CN115375344A - Microgrid two-stage robust optimization low-carbon economic dispatching method considering ladder carbon transaction mechanism - Google Patents

Microgrid two-stage robust optimization low-carbon economic dispatching method considering ladder carbon transaction mechanism Download PDF

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CN115375344A
CN115375344A CN202210820908.1A CN202210820908A CN115375344A CN 115375344 A CN115375344 A CN 115375344A CN 202210820908 A CN202210820908 A CN 202210820908A CN 115375344 A CN115375344 A CN 115375344A
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孟润泉
魏斌
韩肖清
马跃
秦文萍
贾燕冰
窦银科
李婷婷
乔森
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Taiyuan University of Technology
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Abstract

The invention discloses a microgrid two-stage robust optimization low-carbon economic dispatching method considering a ladder carbon transaction mechanism, and relates to an optimization dispatching method of a microgrid. The method comprises the following specific steps: introducing a stepped carbon trading mechanism into the operation process of a microgrid, and reducing the carbon emission of the microgrid by participating in a carbon trading market through the microgrid; the method comprises the following steps of optimizing the running state of the electrical energy storage and power interaction variables in the worst scene in the first stage, optimizing the output variables of each equipment unit in the second stage, and solving the worst scene possibly encountered in the running process of the micro-grid. The scheduling model adopts a box-type uncertain set to describe the uncertainty of the output of the renewable energy source, the scheduling result obtained by solving the model can meet the operation constraint in any scene, the low-carbon economic operation of the microgrid system can be ensured, the source side uncertainty and the carbon emission problem in the operation process of the microgrid system are effectively solved, and the sustainable development of the society is facilitated.

Description

Microgrid two-stage robust optimization low-carbon economic dispatching method considering ladder carbon transaction mechanism
Technical Field
The invention relates to an optimized scheduling method of a microgrid, in particular to a two-stage robust optimized low-carbon economic scheduling method of the microgrid considering a ladder carbon transaction mechanism.
Background
In recent years, the problems that a traditional power system is high in operation cost and difficult to flexibly adjust are increasingly shown, and a microgrid system is suitable for operation for consuming new energy on site, but the uncertainty of the microgrid system brings a great challenge to safe and stable operation because the power generation of the new energy such as wind power and photovoltaic is greatly influenced by natural factors; the operation optimization of the micro-grid is not limited to the economic level, and carbon emission factors are included, so that energy conservation and emission reduction are imperative; the influence on the economic dispatching of the micro-grid caused by the uncertainty of the output of the renewable energy sources can be eliminated, the economical efficiency of the operation of the micro-grid is improved, and the stepped carbon trading mechanism can be introduced into the dispatching process of the micro-grid to reduce the carbon emission generated in the operation process of the micro-grid. Therefore, research is needed to be carried out on the two-stage robust optimization low-carbon economic dispatch of the micro-grid considering the step carbon transaction, so that the micro-grid can realize low-carbon economic operation.
Disclosure of Invention
The invention provides a microgrid two-stage robust optimization low-carbon economic dispatching method considering a stepped carbon trading mechanism, and aims to solve the problem that influence of renewable energy output uncertainty on microgrid economic dispatching and the problem that stepped carbon trading is introduced into a microgrid for dispatching.
The micro-grid system structure researched by the invention is shown in figure 1 and comprises a wind turbine generator, a photovoltaic generator, a fuel cell, a micro gas turbine, an electric energy storage device, a large power grid and an electric load. The invention is realized by the following technical scheme: a micro-grid two-stage robust optimization low-carbon economic dispatching method considering a ladder carbon transaction mechanism comprises the following steps:
step one, modeling each device in the micro-grid system: analyzing the operation characteristics and operation constraints of each device in the micro-grid system, and establishing a scheduling cost model and an operation constraint model of each device in the micro-grid; the micro-grid comprises a wind power generator set, a photovoltaic unit, a fuel cell set, a micro gas turbine, an electric energy storage device, a large power grid and an electric load, and the process is as follows specifically
1) The micro gas turbine and the fuel cell stack in the system belong to controllable equipment, and each equipment meets the output limit expressed by the formula (1) and the climbing constraint expressed by the formula (2):
P i min ≤P i (t)≤P i max (1)
Figure BDA0003744367040000021
in the formula P i (t) represents the output power of the ith device at time t; p i max And P i min Respectively representing the maximum and minimum output power of the ith controllable device; -R i max And R i max Respectively representing the climbing upper and lower limit constants of the ith equipment;
2) The electric energy storage equipment is required to meet the maximum and minimum charge-discharge power limit, the capacity constraint limit and the charge-discharge energy balance constraint related to the service life of the electric energy storage in the scheduling process;
U ch (t)+U dis (t)≤1 (3)
Figure BDA0003744367040000022
Figure BDA0003744367040000023
Figure BDA0003744367040000024
E s (1)＝E s (T) (7)
Figure BDA0003744367040000025
in the formula of U ch (t) and U dis (t) respectively representing the charging/discharging state of the energy storage device at the moment t, wherein the charging/discharging state is not 0 or 1; p ch (t) and P dis (t) respectively representing the charging/discharging power of the energy storage device at the moment t;
Figure BDA0003744367040000026
and
Figure BDA0003744367040000027
respectively representing the maximum charge/discharge power of the energy storage device; e s (t) and E s (t-1) the electric storage quantity of the electric storage equipment at the time t and the time t-1 respectively; eta represents the charge-discharge coefficient of the energy storage device; t is a scheduling period, and is taken for 24 hours;
Figure BDA0003744367040000028
and E max Respectively representing the minimum and maximum energy storage allowed by the energy storage equipment in the scheduling process; e s (1) Capacity is initially scheduled for energy storage;
3) When the internal power supply of the system can not meet the energy demand, the system purchases electricity from an external network to meet the power balance constraint; on the contrary, when the energy supply is excessive in the system, the system can sell energy to an external network to obtain benefits; in the electricity purchasing and selling process, the system should meet the constraints shown in the formulas (9) to (11);
U buy (t)+U sell (t)≤1 (9)
Figure BDA0003744367040000029
Figure BDA00037443670400000210
in the formula of U buy (t) and U sell (t) respectively representing the electricity purchasing/selling states of the system at the time t; p buy (t) and P sell (t) respectively representing the power purchased/sold by the system at the time t;
Figure BDA0003744367040000031
and
Figure BDA0003744367040000032
respectively representing the maximum electricity purchasing/selling power when the micro-grid system exchanges power with the large power grid.
Step two, establishing a mathematical model of the stepped carbon transaction mechanism: establishing a mathematical model of a step carbon trading mechanism by taking the carbon trading base price, the carbon trading interval length and the price increase rate as known data; establishing a mathematical model for the stepped carbon transaction mechanism, and substituting the actual carbon emission intensity of the unit set, the gratuitous carbon quota of the unit set, the carbon transaction base price, the price growth rate and the interval length of the carbon emission as known data to obtain the mathematical model of the stepped carbon transaction mechanism, wherein the mathematical model comprises the following specific steps:
1) The use of the hydrogen fuel cell does not generate carbon emission, so the carbon quota of the microgrid system only considers the use of a gas turbine and the purchased electric energy, and assuming that the purchased electric energy is all from thermal power generation, the carbon quota of the system is expressed as:
Figure BDA0003744367040000033
in the formula D c Total carbon quota for micro gas turbine power generation and microgrid power purchase; sigma e And σ g The emission quota of the unit electric quantity of the thermal power generation and the emission quota of the unit electric quantity of the micro gas turbine are respectively set; p is g (t) output at time t of the micro gas turbine;
2) The actual carbon emissions of the system are generated by both the electricity purchase and the gas turbine, and are expressed by the formula (13):
Figure BDA0003744367040000034
E c total carbon emissions generated for system power generation; gamma ray e Carbon emission intensity of thermal power generation; gamma ray g Carbon emission intensity per unit of electricity generated for the micro gas turbine;
3) And subtracting the carbon emission quota distributed by the system from the carbon emission amount generated by the system operation to obtain the carbon emission right trading amount actually participating in the carbon trading market, wherein when the actual carbon emission amount of the system exceeds the distributed carbon quota value, the part exceeding the quota needs to be purchased on the carbon trading market. The carbon trading price of the traditional carbon trading mechanism is a constant which is not changed, the stepped carbon trading mechanism adopts a stepped pricing principle, the carbon trading prices of the carbon trading volume in different intervals are different, and the more the carbon emission quota required to be purchased, the higher the carbon trading price in the corresponding interval. The stepped carbon transaction cost is shown as equation (14):
Figure BDA0003744367040000041
in the formula: c co2 A step carbon transaction cost; d is the interval length of carbon emission; θ represents a carbon transaction price; λ is the rate of price increase.
Step three, an uncertain set is formulated aiming at the uncertain variables: establishing an uncertain set U by taking the predicted generating power of the photovoltaic and wind generating set and the maximum predicted deviation of the output of the photovoltaic and wind generating set as known conditions; taking the prediction data and the prediction deviation of the renewable energy unit as known data, and introducing the prediction data of the renewable energy unit into the following formula to construct an uncertain set:
Figure BDA0003744367040000042
in the formula, U represents an uncertain set of output of the photovoltaic and wind generating set; u. of wt /u pv The method comprises the following steps of (1) taking wind power/photovoltaic output uncertain variables introduced after wind power/photovoltaic unit output fluctuation into consideration;
Figure BDA0003744367040000043
and
Figure BDA0003744367040000044
respectively representing the predicted power of the fan and the photovoltaic at the moment t;
Figure BDA0003744367040000045
and
Figure BDA0003744367040000046
respectively representing the maximum power errors of the fan and the photovoltaic at the moment t, and taking 20% of prediction data; b is wt (t) and B pv (t) respectively representing binary variables of whether the wind turbine generator and the photovoltaic generator have power errors at the time t, wherein when 1 is taken, the lower limit of the output of the wind turbine generator and the photovoltaic generator at the time t is taken as the predicted power, and when 0 is taken, the output of the renewable energy generator at the time t is taken as the predicted power; r wt And r pv The uncertainty parameter introduced for adjusting the system conservation is an integer between 0 and T, and represents the total time period number of the renewable energy source unit obtaining the minimum output value, and the larger the value is, the more conservative the system scheduling result is.
Step four, constructing a micro-grid two-stage robust optimization low-carbon economic dispatching model considering the step carbon transaction: taking the minimum value of the total running cost of the microgrid, including the system running maintenance cost, the energy purchasing cost, the fuel cost and the carbon transaction cost, as a low-carbon economic dispatching plan target function, taking the constraint condition meeting the safe and stable running of the system as the constraint condition of the economic dispatching plan target function for limitation, respectively taking binary switch variables, continuous variables and uncertain variables as optimization variables of a first stage and a second stage, and constructing a two-stage robust optimization model of the microgrid considering the carbon transaction;
1) The low-carbon economic dispatch plan objective function is as in formula (16), and is the minimum in the worst scene for the operation and maintenance cost, the fuel cost, the carbon emission cost and the energy purchase cost of the system:
Figure BDA0003744367040000051
in the formula: c 1 Scheduling costs for the day ahead; c grid Energy purchase cost for purchasing electricity from the distribution network for the microgrid; c ope Operating and maintaining costs for each distributed power supply; c fuel Operating fuel costs for the microgrid controllable equipment; c CO2 Step carbon transaction cost for the microgrid is shown in a formula (14);
wherein:
Figure BDA0003744367040000052
in the formula, P buy (t) and P sell (t) respectively representing the power purchased/sold by the system at the time t; omega buy And omega sell The time-of-use electricity purchasing price and the time-of-use electricity selling price of the scheduled day are obtained; t is a scheduling period;
wherein:
Figure BDA0003744367040000053
in the formula, ω 1 Representing a maintenance cost factor for the micro gas turbine; omega 2 A maintenance cost coefficient representing the fuel cell; omega 3 A maintenance cost coefficient representing the electrical energy storage; p is fc (t) represents the output power of the fuel cell at time t; p is g (t) represents the output power of the micro gas turbine at time t; p ch (t) and P dis (t) respectively representing the charging/discharging power of the energy storage device at the moment t;
wherein:
Figure BDA0003744367040000054
in the formula, P gas Is the natural gas price; p H Is the hydrogen price; LHV is the low heating value of natural gas; eta g And η fc Respectively representing the power generation efficiency of the micro gas turbine and the fuel cell;
2) The method mainly comprises the following steps of (1) economic dispatching plan constraint conditions, wherein the microgrid mainly comprises power balance of each system, output constraint and climbing constraint of each unit, power interaction constraint with a superior network and constraint of each energy storage device in the dispatching process:
(1) power balance constraint of system:
Figure BDA0003744367040000055
in the formula (I), the compound is shown in the specification,
Figure BDA0003744367040000061
a predicted power representing the load at time t;
(2) and (3) output constraint of the distributed power supply:
P i min ≤P i (t)≤P i max (21)
in the formula P i (t) represents the output power of the ith device at time t; p i max And P i min Respectively representing the maximum and minimum output power of the ith controllable device; -R i max And R i max Respectively representing the climbing upper and lower limit constants of the ith equipment;
(3) and (3) restraining the running state of the electric energy storage equipment:
U ch (t)+U dis (t)≤1 (22)
in the formula of U ch (t) and U dis (t) respectively representing the charging and discharging states of the energy storage device at the moment t, wherein the states are not 0, namely 1;
(4) output constraint of the electric energy storage equipment:
Figure BDA0003744367040000062
Figure BDA0003744367040000063
E s (1)＝E s (T) (25)
Figure BDA0003744367040000064
Figure BDA0003744367040000065
in the formula (I), the compound is shown in the specification,
Figure BDA0003744367040000066
and
Figure BDA0003744367040000067
respectively representing the maximum charge/discharge power of the energy storage device; e s (t) and E s (t-1) the electric storage quantity of the electric storage equipment at the time t and the time t-1 respectively; eta represents the charge-discharge coefficient of the energy storage device; t is a scheduling period, and24 hours;
Figure BDA0003744367040000068
and
Figure BDA0003744367040000069
respectively representing the minimum and maximum energy storage allowed by the energy storage equipment in the scheduling process;
(5) and (3) power interaction constraint of the microgrid:
U buy (t)+U sell (t)≤1 (28)
Figure BDA00037443670400000610
Figure BDA00037443670400000611
in the formula of U buy (t) and U sell (t) respectively representing the electricity purchasing/selling states of the system at the time t; p buy (t) and P sell (t) respectively representing the power purchased/sold by the system at the time t;
Figure BDA00037443670400000612
and
Figure BDA00037443670400000613
respectively representing the maximum electricity purchasing power and the maximum electricity selling power when the micro-grid system and the large power grid exchange power;
3) The uncertainty of the output of the renewable energy is processed by adopting a two-stage robust optimization method, a model is constructed to be a min-max-min structure, and the optimization target is that the day-ahead scheduling cost of the system is minimum; optimizing decision variables in a first stage of the outmost min structure in the model, and giving operating states of energy storage and electricity purchase and sale of the system, wherein the decision variables in the first stage ensure that the system can cope with any uncertain and concentrated scenes in the operating process; after a stage decision variable is determined, a group of worst scenes which enable the daily scheduling cost of the system to be maximum are searched for by a max structure in an inner layer max-min under the value of the stage decision variable; when the decision variables of the outmost min stage and the worst scene of the inner max structure are determined, the innermost is converted into a simple deterministic optimization problem, the purpose is to optimize the output of each equipment unit in the worst scene, so that the day-ahead scheduling cost of the system in the worst scene is the minimum, and the above expression is summarized as follows:
Figure BDA0003744367040000071
wherein x is a first stage decision variable, x = [ U ] ch ,U dis ,U sell ,U buy ](ii) a y is the second stage decision variable, y = [ P = g ,P fc ,P ch ,P dis ,P buy ,P sell ,P wt ,P pv ]。
And step five, solving the micro-grid two-stage robust optimization low-carbon economic dispatching model for constructing and considering the step carbon transaction to obtain a day-ahead dispatching result of power interaction of the controllable power supply, the energy storage unit and the micro-grid.
1) The two-stage robust optimization model is represented as:
Figure BDA0003744367040000072
in the formula: a, C, J, D, E, K, I are coefficient matrices a, C, J, D, u are constant column vectors; because the problem is a multilayer problem and is difficult to solve, the invention adopts the mature C & CG algorithm to solve at present, and decomposes the formula (32) into a main problem shown as the formula (33) and a sub-problem shown as the formula (34);
Figure BDA0003744367040000073
Figure BDA0003744367040000081
the subproblem is a double-layer problem and is difficult to solve under normal conditions, and the subproblem can be converted into a max structure by converting an inner min structure of the subproblem into a max structure and converting the max structure of the subproblem into a max problem shown in the following formula (35) by combining the max structure with an outer max structure according to a strong dual principle;
Figure BDA0003744367040000082
when the uncertain variable is taken as a boundary value, the dual problem correspondingly obtains the maximum value or the minimum value, a bilinear term exists in the formula (35), the BIg-M method is adopted for linearization processing, and the final expression form of the subproblem is shown as the formula (36):
Figure BDA0003744367040000083
in the formula: psi, phi, rho and xi are introduced dual variables; u. of pre Predicting a vector of effort for the renewable energy source; b is c Is an introduced auxiliary continuous variable;
2) The model is solved by adopting a C & CG algorithm, and the solving process is as follows:
(1) given an initial set of adverse scenes (u) wt1 ,u pv1 ) Giving an upper model bound UB = + ∞, and a lower model bound LB = - ∞; the iteration number k is 1; the upper and lower bound convergence difference is taken as a minimum positive real number;
(2) solving the main problem on the basis of the given severe scene set to obtain
Figure BDA0003744367040000084
Taking the solving result of the main problem as a new lower boundary of the model;
(3) the result of solving the main problem
Figure BDA0003744367040000085
Solving a new worst scenario u as a parameter substitution sub-problem k+1 And sub-problem objective function values
Figure BDA0003744367040000086
Updating model upper bound
Figure BDA0003744367040000087
(4) If it is
Figure BDA0003744367040000088
(
Figure BDA0003744367040000089
Convergence threshold), the solution result is output
Figure BDA00037443670400000810
And
Figure BDA00037443670400000811
stopping iteration; otherwise, bringing the new worst scene into the step (2) and generating a new variable y k+1 Adding the following constraints, and adding 1 to the iteration times;
Figure BDA0003744367040000091
compared with the prior art, the invention has the following beneficial effects: the traditional deterministic optimal scheduling method is usually deterministic optimal scheduling based on predicted data, but is limited by the existing predicted power technology, and the actual output of renewable energy units such as wind power and photovoltaic units and the like in the actual scheduling process is usually wrong with the predicted value, so that the scene in the actual scheduling process is usually uncertain. Deterministic optimal scheduling methods do not solve the above problems well; meanwhile, a large amount of carbon emission is generated in the operation process of the micro-grid due to the use of the controllable unit and the exchange of the power of the micro-grid and the power grid. The invention provides a microgrid two-stage robust optimization low-carbon economic dispatching method considering a ladder carbon transaction mechanism, aiming at the problems of source side uncertainty and carbon emission in the dispatching process of a microgrid, which comprises the following steps: introducing a stepped carbon trading mechanism into the operation process of a microgrid, and reducing the carbon emission of the microgrid by participating in a carbon trading market through the microgrid; the method comprises the following steps of optimizing the running state of the electrical energy storage and power interaction variables in the worst scene in the first stage, optimizing the output variables of each equipment unit in the second stage, and solving the worst scene possibly encountered in the running process of the micro-grid. The scheduling model adopts a box-type uncertain set to describe the uncertainty of the output of the renewable energy source, the scheduling result obtained by solving the model can meet the operation constraint in any scene, the low-carbon economic operation of the microgrid system can be ensured, the source side uncertainty and the carbon emission problem in the operation process of the microgrid system are effectively solved, and the sustainable development of the society is facilitated.
Drawings
Fig. 1 is a diagram of a microgrid structure.
FIG. 2 is a flow chart of a two-stage robust optimization.
FIG. 3 is a photovoltaic, wind turbine and load power prediction graph.
Fig. 4 is a two-stage robust optimization scheduling result of the microgrid.
Fig. 5 is a comparison of the scheduling results of the equipment units in the three methods.
FIG. 6 is a graph of carbon emissions at different uncertainties.
FIG. 7 is a graph of carbon transaction costs for different uncertainties.
FIG. 8 is a graph of the day-ahead scheduling cost at different uncertainties.
FIG. 9 shows carbon emissions for different prediction errors.
FIG. 10 shows carbon transaction costs for different prediction errors.
Fig. 11 shows the day-ahead scheduling costs for different prediction errors.
FIG. 12 is a graph of cost increment versus uncertainty.
Detailed Description
The present invention is further illustrated by the following specific examples.
In order to verify the effectiveness of the method designed by the invention, programming is carried out by means of a yalcip tool box under a matlab environment, and a cplex solver is called for solving. In this embodiment, the microgrid structure shown in fig. 1 and the prediction data of the wind power, the photovoltaic generator set and the load given in fig. 3 are taken as examples to perform verification on the proposed model. The operating parameters of the microgrid are shown in tables 1 to 3. The uncertainty of the fan and the photovoltaic unit is 12, which indicates that the wind power and the photovoltaic unit obtain the minimum value of the prediction interval in 12 time intervals, and the rest time intervals are prediction values. The prediction error of the wind turbine and the photovoltaic is 20%, and the maximum prediction deviation of the wind turbine and the photovoltaic unit is twenty percent of the prediction data. The effectiveness of the proposed strategy was developed by comparing the following three schemes. In the method 1, the carbon trading cost is not considered in the objective function, and a robust optimization method is adopted for optimization. In the method 2, the carbon transaction cost of the traditional carbon transaction mechanism is calculated in the objective function, and a robust optimization method is adopted for optimization. In the method 3, according to the method provided by the invention, the objective function takes the carbon transaction cost of the ladder carbon transaction mechanism into account, and a robust optimization method is adopted for optimization. The specific scheduling results are shown in table 4, fig. 4 and fig. 5.
In an embodiment, a microgrid two-stage robust optimization low-carbon economic dispatching method considering a ladder carbon transaction mechanism comprises the following steps:
step one, modeling each device in the micro-grid system: analyzing the operation characteristics and operation constraints of each device in the micro-grid system, and establishing a scheduling cost model and an operation constraint model of each device in the micro-grid; the micro-grid comprises a wind power generator set, a photovoltaic unit, a fuel cell set, a micro gas turbine, an electric energy storage device, a large power grid and an electric load, and the process is as follows specifically
1) The micro gas turbine and the fuel cell stack in the system belong to controllable equipment, and each equipment meets the output limit expressed by the formula (1) and the climbing constraint expressed by the formula (2):
P i min ≤P i (t)≤P i max (1)
Figure BDA0003744367040000101
in the formula P i (t) represents the output power of the ith device at time t; p i max And P i min Respectively represent the i-th controllable devicesMaximum and minimum output power; -R i max And R i max Respectively representing the climbing upper and lower limit constants of the ith equipment;
2) The electric energy storage equipment is required to meet the maximum and minimum charge-discharge power limit, the capacity constraint limit and the charge-discharge energy balance constraint related to the service life of the electric energy storage in the scheduling process;
U ch (t)+U dis (t)≤1 (3)
Figure BDA0003744367040000111
Figure BDA0003744367040000112
Figure BDA0003744367040000113
E s (1)＝E s (T) (7)
Figure BDA0003744367040000114
in the formula of U ch (t) and U dis (t) respectively representing the charging/discharging state of the energy storage device at the moment t, wherein the charging/discharging state is not 0 or 1; p is ch (t) and P dis (t) represents the charging/discharging power of the energy storage device at time t, respectively;
Figure BDA0003744367040000115
and
Figure BDA0003744367040000116
respectively representing the maximum charge/discharge power of the energy storage device; e s (t) and E s (t-1) the electric storage quantity of the electric storage equipment at the time t and the time t-1 respectively; eta represents the charge-discharge coefficient of the energy storage equipment; t is a scheduling period, and is taken for 24 hours;
Figure BDA0003744367040000117
and E max Respectively representing the minimum and maximum energy storage allowed by the energy storage equipment in the scheduling process, and respectively taking 400kWh and 1800kWh; e s (1) The capacity is initially scheduled for energy storage, and is taken as 1000kWh;
3) When the internal power supply of the system can not meet the energy demand, the system purchases electricity from an external network to meet the power balance constraint; on the contrary, when the energy supply in the system is excessive, the system can sell energy to an external network to obtain benefits; in the electricity purchasing and selling process, the system should meet the constraints shown in the formulas (9) to (11);
U buy (t)+U sell (t)≤1 (9)
Figure BDA0003744367040000118
Figure BDA0003744367040000119
in the formula of U buy (t) and U sell (t) respectively representing the electricity purchasing/selling states of the system at the time t; p is buy (t) and P sell (t) respectively representing the power purchased/sold by the system at the time t;
Figure BDA00037443670400001110
and with
Figure BDA00037443670400001111
Respectively representing the maximum electricity purchasing/selling power when the micro-grid system exchanges power with the large power grid.
Step two, establishing a mathematical model of the stepped carbon transaction mechanism: establishing a mathematical model of a step carbon trading mechanism by taking the carbon trading base price, the carbon trading interval length and the price growth rate as known data; establishing a mathematical model for the stepped carbon transaction mechanism, and substituting the actual carbon emission intensity of the unit set, the gratuitous carbon quota of the unit set, the carbon transaction base price, the price growth rate and the interval length of the carbon emission as known data to obtain the mathematical model of the stepped carbon transaction mechanism, wherein the mathematical model comprises the following specific steps:
1) The use of the hydrogen fuel cell does not generate carbon emission, so the carbon quota of the microgrid system only considers the use of a gas turbine and the purchased electric energy, and assuming that the purchased electric energy is all from thermal power generation, the carbon quota of the system is expressed as:
Figure BDA0003744367040000121
in the formula D c Total carbon quota for micro gas turbine power generation and microgrid power purchase; sigma e And σ g The emission quota of the unit electric quantity of the thermal power generation and the emission quota of the unit electric quantity of the micro gas turbine are respectively set; p g (t) output at time t of the micro gas turbine;
2) The actual carbon emissions of the system are generated by both the electricity purchase and the gas turbine, and are expressed by the formula (13):
Figure BDA0003744367040000122
E c total carbon emissions generated for system power generation; gamma ray e Carbon emission intensity of thermal power generation; gamma ray g Carbon emission intensity per unit of electricity generated for the micro gas turbine;
3) The carbon emission quota distributed by the system is subtracted from the carbon emission amount generated by the system operation, so that the carbon emission right trading amount of the system actually participating in the carbon trading market can be obtained, and the stepped carbon trading cost is shown as the formula (14):
Figure BDA0003744367040000123
in the formula: c co2 A ladder carbon transaction cost; d is the interval length of carbon emission, and 0.5t is taken in the embodiment; theta represents the carbon trading price and is taken as 0.3 yuan/kg; lambda is the rate of price increase, taken as 25%.
Step three, an uncertain set is formulated aiming at the uncertain variables: establishing an uncertain set U by taking the predicted power generation power of the photovoltaic and wind turbine generator and the maximum predicted deviation of the output of the photovoltaic and wind turbine generator as known conditions; the prediction data and the prediction deviation of the renewable energy unit are used as known data, and the prediction error is ± 20% of the prediction data, but because the scene considered in this embodiment is the worst wind power and photovoltaic unit output scene, that is, when the photovoltaic and wind power unit reaches the minimum interval, the scheduling cost of the microgrid will be higher, and better conforms to the definition of "crime and bad", therefore, the uncertain set can only consider the downward fluctuation of the source side output, and the prediction data of the renewable energy unit is introduced into the following formula, that is, the uncertain set is constructed:
Figure BDA0003744367040000131
in the formula, U represents an uncertain set of output of the photovoltaic and wind generating set; u. of wt /u pv The method comprises the following steps of (1) taking wind power/photovoltaic output uncertain variables introduced after wind power/photovoltaic unit output fluctuation into consideration;
Figure BDA0003744367040000132
and
Figure BDA0003744367040000133
respectively representing the predicted power of the fan and the photovoltaic at the moment t;
Figure BDA0003744367040000134
and
Figure BDA0003744367040000135
respectively representing the maximum power errors of the fan and the photovoltaic at the moment t, and taking 20% of prediction data; b is wt (t) and B pv (t) binary variables respectively representing whether the wind turbine generator and the photovoltaic generator have power errors at the moment t, wherein when 1 is taken, the lower limit of the output predicted power of the wind turbine generator and the photovoltaic generator at the moment t is represented, and when 0 is taken, the lower limit of the output predicted power of the wind turbine generator and the photovoltaic generator at the moment t is representedTaking the output of the renewable energy output unit as the predicted power; r' s wt And r pv The uncertainty parameter introduced for adjusting the system conservation is an integer between 0 and T, and represents the total time period number of the renewable energy source unit obtaining the minimum output value, and the larger the value is, the more conservative the system scheduling result is.
Step four, constructing a micro-grid two-stage robust optimization low-carbon economic dispatching model considering the step carbon transaction: taking the minimum value of the total running cost of the microgrid, including the system running maintenance cost, the energy purchasing cost, the fuel cost and the carbon transaction cost, as a low-carbon economic dispatching plan target function, taking the constraint condition meeting the safe and stable running of the system as the constraint condition of the economic dispatching plan target function for limitation, respectively taking binary switch variables, continuous variables and uncertain variables as optimization variables of a first stage and a second stage, and constructing a two-stage robust optimization model of the microgrid considering the carbon transaction;
1) The low-carbon economic dispatch plan objective function is as in formula (16), and is the minimum in the worst scene for the operation and maintenance cost, the fuel cost, the carbon emission cost and the energy purchase cost of the system:
Figure BDA0003744367040000136
in the formula: c 1 Scheduling costs for the day ahead; c grid Energy purchase cost for purchasing power from the distribution network for the microgrid; c ope Operating and maintaining costs for each distributed power supply; c fuel Operating fuel costs for the microgrid controllable equipment; c CO2 Step carbon transaction cost for the microgrid, see formula (14);
wherein:
Figure BDA0003744367040000137
in the formula, P buy (t) and P sell (t) respectively representing the power purchased/sold by the system at the time t; omega buy And ω sell For adjusting time-of-day electricity purchasing price and time-of-daySelling electricity price; t is a scheduling period;
wherein:
Figure BDA0003744367040000141
in the formula, ω 1 Representing a maintenance cost factor for the micro gas turbine; omega 2 A maintenance cost coefficient representing the fuel cell; omega 3 A maintenance cost coefficient representing the electrical energy storage; p fc (t) represents the output power of the fuel cell at time t; p is g (t) represents the output power of the micro gas turbine at time t; p ch (t) and P dis (t) respectively representing the charging/discharging power of the energy storage device at the moment t;
wherein:
Figure BDA0003744367040000142
in the formula: p gas For the price of natural gas, 3.5 yuan/m is taken 3 ；P H For the hydrogen price, the market price of hydrogen is 19.2-38.4 yuan/kg, the density of hydrogen is 0.089kg/m < 3 >, 37.87 kW.h/kg is obtained, and the market price of hydrogen is 25.26 yuan/kg; LHV is low heat value of natural gas, and 9.7kWh/m is taken 3 ；η g And η fc The power generation efficiencies of the micro gas turbine and the fuel cell are respectively expressed and are respectively set to 0.6 and 0.5.
2) The method mainly comprises the following steps of (1) economic dispatching plan constraint conditions, wherein the microgrid mainly comprises power balance of each system, output constraint and climbing constraint of each unit, power interaction constraint with a superior network and constraint of each energy storage device in the dispatching process:
(1) power balance constraint of system:
Figure BDA0003744367040000143
in the formula (I), the compound is shown in the specification,
Figure BDA0003744367040000144
a predicted power representing the load at time t;
(2) and (3) output constraint of the distributed power supply:
P i min ≤P i (t)≤P i max (21)
in the formula P i (t) represents the output power of the ith device at time t; p is i max And P i min Respectively representing the maximum and minimum output power of the ith controllable device; -R i max And R i max Respectively representing the climbing upper and lower limit constants of the ith equipment;
(3) and (3) restraining the running state of the electric energy storage equipment:
U ch (t)+U dis (t)≤1 (22)
in the formula of U ch (t) and U dis (t) respectively representing the charging and discharging states of the energy storage device at the moment t, wherein the states are not 0, namely 1;
(4) and (3) output constraint of the electric energy storage equipment:
Figure BDA0003744367040000151
Figure BDA0003744367040000152
E s (1)＝E s (T) (25)
Figure BDA0003744367040000153
Figure BDA0003744367040000154
in the formula (I), the compound is shown in the specification,
Figure BDA0003744367040000155
and
Figure BDA0003744367040000156
respectively representing the maximum charge/discharge power of the energy storage device; e s (t) and E s (t-1) the electric storage quantity of the electric storage equipment at the t moment and the t-1 moment respectively; eta represents the charge-discharge coefficient of the energy storage device; t is a scheduling period, and is taken for 24 hours;
Figure BDA0003744367040000157
and
Figure BDA0003744367040000158
respectively representing the minimum and maximum energy storage allowed by the energy storage equipment in the scheduling process;
(5) microgrid power interaction constraint:
U buy (t)+U sell (t)≤1 (28)
Figure BDA0003744367040000159
Figure BDA00037443670400001510
in the formula of U buy (t) and U sell (t) respectively representing the electricity purchasing/selling states of the system at the time t; p buy (t) and P sell (t) respectively representing the power purchased/sold by the system at the time t;
Figure BDA00037443670400001511
and
Figure BDA00037443670400001512
respectively representing the maximum electricity purchasing power and the maximum electricity selling power when the micro-grid system and the large power grid exchange power;
3) The uncertainty of the output of the renewable energy is processed by adopting a two-stage robust optimization method, a model is constructed to be a min-max-min structure, and the optimization target is that the day-ahead scheduling cost of the system is minimum; optimizing decision variables in a first stage of the outmost min structure in the model, and giving operating states of energy storage and electricity purchase and sale of the system, wherein the decision variables in the first stage ensure that the system can cope with any uncertain and concentrated scenes in the operating process; after a stage decision variable is determined, a group of worst scenes which enable the daily scheduling cost of the system to be maximum are searched for by a max structure in an inner layer max-min under the value of the stage decision variable; when the decision variables of the outmost min stage and the worst scene of the inner max structure are determined, the innermost is converted into a simple deterministic optimization problem, the purpose is to optimize the output of each equipment unit in the worst scene, so that the day-ahead scheduling cost of the system in the worst scene is the minimum, and the above expression is summarized as follows:
Figure BDA00037443670400001513
wherein x is a first stage decision variable, x = [ U ] ch ,U dis ,U sell ,U buy ](ii) a y is a second stage decision variable, y = [ P = [) g ,P fc ,P ch ,P dis ,P buy ,P sell ,P wt ,P pv ]。
And step five, solving the micro-grid two-stage robust optimization low-carbon economic dispatching model for constructing and considering the ladder carbon transaction to obtain a day-ahead dispatching result of the interaction of the controllable power supply, the energy storage unit and the micro-grid power.
1) The two-stage robust optimization model is represented as:
Figure BDA0003744367040000161
in the formula: a, C, J, D, E, K, I are coefficient matrices a, C, J, D, u are constant column vectors; solving by adopting a C & CG algorithm, and decomposing a formula (32) into a main problem shown as a formula (33) and a sub-problem shown as a formula (34);
Figure BDA0003744367040000162
Figure BDA0003744367040000163
the subproblem is a double-layer problem, and an inner-layer min structure of the subproblem is converted into a max structure and combined with an outer-layer max structure according to a strong dual principle to be converted into a max problem shown in the following formula (35);
Figure BDA0003744367040000164
when the uncertain variable is taken as a boundary value, the dual problem correspondingly obtains the maximum value or the minimum value, a bilinear term exists in the formula (35), the BIg-M method is adopted for linearization processing, and the final expression form of the subproblem is shown as the formula (36):
Figure BDA0003744367040000171
in the formula: psi, phi, rho and xi are introduced dual variables; u. of pre Predicting a vector of output for the renewable energy source; b is c Is an introduced auxiliary continuous variable;
2) The model is solved by adopting a C & CG algorithm, and the solving process is as follows:
(1) given an initial set of adverse scenes (u) wt1 ,u pv1 ) Giving an upper model bound UB = + ∞, and a lower model bound LB = - ∞; the iteration number k is 1; the upper and lower bound convergence difference is taken as a minimum positive real number;
(2) solving the main problem on the basis of the given severe scene set to obtain
Figure BDA0003744367040000172
Taking the solving result of the main problem as a new lower boundary of the model;
(3) results of solving the main problem
Figure BDA0003744367040000173
Solving a new worst scenario u as a parameter substitution sub-problem k+1 And the sub-problem objective function value
Figure BDA0003744367040000174
Updating model upper bound
Figure BDA0003744367040000175
(4) If it is
Figure BDA0003744367040000176
(
Figure BDA0003744367040000177
Convergence threshold), the solution result is output
Figure BDA0003744367040000178
And
Figure BDA0003744367040000179
stopping iteration; otherwise, bringing the new worst scene into the step (2) and generating a new variable y k+1 Adding the following constraints, and adding 1 to the iteration number;
Figure BDA00037443670400001710
as shown in fig. 4, at 01; the electric energy storage device charges the stored energy at 02, 05 and 24 with lower electricity price; the fuel cell increases the output power during the load peak period, and reduces the system outsourcing power and the carbon emission together with the energy storage equipment; in the electricity price valley period, the electricity purchasing cost is less than the electricity generating cost of each controllable equipment unit, the power shortage is preferentially met by outsourcing electric energy, and in the electricity price flat-peak period, due to the introduction of a carbon trading mechanism, the carbon emission of the system is greatly increased by outsourcing electric power, so that each controllable equipment preferentially meets the power shortage.
TABLE 1 time-of-use electricity price table
Figure BDA0003744367040000181
TABLE 2 microgrid operating parameters
Figure BDA0003744367040000182
TABLE 3 Integrated scheduling cost for different uncertainties under fixed prediction error
Figure BDA0003744367040000183
TABLE 4 comparison of scheduling results under three methods
Figure BDA0003744367040000184
As can be seen from Table 4, the cost of method 2 is increased by 428.4 yuan compared with method 1, and the cost increase is 4.6%; the carbon emission is reduced by 2114kg, and the reduction range is up to 18.4%; compared with the method 2, the method 3 reduces the carbon emission by 1 757.4kg, and the reduction range reaches 18.7 percent; the day-ahead scheduling cost is increased by 55.6 yuan, and the cost increase amplitude is only 0.57%; the two aspects are considered comprehensively, and the introduction of the stepped carbon transaction can ensure the economical efficiency of the system and simultaneously reduce the carbon emission of the system to the maximum extent, thereby achieving the purposes of energy conservation and emission reduction.
As shown in fig. 5, the output ratio of each of the devices of method 1, method 2, and method 3 is more sufficient in scheduling of the fuel cell and the micro gas turbine in scene three compared with scene one and scene two, and simultaneously reduces the outsourcing power of the system, fills up part of the power shortage by using the flexibility of energy storage scheduling, avoids carbon emission generated by outsourcing power, and simultaneously, because the fuel cell does not generate carbon emission as a cleaning device, the economical efficiency and environmental protection performance of the fuel cell and the energy storage device are better than those of the outsourcing power in the peak of electricity price, and the carbon emission of the system is reduced by the two devices together.
Fig. 6 shows the carbon emissions for three scenarios with different uncertainties. In the method 1 and the method 2, along with the increase of uncertainty, the carbon emission tends to rise obviously, and when the change is not determined to be within 0-18, the carbon emission is increased by 2 136kg and 1579kg respectively; under a stepped carbon trading mechanism, the carbon emission of the system is not increased greatly, the original emission level is maintained, the carbon emission is increased by only 99.4kg, and the increase amplitude is 1.3%; when the uncertainty is greater than 18, the carbon emissions of all three methods show an increasing trend, but in this case, method 3 still has the lowest carbon emissions of the three methods. Therefore, the immune capability of the carbon emission of the system to uncertain parameters is obviously enhanced by introducing a stepped carbon transaction mechanism into the two-stage robust optimization model, and the condition that the carbon emission of the system is greatly increased due to a severe scene is avoided. The carbon emission of the stepped carbon trading mechanism is minimum under the condition of each uncertainty, and compared with the situation that the carbon trading mechanism is not adopted and the traditional carbon trading mechanism is adopted, the average reduction amplitude of the carbon emission under each uncertainty is respectively as high as 31.7% and 14.3%.
Figure 7 shows the carbon trade cost variation for method 2 and method 3 with different uncertainties. The carbon trading cost of method 3 is less than that of method 2 at different uncertainties.
Fig. 8 shows the change of the day-ahead scheduling cost of the three methods under different uncertainties. At each uncertainty, the cost of the stepwise carbon trading model was increased by only 5.1% and 0.54% on average compared to the other two models. In conclusion, the stepped carbon transaction mechanism is introduced into the robust optimization model, so that the problem that a large amount of carbon is discharged by the micro-grid system due to wind and light output fluctuation can be effectively solved, and the low carbon and environmental protection of the system can be guaranteed on the premise of not sacrificing the economy of the system.
Fig. 9 shows the carbon emissions for the three methods for different prediction errors. When the prediction deviation percentage is changed between 5% and 35%, the carbon emission of the method 1 and the method 2 is respectively increased by 1433kg and 1 825.6kg, the increase range respectively reaches 13.5% and 22.5%, the carbon emission of the method 3 is increased by 123kg, and the increase range is only 2%; when the prediction error exceeds 35%, although the carbon emission of the three methods is greatly increased at the same time, the model carbon emission adopting the step-type carbon transaction is the least of the three methods, and it should be noted that the scene occurrence probability when the prediction deviation percentage exceeds 35% is very low. Therefore, the adoption of a stepped carbon trading mechanism can ensure that the carbon emission of the system can be effectively controlled without being greatly increased under the condition that the prediction deviation becomes large.
Fig. 10 shows the variation of carbon trading cost for methods 2 and 3 for different prediction errors. When the prediction error percentage is changed between 0 and 30 percent, the carbon transaction cost of the method 3 is not increased along with the increase of the prediction error; when the prediction error percentage is changed between 30% and 40%, the carbon trading cost of the method 3 and the method 2 is increased along with the increase of the prediction error, but the carbon trading cost of the method 3 is still lower than that of the method 2; the prediction error continues to increase, and the system has to satisfy the power balance by increasing the outsourcing power and increasing the micro-engine output, and the carbon trading cost is higher than that of the method 2 due to the introduction of the step carbon price of the method 3. It should be noted that most of the existing power prediction technologies can control the prediction deviation to be about 20%, so that the introduction of a stepped carbon trading mechanism has practical significance in microgrid optimization scheduling.
Fig. 11 shows the change of the scheduling cost in the day ahead of the three methods under different prediction errors. With the increase of the prediction deviation, the day-ahead scheduling cost of the scheduling scheme obtained by robust optimization continuously increases; when the prediction error percentage is increased from 5 percent to 35 percent, the cost of the three methods is increased by 5203.6 yuan, 5427.3 yuan and 5462.1 yuan; compared with methods 1 and 2, the scheduling cost of the method 3 is averagely increased by 594.16 yuan and 97.5 yuan in the previous days, and the average increase amplitude is respectively 5.5% and 0.8% under each prediction error percentage, so that the cost is not greatly increased due to the introduction of a stepped carbon trading mechanism into a two-stage robust optimization model, and a large amount of emission reduction can be realized while the economy of the system is ensured under the condition that the prediction errors are gradually increased.
Fig. 12 shows the relationship between the uncertainty and the amount of the day-ahead scheduling cost increase in the current uncertainty scenario compared to the previous uncertainty scenario (the uncertainty in the latter scenario is increased by 3 compared to the previous scenario) for the robust optimization model. The increment of the day-ahead scheduling cost is the largest when the scene changes for the first time, and the increment of the day-ahead scheduling cost is gradually reduced along with the increment of the uncertainty, which is the preferential finding of the robust optimization method that the adverse conditions of the scene which is the worst scene of the system operation and the scene which is found after the photovoltaic and fan output is in the boundary value are sequentially reduced. When the uncertainty of the photovoltaic and the fan reaches 14, the uncertainty of the photovoltaic reaches the maximum, only the residual severe scene of the fan is searched, and after the uncertainty reaches 21, the influence of the extreme condition of the residual time interval on the operation result of the system is the minimum.
In order to reflect the effectiveness of the robust optimization method, the embodiment uses the actual output power of the fan and the photovoltaic scheduling on the same day as a reference, calculates the in-day regulation and control cost (the in-day regulation and control cost coefficient is 1.2 times of that of the day-ahead stage) of each scheme under different uncertainties under a fixed prediction error (the prediction deviation percentage is 20%), and then gives the trend that the in-day regulation and control cost and the in-day regulation and control cost change along with the uncertainties under different prediction errors.
As can be seen from table 3, with the increase of uncertainty, the scene that the system needs to consider in the day-ahead scheduling process is worse, and the system is more conservative, so that the cost of the system is continuously increased; in the in-day real-time scheduling stage, when the uncertainty is less than 12, the in-day regulation and control cost is reduced along with the increase of the uncertainty, and the total scheduling cost of the two stages is in a descending trend; when the uncertainty is larger than 12, the day-ahead scheduling scheme of the system is too conservative, the scheduling cost is too high, the output of equipment of the system needs to be reduced according to a day-ahead scheduling plan in a regulation stage, and the two-stage scheduling cost is slightly increased. Therefore, when the prediction bias percentage is taken to be 20% at the time of pre-day scheduling scheme, taking the uncertainty to be 12 will result in the optimal scheduling scheme.
The scope of the invention is not limited to the above embodiments, and various modifications and changes may be made by those skilled in the art, and any modifications, improvements and equivalents within the spirit and principle of the invention should be included in the scope of the invention.

Claims (6)

1. A micro-grid two-stage robust optimization low-carbon economic dispatching method considering a ladder carbon transaction mechanism is characterized by comprising the following steps of: the method comprises the following steps:
step one, modeling each device in the micro-grid system: analyzing the operation characteristics and operation constraints of each device in the micro-grid system, and establishing a scheduling cost model and an operation constraint model of each device in the micro-grid;
step two, establishing a mathematical model of the stepped carbon transaction mechanism: establishing a mathematical model of a step carbon trading mechanism by taking the carbon trading base price, the carbon trading interval length and the price increase rate as known data;
step three, an uncertain set is formulated aiming at the uncertain variables: establishing an uncertain set U by taking the predicted generating power of the photovoltaic and wind generating set and the maximum predicted deviation of the output of the photovoltaic and wind generating set as known conditions;
step four, constructing a microgrid two-stage robust optimization low-carbon economic dispatching model considering the step carbon transaction: taking the minimum value of the total running cost of the microgrid, including the system running maintenance cost, the energy purchasing cost, the fuel cost and the carbon transaction cost, as a low-carbon economic dispatching plan target function, taking the constraint condition meeting the safe and stable running of the system as the constraint condition of the economic dispatching plan target function for limitation, respectively taking binary switch variables, continuous variables and uncertain variables as optimization variables of a first stage and a second stage, and constructing a two-stage robust optimization model of the microgrid considering the carbon transaction;
and step five, solving the micro-grid two-stage robust optimization low-carbon economic dispatching model for constructing and considering the ladder carbon transaction to obtain a day-ahead dispatching result of the interaction of the controllable power supply, the energy storage unit and the micro-grid power.
2. The microgrid two-stage robust optimization low-carbon economic dispatching method considering a ladder carbon transaction mechanism, as claimed in claim 1, is characterized in that: in the first step, the microgrid comprises a wind power generator set, a photovoltaic generator set, a fuel cell set, a micro gas turbine, electric energy storage equipment, a large power grid and an electric load, and the process specifically comprises the following steps:
1) The micro gas turbine and the fuel cell stack in the system belong to controllable equipment, and each equipment meets the output limit expressed by the formula (1) and the climbing constraint expressed by the formula (2):
P i min ≤P i (t)≤P i max (1)
Figure FDA0003744367030000011
in the formula P i (t) represents the output power of the ith device at time t; p is i max And P i min Respectively representing the maximum and minimum output power of the ith controllable device; -R i max And R i max Respectively representing the climbing upper and lower limit constants of the ith equipment;
2) The electric energy storage equipment is required to meet the maximum and minimum charge-discharge power limit, the capacity constraint limit and the charge-discharge energy balance constraint related to the service life of the electric energy storage in the scheduling process;
U ch (t)+U dis (t)≤1 (3)
Figure FDA0003744367030000021
Figure FDA0003744367030000022
Figure FDA0003744367030000023
E s (1)＝E s (T) (7)
Figure FDA0003744367030000024
in the formula of U ch (t) and U dis (t) respectively representing the charging/discharging state of the energy storage device at the moment t, wherein the charging/discharging state is not 0 or 1; p ch (t) and P dis (t) respectively representing the charging/discharging power of the energy storage device at the moment t;
Figure FDA0003744367030000025
and with
Figure FDA0003744367030000026
Respectively representing the maximum charge/discharge power of the energy storage device; e s (t) and E s (t-1) the electric storage quantity of the electric storage equipment at the time t and the time t-1 respectively; eta represents the charge-discharge coefficient of the energy storage device; t is a scheduling period, and is taken for 24 hours;
Figure FDA0003744367030000027
and E max Respectively representing the minimum and maximum energy storage allowed by the energy storage equipment in the scheduling process; e s (1) Initially scheduling capacity for energy storage;
3) When the internal power supply of the system can not meet the energy demand, the system purchases electricity from an external network to meet the power balance constraint; on the contrary, when the energy supply is excessive in the system, the system can sell energy to an external network to obtain benefits; in the electricity purchasing and selling process, the system meets the constraints shown in the formulas (9) to (11);
U buy (t)+U sell (t)≤1 (9)
Figure FDA0003744367030000028
Figure FDA0003744367030000029
in the formula of U buy (t) and U sell (t) respectively representing the electricity purchasing/selling states of the system at the time t; p buy (t) and P sell (t) respectively representing the power purchased/sold by the system at the time t;
Figure FDA00037443670300000210
and
Figure FDA00037443670300000211
respectively representing the maximum electricity purchasing/selling power when the micro-grid system exchanges power with the large power grid.
3. The microgrid two-stage robust optimization low-carbon economic dispatching method considering a ladder carbon transaction mechanism, as claimed in claim 1, is characterized in that: in the second step, a mathematical model is established for the stepped carbon transaction mechanism, and the actual carbon emission intensity of the unit set, the uncompensated carbon quota of the unit set, the carbon transaction base price, the price increase rate and the interval length of the carbon emission are taken as known data to be substituted, so that the mathematical model of the stepped carbon transaction mechanism is established, and the mathematical model is specifically as follows:
1) The use of the hydrogen fuel cell does not generate carbon emission, so the carbon quota of the microgrid system only considers the use of a gas turbine and the purchased electric energy, and assuming that the purchased electric energy is all from thermal power generation, the carbon quota of the system is expressed as:
Figure FDA0003744367030000031
in the formula D c Total carbon quota for micro gas turbine power generation and microgrid power purchase; sigma e And σ g The emission quota of the unit electric quantity of the thermal power generation and the emission quota of the unit electric quantity of the micro gas turbine are respectively; p is g (t) output at time t of the micro gas turbine;
2) The actual carbon emissions of the system are generated by both the electricity purchase and the gas turbine, and are expressed by the formula (13):
Figure FDA0003744367030000032
E c total carbon emissions generated for system power generation; gamma ray e Carbon emission intensity of thermal power generation; gamma ray g Carbon emission intensity per unit of electricity for a micro gas turbine;
3) The carbon emission quota distributed by the system is subtracted from the carbon emission amount generated by the system operation, so that the carbon emission right trading amount of the system actually participating in the carbon trading market can be obtained, and the stepped carbon trading cost is shown as the formula (14):
Figure FDA0003744367030000033
in the formula: c co2 A ladder carbon transaction cost; d is the interval length of carbon emission; θ represents a carbon transaction price; λ is the rate of price increase.
4. The microgrid two-stage robust optimization low-carbon economic dispatching method considering a ladder carbon transaction mechanism, as claimed in claim 1, is characterized in that: and in the third step, the prediction data and the prediction deviation of the renewable energy unit are used as known data, and the prediction data of the renewable energy unit is introduced into the following formula, so that an uncertain set is constructed:
Figure FDA0003744367030000041
in the formula, U represents an uncertain set of output of the photovoltaic and wind generating set; u. of wt /u pv The method comprises the following steps of (1) taking wind power/photovoltaic output uncertain variables introduced after wind power/photovoltaic unit output fluctuation into consideration;
Figure FDA0003744367030000042
and
Figure FDA0003744367030000043
respectively representing the predicted power of the fan and the photovoltaic at the moment t;
Figure FDA0003744367030000044
and
Figure FDA0003744367030000045
respectively representing the maximum power errors of the fan and the photovoltaic at the time t, and taking 20% of prediction data; b is wt (t) and B pv (t) respectively representing binary variables of whether the wind turbine generator and the photovoltaic generator have power errors at the time t, wherein when 1 is taken, the lower limit of the output of the wind turbine generator and the photovoltaic generator at the time t is taken as the predicted power, and when 0 is taken, the output of the renewable energy generator at the time t is taken as the predicted power; r wt And r pv The uncertainty parameter introduced for adjusting the system conservatism is an integer between 0 and T and represents the total time period number of the renewable energy source unit obtaining the minimum output value.
5. The microgrid two-stage robust optimization low-carbon economic dispatching method considering a ladder carbon transaction mechanism, as claimed in claim 1, is characterized in that: the fourth step is as follows:
1) The low-carbon economic dispatch plan objective function is as in formula (16), and is the minimum in the worst scene for the operation and maintenance cost, the fuel cost, the carbon emission cost and the energy purchase cost of the system:
Figure FDA0003744367030000046
in the formula: c 1 Scheduling costs for the day ahead; c grid Energy purchase cost for purchasing power from the distribution network for the microgrid; c ope A cost of operating and maintaining for each distributed power supply; c fuel Operating fuel costs for the microgrid controllable equipment; c CO2 Step carbon transaction cost for the microgrid, see formula (14);
wherein:
Figure FDA0003744367030000047
in the formula, P buy (t) and P sell (t) respectively representing the power purchased/sold by the system at the time t; omega buy And ω sell The time-of-use electricity purchasing price and the time-of-use electricity selling price of the scheduled day are obtained; t is a scheduling period;
wherein:
Figure FDA0003744367030000048
in the formula, ω 1 Representing a maintenance cost factor for the micro gas turbine; omega 2 A maintenance cost coefficient representing the fuel cell; omega 3 A maintenance cost coefficient representing the electrical energy storage; p fc (t) represents the output power of the fuel cell at time t; p g (t) represents the output power of the micro gas turbine at time t; p ch (t) and P dis (t) respectively representing the charging/discharging power of the energy storage device at the moment t;
wherein:
Figure FDA0003744367030000051
in the formula, P gas Is the natural gas price; p H Hydrogen prices; LHV is the low heating value of natural gas; eta g And η fc Respectively representing the power generation efficiency of the micro gas turbine and the fuel cell;
2) The micro-grid mainly comprises power balance of each system, output constraint and climbing constraint of each unit, power interaction constraint with a superior network and constraint of each energy storage device in the scheduling process:
(1) power balance constraint of the system:
Figure FDA0003744367030000052
in the formula (I), the compound is shown in the specification,
Figure FDA0003744367030000053
a predicted power representing the load at time t;
(2) and (3) output constraint of the distributed power supply:
P i min ≤P i (t)≤P i max (21)
in the formula P i (t) represents the output power of the ith device at time t; p i max And P i min Respectively representing the maximum and minimum output power of the ith controllable device; -R i max And R i max Respectively representing the climbing upper and lower limit constants of the ith equipment;
(3) and (3) restraining the running state of the electric energy storage equipment:
U ch (t)+U dis (t)≤1 (22)
in the formula of U ch (t) and U dis (t) respectively representing the charging and discharging states of the energy storage device at the moment t, wherein the states are not 0, namely 1;
(4) and (3) output constraint of the electric energy storage equipment:
Figure FDA0003744367030000054
Figure FDA0003744367030000055
E s (1)＝E s (T) (25)
Figure FDA0003744367030000056
Figure FDA0003744367030000057
in the formula (I), the compound is shown in the specification,
Figure FDA0003744367030000061
and
Figure FDA0003744367030000062
respectively representing the maximum charge/discharge power of the energy storage device; e s (t) and E s (t-1) the electric storage quantity of the electric storage equipment at the time t and the time t-1 respectively; eta represents the charge-discharge coefficient of the energy storage device; t is a scheduling period, and is taken for 24 hours;
Figure FDA0003744367030000063
and
Figure FDA0003744367030000064
respectively representing the minimum and maximum energy storage allowed by the energy storage equipment in the scheduling process;
(5) and (3) power interaction constraint of the microgrid:
U buy (t)+U sell (t)≤1 (28)
Figure FDA0003744367030000065
Figure FDA0003744367030000066
in the formula of U buy (t) and U sell (t) respectively representing the electricity purchasing/selling states of the system at the time t; p buy (t) and P sell (t) respectively representing the power purchased/sold by the system at the time t;
Figure FDA0003744367030000067
and
Figure FDA0003744367030000068
respectively representing the maximum electricity purchasing power and the maximum electricity selling power when the micro-grid system and the large power grid exchange power;
3) The uncertainty of the output of the renewable energy is processed by adopting a two-stage robust optimization method, a model is constructed to be a min-max-min structure, and the optimization target is that the day-ahead scheduling cost of the system is minimum; optimizing decision variables in a first stage of the outmost min structure in the model, and giving operating states of energy storage and electricity purchase and sale of the system, wherein the decision variables in the first stage ensure that the system can cope with any uncertain and concentrated scenes in the operating process; after a stage decision variable is determined, a group of worst scenes which enable the daily scheduling cost of the system to be maximum are searched for by a max structure in an inner layer max-min under the value of the stage decision variable; when the decision variables of the outmost min stage and the worst scene of the inner max structure are determined, the innermost is converted into a simple deterministic optimization problem, the purpose is to optimize the output of each equipment unit in the worst scene, so that the day-ahead scheduling cost of the system in the worst scene is the minimum, and the above expression is summarized as follows:
Figure FDA0003744367030000069
wherein x is a first stage decision variable, x = [ U ] ch ,U dis ,U sell ,U buy ](ii) a y is the second stage decision variable, y = [ P = g ,P fc ,P ch ,P dis ,P buy ,P sell ,P wt ,P pv ]。
6. The microgrid two-stage robust optimization low-carbon economic dispatching method considering a ladder carbon transaction mechanism, as claimed in claim 1, is characterized in that: the fifth step is as follows:
1) The two-stage robust optimization model is represented as:
Figure FDA0003744367030000071
in the formula: a, C, J, D, E, K, I are coefficient matrices a, C, J, D, u are constant column vectors; solving by adopting a C & CG algorithm, and decomposing a formula (32) into a main problem shown as a formula (33) and a sub-problem shown as a formula (34);
Figure FDA0003744367030000072
Figure FDA0003744367030000073
the subproblem is a double-layer problem, and an inner-layer min structure of the subproblem is converted into a max structure and combined with an outer-layer max structure according to a strong dual principle to be converted into a max problem shown in the following formula (35);
Figure FDA0003744367030000074
when the uncertain variable is taken as a boundary value, the dual problem correspondingly obtains the maximum value or the minimum value, a bilinear term exists in the formula (35), the BIg-M method is adopted for linearization processing, and the final expression form of the subproblem is shown as the formula (36):
Figure FDA0003744367030000081
in the formula: psi, phi, rho and xi are introduced dual variables; u. of pre Predicting a vector of output for the renewable energy source; b is c Is an introduced auxiliary continuous variable;
2) The model is solved by adopting a C & CG algorithm, and the solving process is as follows:
(1) given an initial set of adverse scenes (u) wt1 ,u pv1 ) Giving a model upper bound UB = + ∞anda model lower bound LB = - ∞; number of iterationsTaking the number k as 1; taking the convergence difference value of the upper and lower bounds as a minimum positive real number;
(2) solving the main problem on the basis of the given severe scene set to obtain
Figure FDA0003744367030000082
Taking the solving result of the main problem as a new lower boundary of the model;
(3) the result of solving the main problem
Figure FDA0003744367030000083
Solving a new worst scenario u as a parameter substitution sub-problem k+1 And the sub-problem objective function value
Figure FDA0003744367030000084
Updating model upper bound
Figure FDA0003744367030000085
(4) If it is
Figure FDA0003744367030000086
Figure FDA0003744367030000087
If the convergence threshold value is reached, outputting the solving result
Figure FDA0003744367030000088
And with
Figure FDA0003744367030000089
Stopping iteration; otherwise, bringing the new worst scene into the step (2) and generating a new variable y k+1 Adding the following constraints, and adding 1 to the iteration times;
Figure FDA00037443670300000810
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