CN116436099B

CN116436099B - Micro-grid robust optimal scheduling method and system

Info

Publication number: CN116436099B
Application number: CN202310687315.7A
Authority: CN
Inventors: 于浩; 林岩; 刘雨佳; 王亚超; 郭霄宇; 左秀江; 王皓; 王庆彬
Original assignee: Inner Mongolia Hmhj Aluminum Electricity Co ltd; Beijing Herui Energy Storage Technology Co ltd
Current assignee: Inner Mongolia Hmhj Aluminum Electricity Co ltd; Beijing Herui Energy Storage Technology Co ltd
Priority date: 2023-06-12
Filing date: 2023-06-12
Publication date: 2023-11-10
Anticipated expiration: 2043-06-12
Also published as: CN116436099A

Abstract

The invention discloses a robust optimal scheduling method and a robust optimal scheduling system for a micro-grid, wherein the method comprises the steps of modeling each device in the micro-grid system to obtain a mathematical model or a constraint model of each device in the micro-grid system; obtaining a carbon transaction cost model of the micro-grid system according to a mathematical model or a constraint model of each device in the micro-grid system; acquiring an objective function of optimal scheduling of the micro-grid system according to a carbon transaction cost model of the micro-grid system; constructing a micro-grid two-stage robust optimization scheduling model according to the objective function and the operation constraint of each device of the micro-grid system; and constructing an uncertain set according to uncertain variables in the micro-grid system, and solving the two-stage robust optimization scheduling model of the micro-grid by utilizing the uncertain set to obtain a scheduling result. The carbon capture equipment is considered in the running of the micro-grid, and a carbon transaction mechanism is introduced, so that carbon emission generated by the running of the micro-grid is effectively reduced.

Description

Micro-grid robust optimal scheduling method and system

Technical Field

The invention relates to the technical field of energy optimization scheduling, in particular to a micro-grid robust optimization scheduling method and system.

Background

In order to overcome the defects of high construction cost, high operation and maintenance difficulty and the like of a large-scale power grid, a micro-grid is generated, a distributed power supply close to a load is adopted for supplying power, the power generation cost is reduced, the energy utilization rate is improved, however, renewable energy sources (such as wind power, photovoltaic and the like) generally have stronger uncertainty, and a considerable challenge is brought to safe and stable operation of the micro-grid; in order to achieve the goals of carbon peak reaching and carbon neutralization, a carbon trapping technology is adopted to realize large-scale emission reduction; however, the use of the carbon capture apparatus can also lead to an increase in the cost of power generation while reducing carbon dioxide emissions.

Therefore, researches are necessary for robust optimization scheduling of the multi-energy complementary micro-grid considering the carbon capture equipment, so that the micro-grid can realize low-carbon economic operation.

Disclosure of Invention

The invention aims to provide a micro-grid robust optimal scheduling method and system, which consider carbon capture equipment into the running of a micro-grid and introduce a carbon transaction mechanism at the same time, so that the carbon emission generated by the running of the micro-grid is effectively reduced.

In order to achieve the above object, the present invention provides a robust optimal scheduling method for a micro-grid, including:

modeling each device in the micro-grid system to obtain a mathematical model or a constraint model of each device in the micro-grid system;

Obtaining a carbon transaction cost model of the micro-grid system according to a mathematical model or a constraint model of each device in the micro-grid system;

acquiring an objective function of optimal scheduling of the micro-grid system according to a carbon transaction cost model of the micro-grid system;

constructing a micro-grid two-stage robust optimization scheduling model according to the objective function and the operation constraint of each device of the micro-grid system;

and constructing an uncertain set according to uncertain variables in the micro-grid system, and solving the two-stage robust optimization scheduling model of the micro-grid by utilizing the uncertain set to obtain a scheduling result.

Further, modeling each device in the micro-grid system to obtain a mathematical model or constraint model of each device in the micro-grid system, including,

analyzing the operation characteristics and operation constraints of all the devices in the micro-grid system, and modeling all the devices in the micro-grid system to obtain a mathematical model or a constraint model of all the devices in the micro-grid system;

the micro-grid system comprises a wind turbine, an electric energy storage device, a large power grid, an electric load, a gas boiler, a micro gas turbine, carbon capture equipment, a heat storage device and a heat load;

the electric energy storage equipment meets the maximum and minimum charge and discharge power limit, the capacity constraint limit and the charge and discharge energy balance constraint related to the service life of the electric energy storage in the dispatching process; the mathematical model of the electric energy storage device is as follows:

(1)

(2)

(3)

(4)

(5)

Wherein,and->Respectively indicate->Charging power and discharging power of the time period electric energy storage device; />Representation->The charge and discharge states of the electric energy storage equipment in the period of time are represented when the value is 1, and the electric energy storage equipment is in a charge state when the value is 0; />Representing the maximum charging power of the electrical energy storage device, < >>Representing a maximum discharge power of the electrical energy storage device; />Scheduling a total period; />Representation->The capacity of the electrical energy storage device during the period; />And->Respectively representing the maximum capacity and the minimum capacity allowed by the electric energy storage equipment in the scheduling process; />Initial scheduling capacity for the electrical energy storage device; />Representing the capacity of the electrical energy storage device at the last scheduling instant; />Representing the charge-discharge coefficient of the electrical energy storage device;

when an internal power supply of the micro-grid system cannot meet load requirements, the micro-grid system purchases electricity to a large power grid to meet power balance; conversely, when the internal electric energy of the micro-grid system is excessive, the micro-grid system sells electric energy to a large power grid to obtain benefits; in the electricity purchasing and selling process, the power interaction constraint model between the micro-grid system and the large grid should satisfy the formulas (6) to (7):

(6)

(7)

wherein,and->Respectively represent micro-grid system- >The electricity purchasing power and the electricity selling power in the time period; />The method comprises the steps of representing the electricity purchasing and selling state of a micro-grid system in a t period, representing that the micro-grid system purchases electricity to a large grid when the value is 1, and representing that the micro-grid system sells electricity to the large grid when the value is 0; />Representing the maximum purchase and selling electric power allowed when the micro-grid system interacts with the large grid power;

when the output heat power of the micro gas turbine is insufficient to meet the heat load, the gas boiler is used as an auxiliary heat supply device to compensate the heat power shortage; the mathematical model of the gas boiler is as follows:

(8)

wherein,indicating gas boiler->Thermal power output in a time period; />Is natural gas with low calorific value; />Indicating the thermal efficiency of the gas boiler; />Representation->Air inflow of the gas boiler in time period;

the miniature gas turbine can simultaneously supply heat to the outside in the process of gas power generation; the mathematical model of the miniature gas turbine is as follows:

(9)

(10)

(11)

wherein,for miniature gas turbines->The intake air amount of the period; />And->Respectively micro gas turbines at->Electric power and thermal power output in the period; />And->The efficiency of gas-to-electricity conversion and gas-to-heat conversion of the micro gas turbine are respectively; />For miniature gas turbines->Total carbon emissions during the time period; />Carbon emission intensity indicating unit output electric power of micro gas turbine,/- >Carbon emission intensity representing unit output thermal power of the micro gas turbine; />Is the duration of a unit time period;

the heat storage device meets the upper limit of charging and discharging power, the limit of capacity constraint and the balance constraint of charging and discharging capacity in the dispatching process; the mathematical model of the heat storage device is as follows:

(12)

(13)

(14)

(15)

(16)

wherein,and->Respectively indicate->The heat storage device is used for storing heat in a period of time; />Representation->The heat storage device is in a heat charging state when the value is 1, and is in a heat discharging state when the value is 0; />And->Respectively representing the upper limit of the heat storage device on the heat charging power and the heat discharging power; />Scheduling a total period; />Representation->The heat storage amount of the time period heat storage device; />And->Representing the maximum heat storage amount and the minimum heat storage amount allowed by the heat storage device in the dispatching process; />Indicating the initial heat storage capacity of the heat storage device; />Representing the heat storage amount of the heat storage device at the last scheduling moment; />To charge and discharge heat efficiency.

Further, the carbon capture apparatus includes a fixed mode of operation and a flexible mode of operation;

when the carbon capture equipment is in a fixed operation mode, the carbon capture level of the carbon capture equipment is kept unchanged, and the carbon capture energy consumption comprises fixed energy consumption and operation energy consumption; the mathematical model of the carbon capture device in the fixed operation mode is as follows:

(17)

(18)

(19)

Representing the total energy consumption of the carbon capture plant; />The operation energy consumption caused by carbon capture of the carbon capture equipment; />The fixed energy consumption of the carbon capture equipment; />Energy consumption required for capturing carbon dioxide units, < >>For carbon capture level, +.>The split ratio of the flue gas is; />And->Carbon emission intensity of electric power and thermal power output by the micro gas turbine unit respectively; />And->Respectively micro gas turbines at->Electric power and thermal power output in the period; />Indicating that the micro gas turbine is->Total carbon emissions for the period of time; />Representing that the carbon capture device is +.>Carbon dioxide emission of the micro gas turbine captured in a time period;

when the carbon capture equipment is in a flexible operation mode, the carbon capture level is changed according to the current grid electricity price; assuming that the product of the carbon capture level and the time-of-use electricity price is a constant, the product of the carbon capture level and the time-of-use electricity price is:

(20)

wherein,setting a constant of the product of the carbon capture level and the time-of-use electricity price for people; />Is the carbon capture level;is->Electricity purchase price in time period; as electricity purchase rates rise, the carbon capture level correspondingly decreases.

Further, the mathematical model or constraint model of each device in the micro-grid system obtains a carbon transaction cost model of the micro-grid system, including:

Assuming that carbon emission in the micro-grid system comes from a micro gas turbine, a gas boiler and outsourcing power, establishing and acquiring a carbon emission quota model of the micro-grid system according to a mathematical model of the gas boiler, a mathematical model of the micro gas turbine and a power interaction constraint model between the micro-grid system and a large power grid;

calculating and acquiring an actual carbon emission model of the micro power grid system according to a mathematical model of the carbon capture equipment in a fixed operation mode, a mathematical model of the micro gas turbine, a power interaction constraint model between the micro power grid system and a large power grid and a mathematical model of a gas boiler;

and calculating and acquiring a carbon transaction cost model of the micro-grid system according to the carbon emission allowance model and the actual carbon emission quantity model.

Further, it is assumed that carbon emissions inside the micro grid system are from micro gas turbines, gas boilers and outsourced power, and that outsourced power is from coal-fired units; the carbon emission allowance model of the micro-grid system is calculated as follows:

(21)

wherein,carbon emission allowance for the micro-grid system; />、/>And->Carbon emission quota coefficients of the micro gas turbine, the coal-fired unit and the gas boiler are respectively represented; / >And->Respectively micro gas turbines at->Electric power and thermal power output in the period; />To schedule the total period>Representation system->Time period power purchase, < >>Indicating gas boiler->Thermal power output in time period->Is the duration of a unit time period;

the actual carbon emission model of the micro-grid system is as follows:

(22)

wherein,the actual carbon emission is the actual carbon emission of the micro-grid system; />For miniature gas turbines->Total carbon emission in time period->And->The carbon emission intensity coefficients of the coal-fired unit and the gas boiler are respectively; />Representing that the carbon capture device is +.>Carbon dioxide emission of the micro gas turbine captured in a time period;

the traditional carbon transaction pricing mechanism is adopted, and the carbon transaction cost model is obtained by the following steps:

(23)

wherein,representing a carbon trade cost for the microgrid system; />Trade price for unit carbon emissions rights.

Further, according to the carbon transaction cost model of the micro-grid system, obtaining an objective function of optimal scheduling of the micro-grid system comprises:

acquiring the total running cost of the micro-grid system as an objective function of optimal scheduling of the micro-grid system according to the carbon transaction cost, the energy purchasing cost, the running maintenance cost and the energy consumption cost of the carbon capturing equipment of the micro-grid system;

the objective function of the optimal scheduling of the micro-grid system is as follows:

(24)

In the method, in the process of the invention,the total cost of running the micro-grid system; />The energy purchasing cost of the micro-grid system; />Is a micro-grid systemThe operation and maintenance cost of the system; />The energy consumption cost of the carbon capture equipment is reduced; />Cost for carbon trade;

the energy purchasing cost of the micro-grid system is as follows:

(25)

wherein:is->The gas price of the time period; />Is->Electricity selling price in time period; />For miniature gas turbines>Electric power output in time period->The efficiency of gas-to-electricity conversion of the micro gas turbine is improved; />Indicating gas boiler->Thermal power output in time period->Indicating the thermal efficiency of the gas boiler;/>and->Respectively represent micro-grid system->The electricity purchasing power and the electricity selling power in the time period; />Scheduling a total period;

the operation and maintenance cost of the micro-grid system is as follows:

(26)

in the method, in the process of the invention,、/>、/>and->Respectively representing maintenance cost coefficients of the micro gas turbine, the gas boiler, the electric energy storage equipment and the heat storage device; />And->Respectively indicate->Charging power and discharging power of the energy storage in a period; />And (3) withRespectively indicate->The heat storage device is used for storing heat in a period of time;

the energy consumption cost of the carbon capture equipment is as follows:

(27)

in the method, in the process of the invention,the operation energy consumption caused by carbon capture of the carbon capture equipment; />The fixed energy consumption of the carbon capture equipment;is->Electricity purchase price in time period.

Further, according to the objective function and the operation constraint of each device in the micro-grid system, a micro-grid two-stage robust optimization scheduling model is constructed, which comprises,

based on the objective function, limiting by taking the operation constraint of each device in the micro-grid system as a constraint condition, and constructing a micro-grid two-stage robust optimization scheduling model by taking the specific output and interaction power of each device as optimization variables;

in order to cope with uncertainty of wind power output, the two-stage robust optimization scheduling model of the micro-grid is set to be of a three-layer structure of min-max-min, and an uncertain variable is obtained by taking the minimum value of an objective function as a targetIn uncertainty set->A scheduling scheme with optimal economy when the worst scene changes inwards;

the two-stage robust optimal scheduling model structure of the micro-grid is as follows:

(28)

wherein the minimization problem of the outermost layer is the first stage problem, and the optimization variable is，The method comprises the steps of carrying out a first treatment on the surface of the Brackets are the second phase problem, the optimization variable is +.>And->，Wherein the minimization problem is equivalent to the objective function of equation (24), representing minimizing the total cost of operation; />For giving a group->Decision variable +.>Is a feasible region of (2).

Further, the constraint conditions comprise electric heating power balance constraint, micro gas turbine operation constraint, gas boiler constraint, carbon capture equipment constraint, micro power grid system and large power grid power interaction constraint, energy storage device constraint and heat storage device constraint;

The electrothermal power balance constraint is as follows:

(29)

(30)

wherein,and->Respectively represent system->Time period purchase/sell electric power; />For miniature gas turbines>Electric power output in time period->An uncertainty variable of the wind power output introduced after the uncertainty is considered; />Representation->Charging power stored in a time period; />Is the total energy consumption of the carbon capture equipment; />And->Respectively indicate->Charging/discharging power of the time period heat storage device; />For miniature gas turbines>Thermal power output in a time period; />Indicating gas boiler->Thermal power output in a time period; />And->The uncertainty of the electric load power and the thermal load power introduced after the uncertainty is considered are respectively;

the micro gas turbine operating constraints are:

(31)

wherein,representing the maximum output electric power of the micro gas turbine; />Representing the upper limit of the electric power climbing of the micro gas turbine; because of the existence of thermoelectric coupling, the maximum output thermal power and the upper limit of the thermal power climbing of the micro gas turbine are respectively determined by the maximum output electrical power and the upper limit of the electrical power climbing of the micro gas turbine;

the gas boiler is constrained as follows:

(32)

(33)

wherein,the maximum output thermal power of the gas boiler is shown; />The upper limit of the climbing of the thermal power of the gas boiler is indicated.

Further, the uncertainty set is constructed from uncertainty variables within the microgrid system, including,

Assuming that wind power output, electric load and thermal load demand predicted values and maximum predicted deviation are known, constructing an uncertain setThe method comprises the steps of carrying out a first treatment on the surface of the The uncertainty set is:

(34)

wherein U represents an uncertainty set;、/>and->Respectively->The predicted power of the wind turbine generator, the electric load and the thermal load in the period; />、/>And->Respectively->Maximum fluctuation errors of wind power output, electric load power and thermal load power in a period of time; />、/>And->The uncertainty is the uncertainty variables of wind power output, electric load power and thermal load power which are introduced after the uncertainty is considered; />、/>And->The binary variables respectively representing the degree of deviating the uncertain variables of wind power output, electric load power and thermal load power from the predicted value are the same as the predicted value in the uncertain variable value of the corresponding time period when 0 is taken, and the uncertain variables of the corresponding time period when 1 is taken are the boundary of the interval; />、/>And->Uncertainty adjustment parameters of the induced wind power output, the electric load power and the thermal load power are respectively; />A vector formed by the three uncertain variables, namely an uncertain vector; />、/>、/>The matrix constructed for facilitating the subsequent matrix operation has no practical significance; />To schedule the total period.

Further, solving the two-stage robust optimization scheduling model of the micro-grid by using the uncertain set to obtain scheduling results of the controllable unit, the electrothermal energy storage and the interactive power, wherein the method comprises the following steps:

The two-stage robust optimization model is as follows:

(35)

wherein,a coefficient matrix corresponding to the variable under constraint; />And->All of which represent the optimization variables,；/>the vector is composed of three uncertain variables, namely an uncertain vector, namely wind power output, electric load power and thermal load power; u represents an uncertainty set;；/>is a constant column vector;

solving a main problem and a sub-problem shown in a formula (36) and a formula (37) by adopting a column constraint generation algorithm (C & CG);

(36)

(37)

wherein,the auxiliary variables introduced for decomposing the original problem into the main problem and the sub-problem have no practical significance; />To->A matrix of related cost coefficients; t represents the total scheduling period;

at a given pointThe minimization problem of the inner layer of the following formula (37) is a linear problem, and according to the strong pair theory and the corresponding relation of the constraint condition of the formula (37), the minimization problem can be converted into a maximization problem and combined with the maximization problem of the outer layer, so as to obtain the pair problem shown as the formula (38):

(38)

when the uncertain variable quantity reaches the boundary value, the maximum or minimum value is correspondingly obtained for the dual problem, the bilinear term in the formula (38) can be subjected to linearization treatment by adopting a Big-M method, and the final expression form of the sub-problem is shown as the formula (39):

(39)

Wherein,is an uncertainty adjustment parameter of the induced wind power output; />The binary variable representing the degree of deviating the uncertain variable of the wind power output from the predicted value is the same as the predicted value in the uncertain variable of the corresponding period when taking 0, and the uncertain variable of the corresponding period when taking 1 is the boundary of the interval; />As an introduced dual variable; />Is an introduced continuous auxiliary variable; />Is a sufficiently large positive real number.

Based on the same inventive concept, the invention also provides a micro-grid robust optimization scheduling system, which is characterized by comprising a modeling unit, an acquisition unit, a construction unit and a solving unit,

the modeling unit is used for modeling each device in the micro-grid system to obtain a mathematical model or a constraint model of each device in the micro-grid system;

the acquisition unit is used for acquiring a carbon transaction cost model of the micro-grid system according to a mathematical model or a constraint model of each device in the micro-grid system; the method is also used for acquiring an objective function of optimal scheduling of the micro-grid system according to the carbon transaction cost of the micro-grid system;

the construction unit is used for constructing a micro-grid two-stage robust optimization scheduling model according to the objective function and the operation constraint of each device of the micro-grid system;

The solving unit is used for constructing an uncertain set according to uncertain variables in the micro-grid system, and solving the two-stage robust optimization scheduling model of the micro-grid by utilizing the uncertain set to obtain a scheduling result.

The invention has the technical effects and advantages that: because wind power has volatility and intermittence, strong randomness is brought to the running of a micro-grid system after grid connection, and the traditional deterministic optimal scheduling method is difficult to adapt to the random balance requirement of the micro-grid; in order to cope with the uncertainty of renewable energy sources and ensure the stable operation of a micro-grid, a micro-gas turbine is often needed to purchase electricity to a large power grid, and a large amount of carbon dioxide is generated at the same time; aiming at the problems of wind power uncertainty and carbon emission, the carbon capture equipment is considered in the running of the micro-grid, and a carbon transaction mechanism is introduced, so that the carbon emission generated by the running of the micro-grid is effectively reduced; a two-stage robust optimization scheduling model is established, uncertainty of wind power output is described by adopting a box type uncertainty set, scheduling results obtained by solving the model can adapt to any scene in the uncertainty set, low-carbon economic operation of a micro-grid system is ensured, and renewable energy source capacity of the micro-grid is enhanced.

Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a flowchart of a micro-grid robust optimization scheduling method according to an embodiment of the present invention;

fig. 2 is a schematic structural diagram of a micro-grid system according to an embodiment of the present invention;

FIG. 3 is a schematic representation of the pre-day prediction of wind power, electrical load and thermal load in an embodiment of the present invention;

FIG. 4 is a schematic diagram of electricity purchase price, electricity selling price and gas purchase price according to an embodiment of the present invention;

FIG. 5 is a graph of electrical power profiles for various devices of a micro-grid system in accordance with an embodiment of the present invention;

FIG. 6 is a graph of thermal power profiles for various devices of a micro-grid system in accordance with an embodiment of the present invention;

FIG. 7 is a schematic diagram of energy consumption costs of a carbon capture device in two modes according to an embodiment of the present invention;

FIG. 8 is a schematic diagram of carbon trade costs in two modes according to an embodiment of the present invention;

FIG. 9 is a graph showing carbon dioxide capture amounts in two modes according to an embodiment of the present invention;

fig. 10 is a schematic structural diagram of a robust optimal scheduling system for a micro-grid according to an embodiment of the present invention.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

According to the invention, a robust optimization model is constructed by calling a YALMIP tool kit through MATLAB software, a CPLEX solver is adopted for solving, and the micro-grid system shown in FIG. 2 is taken as an embodiment to verify the effectiveness of the method provided by the invention. The predicted conditions of wind power, electric load and thermal load day ahead are shown in fig. 3, the electricity purchase price, the electricity selling price and the gas purchase price are shown in fig. 4, and the uncertainty adjustment parameters of wind power, electric load power and thermal load power are set to be 6, 12 and 12 respectively. The microgrid operating parameters are shown in table 1. The simulation analysis comprises three aspects of day-ahead dispatching results, comparison of different operation modes of the carbon capture equipment and comparison of dispatching cost under different uncertainties.

In order to solve the defects in the prior art, the invention discloses a micro-grid robust optimal scheduling method, which comprises the following steps as shown in fig. 1:

step 1, modeling each device in a micro-grid system to obtain a mathematical model or a constraint model of each device in the micro-grid system; comprising the following steps:

the micro-grid system comprises a wind turbine, an electric energy storage device, a large power grid, an electric load, a gas boiler, a micro gas turbine, carbon capture equipment, a heat storage device and a heat load as shown in fig. 2.

(1) The electric energy storage equipment can carry out bidirectional adjustment on energy, and is beneficial to flexibly maintaining the power balance of the micro-grid system; the electric energy storage equipment meets the maximum and minimum charge and discharge power limit, the capacity constraint limit and the charge and discharge energy balance constraint related to the service life of the electric energy storage in the dispatching process;

the mathematical model of the electric energy storage device is as follows:

(1)

(2)/>

(3)

(4)

(5)

wherein,and->Respectively indicate->Charging power and discharging power of the time period electric energy storage device; / >Representation->The charge and discharge states of the electric energy storage equipment in the period of time are represented when the value is 1, and the electric energy storage equipment is in a charge state when the value is 0; />And->Respectively representing the maximum charging power and the maximum discharging power of the electric energy storage equipment, and taking 500kW; />Taking 24h as a scheduling total period; />Representation->The capacity of the electrical energy storage device during the period; />And (3) withRespectively representing the maximum capacity and the minimum capacity allowed by the electric energy storage equipment in the dispatching process, wherein the maximum capacity and the minimum capacity are respectively 1800kWh and 400kWh; />The initial scheduling capacity of the electric energy storage equipment is taken as 1000kWh; />Representing the capacity of the electrical energy storage device at the last scheduling instant; />The charge and discharge coefficient of the electrical energy storage device is represented and taken as 0.95.

(2) When an internal power supply of the micro-grid system cannot meet load requirements, the micro-grid system purchases electricity to a large power grid to meet power balance; conversely, when the internal electric energy of the micro-grid system is excessive, the micro-grid system sells electric energy to a large power grid to obtain benefits; in the electricity purchasing and selling process, the power interaction constraint model between the micro-grid system and the large grid should satisfy the formulas (6) to (7):

(6)

(7)

wherein,and->Respectively represent micro-grid system- >The electricity purchasing power and the electricity selling power in the time period; />The method comprises the steps of representing the electricity purchasing and selling state of a micro-grid system in a t period, representing that the micro-grid system purchases electricity to a large grid when the value is 1, and representing that the micro-grid system sells electricity to the large grid when the value is 0; />The maximum purchase and selling power allowed when the micro-grid system interacts with the large grid power is represented as 1000kW.

(3) When the output heat power of the micro gas turbine is insufficient to meet the heat load, the gas boiler is used as an auxiliary heat supply device to compensate the heat power shortage; the mathematical model of the gas boiler is as follows:

(8)

wherein,indicating gas boiler->Thermal power output in a time period; />The natural gas has a low calorific value of 9.7 kWh/m 3; />The thermal efficiency of the gas boiler was 0.9; />Representation->The air inflow of the gas boiler in the period.

(4) The miniature gas turbine can simultaneously supply heat to the outside in the process of gas power generation, so that the economic performance and efficiency of the whole system are improved; the mathematical model of the miniature gas turbine is as follows:

(9)

(10)

(11)/>

wherein,for miniature gas turbines->The intake air amount of the period; />Is natural gas with low calorific value; />And (3) withRespectively micro gas turbines at->Electric power and thermal power output in the period; />And->The gas-to-electricity and gas-to-heat efficiencies of the micro gas turbine are respectively 0.35 and 0.4; / >For miniature gas turbines->Total carbon emissions during the time period;the carbon emission intensity representing the unit output electric power of the micro gas turbine is taken as 0.7g/kWh; />The carbon emission intensity representing the unit output heat power of the micro gas turbine is taken as 0.4g/kWh; />The time length of the unit time period is 1h;

(5) The heat storage device is similar to the electric energy storage device, and the heat storage device is required to meet the upper limit of charging and discharging power, the limit of capacity constraint and the balance constraint of charging and discharging quantity in the dispatching process; the mathematical model of the heat storage device is as follows:

(12)

(13)

(14)

(15)

(16)

wherein,and->Respectively indicate->The heat storage device is used for storing heat in a period of time; />Representation->The heat storage device is in a heat charging state when the value is 1, and is in a heat discharging state when the value is 0; />And->Respectively representing the upper limit of the heat storage device on the heat charging power and the heat releasing power, and taking 500kW; />Taking 24h as a scheduling total period; />Representation->The heat storage amount of the time period heat storage device; />And->Representing the maximum heat storage capacity and the minimum heat storage capacity allowed by the heat storage device in the dispatching process, and respectively taking 1800kW and 400kW;indicating the initial heat storage capacity of the heat storage device; />Representing the heat storage amount of the heat storage device at the last scheduling moment; / >The heat charging and discharging efficiency was taken to be 0.9.

(6) The carbon capture device includes a fixed mode of operation and a flexible mode of operation;

(1) when the carbon capture equipment is in a fixed operation mode, the carbon capture level of the carbon capture equipment is kept unchanged, and the carbon emission reduction effect is obvious in the mode, but the cost is higher; and the carbon capture energy consumption comprises fixed energy consumption and operation energy consumption;

wherein, the fixed energy consumption is irrelevant to the running state of the carbon capture equipment, and the value of the fixed energy consumption is not changed; the operation energy consumption is related to the carbon capturing amount; the mathematical model of the carbon capture device in the fixed operation mode is as follows:

(17)

(18)

(19)

is the total energy consumption of the carbon capture equipment; />The operation energy consumption caused by carbon capture of the carbon capture equipment;the fixed energy consumption of the carbon capture equipment is 50kW; />The energy consumption required for capturing the carbon dioxide unit is taken as 0.269kWh/g; />At carbon capture level, the fixed pattern was taken to be 0.85; />The split ratio of the flue gas is 0.7; />And->Carbon emission intensity of electric power and thermal power output by the micro gas turbine unit respectively; />And->Respectively micro gas turbines at->Electric power and thermal power output in the period; />Representing micro gas turbine->Total carbon emissions during the time period; />Representing that the carbon capture device is +. >Carbon dioxide emissions from the micro gas turbine captured over time.

(2) When the carbon capture equipment is in a flexible operation mode, the carbon capture level is changed according to the current power grid electricity price, so that the reduction of carbon capture energy consumption is realized, and finally, the carbon capture cost is reduced; for simplicity of discussion, the present invention assumes that the product of the carbon capture level and the time-of-use electricity price is a constant, and the product of the carbon capture level and the time-of-use electricity price is:

(20)

wherein,setting a constant of the product of the carbon capture level and the time-of-use electricity price for people; />Is the carbon capture level;is->Electricity purchase price of time period, units (yuan/kWh); when electricity purchase price is increased, the carbon capture level is correspondingly lowerThereby achieving the purpose of reducing the energy consumption cost.

Step 2, obtaining a carbon transaction cost model of the micro-grid system according to a mathematical model or a constraint model of each device in the micro-grid system, wherein the step comprises the following steps:

(1) Assuming that carbon emissions inside the micro-grid system are from micro gas turbines, gas boilers and outsourcing power, and assuming that outsourcing power is from coal-fired units; establishing and acquiring a carbon emission allowance model of the micro power grid system according to a mathematical model of the gas boiler, a mathematical model of the micro gas turbine and a power interaction constraint model between the micro power grid system and the large power grid;

The carbon emission allowance model of the micro-grid system is calculated as follows:

(21)

wherein,carbon emission allowance for the micro-grid system; />、/>And->The carbon emission quota coefficients of the micro gas turbine, the coal-fired unit and the gas boiler are respectively 0.224g/kWh, 0.302g/kWh and 0.152g/kWh;and->Respectively micro gas turbines at->Electric power and thermal power output in the period; />Taking 24h as a scheduling total period; />Representation system->Time period power purchase, < >>Indicating gas boiler->Thermal power output in a time period; />Is a unit time period of 1h.

(2) Calculating and acquiring an actual carbon emission model of the micro power grid system according to a mathematical model of the carbon capture equipment in a fixed operation mode, a mathematical model of the micro gas turbine, a power interaction constraint model between the micro power grid system and a large power grid and a mathematical model of a gas boiler;

the actual carbon emission model of the micro-grid system is as follows:

(22)

wherein,the actual carbon emission is the actual carbon emission of the micro-grid system; />For miniature gas turbines->Total carbon emission in time period->And->The carbon emission intensity coefficients of the coal-fired unit and the gas-fired boiler are respectively 0.8g/kWh and 0.2g/kWh; />Representation system- >Time period power purchase, < >>Indicating gas boiler->Thermal power output in a time period; />Taking 24h as a scheduling total period; />Representing that the carbon capture device is +.>Carbon dioxide emissions of the micro gas turbine captured in time period are in g.

(3) According to the carbon emission allowance model and the actual carbon emission quantity model, calculating and acquiring a carbon transaction cost model of the micro-grid system;

(23)

Step 3, obtaining an objective function of optimal scheduling of the micro-grid system according to a carbon transaction cost model of the micro-grid system; the method comprises the following steps:

acquiring the total running cost of the micro-grid system according to the carbon transaction cost, the energy purchasing cost, the running maintenance cost and the energy consumption cost of the carbon capturing equipment of the micro-grid system; taking the total running cost of the micro-grid system as an objective function of the optimal dispatching of the micro-grid system;

(24)

in the method, in the process of the invention,the total cost of running the micro-grid system; />The energy purchasing cost of the micro-grid system; />The operation and maintenance cost of the micro-grid system is realized; / >The energy consumption cost of the carbon capture equipment is reduced; />Cost for carbon trade;

the energy purchasing cost of the micro-grid system is as follows:

(25)

wherein:is->Air for a period of timeValence, units (yuan/kWh); />Is->Electricity selling price of time period, unit (yuan/kWh); />For miniature gas turbines>Electric power output in time period->The gas-to-electricity efficiency of the micro gas turbine is taken to be 0.35; />Indicating gas boiler->Thermal power output in time period->The thermal efficiency of the gas boiler was 0.9; />And->Respectively represent micro-grid system->The electricity purchasing power and the electricity selling power in the time period; />Taking 24h as a scheduling total period;

the operation and maintenance cost of the micro-grid system is as follows:

(26)

in the method, in the process of the invention,、/>、/>and->The maintenance cost coefficients of the micro gas turbine, the gas boiler, the electric energy storage equipment and the heat storage device are respectively represented and respectively taken as 0.25 yuan/kW, 0.3 yuan/kW and 0.3 yuan/kW; />Indicating gas boiler->Thermal power output in time period->The thermal efficiency of the gas boiler was 0.9; />And->Respectively indicate->Charging power and discharging power of the energy storage in a period; />And->Respectively indicate->The heat storage device is used for storing heat in a period of time;

the energy consumption cost of the carbon capture equipment is as follows:

(27)

Wherein:the operation energy consumption caused by carbon capture of the carbon capture equipment; />The fixed energy consumption of the carbon capture equipment;is->Electricity purchase price in time of day, units (yuan/kWh).

Step 4, constructing a micro-grid two-stage robust optimization scheduling model according to the objective function and the operation constraint of each device in the micro-grid system; comprising the following steps:

(1) Based on the objective function, limiting by taking the operation constraint of each device in the micro-grid system as a constraint condition, taking the specific output and interaction power of each device as optimization variables, and calling a YALMIP tool box through MATLAB software to construct a multi-energy complementary micro-grid two-stage robust optimization scheduling model considering carbon capture devices;

the constraint conditions comprise electrothermal power balance constraint, micro gas turbine operation constraint, gas boiler constraint, carbon capture equipment constraint, micro power grid system and large power grid power interaction constraint, energy storage device constraint and heat storage device constraint;

namely, the micro-grid system should meet the operation constraint of the micro-gas turbine, the gas boiler constraint, the carbon capture equipment constraint, the power interaction constraint of the micro-grid system and the large power grid, the energy storage device constraint and the heat storage device constraint in the dispatching process.

(1) The electrothermal power balance constraint is as follows:

(29)

(30)

wherein,and->Respectively represent system->Time period purchase/sell electric power; />For miniature gas turbines>Electric power output in time period->An uncertainty variable of the wind power output introduced after the uncertainty is considered;representation->Charging power stored in a time period; />Is the total energy consumption of the carbon capture equipment; />And->Respectively indicate->Charging/discharging power of the time period heat storage device; />For miniature gas turbines>Thermal power output in a time period;indicating gas boiler->Thermal power output in a time period; />And->The electrical load power and the thermal load power uncertainty variables introduced after the uncertainty is considered, respectively.

(2) The micro gas turbine operating constraints are:

(31)

wherein,for miniature gas turbines>Electric power output in a period; />Representing the maximum output electric power of the micro gas turbine, and taking 1000kW; />Representing the upper limit of the electromechanical power climbing of the miniature gas turbine, and taking the upper limit as 500kW/h; because of the existence of thermoelectric coupling, the maximum output thermal power and the upper limit of the thermal power climbing of the micro gas turbine are respectively controlled by the maximum output power of the micro gas turbineAnd determining the upper limit of power and electric power climbing.

(3) The gas boiler is constrained as follows:

(32)

(33)

wherein,the maximum output thermal power of the gas boiler is shown; / >The upper limit of the climbing of the thermal power of the gas boiler is expressed as 300kW/h.

(4) Carbon capture equipment constraints, including fixed modes of operation and flexible modes of operation;

the mathematical model of the carbon capture device in the fixed operation mode is as follows:

(17)

(18)

(19)

is the total energy consumption of the carbon capture equipment; />The operation energy consumption caused by carbon capture of the carbon capture equipment;fixing energy for carbon capture equipmentConsumption; />Energy consumption required for capturing carbon dioxide units, < >>In order to achieve a level of carbon capture,the split ratio of the flue gas is; />And->Carbon emission intensity of electric power and thermal power output by the micro gas turbine unit respectively;and->Respectively micro gas turbines at->Electric power and thermal power output in the period; />Representing micro gas turbine->Total carbon emissions during the time period; />Representing that the carbon capture device is +.>Carbon dioxide emissions from the micro gas turbine captured over time.

(20)

wherein,setting a constant of the product of the carbon capture level and the time-of-use electricity price for people; />Is the carbon capture level;is- >Electricity purchase price of time period, units (yuan/kWh); as electricity purchase rates rise, the carbon capture level correspondingly decreases.

(5) Micro-grid power interaction constraint:

(6)

(7)

wherein,and->Respectively represent micro-grid system->The electricity purchasing power and the electricity selling power in the time period;the method comprises the steps of representing the electricity purchasing and selling state of a micro-grid system in a t period, representing that the micro-grid system purchases electricity to a large grid when the value is 1, and representing that the micro-grid system sells electricity to the large grid when the value is 0; />The maximum purchase and selling power allowed when the micro-grid system interacts with the large grid power is represented as 1000kW.

(6) Electric energy storage device constraints:

(1)/>

(2)

(3)

(4)

(5)

wherein,and->Respectively indicate->Charging power and discharging power of the time period electric energy storage device; />Representation->The charge and discharge states of the electric energy storage equipment in the period of time are represented when the value is 1, and the electric energy storage equipment is in a charge state when the value is 0; />And->Respectively representing the maximum charging power and the maximum discharging power of the electric energy storage equipment, and taking 500kW; />Taking 24h as a scheduling total period; />Representation->The capacity of the electrical energy storage device during the period; />And->Respectively representing the maximum capacity and the minimum capacity allowed by the electric energy storage equipment in the dispatching process, wherein the maximum capacity and the minimum capacity are respectively 1800kWh and 400kWh; / >The initial scheduling capacity of the electric energy storage equipment is taken as 1000kWh; />Representing the capacity of the electrical energy storage device at the last scheduling instant; />The charge and discharge coefficient of the electrical energy storage device is represented and taken as 0.95.

(7) And (3) restraining a heat storage device:

(12)

(13)

(14)

(15)

(16)

wherein:and->Respectively indicate->The heat storage device is used for storing heat in a period of time; />Representation->The heat storage device is in a heat charging state when the value is 1, and is in a heat discharging state when the value is 0; />And->Respectively representing the upper limit of the heat storage device heat charging power and the upper limit of the heat releasing power, and taking 500kW; />Taking 24h as a scheduling total period; />Representation->Time period heat storageThe heat storage capacity of the device; />And->Respectively representing the maximum heat storage amount and the minimum heat storage amount allowed in the dispatching process of the heat storage device, and respectively taking 1800kW and 400kW;indicating the initial heat storage capacity of the heat storage device; />Representing the heat storage amount of the heat storage device at the last scheduling moment; />The heat charging and discharging efficiency was taken to be 0.9.

(2) In order to cope with uncertainty of wind power output, the two-stage robust optimization scheduling model of the micro-grid is set to be of a three-layer structure of min-max-min, and an uncertain variable is obtained by taking the minimum value of an objective function as a target In uncertainty set->A scheduling scheme with optimal economy when the worst scene changes inwards;

the two-stage robust optimization model of the micro-grid is as follows:

(28)/>

wherein the minimization problem of the outermost layer is the first stage problem, and the optimization variable is，The method comprises the steps of carrying out a first treatment on the surface of the Brackets are the second stage problemThe optimization variable is +.>And->，Wherein the minimization problem is equivalent to the objective function of equation (24), representing minimizing the total cost of operation; />For giving a group->Decision variable +.>Is a feasible region of (2).

Step 5, constructing an uncertainty set according to uncertainty variables in the micro-grid system; solving the two-stage robust optimization scheduling model of the micro-grid by using a CPLEX solver and an uncertain set to obtain scheduling results of a controllable unit, electrothermal energy storage and interactive power, wherein the method comprises the following steps:

(1) Assuming that wind power output, electric load and thermal load demand predicted values and maximum predicted deviation are known, constructing a box type uncertain set；

The uncertainty set is:

(34)

wherein U represents an uncertainty set;、/>and->Respectively->The predicted power of the wind turbine generator, the electric load and the thermal load in the period; />、/>And->Respectively->Maximum fluctuation errors of wind power output, electric load power and thermal load power in a period are respectively taken as 15%, 10% and 10% of predicted power; / >、/>And->The uncertainty is the uncertainty variables of wind power output, electric load power and thermal load power which are introduced after the uncertainty is considered; />、/>And->The binary variables respectively representing the degree of deviating the uncertain variables of wind power output, electric load power and thermal load power from the predicted value are the same as the predicted value in the uncertain variable value of the corresponding time period when 0 is taken, and the uncertain variables of the corresponding time period when 1 is taken are the boundary of the interval; />、/>And->Uncertainty adjustment parameters of the induced wind power output, the electric load power and the thermal load power are respectively; />A vector formed by the three uncertain variables, namely an uncertain vector; />、/>、/>The matrix constructed for facilitating the subsequent matrix operation has no practical significance; />Scheduling a total period;

the predicted conditions of wind power, electric load and thermal load are shown in figure 3.

(2) The two-stage robust optimization model is expressed as:

(35)

in the method, in the process of the invention,a coefficient matrix corresponding to the variable under constraint; />And->All of which represent the optimization variables,；/>the vector is composed of three uncertain variables, namely an uncertain vector, namely wind power output, electric load power and thermal load power; />The method comprises the steps of carrying out a first treatment on the surface of the U represents an uncertainty set; />Is a constant column vector;

(36)

(37)

at a given pointThe minimization problem of the inner layer of the following formula (37) is a linear problem, and can be converted into a maximization problem and combined with the maximization problem of the outer layer according to the strong pair theory and the corresponding relation of the constraint condition of the formula (37) to obtain the product such asThe dual problem of formula (38): />

(38)

When the uncertain variable quantity reaches the boundary value, the maximum or minimum value is correspondingly obtained for the dual problem, the bilinear term in the formula (54) can be subjected to linearization processing by adopting a Big-M method, and the final expression form of the sub-problem is shown as the formula (39):

(39)

in the method, in the process of the invention,is an uncertainty adjustment parameter of the induced wind power output; />The binary variable representing the degree of deviating the uncertain variable of the wind power output from the predicted value is the same as the predicted value in the uncertain variable of the corresponding period when taking 0, and the uncertain variable of the corresponding period when taking 1 is the boundary of the interval; />As an introduced dual variable; />Is an introduced continuous auxiliary variable; />Is a sufficiently large positive real number.

The two-stage robust optimization model adopts a column constraint generation algorithm (C & CG) solving flow as follows:

(1) Given a set of uncertainty variablesSetting the upper limit of the total cost of the scheduling scheme as the initial worst scene>Model lower bound->The method comprises the steps of carrying out a first treatment on the surface of the Iteration count->；

(2) According to the worst sceneSolving the main problem to get (+)>,/>,/>……/>) The objective function value of the main problem is taken as a new lower bound;

(3) results of solving the Main problemSubstituting the sub-problem and solving to obtain worst scene +.>Objective function value of sub-problem->Updating the upper bound of the model->；

(4) If it isThen output the solving result +.>And->Stopping iteration; otherwise, bringing the new worst scene into step (2) and generating a new variable +.>Adding the following constraint, and adding 1 to the iteration times;

(40)

as can be seen from fig. 5 and 6, at 1-7 h, although the wind power output can basically meet the electrical load demand, the micro gas turbine still outputs the maximum power, because if the micro gas turbine output is smaller, the thermal power correspondingly output under the condition of thermoelectric coupling is also smaller, and the heat storage device and the gas boiler are not enough to meet the thermal load demand. At 8-18 h, electricity is purchased through a large power grid to meet the electric load demand, and the electric load demand is also smaller at 8-18 hours because the output thermal power is also larger if the micro-combustion engine is excessively high in output, and the redundant thermal power cannot be completely consumed through the heat storage device. Finally, at 19-24 hours, because the heat load demand is larger, the micro-combustion engine is required to be mainly used for supplying heat, and the corresponding output electric power is too much, so that the excessive electric energy generated by electricity selling and electricity energy storage is consumed.

Considering the case where the electricity price is 0.4 as a standard,2.125 so that when the electricity price is changed, the carbon capture level of each period is also changed with the electricity price, in practice +.>Can be flexibly set according to the situation. As shown in fig. 7 to 9, when the electricity prices are the same, the energy consumption cost, the carbon transaction cost, and the carbon dioxide capturing amount of the carbon capturing device in the two modes are the same. When the electricity price is increased, the energy consumption cost of the carbon capture equipment in the flexible mode is generally lower than that in the fixed mode; carbon trade costs are generally higher than fixed patternsThe method comprises the steps of carrying out a first treatment on the surface of the The carbon dioxide capture amount is generally lower than the fixed mode. This is because as electricity prices rise, the carbon capture level in the flexible mode decreases with the resulting decrease in the energy consumption of the carbon capture plant operation, the amount of captured carbon dioxide decreases, and the carbon trade cost increases. At the same time, due to the complex relationship between the carbon capture level and the output power of the micro-combustion engine, the situation opposite to the general trend can occur in a very small part of time. In general, when the carbon capture apparatus is in a flexible mode of operation, the operating energy consumption is reduced, the carbon capture amount is reduced, equivalent to sacrificing low carbon to ensure economy.

In order to verify that the optimization method provided by the invention can flexibly adjust the conservation of the scheduling scheme, three groups of uncertainty adjustment parameters are selected for comparison, and the parameter setting and the corresponding total running cost are shown in table 2. It can be seen that as the uncertainty adjustment parameters increase, the overall cost of operation of the micro-grid increases accordingly, as the uncertainty is more accounted for, resulting in a more conservative solution, resulting in an increase in overall cost.

Table 1 microgrid system operating parameters

TABLE 2 Total cost of microgrid operation with different uncertainty adjustment parameters

the modeling unit is used for modeling each device in the micro-grid system and obtaining a mathematical model or a constraint model of each device in the micro-grid system;

The specific manner in which the respective unit modules perform the operations in the above-described embodiments has been described in detail in relation to the embodiments of the method, and will not be described in detail herein.

The beneficial effects of the invention include: because wind power has volatility and intermittence, strong randomness is brought to the running of a micro-grid system after grid connection, and the traditional deterministic optimal scheduling method is difficult to adapt to the random balance requirement of the micro-grid; in order to cope with the uncertainty of renewable energy sources and ensure the stable operation of the micro-grid, a micro-gas turbine is often needed to purchase electricity to a large power grid, and a large amount of carbon dioxide is generated at the same time. Aiming at the problems of wind power uncertainty and carbon emission, the invention provides a robust optimization scheduling method of a multi-energy complementary micro-grid considering carbon capture equipment, which comprises the following steps: considering the carbon capture equipment into the running of the micro-grid, and introducing a carbon transaction mechanism at the same time, so that the carbon emission generated by the running of the micro-grid is effectively reduced; a two-stage robust optimization scheduling model is established, uncertainty of wind power output is described by adopting a box type uncertainty set, scheduling results obtained by solving the model can adapt to any scene in the uncertainty set, low-carbon economic operation of a micro-grid system is ensured, and renewable energy source capacity of the micro-grid is enhanced.

Finally, it should be noted that: the foregoing description is only illustrative of the preferred embodiments of the present invention, and although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described, or equivalents may be substituted for elements thereof, and any modifications, equivalents, improvements or changes may be made without departing from the spirit and principles of the present invention.

Claims

1. The utility model provides a micro-grid robust optimal scheduling method, which is characterized by comprising the following steps:

constructing an uncertain set according to uncertain variables in a micro-grid system, and solving a two-stage robust optimization scheduling model of the micro-grid by utilizing the uncertain set to obtain a scheduling result;

wherein the uncertainty set is constructed from uncertainty variables within the microgrid system, including,

(34)

wherein U represents an uncertainty set;、/>and->Respectively->The predicted power of the wind turbine generator, the electric load and the thermal load in the period; / >、/>And->Respectively->Maximum fluctuation errors of wind power output, electric load power and thermal load power in a period of time; />、/>And->The uncertainty is the uncertainty variables of wind power output, electric load power and thermal load power which are introduced after the uncertainty is considered; />、/>And->Uncertainty deviations of wind power output, electrical load power and thermal load power, respectivelyThe binary variable of the predicted value degree is the same as the predicted value in the uncertain variable value of the corresponding time interval when taking 0, and the uncertain variable of the corresponding time interval when taking 1 is the boundary of the interval; />、And->Uncertainty adjustment parameters of the induced wind power output, the electric load power and the thermal load power are respectively; />A vector formed by the three uncertain variables, namely an uncertain vector; />、/>、/>The matrix constructed for facilitating the subsequent matrix operation has no practical significance; />To schedule the total period.

2. The method for robust optimal scheduling of a micro-grid according to claim 1, wherein modeling each device in the micro-grid system to obtain a mathematical model or constraint model of each device in the micro-grid system comprises,

(1)

(2)/>(3)

(4)

(5)

wherein,and->Respectively indicate->Charging power and discharging power of the time period electric energy storage device; />Representation->The charge and discharge states of the electric energy storage equipment in the period of time are represented when the value is 1, and the electric energy storage equipment is in a charge state when the value is 0; />Representing the maximum charging power of the electrical energy storage device, < >>Representing a maximum discharge power of the electrical energy storage device; />Scheduling a total period; />Representation->The capacity of the electrical energy storage device during the period; />And->Respectively representing the maximum capacity and the minimum capacity allowed by the electric energy storage equipment in the scheduling process; />Initial scheduling capacity for the electrical energy storage device;representing the capacity of the electrical energy storage device at the last scheduling instant; />Representing the charge-discharge coefficient of the electrical energy storage device;

(6)

(7)

Wherein,and->Respectively represent micro-grid system->The electricity purchasing power and the electricity selling power in the time period; />The method comprises the steps of representing the electricity purchasing and selling state of a micro-grid system in a t period, representing that the micro-grid system purchases electricity to a large grid when the value is 1, and representing that the micro-grid system sells electricity to the large grid when the value is 0; />Representing the maximum purchase and selling electric power allowed when the micro-grid system interacts with the large grid power;

(8)

wherein the method comprises the steps of，Indicating gas boiler->Thermal power output in a time period; />Is natural gas with low calorific value; />Indicating the thermal efficiency of the gas boiler; />Representation->Air inflow of the gas boiler in time period;

(9)

(10)

(11)

wherein,for miniature gas turbines->The intake air amount of the period; />And->Respectively micro gas turbines at->Electric power and thermal power output in the period; />And->The efficiency of gas-to-electricity conversion and gas-to-heat conversion of the micro gas turbine are respectively;for miniature gas turbines->Total carbon emissions during the time period; / >Carbon emission intensity indicating unit output electric power of micro gas turbine,/->Carbon emission intensity representing unit output thermal power of the micro gas turbine; />Is the duration of a unit time period;

(12)

(13)

(14)

(15)

(16)

3. A micro-grid robust optimal scheduling method according to claim 2, wherein,

the carbon capture device includes a fixed mode of operation and a flexible mode of operation;

(17)

(18)

(19)

representing the total energy consumption of the carbon capture plant; />The operation energy consumption caused by carbon capture of the carbon capture equipment; />The fixed energy consumption of the carbon capture equipment; />Energy consumption required for capturing carbon dioxide units, < >>For carbon capture level, +.>The split ratio of the flue gas is; />And->Carbon emission intensity of electric power and thermal power output by the micro gas turbine unit respectively;and->Respectively micro gas turbines at->Electric power and thermal power output in the period; />Indicating that the micro gas turbine is->Total carbon emissions for the period of time; />Representing that the carbon capture device is +.>Carbon dioxide emission of the micro gas turbine captured in a time period;

(20)

wherein,setting a constant of the product of the carbon capture level and the time-of-use electricity price for people; />Is the carbon capture level; Is->Electricity purchase price in time period; as electricity purchase rates rise, the carbon capture level correspondingly decreases.

4. The method for robust optimization scheduling of a micro-grid according to claim 1, wherein the obtaining a carbon trade cost model of the micro-grid system by using a mathematical model or a constraint model of each device in the micro-grid system comprises:

5. A micro-grid robust optimized scheduling method according to claim 4, wherein,

Assuming that carbon emissions inside the micro-grid system are from micro gas turbines, gas boilers and outsourcing power, and assuming that outsourcing power is from coal-fired units; the carbon emission allowance model of the micro-grid system is calculated as follows:

(21)

wherein,carbon emission allowance for the micro-grid system; />、/>And->Carbon emission quota coefficients of the micro gas turbine, the coal-fired unit and the gas boiler are respectively represented; />And->Respectively micro gas turbines at->Electric power and thermal power output in the period; />To schedule the total period>Representation system->Time period power purchase, < >>Indicating gas boiler->Thermal power output in time period->Is the duration of a unit time period;

the actual carbon emission model of the micro-grid system is as follows:

(22)

(23)

6. The method for robust optimal scheduling of a micro-grid according to claim 1, wherein obtaining an objective function of optimal scheduling of the micro-grid system according to a carbon trade cost model of the micro-grid system comprises:

(24)

in the method, in the process of the invention,the total cost of running the micro-grid system; />The energy purchasing cost of the micro-grid system; />The operation and maintenance cost of the micro-grid system is realized; />The energy consumption cost of the carbon capture equipment is reduced; />Cost for carbon trade;

the energy purchasing cost of the micro-grid system is as follows:

(25)

wherein:is->The gas price of the time period; />Is->Electricity selling price in time period; />For miniature gas turbines>Electric power output in time period->The efficiency of gas-to-electricity conversion of the micro gas turbine is improved; />Indicating gas boiler->Thermal power output in time period->Indicating the thermal efficiency of the gas boiler; />And->Respectively represent micro-grid system->The electricity purchasing power and the electricity selling power in the time period; />Scheduling a total period;

The operation and maintenance cost of the micro-grid system is as follows:

(26)

the energy consumption cost of the carbon capture equipment is as follows:

(27)

in the method, in the process of the invention,the operation energy consumption caused by carbon capture of the carbon capture equipment; />The fixed energy consumption of the carbon capture equipment; />Is->Electricity purchase price in time period.

7. The method for robust optimal scheduling of a micro-grid according to claim 1, wherein the method for robust optimal scheduling of a micro-grid comprises constructing a two-stage robust optimal scheduling model of a micro-grid according to the objective function and the operation constraint of each device in the micro-grid system,

in order to cope with uncertainty of wind power output, the two-stage robust optimization scheduling model of the micro-grid is set to be of a three-layer structure of min-max-min, and an uncertain variable is obtained by taking the minimum value of an objective function as a target In uncertainty set->A scheduling scheme with optimal economy when the worst scene changes inwards;

(28)

8. The method for robust optimized dispatch of a micro-grid as claimed in claim 7, wherein,

the electrothermal power balance constraint is as follows:

(29)

(30)

wherein,and->Respectively represent system->Time period purchase/sell electric power; />For miniature gas turbines>Electric power output in time period->An uncertainty variable of the wind power output introduced after the uncertainty is considered; />Representation->Charging power stored in a time period; />Is the total energy consumption of the carbon capture equipment; / >And->Respectively indicate->Charging/discharging power of the time period heat storage device; />For miniature gas turbines>Thermal power output in a time period; />Indicating gas boilerThermal power output in a time period; />And->The uncertainty of the electric load power and the thermal load power introduced after the uncertainty is considered are respectively;

the micro gas turbine operating constraints are:

(31)

wherein,representing the maximum output electric power of the micro gas turbine; />Representing the upper limit of the electric power climbing of the micro gas turbine; micro-scale due to the presence of thermoelectric couplingThe maximum output thermal power and the upper limit of the thermal power climbing of the gas turbine are respectively determined by the maximum output electric power and the upper limit of the electric power climbing of the micro gas turbine;

the gas boiler is constrained as follows:

(32)

(33)

9. The micro-grid robust optimization scheduling method according to claim 8, wherein solving the micro-grid two-stage robust optimization scheduling model by using the uncertain set to obtain scheduling results of controllable units, electrothermal energy storage and interactive power comprises:

the two-stage robust optimization model is as follows:

(35)

wherein, A coefficient matrix corresponding to the variable under constraint; />And->All of which represent the optimization variables,；/>the vector is composed of three uncertain variables, namely an uncertain vector, namely wind power output, electric load power and thermal load power; u represents an uncertainty set;；/>is a constant column vector;

(36)

(37)

(38)

(39)

wherein,is an uncertainty adjustment parameter of the induced wind power output; / >The binary variable representing the degree of deviating the uncertain variable of the wind power output from the predicted value is the same as the predicted value in the uncertain variable of the corresponding period when taking 0, and the uncertain variable of the corresponding period when taking 1 is the boundary of the interval; />As an introduced dual variable; />Is an introduced continuous auxiliary variable; />Is a sufficiently large positive real number.

10. A micro-grid robust optimization scheduling system is characterized by comprising a modeling unit, an acquisition unit, a construction unit and a solving unit,

the solving unit is used for constructing an uncertain set according to uncertain variables in the micro-grid system, and solving the two-stage robust optimization scheduling model of the micro-grid by utilizing the uncertain set to obtain a scheduling result;

(34)

wherein U represents an uncertainty set;、/>and->Respectively->The predicted power of the wind turbine generator, the electric load and the thermal load in the period; />、/>And->Respectively->Maximum fluctuation errors of wind power output, electric load power and thermal load power in a period of time; />、/>And->The uncertainty is the uncertainty variables of wind power output, electric load power and thermal load power which are introduced after the uncertainty is considered; />、/>And->The binary variables respectively representing the degree of deviating the uncertain variables of wind power output, electric load power and thermal load power from the predicted value are the same as the predicted value in the uncertain variable value of the corresponding time period when 0 is taken, and the uncertain variables of the corresponding time period when 1 is taken are the boundary of the interval; />、And->Uncertainty adjustment parameters of the induced wind power output, the electric load power and the thermal load power are respectively; />A vector formed by the three uncertain variables, namely an uncertain vector; />、/>、/>The matrix constructed for facilitating the subsequent matrix operation has no practical significance; / >To schedule the total period.