A Multi-Time-Scale Energy Dispatch Method and Device for Microgrids Considering Source-Load Uncertainty

Abstract

This invention discloses a microgrid multi-timescale energy dispatching method and apparatus considering source-load uncertainty, comprising: acquiring historical data of wind, solar, and load; determining the initial probability distribution value of the historical scenarios of wind, solar, and load after reduction based on the historical data of wind, solar, and load; solving for the day-ahead dispatch plan based on a pre-constructed microgrid system model and a day-ahead two-stage sub-Blu-ray bar optimization dispatching model, according to the initial probability distribution value of the historical scenarios of wind, solar, and load after reduction; solving for the day-ahead rolling dispatch plan based on a pre-constructed intraday rolling optimization dispatching model, according to the day-ahead dispatch plan; and solving for the real-time output value of each energy coupling device based on a pre-constructed real-time adjustment optimization dispatching model, according to the intra-day rolling dispatch plan, to determine the final dispatch plan of the system.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of micro-grid energy scheduling, and particularly relates to a micro-grid multi-time scale energy scheduling method and device considering source-load uncertainty. BACKGROUND

[0002] As a high-level development stage of distributed power supply and an important component of future energy internet, micro-grid provides an effective way to improve clean energy utilization rate. Micro-grid refers to a controllable micro energy system containing distributed power supply, energy storage equipment, monitoring and protection devices, etc., which can realize efficient and gradient utilization of energy by planning and scheduling the output of each device on the basis of meeting user-side load demand.

[0003] The penetration of high proportion of renewable energy in micro-grid and the randomness of user load make the source-load in micro-grid have multiple uncertain factors in actual operation. How to reduce the prediction error and uncertainty of renewable energy and user load in micro-grid is the first problem to be considered in micro-grid energy scheduling. The traditional multi-time scale optimization scheduling does not consider source-load uncertainty in day-ahead, and the system may have large power fluctuation in the day-ahead stage due to adjustment needs, which is not conducive to the economic and stable operation of the system. Meanwhile, micro-grid is a complex system with multiple operating conditions and coupling relationship of different energy equipment. Although the traditional model predictive control method can meet the real-time adjustment requirements of micro-grid system through rolling optimization and feedback correction means, it is not conducive to the rapid adjustment of the system due to long solving time and high order.

[0004] Therefore, it is an urgent problem to be solved in the field of micro-grid energy scheduling to fully utilize the characteristics that the prediction accuracy of source-load in micro-grid improves with the decrease of time scale and find a suitable micro-grid multi-time scale energy scheduling method. SUMMARY

[0005] To solve the problem that the existing method does not fully utilize global information and local information, the application provides a micro-grid multi-time scale energy scheduling method and device considering source-load uncertainty, which can suppress the power fluctuation of renewable energy and load on the basis of ensuring the balance between supply and demand of micro-grid and the economic and stable operation of micro-grid, and ensure that the day-ahead scheduling plan of micro-grid has a certain robustness, so that the power fluctuation of the system in the day-ahead stage due to adjustment needs is minimized. A two-stage distributed robust optimization scheduling method is used in the day-ahead scheduling stage. Meanwhile, a distributed model predictive control method is used to solve the problem of large calculation and high order of traditional model predictive control in real-time adjustment of micro-grid.

[0006] Based on the characteristics that the prediction accuracy of micro-grid source and load is improved with the decrease of time scale, the power fluctuation of renewable energy and load is smoothed through multi-time scale energy scheduling; and, in order to ensure that the day-ahead scheduling plan has a certain robustness, so that the power fluctuation required by the adjustment in the day stage is minimized, the two-stage distribution robust optimization scheduling method is used in the day-ahead scheduling stage, so as to avoid excessive power adjustment due to the existence of extreme scenarios in the day-ahead plan; at the same time, in the real-time adjustment of the micro-grid, in view of the defects of large calculation amount and high order number of the traditional model predictive control, the distributed model predictive control is used to improve the control performance of the online adjustment of the micro-grid.

[0007] Technical scheme: To solve the above technical problems, the technical scheme adopted by the present application is:

[0008] In a first aspect, a micro-grid multi-time scale energy scheduling method considering source and load uncertainty is provided, comprising:

[0009] Obtaining historical data of wind, light and load;

[0010] Determining the initial probability distribution value p 0,n of the historical scene of the wind, light and load scene after reduction according to the historical data of the wind, light and load;

[0011] Based on the pre-constructed micro-grid system model and the day-ahead two-stage distribution robust optimization scheduling model, the day-ahead scheduling plan is solved according to the initial probability distribution value p 0,n of the historical scene of the wind, light and load scene after reduction;

[0012] Based on the pre-constructed day-by-day rolling optimization scheduling model, the day-by-day rolling scheduling plan is solved according to the day-ahead scheduling plan;

[0013] Based on the pre-constructed real-time adjustment optimization scheduling model, the real-time output value of each energy coupling device is solved according to the day-by-day rolling scheduling plan, and the final scheduling plan of the system is determined.

[0014] In some embodiments, the micro-grid system model comprises:

[0015] (1) The electric heating output model of the gas turbine and the constraint condition:

[0016] ① The gas turbine generates electricity and heat by consuming natural gas, and the mathematical model is as follows:

[0017]

[0018]

[0019] In the formula, LHV represents the low heat value of natural gas; η gt,e represents the power generation efficiency of the gas turbine; η gt,hrepresenting the heat production efficiency of the gas turbine; representing the electricity production of the gas turbine at time t; representing the heat production of the gas turbine at time t; representing the volume of natural gas consumed by the gas turbine at time t;

[0020] ②The gas turbine needs to consider the upper and lower power constraints and the ramping constraints when running:

[0021]

[0022]

[0023]

[0024]

[0025] wherein, representing the upper limit of electricity production of the gas turbine; representing the upper limit of heat production of the gas turbine; representing the ramping power limit of electricity production of the gas turbine; representing the ramping power limit of heat production of the gas turbine; representing the electricity production of the gas turbine at time t-1; representing the heat production of the gas turbine at time t-1;

[0026] (2) Power output model of the electric-to-gas equipment and constraint conditions:

[0027] ①The electric-to-gas equipment can convert electricity into natural gas, and the mathematical model is as follows:

[0028]

[0029] wherein, η P2G representing the electricity production efficiency of the gas turbine; representing the gas volume produced by the electric-to-gas equipment at time t; representing the electricity consumed by the electric-to-gas equipment at time t;

[0030] ②The electric-to-gas equipment needs to consider the upper and lower power constraints and the ramping constraints when running:

[0031]

[0032]

[0033] wherein, representing the upper limit of gas production of the electric-to-gas equipment; representing the ramping power limit of electricity production of the gas turbine; representing the gas production of the electric-to-gas equipment at time t-1;

[0034] (3) Gas-fired boiler output model and constraint conditions:

[0035] ① The gas-fired boiler uses natural gas as raw material to produce heat energy, and the mathematical model is as follows:

[0036]

[0037] In the formula, η gb represents the heat production efficiency of the gas-fired boiler; represents the volume of natural gas consumed by the gas-fired boiler at time t; represents the heat production of the gas turbine at time t;

[0038] ② The gas-fired boiler needs to consider the upper and lower limit constraints of power and climbing constraints when running:

[0039]

[0040]

[0041] In the formula, represents the upper limit of heat production of the gas-fired boiler; represents the climbing power limit of heat production of the gas-fired boiler; represents the heat production of the gas-fired boiler at time t-1;

[0042] (4) Energy storage device model and constraint conditions:

[0043] In terms of energy storage devices, batteries, heat storage tanks and gas storage tanks are used to coordinate energy supply and demand balance, and take advantage of the peak and valley mechanism of electricity price to store excess energy or energy with higher price, and release energy to supply when there is energy demand or energy price is high, so as to achieve the effect of peak shaving and valley filling; The constraint conditions of the energy storage device include device recharge constraint and capacity constraint;

[0044] The energy storage device constraint is:

[0045]

[0046] E min,j ≤E t,j ≤E max,j (58)

[0047]

[0048]

[0049]

[0050]

[0051] Equation (13) is the energy storage balance constraint, where E t,j and E t-1,j These represent the energy stored by the j-th type of energy storage device at time t and time t-1, respectively. and These represent the charging and discharging energy of the j-th energy storage device at time t, respectively; η ch,j and η dis,j Let E be the energy storage efficiency of the j-th type of energy storage device; Equations (14)-(15) are the energy storage constraints, where E is the energy storage efficiency. min,j and E max,j These represent the minimum and maximum energy storage capacity of the j-th type of energy storage device, respectively; Equations (16)-(17) are the upper and lower limits of charge / discharge energy and the charge / discharge flag constraints, where... Let the charging flag of the j-th type of energy storage device at time t be a variable of 0-1. A value of 1 indicates that the energy storage device is charging, while a value of 0 indicates that the energy storage device is not charging. Let the energy release flag of the j-th type of energy storage device at time t be a variable between 0 and 1. A value of 1 indicates that the energy storage device is releasing energy, while a value of 0 indicates that the energy storage device is not releasing energy. and These are the maximum charging energy and maximum discharging energy of the j-th type of energy storage device, respectively.

[0052] (5) Constraints on the interconnection lines between the microgrid and the external power grid and gas grid:

[0053] To prevent excessive electricity and gas purchases by the microgrid, and to ensure the safety of the interconnection lines between the microgrid and the external power grid and natural gas grid, the transmission constraints of the interconnection lines are as follows:

[0054]

[0055]

[0056]

[0057]

[0058] In the formula, This represents the amount of electricity consumed through online shopping at time t. This represents the electricity sold by the microgrid at time t; This represents the upper limit for electricity purchases via microgrids; Represents the upper limit of electricity sales from microgrids; This is a variable with a microgrid electricity purchase flag of 0-1; a value of 1 indicates microgrid electricity purchase. This is a microgrid electricity sales flag variable (0-1), where a value of 1 indicates microgrid electricity sales. This represents the volume of micro-online shopping at time t; This represents the upper limit of online shopping volume.

[0059] In some embodiments, based on a pre-built microgrid system model and a two-stage pre-planned bar optimization scheduling model, the initial probability distribution value p of the wind-solar-load scenario after historical scenario reduction is used. 0,n The day-ahead scheduling plan is obtained by solving for:

[0060] (1) The objective function is mainly divided into two stages: the first stage is to formulate the scheduling plan for each unit equipment with the overall operating cost of the microgrid as the objective; the second stage is to optimize and adjust the scheduling plan of the first stage under the probability distribution of the worst wind-solar-load scenario, as shown in the following formula:

[0061]

[0062] In the formula, E1 represents the energy purchase cost of the microgrid; E2 represents the environmental emission control cost of the microgrid; E3 represents the maintenance cost of each energy coupling device and energy storage device in the microgrid; Ω represents the feasible region of the probability distribution of the wind-solar load scenario; p n Let be the expected probability value of the nth specific wind-solar-load scenario; s represents each distributed energy device in the microgrid; Let be the adjustment cost of the s-th type of distributed energy device at time t; Let T be the power adjustment of the s-th device in the n-th scenario at time t; T is the total day-ahead scheduling timescale, and N is the total number of scenarios.

[0063] In Equation (23), the specific meaning of the internal max-min problem is: when the total adjustment cost of each distributed energy device in the microgrid is minimized during the rescheduling phase, solve for the worst probability distribution of the wind-solar-load scenario; the specific meaning of the external min problem is: solve for the microgrid scheduling scheme with the minimum total cost under the worst probability distribution of the wind-solar-load scenario.

[0064] ①The energy purchase cost is:

[0065]

[0066] In the formula, and λ t These represent the microgrid's electricity purchase, electricity sales, and gas purchase prices at time t. and These represent the microgrid's electricity purchase, electricity sales, and gas purchase volume at time t; Δt is the day-ahead dispatching division value.

[0067] ②The environmental emission treatment cost is:

[0068]

[0069] In the formula, γ grid γ gt and γ gbε represents the carbon dioxide emission coefficients for the power grid, gas turbine, and gas boiler, respectively; ε is the carbon dioxide treatment cost coefficient; P t gt This represents the power output of the gas turbine at time t; This represents the heat generated by the gas turbine at time t;

[0070] ③The operating and maintenance cost of the equipment is:

[0071]

[0072] In the formula, i represents the gas boiler, gas turbine, and electro-gas conversion equipment; j represents the battery, thermal storage tank, and gas storage tank equipment; μ and ν are the maintenance costs of the i-th energy coupling equipment and the j-th energy storage equipment, respectively; P t,i The i-th type of energy coupling device outputs power at time t; and These represent the charging and discharging energy of the j-th type of energy storage device at time t;

[0073] (2) When performing optimized scheduling, the microgrid system needs to satisfy not only the device operation constraints, energy upper and lower limit constraints, and capacity constraints of the microgrid system model, but also energy balance constraints and scenario probability distribution constraints:

[0074] ①The energy balance constraint is:

[0075] P t buy +P t wt +P t gt +P t dis =P t sell +P t P2G +P t ch +P t L (71)

[0076]

[0077]

[0078] In the formula, This represents the amount of electricity consumed through online shopping at time t. This represents the electricity sold by the microgrid at time t; This represents the volume of micro-online shopping at time t; This represents the predicted output of the wind turbine at time t; This represents the microgrid load at time t; This represents the microgrid heat load at time t; This represents the microgrid gas load at time t; This represents the power output of the gas turbine at time t; This represents the heat generated by the gas turbine at time t; This represents the volume of natural gas consumed by the gas turbine at time t; This represents the gas production volume of the electro-gas converter at time t; This represents the electrical energy consumed by the electro-gas conversion equipment at time t. This represents the volume of natural gas consumed by the gas-fired boiler at time t. This represents the heat generated by the gas turbine at time t; and These represent the battery charge and discharge amounts at time t, respectively. and These represent the heat charge and heat release of the thermal storage tank at time t, respectively. and These represent the gas filling and venting volumes of the gas storage tank at time t, respectively.

[0079] ② The scenario probability distribution constraint is a comprehensive norm constraint Ω composed of the 1-norm condition and the ∞-norm condition, specifically:

[0080]

[0081] In the formula, p n Let p be the expected probability of the nth specific landscape scene; 0,n θ represents the initial probability distribution value of the historical scene of the wind, light, and lotus scene after reduction, and θ1 represents the allowable deviation value of the actual scene probability under the 1-norm condition; θ ∞ This represents the allowable deviation value for the probability of a real-world scenario under the ∞-norm condition.

[0082] In equation (30), p n Satisfying the confidence set:

[0083]

[0084] Where Pr represents the confidence level, N is the total number of scenarios, and M is the number of historical scenarios;

[0085] Let the confidence levels on the right sides of equations (31) be the set parameters α1 and α2, respectively. ∞ Then we have:

[0086]

[0087] The column constraint generation algorithm is used to solve the day-ahead two-stage sub-Blu-ray bar optimization scheduling model to obtain the day-ahead scheduling plan of the microgrid, which can be used as a reference for the intraday rolling optimization scheduling and real-time adjustment optimization scheduling stages.

[0088] In some embodiments, the total timescale T for day-ahead scheduling is 24 hours, and the day-ahead scheduling interval is 1 hour.

[0089] In some embodiments, the intraday rolling optimization scheduling model includes:

[0090] The objective function is to minimize the start-up and shutdown penalty costs of each energy storage device, and the tie-line power fluctuation and energy storage adjustment are added as penalty functions to the objective function:

[0091]

[0092] In equation (33), L is the starting time of each intraday optimal scheduling; ΔL is the intraday optimal scheduling scale value; k is the number of intraday scheduling cycles; P buy,dh (t) and P buy (t) represents the tie-line power of the microgrid's day-ahead and intraday plans, respectively; I n,dh (t) and I n (t) is a 0-1 variable, representing the start-up and shutdown status of the nth energy storage device at time t according to the day-ahead and intraday plans; C n W represents the penalty cost for changes in the start-up and shutdown of the nth energy storage device. n,dh (t) and W n (t) represents the day-ahead and intraday planned energy storage capacity at time t for the nth energy storage device, respectively; σ and These are the penalty coefficients for changes in tie line power and stored energy, respectively.

[0093] In some embodiments, the intraday rolling optimization scheduling timescale is 4 hours, and the intraday optimization scheduling division value ΔL is 15 minutes.

[0094] In some embodiments, the real-time adjustment and optimization scheduling model includes:

[0095] According to the requirements of distributed model predictive control algorithms, it is necessary to establish a predictive model for the system.

[0096] The prediction model is represented as follows:

[0097]

[0098] In equation (34), P gt (t+Δt|t), P gb (t+Δt|t), P p2g (t+Δt|t), P buy (t+Δt|t) and G buy (t+Δt|t) represent the final dispatch plan values ​​issued after real-time adjustments for the gas turbine, gas boiler, electricity-to-gas conversion, microgrid's electricity purchase and sale from the grid, and microgrid's gas purchase from the gas grid; P gt (t), P gb(t), P p2g (t), P buy (t) and G buy (t) represents the scheduling plan values ​​for gas turbines, gas boilers, electricity-to-gas conversion, microgrids purchasing and selling electricity from the grid, and microgrids purchasing gas from the gas grid, respectively, in the previous stage; ΔP gt (t), ΔP gb (t) and ΔP p2g (t) represents the correction values ​​for the gas turbine, gas boiler, and electric-to-gas converter in the real-time stage; ΔP w (t) and ΔP pv (t) represents the disturbance of wind power and photovoltaic output; ΔL e (t), ΔL h (t) and ΔL g (t) represents the disturbance of electricity, heat, and gas loads; Δt represents the real-time adjustment and optimization scheduling division value;

[0099] Based on the aforementioned prediction model, after predicting m steps forward, the various energy coupling devices are treated as interconnected subsystems using the concept of "distributed" systems, and an objective function min J is established. i This minimizes the difference between the output vector and the target vector, as well as the variable in the control vector.

[0100]

[0101]

[0102]

[0103]

[0104] In equations (36)-(38), Y i,fore The predicted output vector is composed of the electricity and gas purchase and sale value of the i-th subsystem; and These represent the electricity and gas purchase values ​​for the i-th subsystem, respectively; Y i,ref The reference output vector is composed of the reference values ​​for electricity purchase and sale and gas purchase for the i-th subsystem; and These are the reference values ​​for electricity purchase and sale and gas purchase for the i-th subsystem, respectively; U i This represents the adjustment amount for the i-th subsystem; and These are the adjustment values ​​for the gas turbine, electric-to-gas conversion equipment, and gas boiler in the i-th subsystem, respectively; W i and Q i Let be the weight matrices for the output and control quantities of the i-th subsystem, respectively; m is the number of time periods in the control time domain, and T represents the matrix transpose;

[0105] In solving the objective function min J i First, information exchange occurs between the various subsystems; then, each subsystem, based on the information obtained from other subsystems, calculates its target value based on the objective function min J. i Find its own optimal solution; finally, when the accuracy requirements are met, calculate the final output based on the control variables of all subsystems.

[0106] Furthermore, the real-time adjustment phase not only needs to satisfy the prediction model described in equation (34), but also needs to satisfy the upper and lower limit constraints of the coupling device's adjustment:

[0107]

[0108]

[0109]

[0110]

[0111]

[0112]

[0113] In equations (39)-(40), and These are the lower limits for adjustment of gas turbines, electric-to-gas conversion equipment, and gas boilers, respectively. and These are the upper limits for adjustment for gas turbines, electric-to-gas conversion equipment, and gas boilers, respectively. Represents the upper and lower limits of electricity production from a gas turbine; This represents the upper and lower limits of electricity consumption for electro-gas conversion equipment. This represents the upper and lower limits of heat production for the gas-fired boiler; k represents the prediction step size.

[0114] In some embodiments, the real-time adjustment and optimization scheduling control time domain is 15 minutes, and the real-time adjustment and optimization scheduling division value Δt is 5 minutes.

[0115] In a second aspect, the present invention provides a microgrid multi-timescale energy scheduling device that considers source-load uncertainty, including a processor and a storage medium;

[0116] The storage medium is used to store instructions;

[0117] The processor is configured to operate according to the instructions to perform the steps of the method according to the first aspect.

[0118] Thirdly, the present invention provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0119] Beneficial Effects: This invention provides a multi-timescale optimization scheduling method for microgrids that considers source-load uncertainty. It employs a two-stage bibloc optimization scheduling approach, an intraday rolling optimization scheduling approach, and a real-time adjustment optimization scheduling approach, and performs energy scheduling on the microgrid based on source-load predictions at different time scales. This minimizes the impact of uncertainties in renewable energy and load on the stable operation of the microgrid system. Furthermore, to ensure the robustness of the microgrid's day-ahead scheduling plan and minimize power fluctuations required for intraday adjustments, a two-stage bibloc optimization scheduling method is used in the day-ahead scheduling phase to avoid excessive power adjustments due to extreme scenarios. Simultaneously, addressing the drawbacks of traditional centralized model predictive control (MMC) such as high computational load and slow solution speed, a distributed MMC method is used to improve the approach, resulting in a final scheduling method that balances economy and conservatism. Attached Figure Description

[0120] Figure 1 This is a diagram of a multi-energy coupled microgrid structure of electricity, heat, and gas in an embodiment of the present invention.

[0121] Figure 2 This is a flowchart illustrating the implementation of the method in an embodiment of the present invention. Detailed Implementation

[0122] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0123] In the description of this invention, "several" means one or more, "multiple" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0124] In the description of this invention, the terms "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0125] Example 1

[0126] A multi-timescale energy scheduling method for microgrids considering source-load uncertainty includes:

[0127] Obtain historical data on wind-blown lotus;

[0128] Based on historical data of wind-flower-helix scenes, the initial probability distribution value p of the historical scenes of wind-flower-helix scenes after reduction was determined. 0,n ;

[0129] Based on the pre-constructed microgrid system model and the two-stage split-bar optimization scheduling model, and according to the initial probability distribution value p of the historical scenarios of wind, solar and load scenarios after reduction. 0,n Solving for the day-ahead scheduling plan yields the solution.

[0130] Based on the pre-built intraday rolling optimization scheduling model, the intraday rolling scheduling plan is obtained by solving according to the day-ahead scheduling plan;

[0131] Based on the pre-built real-time adjustment and optimization scheduling model, the real-time output value of each energy coupling device is obtained according to the intraday rolling scheduling plan, and the final scheduling plan of the system is determined.

[0132] The microgrid system model includes:

[0133] (1) Electrothermal output model and constraints of gas turbine:

[0134] ① Gas turbines generate electricity and heat by consuming natural gas. The mathematical model is shown below:

[0135]

[0136]

[0137] In the formula, LHV represents the lower calorific value of natural gas; η gt,e η represents the power generation efficiency of a gas turbine. gt,h This represents the heat production efficiency of the gas turbine; This represents the power output of the gas turbine at time t; This represents the heat generated by the gas turbine at time t; This represents the volume of natural gas consumed by the gas turbine at time t;

[0138] ② When operating a gas turbine, upper and lower power limits and ramping constraints need to be considered:

[0139]

[0140]

[0141]

[0142]

[0143] In the formula, This represents the upper limit of electricity production from a gas turbine. This represents the upper limit of heat production of a gas turbine; This represents the power ramp-up limit for the electricity generation of a gas turbine; This represents the power limit for heat generation ramp-up of a gas turbine. This represents the electricity generated by the gas turbine at time t-1; This represents the heat generated by the gas turbine at time t-1;

[0144] (2) Output model and constraints of the electro-gas conversion equipment:

[0145] ① The power-to-gas (EPG) equipment can convert electricity into natural gas. The mathematical model is shown below:

[0146]

[0147] In the formula, η P2G This represents the power generation efficiency of the gas turbine. This represents the gas production volume of the electro-gas converter at time t; This represents the electrical energy consumed by the electro-gas conversion equipment at time t.

[0148] ② Power upper and lower limit constraints and ramping constraints need to be considered when operating electro-gas conversion equipment:

[0149]

[0150]

[0151] In the formula, This represents the upper limit of gas production for the electro-gas conversion equipment; This represents the power ramp-up limit for the electricity generation of a gas turbine; This represents the gas production of the electro-gas converter at time t-1;

[0152] (3) Gas-fired boiler output model and constraints:

[0153] ① A gas-fired boiler uses natural gas as a raw material to produce heat energy. The mathematical model is shown below:

[0154]

[0155] In the formula, η gb This represents the heat production efficiency of a gas-fired boiler. This represents the volume of natural gas consumed by the gas-fired boiler at time t. This represents the heat generated by the gas turbine at time t;

[0156] ② When operating a gas-fired boiler, upper and lower power limits and ramp-up constraints need to be considered:

[0157]

[0158]

[0159] In the formula, This represents the upper limit of heat production of a gas-fired boiler; This represents the power limit for the heat production ramp-up of a gas-fired boiler. This represents the heat output of the gas-fired boiler at time t-1;

[0160] (4) Energy storage device model and constraints:

[0161] In terms of energy storage equipment, batteries, thermal storage tanks, and gas storage tanks are used to coordinate the balance of energy supply and demand. By utilizing the peak-valley mechanism of electricity prices, excess energy or energy with high prices is stored and released to supply energy when the system has energy demand or energy prices are high, thereby achieving the effect of peak shaving and valley filling. The constraints of energy storage equipment include equipment re-release energy constraints and capacity constraints.

[0162] The constraints of the energy storage device are:

[0163]

[0164] E min,j ≤E t,j ≤E max,j (102)

[0165]

[0166]

[0167]

[0168]

[0169] Equation (13) is the energy storage balance constraint, where E t,j and E t-1,j These represent the energy stored by the j-th type of energy storage device at time t and time t-1, respectively. and These represent the charging and discharging energy of the j-th energy storage device at time t, respectively; η ch,j and η dis,j Let E be the energy storage efficiency of the j-th type of energy storage device; Equations (14)-(15) are the energy storage constraints, where E is the energy storage efficiency. min,j and E max,jThese represent the minimum and maximum energy storage capacity of the j-th type of energy storage device, respectively; Equations (16)-(17) are the upper and lower limits of charge / discharge energy and the charge / discharge flag constraints, where... Let the charging flag of the j-th type of energy storage device at time t be a variable of 0-1. A value of 1 indicates that the energy storage device is charging, while a value of 0 indicates that the energy storage device is not charging. Let the energy release flag of the j-th type of energy storage device at time t be a variable between 0 and 1. A value of 1 indicates that the energy storage device is releasing energy, while a value of 0 indicates that the energy storage device is not releasing energy. and These are the maximum charging energy and maximum discharging energy of the j-th type of energy storage device, respectively.

[0170] (5) Constraints on the interconnection lines between the microgrid and the external power grid and gas grid:

[0171] To prevent excessive electricity and gas purchases by the microgrid, and to ensure the safety of the interconnection lines between the microgrid and the external power grid and natural gas grid, the transmission constraints of the interconnection lines are as follows:

[0172]

[0173]

[0174]

[0175]

[0176] In the formula, This represents the amount of electricity consumed through online shopping at time t. This represents the electricity sold by the microgrid at time t; This represents the upper limit for electricity purchases via microgrids; Represents the upper limit of electricity sales from microgrids; This is a variable with a microgrid electricity purchase flag of 0-1; a value of 1 indicates microgrid electricity purchase. This is a microgrid electricity sales flag variable (0-1), where a value of 1 indicates microgrid electricity sales. This represents the volume of micro-online shopping at time t; This represents the upper limit of online shopping volume.

[0177] In some embodiments, based on a pre-built microgrid system model and a two-stage pre-planned bar optimization scheduling model, the initial probability distribution value p of the wind-solar-load scenario after historical scenario reduction is used. 0,n The day-ahead scheduling plan is obtained by solving for:

[0178] (1) The objective function is mainly divided into two stages: the first stage is to formulate the scheduling plan for each unit equipment with the overall operating cost of the microgrid as the objective; the second stage is to optimize and adjust the scheduling plan of the first stage under the probability distribution of the worst wind-solar-load scenario, as shown in the following formula:

[0179]

[0180] In the formula, E1 represents the energy purchase cost of the microgrid; E2 represents the environmental emission control cost of the microgrid; E3 represents the maintenance cost of each energy coupling device and energy storage device in the microgrid; Ω represents the feasible region of the probability distribution of the wind-solar load scenario; p n Let be the expected probability value of the nth specific wind-solar-load scenario; s represents each distributed energy device in the microgrid; Let be the adjustment cost of the s-th type of distributed energy device at time t; Let T be the power adjustment of the s-th device in the n-th scenario at time t; T is the total day-ahead scheduling timescale, and N is the total number of scenarios.

[0181] In Equation (23), the specific meaning of the internal max-min problem is: when the total adjustment cost of each distributed energy device in the microgrid is minimized during the rescheduling phase, solve for the worst probability distribution of the wind-solar-load scenario; the specific meaning of the external min problem is: solve for the microgrid scheduling scheme with the minimum total cost under the worst probability distribution of the wind-solar-load scenario.

[0182] ①The energy purchase cost is:

[0183]

[0184] In the formula, and λ t These represent the microgrid's electricity purchase, electricity sales, and gas purchase prices at time t. and These represent the microgrid's electricity purchase, electricity sales, and gas purchase volume at time t; Δt is the day-ahead dispatching division value.

[0185] ②The environmental emission treatment cost is:

[0186]

[0187] In the formula, γ grid γ gt and γ gb ε represents the carbon dioxide emission coefficients for the power grid, gas turbine, and gas boiler, respectively; ε is the carbon dioxide treatment cost coefficient; P t gt This represents the power output of the gas turbine at time t; This represents the heat generated by the gas turbine at time t;

[0188] ③The operating and maintenance cost of the equipment is:

[0189]

[0190] In the formula, i represents the gas boiler, gas turbine, and electro-gas conversion equipment; j represents the battery, thermal storage tank, and gas storage tank equipment; μ and ν are the maintenance costs of the i-th energy coupling equipment and the j-th energy storage equipment, respectively; P t,i The i-th type of energy coupling device outputs power at time t; and These represent the charging and discharging energy of the j-th type of energy storage device at time t;

[0191] (2) When performing optimized scheduling, the microgrid system not only needs to meet the device operation constraints, energy upper and lower limit constraints, and capacity constraints of the microgrid system model, but also needs to meet energy balance constraints and scenario probability distribution constraints:

[0192] ①The energy balance constraint is:

[0193] P t buy +P t wt +P t gt +P t dis =P t sell +P t P2G +P t ch +P t L (115)

[0194]

[0195]

[0196] In the formula, This represents the amount of electricity consumed through online shopping at time t. This represents the electricity sold by the microgrid at time t; This represents the volume of micro-online shopping at time t; This represents the predicted output of the wind turbine at time t; This represents the microgrid load at time t; This represents the microgrid heat load at time t; This represents the microgrid gas load at time t; This represents the power output of the gas turbine at time t; This represents the heat generated by the gas turbine at time t; This represents the volume of natural gas consumed by the gas turbine at time t; This represents the gas production volume of the electro-gas converter at time t; This represents the electrical energy consumed by the electro-gas conversion equipment at time t. This represents the volume of natural gas consumed by the gas-fired boiler at time t. This represents the heat generated by the gas turbine at time t; and These represent the battery charge and discharge amounts at time t, respectively. and These represent the heat charge and heat release of the thermal storage tank at time t, respectively. and These represent the gas filling and venting volumes of the gas storage tank at time t, respectively.

[0197] ② The scenario probability distribution constraint is a comprehensive norm constraint Ω composed of the 1-norm condition and the ∞-norm condition, specifically:

[0198]

[0199] In the formula, p n Let p be the expected probability of the nth specific landscape scene; 0,n θ represents the initial probability distribution value of the historical scene of the wind, light, and lotus scene after reduction, and θ1 represents the allowable deviation value of the actual scene probability under the 1-norm condition; θ ∞ This represents the allowable deviation value for the probability of a real-world scenario under the ∞-norm condition.

[0200] In equation (30), p n Satisfying the confidence set:

[0201]

[0202] Where Pr represents the confidence level, N is the total number of scenarios, and M is the number of historical scenarios;

[0203] Let the confidence levels on the right sides of equations (31) be the set parameters α1 and α2, respectively. ∞ Then we have:

[0204]

[0205] The column constraint generation algorithm is used to solve the day-ahead two-stage sub-Blu-ray bar optimization scheduling model to obtain the day-ahead scheduling plan of the microgrid, which can be used as a reference for the intraday rolling optimization scheduling and real-time adjustment optimization scheduling stages.

[0206] In some embodiments, the total timescale T for day-ahead scheduling is 24 hours, and the day-ahead scheduling interval is 1 hour.

[0207] In some embodiments, the intraday rolling optimization scheduling model includes:

[0208] The objective function is to minimize the start-up and shutdown penalty costs of each energy storage device, and the tie-line power fluctuation and energy storage adjustment are added as penalty functions to the objective function:

[0209]

[0210] In equation (33), L is the starting time of each intraday optimal scheduling; ΔL is the intraday optimal scheduling scale value; k is the number of intraday scheduling cycles; P buy,dh (t) and P buy (t) represents the tie-line power of the microgrid's day-ahead and intraday plans, respectively; I n,dh (t) and I n (t) is a 0-1 variable, representing the start-up and shutdown status of the nth energy storage device at time t according to the day-ahead and intraday plans; C n W represents the penalty cost for changes in the start-up and shutdown of the nth energy storage device. n,dh (t) and W n (t) represents the day-ahead and intraday planned energy storage capacity at time t for the nth energy storage device, respectively; σ and These are the penalty coefficients for changes in tie line power and stored energy, respectively.

[0211] In some embodiments, the intraday rolling optimization scheduling timescale is 4 hours, and the intraday optimization scheduling division value ΔL is 15 minutes.

[0212] In some embodiments, the real-time adjustment and optimization scheduling model includes:

[0213] According to the requirements of distributed model predictive control algorithms, it is necessary to establish a predictive model for the system.

[0214] The prediction model is represented as follows:

[0215]

[0216] In equation (34), P gt (t+Δt|t), P gb (t+Δt|t), P p2g (t+Δt|t), P buy (t+Δt|t) and G buy (t+Δt|t) represent the final dispatch plan values ​​issued after real-time adjustments for the gas turbine, gas boiler, electricity-to-gas conversion, microgrid's electricity purchase and sale from the grid, and microgrid's gas purchase from the gas grid; P gt (t), P gb (t), P p2g (t), P buy (t) and G buy (t) represents the scheduling plan values ​​for gas turbines, gas boilers, electricity-to-gas conversion, microgrids purchasing and selling electricity from the grid, and microgrids purchasing gas from the gas grid, respectively, in the previous stage; ΔP gt (t), ΔP gb (t) and ΔP p2g (t) represents the correction values ​​for the gas turbine, gas boiler, and electric-to-gas converter in the real-time stage; ΔP w (t) and ΔPpv (t) represents the disturbance of wind power and photovoltaic output; ΔL e (t), ΔL h (t) and ΔL g (t) represents the disturbance of electricity, heat, and gas loads; Δt represents the real-time adjustment and optimization scheduling division value;

[0217] Based on the aforementioned prediction model, after predicting m steps forward, the various energy coupling devices are treated as interconnected subsystems using the concept of "distributed" systems, and an objective function min J is established. i This minimizes the difference between the output vector and the target vector, as well as the variable in the control vector.

[0218]

[0219]

[0220]

[0221]

[0222] In equations (36)-(38), Y i,fore The predicted output vector is composed of the electricity and gas purchase and sale value of the i-th subsystem; and These represent the electricity and gas purchase values ​​for the i-th subsystem, respectively; Y i,ref The reference output vector is composed of the reference values ​​for electricity purchase and sale and gas purchase for the i-th subsystem; and These are the reference values ​​for electricity purchase and sale and gas purchase for the i-th subsystem, respectively; U i This represents the adjustment amount for the i-th subsystem; and These are the adjustment values ​​for the gas turbine, electric-to-gas conversion equipment, and gas boiler in the i-th subsystem, respectively; W i and Q i Let be the weight matrices for the output and control quantities of the i-th subsystem, respectively; m is the number of time periods in the control time domain, and T represents the matrix transpose;

[0223] In solving the objective function min J i First, information exchange occurs between the various subsystems; then, each subsystem, based on the information obtained from other subsystems, calculates its target value based on the objective function min J. i Find its own optimal solution; finally, when the accuracy requirements are met, calculate the final output based on the control variables of all subsystems.

[0224] Furthermore, the real-time adjustment phase not only needs to satisfy the prediction model described in equation (34), but also needs to satisfy the upper and lower limit constraints of the coupling device's adjustment:

[0225]

[0226]

[0227]

[0228]

[0229]

[0230]

[0231] In equations (39)-(40), and These are the lower limits for adjustment of gas turbines, electric-to-gas conversion equipment, and gas boilers, respectively. and These are the upper limits for adjustment for gas turbines, electric-to-gas conversion equipment, and gas boilers, respectively. Represents the upper and lower limits of electricity production from a gas turbine; This represents the upper and lower limits of electricity consumption for electro-gas conversion equipment. This represents the upper and lower limits of heat production for the gas-fired boiler; k represents the prediction step size.

[0232] In some embodiments, the real-time adjustment and optimization scheduling control time domain is 15 minutes, and the real-time adjustment and optimization scheduling division value Δt is 5 minutes.

[0233] In some embodiments, such as Figure 1 As shown, the microgrid contains various distributed power supply devices, energy coupling devices, and energy storage devices. By scheduling the output of each unit, it can meet the diverse loads on the user side while optimizing the system's own economic efficiency and environmental friendliness during operation.

[0234] On the energy supply side, this mainly includes the upstream power grid and upstream gas grid, as well as wind power generation equipment and photovoltaic power generation equipment. The upstream power grid and microgrids are bidirectionally connected; when the microgrid's power is insufficient, it supplies power to the microgrid, and it can also absorb excess power from the microgrid. Wind power and photovoltaic power generation prioritize supplying the electricity needed by the microgrid. The upstream gas grid is responsible for supplying natural gas to gas turbines, gas boilers, and users. Regarding energy coupling equipment, power-to-gas (EPG) equipment can convert excess electricity from wind power and photovoltaic power generation into natural gas; gas turbines consume natural gas to produce electricity and heat; gas boilers use natural gas as raw material to produce heat. Regarding energy storage equipment, batteries, thermal storage tanks, and gas storage tanks are used to coordinate energy supply and demand balance, achieving peak shaving and valley filling effects through charging and discharging. The electrical load measured by users can be supplied by the power grid, photovoltaic power generation, wind power generation, gas turbines, and batteries; the thermal load can be supplied by gas turbines, gas boilers, and thermal storage tanks; and the gas load can be supplied by the gas grid, EPG equipment, and gas storage tanks.

[0235] The implementation process of the entire multi-timescale scheduling framework is as follows: Figure 2 As shown, the daily scheduling interval is 1 hour. Every 24 hours, a two-stage split-loop optimization scheduling model is used to formulate a scheduling plan, and the entire 24-hour scheduling plan is issued for reference by intraday rolling optimization scheduling and real-time adjustment optimization scheduling. The interval of the intraday rolling optimization scheduling stage is 15 minutes. Rolling optimization scheduling is performed with a 4-hour control time domain, but only the first 15-minute scheduling instruction of each rolling optimization is sent to the next stage. Real-time adjustment optimization scheduling uses a 15-minute control time domain and a 5-minute interval value. Based on the intraday rolling optimization plan, further adjustments and optimizations are performed to ensure the safety of the tie line and the stable and economical operation of the microgrid system.

[0236] It is important to note that each time the intraday rolling optimization scheduling optimizes equipment output within the control time domain while meeting system constraints, only the scheduling plan for the first 15-minute interval is issued to complete the next stage, the real-time adjustment stage. After completing this 15-minute real-time adjustment and optimization scheduling, the optimization window is shifted forward by 15 minutes. Based on the latest source-load forecast information, with a 4-hour control time domain and a 15-minute interval, intraday rolling optimization scheduling is performed again, and the above operation is repeated. This rolling optimization is repeated until the entire intraday rolling optimization stage is completed.

[0237] A multi-timescale energy scheduling method for microgrids considering source-load uncertainty includes the following steps:

[0238] Step 1: Construct a microgrid system and model the energy input, conversion, and storage devices within the microgrid;

[0239] Step 2: Using 24 hours as the time scale and 1 hour as the division value, construct a two-stage day-ahead split-bar optimal scheduling model; use historical data of wind, solar and load to determine the initial probability distribution value of the historical scenarios of wind, solar and load after reduction, characterize the uncertain parameters and their probability distribution confidence set, and construct a two-stage day-ahead split-bar optimal scheduling model: the first stage is to formulate the scheduling plan of each unit equipment with the comprehensive operating cost of the microgrid as the objective; the second stage optimizes and adjusts the scheduling plan of the first stage under the probability distribution of the worst scenario of wind, solar and load, and obtains the day-ahead scheduling plan of the microgrid system.

[0240] Step 3: Establish an intraday rolling optimization scheduling model with a time scale of 4 hours and a division value of 15 minutes; after updating the intraday source load forecast information under the above time domain, take the minimum start-up and shutdown penalty cost of each energy storage device as the objective function, and add tie line power fluctuation and energy storage adjustment amount as penalty functions into the objective function. Under the premise of satisfying system operation constraints, adjust the day-ahead scheduling plan to obtain the intraday rolling scheduling plan.

[0241] Step 4: Establish a real-time adjustment and optimization scheduling model with 15 minutes as the control time domain and 5 minutes as the division value; based on the more accurate intraday ultra-short-term source and load forecast values, adopt a distributed model predictive control method, adjust each energy coupling device according to the intraday rolling scheduling plan, obtain the real-time output value of the system, and determine the final scheduling plan of the system.

[0242] The above-mentioned two-stage daytime optimization scheduling, intraday rolling optimization scheduling, and real-time adjustment optimization scheduling constitute the microgrid multi-timescale optimization scheduling strategy proposed in this invention that considers source-load uncertainty.

[0243] Example 2

[0244] Secondly, this embodiment provides a microgrid multi-timescale energy scheduling device that considers source-load uncertainty, including a processor and a storage medium;

[0245] The storage medium is used to store instructions;

[0246] The processor is configured to operate according to the instructions to perform the steps of the method according to Embodiment 1.

[0247] Example 3

[0248] Thirdly, this embodiment provides a storage medium on which a computer program is stored, which, when executed by a processor, implements the steps of the method described in Embodiment 1.

[0249] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0250] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0251] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0252] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0253] As is known from common technical knowledge, this invention can be implemented through other embodiments that do not depart from its spirit or essential characteristics. Therefore, the disclosed embodiments described above are merely illustrative in all respects and are not the only ones. All modifications within the scope of this invention or its equivalents are included in this invention.

Claims

1. A microgrid multi-time scale energy scheduling method considering source load uncertainty, characterized in that, The method comprises: acquiring historical data of wind and light loads; determining initial probability distribution values of historical scenes of wind and light load scenes after reduction according to the historical data of wind and light loads; solving a day-ahead scheduling plan according to the initial probability distribution values of the historical scenes of wind and light load scenes after reduction based on a pre-constructed micro-grid system model and a day-ahead two-stage distribution robust optimization scheduling model; solving a day-in rolling scheduling plan according to the day-ahead scheduling plan based on a pre-constructed day-in rolling optimization scheduling model; solving real-time output values of each energy coupling device according to the day-in rolling scheduling plan based on a pre-constructed real-time adjustment optimization scheduling model to determine a final scheduling plan of the system; wherein the real-time adjustment optimization scheduling model comprises: a prediction model of the system is established and is expressed as: In the formula, and respectively are the final dispatching plan values of the gas turbine, gas boiler, electric-gas conversion, micro-grid buying and selling electricity to the grid and micro-grid buying gas from the gas grid issued by the dispatching center after real-time stage adjustment; and respectively are the dispatching plan values of the gas turbine, gas boiler, electric-gas conversion, micro-grid buying and selling electricity to the grid and micro-grid buying gas from the gas grid in the last stage; and respectively are the correction values of the gas turbine, gas boiler and electric-gas conversion in the real-time stage; and are the disturbance amounts of wind power and photovoltaic output; and are the disturbance amounts of electric, heat and gas load; Δt is the dispatching interval value of day-ahead scheduling; η gt,e represents the power generation efficiency of the gas turbine; η gb represents the heat generation efficiency of the gas boiler; η P2G represents the power generation efficiency of the gas turbine; Based on the prediction model, after m time periods are predicted forward, each energy coupling device is regarded as an interconnected subsystem by using the idea of distribution, a target function min J i is set, so as to minimize the difference between the output vector and the target vector and the variable of the control vector. wherein Y i,fore is the predicted output vector of the purchase and sale electricity value and the purchase gas value of the ith subsystem; m is the number of time periods in the control time domain; and are the purchase and sale electricity value and the purchase gas value of the corresponding time period of the ith subsystem respectively; Y i,ref is the reference output vector of the purchase and sale electricity reference value and the purchase gas reference value of the ith subsystem; and are the purchase and sale electricity reference value and the purchase gas reference value of the corresponding time period of the ith subsystem respectively; U i is the adjustment value of the ith subsystem; and are the adjustment values of the gas turbine, the electric-to-gas equipment and the gas boiler of the corresponding time period of the ith subsystem respectively; W i and Q i are the weight matrices of the output and the control value of the ith subsystem respectively; When solving the objective function min J i , first, information exchange between each subsystem is performed; then, each subsystem solves its own optimal solution according to the obtained information of other subsystems and the objective function min J i ; finally, when the accuracy requirement is met, the final output is solved according to the control variables of all subsystems. The real-time adjustment stage also needs to satisfy the upper and lower limit constraints of the coupling device and is expressed as: wherein and respectively represent lower and upper limits of the adjustment amount of the gas turbine, the electric-gas device, and the gas boiler; and respectively represent lower and upper limits of the adjustment amount of the gas turbine, the electric-gas device, and the gas boiler; represent upper and lower limits of the power generation of the gas turbine; represent upper and lower limits of the power consumption of the electric-gas device; represent upper and lower limits of the heat generation of the gas boiler; k represents a prediction period, respectively represent the adjustment amount of the gas turbine, the power generation of the gas turbine, the adjustment amount of the electric-gas device, the power consumption of the electric-gas device, the adjustment amount of the gas boiler, and the heat generation of the gas boiler in the kth period. 2.The microgrid multi-time scale energy scheduling method considering source load uncertainty according to claim 1, wherein, The micro-grid system model comprises: (1) an electric-thermal output model and constraint conditions of the gas turbine: ① The gas turbine generates electricity and heat by consuming natural gas, and the mathematical model is as shown below: where LHV represents the lower heating value of natural gas; η gt,e represents the power generation efficiency of the gas turbine; η gt,h represents the heat generation efficiency of the gas turbine; represents the power generation amount of the gas turbine at time t; represents the heat generation amount of the gas turbine at time t; represents the volume of natural gas consumed by the gas turbine at time t; ② The gas turbine needs to consider the power upper and lower limit constraints and the climbing constraints when running: wherein represents the upper limit of power generation of the gas turbine; represents the upper limit of heat generation of the gas turbine; represents the ramping power limit of power generation of the gas turbine; represents the ramping power limit of heat generation of the gas turbine; represents the power generation amount of the gas turbine at time t-1; represents the heat generation amount of the gas turbine at time t-1; (2) an output model and constraint conditions of the electric-to-gas device: ① The electric-to-gas device can convert electricity into natural gas, and the mathematical model is as shown below: wherein η P2G represents the power generation efficiency of the gas turbine; represents the gas production volume of the electric-gas conversion facility at time t; represents the electric energy consumed by the electric-gas conversion facility at time t; ② The electric-to-gas device needs to consider the power upper and lower limit constraints and the climbing constraints when running: wherein, represents the upper limit of gas production of the electric-gas conversion device; represents the electric production ramping power limit of the gas turbine; represents the gas production of the electric-gas conversion device at time t-1; (3) an output model and constraint conditions of the gas boiler: ① The gas boiler produces heat energy by using natural gas as raw material, and the mathematical model is as shown below: wherein η gb represents the heat production efficiency of the gas boiler; represents the volume of natural gas consumed by the gas boiler at time t; represents the heat production of the gas turbine at time t; ② The gas boiler needs to consider the power upper and lower limit constraints and the climbing constraints when running: wherein, represents the upper limit of heat production of the gas boiler; represents the ramping power limit of heat production of the gas boiler; represents the amount of heat produced by the gas boiler at time t-1; (4) a model and constraint conditions of the energy storage device: As for the energy storage device, the battery, the heat storage tank and the gas storage tank are used to coordinate the energy supply and demand balance, and the peak and valley electricity price mechanism is used to store the excess energy or the energy with a higher price, and the energy is released to supply when the system has energy demand or the energy price is high, so as to achieve the effect of peak shaving and valley filling; the constraint conditions of the energy storage device include device re-energizing constraints and capacity constraints; The energy storage device constraint is: E min,j ≤E t,j ≤E max,j (14) Equation (13) is the energy storage balance constraint, where E t,j and E t-1,j These represent the energy stored by the j-th type of energy storage device at time t and time t-1, respectively. and These represent the charging and discharging energy of the j-th energy storage device at time t, respectively; η ch,j and η dis,j Let E be the energy storage efficiency of the j-th type of energy storage device; Equations (14)-(15) are the energy storage constraints, where E is the energy storage efficiency. min,j and E max,j These represent the minimum and maximum energy storage capacity of the j-th type of energy storage device, respectively; Equations (16)-(17) are the upper and lower limits of charge / discharge energy and the charge / discharge flag constraints, where... Let the charging flag of the j-th type of energy storage device at time t be a variable of 0-1. A value of 1 indicates that the energy storage device is charging, while a value of 0 indicates that the energy storage device is not charging. Let the energy release flag of the j-th type of energy storage device at time t be a variable between 0 and 1. A value of 1 indicates that the energy storage device is releasing energy, while a value of 0 indicates that the energy storage device is not releasing energy. and These represent the maximum charging and maximum discharging energy of the j-th type of energy storage device, respectively; Δt is the day-ahead dispatching division value; and T is the total day-ahead dispatching time scale. (5) constraint conditions of the micro-grid and the external power grid and gas grid tie line: In order to prevent the micro-grid from excessively purchasing electricity and gas, and at the same time ensure the safety of the tie line between the micro-grid and the external power grid and the natural gas grid, the tie line transmission constraint is: In the formula, represents the micro-grid electricity purchase amount at time t; represents the micro-grid electricity sale amount at time t; represents the upper limit of the micro-grid electricity purchase; represents the upper limit of the micro-grid electricity sale; is a micro-grid electricity purchase flag bit 0-1 variable, and represents the micro-grid electricity purchase when being 1; is a micro-grid electricity sale flag bit 0-1 variable, and represents the micro-grid electricity sale when being 1; represents the micro-grid gas purchase amount at time t; represents the upper limit of the micro-grid gas purchase. 3.The microgrid multi-time scale energy scheduling method considering source load uncertainty according to claim 2, wherein, Based on the pre-constructed micro-grid system model and the day-ahead two-stage distribution robust optimization scheduling model, according to the initial probability distribution value p of the wind-solar-load scene after the historical scene is reduced 0,n , the day-ahead scheduling plan is obtained, including: (1) The objective function is mainly divided into two stages: the first stage takes the comprehensive operation cost of the micro-grid as the target to develop the scheduling scheme of each unit, and the second stage optimizes and adjusts the scheduling plan of the first stage under the worst scenario probability distribution of wind and light load, which is specifically shown in the following formula: wherein E1 is the energy purchasing cost of the microgrid; E2 is the environmental emission treatment cost of the microgrid; E3 is the maintenance cost of each energy coupling device and energy storage device of the microgrid; Ω is a scenario probability distribution constraint; p n is the probability expectation value of the nth specific wind-solar load scenario; s represents each distributed energy device in the microgrid; is the adjustment cost of the st distributed energy device at time t; is the adjustment power size of the st device at time t in the nth scenario; T is the total time scale of day-ahead scheduling, and N is the total number of scenarios. In formula (23), the specific meaning of the internal max-min problem is that when the total adjustment cost of each distributed energy device of the micro-grid in the rescheduling stage is the smallest, the worst probability distribution of the wind and light load scene is solved; the specific meaning of the external min problem is that the micro-grid scheduling scheme with the minimum total cost under the worst wind and light load scene probability distribution is solved; ① The energy purchasing cost is: In the formula, and λ t are the purchase electricity price, the sale electricity price and the purchase gas price of the micro-grid at time t, respectively; and are the purchase electricity quantity, the sale electricity quantity and the purchase gas quantity of the micro-grid at time t, respectively; and Δt is the dispatching graduation value. ② The environmental emission treatment cost is: wherein γ grid , γ gt and γ gb are the carbon dioxide emission coefficients of the power grid, the gas turbine and the gas boiler, respectively; ε is the carbon dioxide treatment cost coefficient; represents the power generation of the gas turbine at time t; represents the heat production of the gas turbine at time t; ③ The device operation and maintenance cost is: where i represents a gas boiler, a gas turbine, and an electric-to-gas device; j represents a battery, a heat storage tank, and a gas storage tank device; μ and v are maintenance costs of the i-th energy coupling device and the j-th energy storage device, respectively; P t,i is the output of the i-th energy coupling device at time t; and are the charging energy and the discharging energy of the j-th energy storage device at time t, respectively. (2) When performing the optimal scheduling, the micro-grid system needs to meet the device operation constraints, the upper and lower energy constraints, the capacity constraints of the micro-grid system model, and also needs to meet the energy balance constraints and the scenario probability distribution constraints: ① The energy balance constraint is: wherein, represents the microgrid electricity purchase amount at time t; represents the microgrid electricity sale amount at time t; represents the microgrid gas purchase amount at time t; represents the wind turbine predicted output at time t; represents the microgrid electricity load at time t; represents the microgrid heat load at time t; represents the microgrid gas load at time t; represents the electricity production amount of the gas turbine at time t; represents the heat production amount of the gas turbine at time t; represents the natural gas volume consumed by the gas turbine at time t; represents the gas volume produced by the electricity-to-gas device at time t; represents the electricity energy consumed by the electricity-to-gas device at time t; represents the natural gas volume consumed by the gas boiler at time t; represents the heat production amount of the gas turbine at time t; and respectively represent the battery charge amount and discharge amount at time t; and respectively represent the heat charging amount and discharging amount of the heat storage tank at time t; and respectively represent the gas charging amount and discharging amount of the gas storage tank at time t. ② The scenario probability distribution constraint is a comprehensive norm constraint Ω composed of a 1-norm condition and an ∞-norm condition, and specifically is: In the formula, p n is the probability expectation value of the nth specific wind and light load scene; p 0,n is the initial probability distribution value of the historical scene of the wind and light load scene after reduction, θ1 is the actual scene probability allowable deviation value under the 1-norm condition; θ ∞ is the actual scene probability allowable deviation value under the ∞-norm condition; In formula (30), p n satisfies the confidence set: Wherein, Pr represents the confidence level, N is the total number of scenarios, and M is the number of historical scenarios; Let the confidence of the right side of equation (31) be set as parameters a1 and a ∞ Then, we have: The day-ahead two-stage distribution robust optimization scheduling model is solved by using a column constraint generation algorithm to obtain a day-ahead scheduling plan of the micro-grid.

4. The microgrid multi-time scale energy scheduling method considering source load uncertainty according to claim 1 or 3, characterized in that, The total time scale of the day-ahead scheduling is 24 hours, and the day-ahead scheduling scale is 1 hour. 5.The microgrid multi-time scale energy scheduling method considering source load uncertainty according to claim 1, wherein, The intra-day rolling optimization scheduling model comprises: Taking the minimum start-stop penalty cost of each energy storage device as an objective function, and adding the tie-line power fluctuation and energy storage adjustment amount as a penalty function into the objective function: In formula (33), L is the starting time of each intra-day optimal dispatch; ΔL is the intra-day optimal dispatch index value; D is the number of intra-day dispatch periods; P buy,dh (t) respectively represent the tie-line power of the microgrid day-ahead plan and intra-day plan; I buy (t) respectively represent the tie-line power of the microgrid day-ahead plan and intra-day plan; I n,dh (t) respectively represent the tie-line power of the microgrid day-ahead plan and intra-day plan; I n (t) is a 0-1 variable, representing the start-stop state of the nth energy storage device at the day-ahead plan and intra-day plan time t; C n is the start-stop change penalty cost of the nth energy storage device; W n,dh (t) is the start-stop change penalty cost of the nth energy storage device; W n (t) is the start-stop change penalty cost of the nth energy storage device; W are the tie-line power and energy storage capacity change penalty coefficients, respectively. 6.The microgrid multi-time scale energy scheduling method considering source load uncertainty according to claim 1 or 5, wherein, The time scale of the intra-day rolling optimization scheduling is 4 hours, and the intra-day optimization scheduling scale is 15 minutes. 7.The microgrid multi-time scale energy scheduling method considering source load uncertainty according to claim 1, wherein, The real-time adjustment optimization scheduling control time domain is 15 minutes, and the real-time adjustment optimization scheduling scale is 5 minutes.

8. A microgrid multi-time scale energy scheduling device considering source load uncertainty, characterized in that, It comprises a processor and a storage medium. The storage medium is used to store instructions. The processor is used to operate according to the instructions to perform the steps of the method according to any one of claims 1 to 7.

9. A storage medium having stored thereon a computer program, characterized in that The computer program, when executed by the processor, implements the steps of the method according to any one of claims 1 to 7.

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