Method and system for robust optimization of microgrid scheduling

Abstract

A method and system for robust optimization of microgrid scheduling, relating to the technical field of microgrid scheduling. The method comprises: constructing a multi-interval uncertainty set by means of an uncertainty prediction parameter (S1); on the basis of the established multi-interval uncertainty set, constructing a robust scheduling model of a microgrid (S2); and using a column constraint generation algorithm to iteratively solve the constructed robust scheduling model, to obtain a net load curve and an operation plan for energy output (S3). In the present invention, on the basis of traditional single-interval robust optimization, a multi-interval uncertainty set is constructed on the basis of wind power prediction data, a multi-interval two-stage robust optimization model is established on the basis of the foregoing, and the conservative nature of single-interval robustness is reduced. A nested column and a constraint generation algorithm are used for solving. In the first stage, using minimum net load fluctuation as a goal, planning is carried out on the basis of the prediction data, and in the second stage, considering the uncertainty of wind power, wind power output in a worst-case scenario is searched for, and the policy of the first stage is adjusted, so that the stability of the microgrid is ensured.

Description

Robust optimization microgrid scheduling method and system TECHNICAL FIELD

[0001] The present application relates to the technical field of microgrid scheduling, in particular to a robust optimization microgrid scheduling method and system. BACKGROUND

[0002] Traditional fossil fuel power generation has brought great pollution to the natural environment, so now more and more renewable energy has emerged. Renewable energy is connected to the microgrid power system through the microgrid, and the proportion is getting higher and higher. The penetration rate of renewable energy in the microgrid is gradually increasing. The uncertainty of renewable energy will affect the stability of the microgrid. Therefore, in order to ensure the stable and efficient operation of the microgrid, it is necessary to reasonably plan the microgrid system and realize the coordinated operation of traditional units, renewable units and energy storage devices.

[0003] Renewable energy is affected by natural conditions and has uncertainty. It is difficult to accurately predict renewable energy in practice. These factors have brought great challenges to microgrid optimization scheduling. Robust optimization has become one of the important methods to solve the uncertainty problem of microgrid. The uncertainty set of robust optimization method is easy to obtain, does not depend on the probability curve, and the optimal solution obtained can meet all scenarios in the uncertainty set, has strong stability, and its principle is simple and suitable for practical engineering application. The existing robust optimization research constructs a single-interval uncertainty set based on the bias interval and the budget parameter to describe the uncertainty in the microgrid. The optimization result of the uncertainty parameter is that the boundary value of the bias interval is located in part of the time period and is biased in the same direction, and the remaining time period is the nominal value. Such extreme scenarios are almost impossible to occur in practice, resulting in strong conservatism of the robust optimization result. Therefore, it is necessary to reduce the conservatism of robust optimization and improve the practicality of the robust optimization result.

[0004] SUMMARY

[0005] In view of the problems existing in the prior art robust optimization microgrid scheduling and system, the present application is proposed.

[0006] Therefore, the problem to be solved by the present application is that the robust optimization result is highly conservative.

[0007] To solve the above technical problems, the present application provides the following technical scheme:

[0008] In a first aspect, the present application embodiment provides a robust optimization microgrid scheduling method, which comprises the following steps,

[0009] constructing a multi-interval uncertainty set through the prediction parameter of uncertainty;

[0010] constructing a robust scheduling model of the microgrid according to the established multi-interval uncertainty set;

[0011] Solving the constructed robust scheduling model by using column generation algorithm iteration, obtaining the net load curve and energy output operation plan.

[0012] As a preferred scheme of the robust optimization microgrid scheduling method of the application, wherein: the multi-interval uncertainty set includes a wind power uncertainty set and a photovoltaic uncertainty set;

[0013] The wind power uncertainty set is expressed by a mathematical formula as follows:

[0014] The photovoltaic uncertainty set is expressed by a mathematical formula as follows:

[0015] In the formula, Z WT is expressed as a wind power uncertainty set, and respectively represent the actual value, the predicted value, the predicted upper limit and the predicted lower limit of the i-th microgrid at t time of wind power, and respectively represent the fluctuation upper limit and the fluctuation lower limit of the i-th microgrid at t time of wind power, Z PV is expressed as a photovoltaic uncertainty set, and respectively represent the actual value, the predicted value, the predicted upper limit and the predicted lower limit of the i-th microgrid at t time of photovoltaic, and respectively represent the fluctuation upper limit and the fluctuation lower limit of the i-th microgrid at t time of photovoltaic, and [-∧, ∧] represents the error range of the deviation power and the uncertainty predicted nominal value in a scheduling period.

[0016] As a preferred scheme of the robust optimization microgrid scheduling method of the application, wherein: the robust scheduling model includes an objective function and a constraint condition;

[0017] In the formula, the objective function is expressed as:

[0018] In the formula, C MG represents the total cost, C s represents the microgrid purchase and sale power start-stop cost, C buy represents the microgrid purchase and sale power cost; C gas represents the microgrid purchase and sale gas cost, C inv represents the energy storage investment cost, C e represents the energy storage operation and maintenance cost, c s represents the purchase and sale power behavior unit price, and represent the microgrid purchase and sale power price, cgas denoted as natural gas unit price, c e denoted as energy storage operation and maintenance cost unit price;

[0019] The constraint condition is expressed by a mathematical formula as follows:

[0020] In the formula, denoted as the electric power generated by the CHP at time period t, denoted as the gas consumption of the CHP at time period t, η GE denoted as the power generation efficiency of the micro gas turbine, and denoted as the heat power generated by the gas boiler at time period t and the gas consumption, ξ GB denoted as the heating efficiency of the gas boiler, P e，max is the upper limit of the power stored by the energy storage device, and denoted as the binary state variable of the electric energy storage device at time t for charging and discharging;

[0021] A robust scheduling model of the microgrid is constructed and expressed by a mathematical formula as follows:

[0022] In the formula, denoted as the first-stage decision variable set, U is denoted as the uncertain set variable, denoted as the second-stage decision variable set.

[0023] As a preferred scheme of the robust optimization microgrid scheduling method of the present application, when the robust scheduling model is solved, the robust scheduling model is transformed into a matrix expression form, and the expression is as follows:

[0024] In the formula, x is denoted as the first-stage discrete decision variable set, y is denoted as the second-stage continuous decision variable set, A, B, C, E, F are all denoted as the matrix form of the corresponding parameters, a and b are denoted as the parameter column vectors in the objective function, and c, e, g, h are denoted as the column vectors of the related parameters of the constraint condition.

[0025] The min-max-min of the matrix expression is decomposed into a master problem and a sub-problem, and the master problem and the sub-problem are solved by iteration to obtain the optimal scheduling result.

[0026] As a preferred scheme of the robust optimization microgrid scheduling method of the present application, the solution method of the robust scheduling model comprises, at each iteration, obtaining the worst wind power and photovoltaic output scenario by the sub-problem, and adding the corresponding constraint to the master problem, wherein the mathematical expression of the constraint condition is as follows:

[0027] where k represents the iteration number, v represents the auxiliary variable introduced, y l represents the optimal strategy of the sub-problem obtained in the lth iteration, represents the worst scenario of photovoltaic, wind power output and load fluctuation obtained by solving the sub-problem in the lth iteration;

[0028] After the first-stage decision of the main problem is made, the second-stage decision of the sub-problem is made, and in each iteration, the optimal configuration capacity of the energy storage obtained under the main problem is given, the sub-problem is solved to obtain the worst photovoltaic, wind power output and load fluctuation scenario and the corresponding optimal operation strategy, which is expressed by a mathematical formula as follows:

[0029] Ey≤e

[0030] Ay+Bd=c

[0031] Ex+Fy≤h

[0032] where c, e and h represent column vectors of related parameters.

[0033] As a preferred scheme of the micro-grid scheduling method based on robust optimization, the sub-problem is a max-min type double-layer optimization problem, the sub-problem is converted into a single-layer optimization problem through KKT condition, the conversion expression is a Lagrange function formula of min, and the specific expression is as follows:

[0034] The original constraint is equivalently converted, and the specific formula is as follows:

[0035] Then, partial derivatives of are calculated, and the calculation formula is expressed as follows:

[0036] where respectively represent the second-stage decision variables.

[0037] As a preferred scheme of the micro-grid scheduling method based on robust optimization, the inequality condition is relaxed by Big-M, and the mathematical formula is expressed as follows:

[0038] where 0≤a⊥b≥0 represents a≥0, b≥0 and ab=0, M represents a positive number, Z1, Z2, Z3, Z4, Z5, Z6, Z7, Z8, Z9 and Z 10 respectively represent binary variables in the complementary condition;

[0039] By converting the original nonlinear constraints into linear constraints, inputting into the upper model, and then calculating the data by using the upper model, the operation plan of energy output is obtained.

[0040] In a second aspect, the embodiment of the present application provides a robust optimization micro-grid scheduling system, which comprises a construction module, an optimization solving module, a strategy generating module, and a calculation module.

[0041] The construction module is used for constructing the uncertain model and the robust scheduling model, updating and optimizing the collected data, and setting the objective function and the constraint condition.

[0042] The optimization solving module is used for solving the constructed robust scheduling model, adopting the column constraint generating algorithm to convert the problem into a matrix expression, and solving the main problem and the sub-problem through iteration, so that the optimized operation plan is obtained.

[0043] The strategy generating module is used for constantly adjusting and optimizing the decision variables in the first stage and the second stage during the solving process of the robust scheduling model.

[0044] The calculation module is used for processing the nonlinear constraints in each model, converting the nonlinear constraints into linear constraints, and then converting the double-layer optimization problem into a single-layer problem, and solving by using the Lagrange function.

[0045] In a third aspect, the embodiment of the present application provides a computer device, which comprises a memory and a processor, and the memory stores a computer program, wherein the processor implements any step of the robust optimization micro-grid scheduling method when executing the computer program.

[0046] In a fourth aspect, the embodiment of the present application provides a computer readable storage medium, which stores a computer program, wherein the computer program is executed by the processor to implement any step of the robust optimization micro-grid scheduling method.

[0047] The present application has the following beneficial effects: on the basis of the traditional single-interval robust optimization, a multi-interval uncertainty set is constructed based on the wind power prediction data, and a multi-interval two-stage robust optimization model is established, so that the conservativeness of the single-interval robust optimization is reduced.

[0048] The nested column and constraint generating algorithm is used for solving, in the first stage, the minimum net load fluctuation is taken as the target, the planning is performed based on the prediction data, in the second stage, the wind power output in the worst scenario is found considering the wind power uncertainty, and the strategy in the first stage is adjusted to ensure the stability of the micro-grid. BRIEF DESCRIPTION OF DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments description will be briefly introduced as follows. Obviously, the drawings in the following description only represent some embodiments of the present application, and all other drawings obtained by those skilled in the art without creative effort based on these drawings should also fall within the protection scope of the present application.

[0050] Fig. 1 is a flow chart of the robust optimization micro-grid scheduling method.

[0051] Fig. 2 is a schematic diagram of a single-interval uncertainty set of a bad scenario.

[0052] Fig. 3 is a schematic diagram of a multi-interval uncertainty set of a bad scenario of the robust optimization micro-grid scheduling method.

[0053] Fig. 4 is a schematic diagram of a wind power multi-interval robust optimization of the robust optimization micro-grid scheduling method.

[0054] Fig. 5 is a schematic diagram of a photovoltaic multi-interval robust optimization of the robust optimization micro-grid scheduling method.

[0055] Fig. 6 is a schematic diagram of the net load curve before and after optimization of the robust optimization micro-grid scheduling method.

[0056] Fig. 7 is a schematic diagram of the electric power output result of the robust optimization micro-grid scheduling method. DETAILED DESCRIPTION

[0057] In order to make the above objectives, features and advantages of the present application more apparent, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present application.

[0058] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application. However, the present application can be practiced without the specific details, other than those described herein, and it is understood that the present application is not limited to the embodiments described herein and can be practiced with or without other apparatuses, systems, structures, techniques, or components not expressly described herein. Therefore, the scope of the present application is not intended to be limited to the following description and the accompanying drawings.

[0059] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. The "in one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments.

[0060] The application is described in detail in combination with the schematic diagram. In the detailed description of the embodiments of the application, the cross-sectional view of the device structure is partially enlarged without the general proportion for the convenience of illustration, and the schematic diagram is only an example, which should not limit the scope of protection of the application herein. In addition, the three-dimensional spatial dimensions of length, width and depth should be included in the actual manufacture.

[0061] Meanwhile, in the description of the application, it should be noted that the terms "upper, lower, inner and outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the application. In addition, the terms "first, second or third" are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0062] In the application, unless otherwise explicitly specified and limited, the terms "mounting, connection, connection" should be understood broadly, for example: it can be fixed connection, detachable connection or integral connection; it can also be mechanical connection, electrical connection or direct connection, it can also be indirectly connected through an intermediate medium, or it can be the communication between two elements. For those skilled in the art, the specific meaning of the above terms in the application can be understood according to the specific circumstances.

[0063] Embodiment 1

[0064] Referring to FIGS. 1-5, the first embodiment of the application provides a robust optimization micro-grid scheduling method, which includes the following steps,

[0065] S1, constructing a multi-interval uncertainty set through the prediction parameters of uncertainty.

[0066] The multi-interval uncertainty set includes a wind power uncertainty set and a photovoltaic uncertainty set;

[0067] The wind power uncertainty set is expressed by a mathematical formula as follows:

[0068] The photovoltaic uncertainty set is expressed by a mathematical formula as follows:

[0069] In the formula, Z WT is expressed as the wind power uncertainty set, and respectively represent the actual value, the predicted value, the upper limit of the predicted value and the lower limit of the predicted value of the i-th micro-grid of wind power at time t, and respectively represent the fluctuation upper limit and the fluctuation lower limit of the i-th micro-grid of wind power at time t, Z PVdenotes the uncertainty set of photovoltaic, and respectively denote the actual value, the predicted value, the upper limit of prediction and the lower limit of prediction of the ith micro-grid at time t, and respectively denote the upper limit and the lower limit of fluctuation of the ith micro-grid at time t, [-∧, ∧] denotes the error range of the deviation power and the nominal value of the uncertainty prediction in a scheduling period.

[0070] S2, according to the established multi-interval uncertainty set, a robust scheduling model of the micro-grid is constructed.

[0071] The robust scheduling model comprises a target function and a constraint condition;

[0072] The target function is expressed as:

[0073] In the formula, C MG denotes the total cost, C s denotes the start-stop cost of purchasing and selling electricity of the micro-grid, C buy denotes the cost of purchasing and selling electricity of the micro-grid; C gas denotes the cost of purchasing and selling gas of the micro-grid, C inv denotes the investment cost of energy storage, C e denotes the operation and maintenance cost of energy storage, c s denotes the unit price of purchasing and selling electricity, and denote the price of purchasing and selling electricity of the micro-grid, c gas denotes the unit price of natural gas, c e denotes the unit price of operation and maintenance cost of energy storage;

[0074] The constraint condition is expressed by a mathematical formula as:

[0075] In the formula, denotes the electric power generated by the CHP at time t, G denotes the gas consumption of the CHP at time t, η GE denotes the power generation efficiency of the micro gas turbine, and denote the heat power generated and the gas consumption of the gas boiler at time t, ξ GB denotes the heat generation efficiency of the gas boiler, P e，max is the upper limit of the power of the energy storage device, and denote the binary state variables of the electric energy storage device at time t;

[0076] The robust scheduling model of the micro-grid is constructed, which is expressed by a mathematical formula as:

[0077] In the formula, U represents a first-stage decision variable set, and U represents an uncertain set variable, Y represents a second-stage decision variable set.

[0078] S3, using a column constraint generation algorithm to iteratively solve the constructed robust scheduling model, to obtain a net load curve and an energy output operation plan.

[0079] When the robust scheduling model is solved, the robust scheduling model is transformed into a matrix expression form, and the expression is:

[0080] In the formula, x represents a first-stage discrete decision variable set, y represents a second-stage continuous decision variable set, A, B, C, E, and F all represent matrix forms of corresponding parameters, a and b represent parameter column vectors in the objective function, and c, e, g, and h represent column vectors of related parameters of constraint conditions;

[0081] The min-max-min of the matrix expression is decomposed into a master problem and a sub-problem, and the master problem and the sub-problem are iteratively solved to obtain an optimal scheduling result.

[0082] The robust scheduling model solving method comprises the following steps: in each iteration, the worst wind power and photovoltaic output scene is obtained through the sub-problem, and corresponding constraints are added to the master problem, wherein the mathematical expression of the constraint condition is:

[0083] In the formula, k represents the number of iterations, v represents an auxiliary variable introduced, y l Y represents an optimal strategy of the sub-problem obtained in the lth iteration, Y represents the worst photovoltaic and wind power output and load fluctuation scene solved by the sub-problem in the lth iteration;

[0084] After the master problem makes a first-stage decision, the sub-problem makes a second-stage decision, in each iteration, the optimal configuration capacity of the energy storage obtained under the master problem is given, the sub-problem is solved, the worst photovoltaic and wind power output and load fluctuation scene and the corresponding optimal operation strategy are obtained, and the mathematical formula is:

[0085] Ey≤e

[0086] Ay+Bd=c

[0087] Ex+Fy≤h

[0088] In the formula, c, e, and h all represent column vectors of related parameters.

[0089] The sub-problem is a bi-level optimization problem in max-min form. The sub-problem is converted into a single-level optimization problem through KKT conditions, and the conversion expression is a Lagrange function formula of min, which is specifically expressed as:

[0090] The original constraint is equivalently converted, and the specific formula is:

[0091] The partial derivative is calculated, and the calculation formula is expressed as:

[0092] In the formula, and respectively represent the second-stage decision variables.

[0093] The inequality condition is relaxed by Big-M, which is expressed by a mathematical formula as:

[0094] In the formula, 0≤a⊥b≥0 represents a≥0, b≥0 and ab=0, M represents a positive number, Z1, Z2, Z3, Z4, Z5, Z6, Z7, Z8, Z9 and Z 10 respectively represent binary variables in the complementary condition;

[0095] By converting the original nonlinear constraint into a linear constraint, input it into the upper model, and then calculate the data using the upper model to obtain the operation plan of energy output.

[0096] As shown in FIGS. 2 and 3, the time in this range is divided into multiple continuous sub-intervals in detail. Due to the uncertainty of the system, only one sub-interval will exhibit a specific deviation value at any time t. From the perspective of scheduling, these sub-intervals not only differ in space but also differ in time. Within the entire scheduling period, each sub-interval has a time period budget constraint, ensuring that the system's operation remains within the predetermined limits when considering uncertainty. As shown in FIGS. 4 and 5, compared with single-interval robust optimization, the multi-interval set two-stage method not only adapts to the uncertainty of wind power and photovoltaic but also exhibits high flexibility and resilience in dealing with emergencies and energy fluctuations. Overall, the multi-interval set two-stage robust optimization method exhibits greater advantages in ensuring stable operation of the microgrid, improving energy utilization efficiency, and economic efficiency compared with single-interval robust optimization.

[0097] Embodiment 2

[0098] Based on the first embodiment, the embodiment further provides a robust optimization microgrid scheduling system, which includes a construction module, an optimization solving module, a strategy generating module, and a calculation module.

[0099] ​​The construction module is used for construction of the uncertain model and the robust scheduling model, and updating and optimization of collected data, setting of a target function and a constraint condition;

[0100] The optimization solving module is used for solving the constructed robust scheduling model, adopting a column constraint generation algorithm, converting the problem into a matrix expression, and finally obtaining an optimized operation plan by solving a main problem and a sub-problem through iteration;

[0101] The strategy generation module is used for constantly adjusting and optimizing the decision variables of the first stage and the second stage in the solving process of the robust scheduling model;

[0102] The calculation module is used for processing nonlinear constraints in each model, converting the nonlinear constraints into linear constraints, and secondly converting a double-layer optimization problem into a single-layer problem, and solving through a Lagrange function.

[0103] The embodiment also provides a computer device suitable for the robust optimization micro-grid scheduling method, including a memory and a processor; the memory is used for storing computer executable instructions, and the processor is used for executing the computer executable instructions to realize the robust optimization micro-grid scheduling method proposed in the above embodiment.

[0104] The computer device can be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. The processor of the computer device is used for providing calculation and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for running of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used for wired or wireless communication with external terminals. The wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, a trackball or a touchpad arranged on the shell of the computer device. The input device of the computer device can also be an external keyboard, a touchpad or a mouse, etc.

[0105] The embodiment also provides a storage medium having a computer program stored thereon, and the program is executed by a processor to realize the robust optimization micro-grid scheduling method proposed in the above embodiment.

[0106] The storage medium proposed in the embodiment belongs to the same inventive concept as the data storage method proposed in the above embodiment, and the technical details not described in detail in the embodiment can be referred to the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.

[0107] Embodiment 3

[0108] With reference to FIGS. 6 and 7, on the basis of the first two embodiments, the present embodiment provides a robust optimization micro-grid scheduling method. In order to verify the beneficial effects of the present application, economic benefit calculation and simulation experiments are carried out for scientific demonstration.

[0109] As can be seen from FIG. 6, through the optimization of the present model, the net load power is significantly reduced, and the maximum peak value is greatly reduced, achieving significant peak shaving effect. The addition of energy storage devices significantly reduces the net load peak-valley difference, which shows the important role of energy storage devices for micro-grid stability. This difference means that the energy storage devices effectively absorb the excess energy in the micro-grid and release it at the peak demand time, further reducing the peak load. As shown in FIG. 7, the optimization results of the electric power are shown. In the 01:00-08:00 and 22:00-24:00 time periods, the grid electricity price is in the valley time electricity price and the normal time electricity price interval, respectively, which is mainly supplied by CHP and wind and light output, and the battery is mainly in the charging state; in the 09:00-12:00 and 18:00-21:00 time periods, the system is in the peak time electricity price interval, and the electric load is mainly supplied by the battery discharge power and the micro gas turbine output; in the 13:00-17:00 time period, the system is in the normal time electricity price interval, and the electric load is in the peak period, which is supplied by CHP output and energy storage.

[0110] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. A robust optimization based microgrid scheduling method, characterized in that: The method comprises the following steps: a multi-interval uncertainty set is constructed by a prediction parameter of uncertainty; a robust scheduling model of the micro-grid is constructed according to the established multi-interval uncertainty set; a net load curve and an energy output operation plan are obtained by iteratively solving the constructed robust scheduling model by using a column constraint generation algorithm.

2. The robust optimization microgrid scheduling method of claim 1, wherein: The multi-interval uncertainty set comprises a wind power uncertainty set and a photovoltaic uncertainty set; The uncertainty set of the wind power is expressed by a mathematical formula as follows: The set of photovoltaic uncertainties is expressed mathematically as: In the formula, Z WT Let P be the set of uncertainties in wind power. t WT P t WT,* P t WT,b+ and P t WT,b- Let represent the actual value, predicted value, upper prediction limit, and lower prediction limit of the i-th microgrid of wind power at time t, respectively. and respectively represent the upper and lower bounds of the wind power fluctuation of the ith microgrid at time t, Z PV represent the uncertainty set of the photovoltaic, P t PV , P t PV,* , P t PV,b+ and P t PV,b- respectively represent the actual value, the predicted value, the upper and lower bounds of the predicted value of the ith microgrid of the photovoltaic at time t, and are respectively an upper limit and a lower limit of photovoltaic fluctuation of the ith micro-grid at the tth time point, and [-∧, ∧] represents an error range of a deviation power and a nominal value of uncertainty prediction in a scheduling period.

3. The robust optimization microgrid scheduling method of claim 2, wherein: The robust scheduling model comprises an objective function and a constraint condition; wherein the objective function is expressed as: C inv = (υP e,max + θ e E e,max ) / (n·365) where C MG denotes the total cost, C s denotes the microgrid electricity purchase and sale start-stop cost, C buy denotes the microgrid electricity purchase and sale cost; C gas denotes the microgrid gas purchase and sale cost, C inv denotes the energy storage investment cost, C e denotes the energy storage operation and maintenance cost, c s denotes the purchase and sale of electricity behavior unit price, and denoted as microgrid electricity purchase and sale price, c gas denoted as natural gas unit price, c e denoted as energy storage operation and maintenance cost unit price; The constraints are expressed mathematically as: P t PV +P t WT +P t CHP +P t dis +P t net =P t ch +P t HP In the formulae, represents the electrical power output by the CHP at the time period t, represents the amount of gas consumed by the CHP at the time period t, η GE represents the power generation efficiency of the micro gas turbine, and respectively represent the heat output and the gas consumption of the gas boiler at the t period, and ξ GB represents the heating efficiency of the gas boiler, P e，max is the upper limit of the power stored by the energy storage device, and is a binary state variable of charging and discharging of the electric energy storage device at the tth time point; A robust scheduling model of microgrid is constructed, which is expressed by mathematical formula as follows: In the formulae, denoted as a first-stage decision variable set, U, denoted as an uncertain set variable, is a second-stage decision variable set.

4. The robustly optimized microgrid scheduling method of claim 3, wherein: When the robust scheduling model is solved, the robust scheduling model is transformed into a matrix expression form, and the expression is: In the formula, x represents a first-stage discrete decision variable set, y represents a second-stage continuous decision variable set, A, B, C, E, and F all represent matrix forms of corresponding parameters, a and b represent parameter column vectors in the objective function, and c, e, g, and h represent column vectors of parameters related to the constraint condition; The min-max-min of the matrix expression is decomposed into a main problem and a sub-problem, and the main problem and the sub-problem are iteratively solved to obtain an optimal scheduling result.

5. The robustly optimized microgrid scheduling method of claim 4, wherein: The solving method of the robust scheduling model comprises, at each iteration, obtaining, by a sub-problem, a worst wind power and photovoltaic output scenario, adding a corresponding constraint to a main problem, wherein a mathematical expression of the constraint condition is: Cx≤g where k denotes the iteration number, v denotes an auxiliary variable introduced, y l denotes the optimal policy of the subproblem obtained in the lth iteration, are respectively photovoltaic and wind power outputs and a most adverse scenario of load fluctuation solved by the sub-problem in the lth iteration; After the first-stage decision of the main problem, the second-stage decision of the sub-problem is made. In each iteration, given the optimal capacity of the energy storage obtained under the main problem, the sub-problem is solved to obtain the worst photovoltaic and wind power output, load fluctuation scenario and the corresponding optimal operation strategy, which is expressed by a mathematical formula as follows: Ey≤e Ay+Bd=c Ex+Fy≤h In the formula, c, e, and h all represent column vectors of related parameters.

6. The robustly optimized microgrid scheduling method of claim 5, wherein: The sub-problem is a double-layer optimization problem in max-min form. The sub-problem is converted into a single-layer optimization problem through KKT conditions, and the conversion expression is a Lagrange function formula in min form, which is specifically expressed as: The original constraints are equivalently transformed by the following formula: Again, respectively, to Taking the partial derivative, the formula is expressed as: In the formulae, are respectively second-stage decision variables.

7. The robust optimization microgrid scheduling method of claim 6, wherein: The inequality condition is adopted Big-M relaxation, expressed by mathematical formula as: where 0≤a⊥b≥0 is expressed as a≥0, b≥0 and ab=0, M is expressed as a positive number, Z1, Z2, Z3, Z4, Z5, Z6, Z7, Z8, Z9, and Z 10 respectively represent binary variables in a complementary condition; By converting the original nonlinear constraint into a linear constraint, the data are input into the upper model, and the energy output operation plan is obtained by using the upper model to calculate the data.

8. A robust optimization microgrid scheduling system based on the robust optimization microgrid scheduling method of any one of claims 1-7, characterized in that: The method comprises a construction module, an optimization solving module, a strategy generation module, and a calculation module; The construction module is used for constructing an uncertainty model and a robust scheduling model, updating and optimizing collected data, and setting an objective function and a constraint condition; The optimization solving module is used for solving the constructed robust scheduling model, converting the problem into a matrix expression by using a column constraint generation algorithm, and iteratively solving the main problem and the sub-problem to finally obtain an optimized operation plan; The strategy generation module is used for adjusting and optimizing first-stage and second-stage decision variables in a solving process of the robust scheduling model; The calculation module is used for processing nonlinear constraints in each model, converting the nonlinear constraints into linear constraints, converting a double-layer optimization problem into a single-layer problem, and solving by using a Lagrange function. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: The processor implements the steps of the robust optimization micro-grid scheduling method of any one of claims 1-7 when executing the computer program.

10. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to implement the steps of the robust optimization micro-grid scheduling method of any one of claims 1-7.

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