij 、B ij Respectively a real part and an imaginary part of i rows and j columns in the node admittance matrix; u (i, t) is the voltage amplitude of the node i at the time t; theta ij (t) is the phase angle difference between the two ends of the branch ij at the moment t; nbus is the number of network nodes;
and (3) power grid node voltage amplitude constraint:
U min (i)≤U(i,t)≤U max (i) (21)
in the formula: u shape max (i)、U min (i) The upper limit and the lower limit of the voltage amplitude allowed by the node i are respectively set;
5) Other constraints
And (3) new energy output constraint:
Figure FDA0003817291960000067
in the formula:
Figure FDA0003817291960000071
respectively taking the maximum output values of wind power and photovoltaic power;
wind-solar unit climbing restraint:
Figure FDA0003817291960000072
in the formula:
Figure FDA0003817291960000073
the up-down climbing rate of the wind turbine generator;
Figure FDA0003817291960000074
the up-down climbing speed of the photovoltaic unit.
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CN117639069A (en) * 2023-11-28 2024-03-01 国网青海省电力公司清洁能源发展研究院 Light-hydrogen-storage comprehensive energy system suitable for remote agriculture and animal husbandry area
CN117371669A (en) * 2023-12-06 2024-01-09 江苏米特物联网科技有限公司 Park comprehensive energy system operation method considering carbon transaction risk cost
CN117371669B (en) * 2023-12-06 2024-03-12 江苏米特物联网科技有限公司 Park comprehensive energy system operation method considering carbon transaction risk cost

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