A multi-energy microgrid affine optimization scheduling method based on model predictive control

Abstract

The present application relates to a kind of affine optimization scheduling method of multi-energy microgrid based on model predictive control.First, the uncertainty of wind and light output and load demand in multi-energy microgrid is characterized using affine algorithm, the correlation between each uncertain variable is reflected by sharing noise element, and the interval expansion problem is solved.Second, with the minimum multi-energy microgrid operation cost and its fluctuation range as the goal, an affine optimization scheduling model of multi-energy microgrid is constructed.Finally, the affine optimization scheduling model is combined into the model predictive control method, and by continuously obtaining updated prediction information within a day, the interval of scheduling result is further reduced by solving the constructed multi-energy microgrid affine optimization scheduling model on the basis of ensuring the accuracy of prediction information.The present application combines affine optimization method with model predictive control method, which can effectively deal with the uncertainty of wind and light output and load demand in multi-energy microgrid, and reduce the conservatism of scheduling result.

Description

TECHNICAL FIELD

[0001] The present application relates to a multi-energy microgrid affine optimization scheduling method based on model predictive control. BACKGROUND

[0002] Multi-energy microgrid (MEMG) can utilize the coupling and complementary characteristics among electricity, heat, gas and other multiple energies, and is an important way to realize sustainable development and efficient utilization of energy. However, the prediction uncertainty of wind and light output and load demand in the multi-energy microgrid will expand the conservativeness of the scheduling result and affect the stability of the operation. Therefore, it is necessary to study the uncertain optimization scheduling method of the multi-energy microgrid to ensure the reliable operation of the multi-energy microgrid under the influence of uncertainty. In addition, the traditional multi-energy microgrid optimization scheduling method mainly focuses on day-ahead optimization scheduling, ignoring the characteristics that the accuracy of day-ahead prediction decreases with the lengthening of the prediction time domain. Higher prediction uncertainty will further expand the scheduling result interval of the multi-energy microgrid. Therefore, it is necessary to optimize the scheduling of the multi-energy microgrid based on the prediction information in a shorter prediction time domain to reduce the influence of prediction uncertainty on the scheduling result.

[0003] At present, the uncertain optimization scheduling method of the multi-energy microgrid mainly includes random optimization method, robust optimization method and interval optimization method. The random optimization method converts the uncertain problem into multiple deterministic problems for solving by generating a large number of scenarios, but it needs to master the accurate distribution of uncertain factors and has many scenarios, which makes the modeling difficult and the solving burden heavy. The robust optimization method has the advantage of easy modeling, but in order to ensure the reliable operation of the multi-energy microgrid in the worst case, the scheduling result is too conservative. The interval optimization method only needs to model and solve based on the upper and lower limits of the uncertain factors, and gives the optimal solution in the form of interval for selection, which is more suitable for practical engineering compared with the robust optimization method. However, due to the difficulty of considering the correlation between uncertain variables in the interval algorithm, there is an interval expansion problem, which makes the scheduling result of the multi-energy microgrid also conservative.

[0004] In addition, the current uncertain optimization scheduling method of the multi-energy microgrid is mostly based on day-ahead prediction data for day-ahead optimization scheduling, but the accuracy of day-ahead prediction decreases with the lengthening of the prediction time domain, which will further expand the conservativeness of the uncertain optimization scheduling method of the multi-energy microgrid. SUMMARY

[0005] The present application aims at the problem that the uncertainty of wind and light output and load demand in the multi-energy microgrid leads to the expansion of the conservativeness of the scheduling result and the influence on the stability of the operation, and provides a multi-energy microgrid affine optimization scheduling method based on model predictive control, which can effectively cope with the uncertainty of wind and light output and load demand in the multi-energy microgrid and reduce the conservativeness of the scheduling result.

[0006] To achieve the above object, the technical scheme of the present application is: a multi-energy microgrid affine optimization scheduling method based on model predictive control, comprising:

[0007] Considering the synergistic effect of electricity, heat and gas in the multi-energy microgrid, a multi-energy microgrid optimization scheduling model is constructed, and on this basis, based on the prediction information of wind and light output and load demand, an affine algorithm is used to represent the uncertainty of wind and light output and load demand in the multi-energy microgrid, and an affine optimization scheduling model of the multi-energy microgrid is constructed.

[0008] On the basis of constructing the affine optimization scheduling model of the multi-energy microgrid, the model predictive control (MPC) method is used to continuously obtain the updated prediction information of wind and light output and load demand within a day, and combined with the rolling optimization idea, the proposed affine optimization scheduling model of the multi-energy microgrid is solved.

[0009] Compared with the prior art, the present application has the following beneficial effects:

[0010] 1. The multi-energy microgrid affine optimization scheduling method based on model predictive control proposed in the present application can effectively cope with the uncertainty of wind and light output and load demand in the multi-energy microgrid, and reduce the conservatism of the scheduling result. The affine algorithm is used to represent the uncertainty of wind and light output and load demand in the multi-energy microgrid, and the correlation between each uncertain variable is reflected by sharing noise elements, solving the problem of interval expansion. The affine optimization scheduling model of the multi-energy microgrid is constructed by minimizing the operating cost and its fluctuation range, and the affine optimization scheduling model is combined into the model predictive control method. By continuously obtaining the updated prediction information of wind and light output and load demand within a day, the affine optimization scheduling model of the multi-energy microgrid is solved on the basis of ensuring the accuracy of the prediction information, and the interval of the scheduling result is further reduced.

[0011] 2. The multi-energy microgrid affine optimization scheduling method based on model predictive control proposed in the present application overcomes the shortcomings of large modeling difficulty and heavy solving burden of the stochastic optimization method, and the problem of large conservatism of the scheduling result of the robust and interval optimization method. Compared with the day-ahead optimization method of the multi-energy microgrid, the method proposed in the present application uses the continuously updated prediction information of wind and light output and load demand, which can reduce the influence of uncertainty on the scheduling result and reduce the interval of the scheduling result. BRIEF DESCRIPTION OF DRAWINGS

[0012] Figure 1 The figure is a system structure diagram of the multi-energy microgrid.

[0013] Figure 2 The figure is a flow chart of the multi-energy microgrid affine optimization scheduling method based on model predictive control. DETAILED DESCRIPTION

[0014] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings.

[0015] This invention discloses an affine optimization scheduling method for multi-energy microgrids based on model predictive control, comprising:

[0016] Considering the synergistic effect of electricity, heat and gas in multi-energy microgrids, an optimal scheduling model for multi-energy microgrids is constructed. Based on this, and using the predicted information of wind and solar power output and load demand, an affine algorithm is used to characterize the uncertainty of wind and solar power output and load demand in multi-energy microgrids, and an affine optimal scheduling model for multi-energy microgrids is constructed.

[0017] Based on the construction of a multi-energy microgrid affine optimization scheduling model, the model predictive control (MPC) method is used to continuously obtain the daily updated wind and solar power output and load demand forecast information. Combined with the rolling optimization idea, the proposed multi-energy microgrid affine optimization scheduling model is solved in a rolling manner.

[0018] The following is a detailed implementation process of the method of the present invention.

[0019] 1. Multi-energy microgrid system

[0020] 1.1 System Structure

[0021] like Figure 1 As shown, a multi-energy microgrid consists of four parts: energy supply, energy conversion equipment, energy storage equipment, and loads, achieving synergistic support from electricity, heat, and gas. The energy supply part includes two new energy power generation methods: wind power and photovoltaic power, as well as energy interaction with the power grid and gas grid. Energy conversion is achieved by four types of equipment: combined heat and power (CHP) units, electricity-to-gas converters, electric boilers, and gas boilers. The CHP units are further composed of micro gas turbines and waste heat recovery devices. Energy storage equipment includes electrical energy storage, thermal energy storage, and gas energy storage. The load part includes three types of loads: electricity, heat, and gas.

[0022] 1.2 Equipment Mathematical Model

[0023] (1) Cogeneration unit

[0024] The mathematical model for a combined heat and power (CHP) unit is:

[0025]

[0026] In the formula, and These represent the electrical power and thermal power generated by the combined heat and power unit during time period t, respectively. η is the natural gas power consumed by the combined heat and power unit during time period t; GE and η GH These represent the power generation and heat generation efficiencies of the micro gas turbine, respectively; ηHR This refers to the recovery efficiency of the waste heat recovery device.

[0027] (2) Electric to gas conversion

[0028] The mathematical model for electro-gas conversion is:

[0029]

[0030] In the formula, and η represents the power of natural gas produced and the power of electricity consumed during time period t, respectively, in the power-to-gas conversion process. P2G This refers to the energy conversion efficiency of electricity to gas.

[0031] (3) Electric boiler

[0032] The mathematical model of an electric boiler is:

[0033]

[0034] In the formula, and η represents the thermal power generated and the electrical power consumed by the electric boiler during time period t, respectively. EB This refers to the electrothermal conversion efficiency of the electric boiler.

[0035] (4) Gas-fired boiler

[0036] The mathematical model for a gas-fired boiler is as follows:

[0037]

[0038] In the formula, and η represents the thermal power produced by the gas-fired boiler and the natural gas power consumed during time period t, respectively. GB This refers to the heating efficiency of a gas-fired boiler.

[0039] (5) Energy storage equipment

[0040] The mathematical model for energy storage devices is as follows:

[0041]

[0042] In the formula, tp represents the type of energy storage device, tp∈[ES,HS,GS], where ES represents electrical energy storage, HS represents thermal energy storage, and GS represents gas energy storage; Let t be the capacity of the energy storage device during time period t; and These represent the charging and discharging power of the energy storage device during time period t; η c,tp and η d,tp Δt represents the charging and discharging efficiency of the energy storage device, respectively; Δt is the scheduling time interval.

[0043] 1.3 Operational Constraints of Multi-Energy Microgrids

[0044] (1) Power balance constraint

[0045] Multi-energy microgrids need to satisfy the power balance constraints of electricity, heat, and gas energy sources respectively:

[0046]

[0047] In the formula, and These represent the electricity purchase and sales volume of the multi-energy microgrid during time period t; The gas purchase volume of the multi-energy microgrid during time period t; and These represent the power generation capacity of the wind turbine and the photovoltaic system during time period t, respectively. and These represent the charging and discharging power of the energy storage device during time period t; and These represent the charging and discharging power of the thermal energy storage device during time period t; and These represent the charging and discharging power of the gas storage device during time period t. and These represent the electricity, heat, and gas load demands for time period t.

[0048] (2) External network interaction constraints

[0049] The constraints that a multi-energy microgrid must meet to engage in electricity purchase and sale with the external power grid and to purchase gas from the external gas grid are as follows:

[0050]

[0051] In the formula, and These are the lower and upper limits of the power purchase capacity, respectively. and These represent the lower and upper limits of the electricity sales capacity, respectively; binary variable v t This indicates the power purchase and sale status of the multi-energy microgrid during time period t. If v t =1 indicates that the multi-energy microgrid purchases electricity during time period t, and vice versa if v t =0 indicates that electricity is sold during time period t; and These represent the lower and upper limits of the gas purchase volume, respectively.

[0052] (3) Constraints on energy conversion equipment

[0053] The output and ramping constraints of the energy conversion equipment are as follows:

[0054]

[0055] In the formula, These are the lower and upper limits of the output of a combined heat and power (CHP) unit, respectively. These are the lower and upper limits of the output power for the electro-gas conversion, respectively. These are the lower and upper limits of the output of the electric boiler, respectively. These are the lower and upper limits of the output of the gas-fired boiler, respectively. These are the lower and upper limits of ramp speed for combined heat and power (CHP) units, respectively. These are the lower and upper limits of the ramp rate for electric boilers, respectively. These are the lower and upper limits for ramping up gas-fired boilers, respectively.

[0056] (4) Constraints of energy storage devices

[0057] The constraints of energy storage devices are:

[0058]

[0059] In the formula, and These represent the lower and upper limits of the capacity of energy storage devices, respectively. and These are the lower and upper limits of the charging power for energy storage devices, respectively. and These represent the lower and upper limits of the power that energy storage devices can release, respectively. and These represent the capacity of the energy storage devices before and at the end of the scheduling cycle, respectively; T is the total number of periods in the scheduling cycle. This is achieved through binary variables. This indicates the energy storage and release status of the energy storage device during time period t. This indicates that the energy storage device stores energy during time period t, and vice versa. This indicates that the energy storage device releases energy during time period t, and the availability of the energy storage device is guaranteed by ensuring that its capacity remains consistent at the beginning and end of the scheduling cycle.

[0060] 2. Affine Optimization Scheduling Model for Multi-Energy Microgrids

[0061] 2.1 Definition of Affine Variables

[0062] (1) Affine form of uncertain factors

[0063] The uncertainties in multi-energy microgrids mainly consist of prediction errors for wind power, photovoltaic power generation, and the three types of load demand. Based on affine algorithms, these uncertainties can be described in the following affine form:

[0064]

[0065] In the formula, and These are the affine forms corresponding to wind power, photovoltaic power generation, and electricity, heat, and gas load demands during time period t, respectively. and These are the corresponding center values, representing the predicted values ​​of each uncertain factor; and These are the corresponding noise element coefficients, representing the fluctuation limits of each uncertainty factor; and These are the corresponding noise elements, representing the deviation between the actual and predicted values ​​of each uncertain factor.

[0066] (2) Affine form of decision variables

[0067] In the affine optimization scheduling model of a multi-energy microgrid, the decision variables will be affected by all uncertainties, and its affine form will contain the noise elements corresponding to all uncertainties, as shown in the equation:

[0068]

[0069] In the formula, For a decision variable X in a multi-energy microgrid t affine forms; Its central value represents the decision variable X when it is unaffected by uncertainties. t Optimized scheduling values; The noise element coefficients corresponding to each uncertainty factor represent the impact of each uncertainty factor on the decision variable X. t The degree of influence; i is the set of various uncertainty factor types.

[0070] 2.2 Definition of Affine Constraint

[0071] The constraints in multi-energy microgrids are mainly of three types: equality constraints, inequality constraints, and time-transition constraints. Each of these three types of constraints can be defined in affine form.

[0072] (1) Equality constraints

[0073] The equality constraints in multi-energy microgrids mainly fall into two categories: energy conversion relationships and power balance, and their affine forms are shown in the equation. Since the decision variables in the affine form of a multi-energy microgrid have the same noise element, the affine form of the equality constraint can be defined as the central values ​​of the affine variables on both sides of the equation and the coefficients of each noise element corresponding to each other. Therefore, the equation can be further expressed as equation.

[0074]

[0075] In the formula, λ represents the affine variable contained in the equality constraints of the multi-energy microgrid; A , λ B …λC These are the corresponding coefficients of each affine variable; The central values ​​of each affine variable; These are the noise element coefficients for each affine variable.

[0076] (2) Inequality constraints

[0077] The inequality constraints in a multi-energy microgrid mainly consist of upper and lower limits on the output of energy conversion and storage devices, as well as upper and lower limits on the energy interaction between the microgrid and the external grid. Their affine forms are shown in the equation. To meet the completeness requirement, that is, to ensure that the inequality constraints can still be satisfied under the influence of all possible uncertainties, constraints are imposed on their possible minimum and maximum values, as shown in the equation.

[0078]

[0079] In the formula, D represents the affine variables involved in the inequality constraints of multi-energy microgrids. min and D max Affine variables The minimum and maximum allowed values; and These are its center value and noise element coefficient, respectively.

[0080] (3) Time constraints

[0081] The time constraints in multi-energy microgrids mainly fall into two categories: time-series variation constraints of energy storage capacity and ramp-up constraints of energy conversion equipment.

[0082] For constraints on the time-series variation of energy storage device capacity, its affine form is:

[0083]

[0084] In the formula, and These are the affine forms of the energy storage device capacity during time period t and time period t-1, respectively. and These represent the charging and discharging power of the affine energy storage device during time period t; η c and η d These refer to the charging and discharging efficiencies of the energy storage device, respectively.

[0085] It can be assumed that in the period before the start of the optimized scheduling cycle, the capacity of the energy storage device is equal to the set initial capacity, which is a fixed value. Therefore, the noise element coefficients in its corresponding affine form are all zero. Furthermore, to ensure the periodic availability of the energy storage device at the end of the scheduling cycle, and to still allow for adjustment space to cope with uncertainties, the capacity of the energy storage device at the end of the scheduling cycle is constrained only by its central value. Therefore, the affine form of the constraint on the time-series change of the energy storage device capacity can be more specifically expressed as follows:

[0086]

[0087] In the formula, They are respectively The center value and noise element coefficient; They are respectively The center value and noise element coefficient; E 0 The capacity of energy storage devices for the period preceding the start of the scheduling cycle; They are respectively The center value and noise element coefficient; They are respectively The center value and noise element coefficient.

[0088] The affine form of the ramp constraint for energy conversion equipment is shown in the equation. To meet the completeness requirement, the equation can also be transformed into the equation.

[0089]

[0090] In the formula, and These are the affine forms of the power output of the energy conversion equipment in a multi-energy microgrid during time period t and time period t-1, respectively; K down and K up These are the set lower and upper limits for climbing, respectively; They are respectively The center value and noise element coefficient; They are respectively The center value and noise element coefficient.

[0091] 2.3 Definition of Affine Objective Function

[0092] With the goal of minimizing the operating cost of a multi-energy microgrid, the objective function of the affine optimization scheduling model for the multi-energy microgrid is:

[0093]

[0094] in:

[0095]

[0096] In the formula: and These are the operating costs of the affine multi-energy microgrid for time period t, including gas purchase costs, electricity purchase costs, electricity sales costs, and the operation and maintenance costs of the energy conversion equipment. and These are the affine forms of the gas purchase volume, electricity purchase volume, and electricity sales volume of the multi-energy microgrid during time period t; and The affine forms represent the power consumption of the combined heat and power unit, the electric-to-gas converter, the electric boiler, and the gas boiler during time period t, respectively; c buy,g and These represent the unit cost of gas purchase for multi-energy microgrids and the unit cost of electricity purchase and sale during time period t; c CHP c P2G c EB and c GB These are the unit maintenance costs for combined heat and power units, electric-to-gas conversion units, electric boilers, and gas-fired boilers, respectively.

[0097] Based on the basic structure of affine variables, the objective function in affine form can be further defined and transformed, dividing it into two parts: the central value of operating costs and the fluctuation range. Therefore, the equation can be further defined as:

[0098]

[0099] In the formula, and These are the central value of the multi-energy microgrid operating cost and the noise element coefficient for time period t, respectively. These are the center value and noise element coefficient of the gas purchase cost of the multi-energy microgrid during time period t; These are the center value of the electricity purchase cost of the multi-energy microgrid and the noise element coefficient, respectively, for time period t. These are the center value of the electricity sales cost of the multi-energy microgrid and the noise element coefficient, respectively, for time period t. , respectively, are the center value of the operation and maintenance cost of the multi-energy microgrid and the noise element coefficient during time period t; w is the optimization weight, and its value range is [0,1].

[0100] In the formula, the central value of the multi-energy microgrid operating cost represents the operating cost of the multi-energy microgrid when it is not affected by uncertain factors; the noise element coefficients of the operating cost represent the fluctuations in the operating cost of the multi-energy microgrid under the influence of corresponding uncertain factors. The optimization weight w represents the emphasis of the optimization scheduling objective. In actual engineering, dispatchers can adjust the value of w according to the actual needs of economic or conservative operating costs, so that the optimization scheduling objective focuses more on economic or conservative factors.

[0101] 3. Affine Optimization Scheduling of Multi-Energy Microgrids Based on Model Predictive Control

[0102] Based on the affine optimization objective function defined in the equation, and combined with the rolling optimization concept in the model predictive control method, the objective function of the multi-energy microgrid affine optimization scheduling method based on model predictive control can be defined as shown in the equation.

[0103]

[0104] The absolute value term of the noise element coefficients involved in the multi-energy microgrid operating costs increases the difficulty and reduces the efficiency of the solution. To shorten the solution time and meet the needs of online optimization, this invention introduces an auxiliary variable z. i,+ z i,- With constraints, the absolute value terms in the objective function are replaced with the equivalent substitution shown in the equation.

[0105]

[0106] Due to auxiliary variables z i,- It is only reflected in the objective function, and the optimization trend is to minimize the objective function, which guarantees that in the optimized scheduling results... z i,- The fact that the values ​​are not both non-zero ensures the validity of the equation, guaranteeing the equivalence of the substitution. Ultimately, the affine optimization scheduling model M for a multi-energy microgrid based on model predictive control can be constructed as follows:

[0107]

[0108] Before each optimization, the system status information that needs to be obtained includes the output of each energy conversion device in the multi-energy microgrid and the capacity of the energy storage device in the previous period. The forecast data that needs to be updated includes the power generation of wind and photovoltaic in the multi-energy microgrid, as well as the demand of the three types of loads: electricity, heat, and gas.

[0109] 4. For example Figure 2 As shown, the present invention provides an affine optimization scheduling method for multi-energy microgrids based on model predictive control. The specific implementation steps are as follows:

[0110] S1. Initialize the start time period of the optimized scheduling, let t=1;

[0111] S2. Obtain the energy storage status information of the multi-energy microgrid in the previous time period, i.e., the time period t-1, and the wind and solar power output and load demand forecast information of the multi-energy microgrid in the time period from t to t+N-1.

[0112] S3. Define the prediction information obtained in S2 as an affine form;

[0113] S4. Solve the affine optimization scheduling model of the multi-energy microgrid to obtain the optimal scheduling scheme of the multi-energy microgrid in the time period t to t+N-1.

[0114] S5. Send the value of the first time period in the optimized scheduling scheme to the t time period for execution;

[0115] S6. Determine whether the current optimized scheduling period t is less than the total number of scheduling periods T;

[0116] S7. If t < T, then t = t + 1, and return to S2; if t ≥ T, then end the entire optimization scheduling process, and the optimization scheduling of T time periods is completed.

[0117] The above are preferred embodiments of the present invention. Any changes made to the technical solution of the present invention that do not exceed the scope of the technical solution of the present invention shall fall within the protection scope of the present invention.

Claims

1. An affine optimization scheduling method for multi-energy microgrids based on model predictive control, characterized in that, include: Considering the synergistic effect of electricity, heat, and gas in a multi-energy microgrid, an optimal scheduling model for the multi-energy microgrid is constructed. Based on this, and using the predicted information of wind and solar power output and load demand, an affine algorithm is used to characterize the uncertainties of wind and solar power output and load demand in the multi-energy microgrid, thus constructing an affine optimal scheduling model for the multi-energy microgrid; the details are as follows: (1) Definition of affine variables: (1.1) Affine form of uncertainties The uncertainties in multi-energy microgrids consist of prediction errors for wind power, photovoltaic power generation, and three types of load demand. Based on an affine algorithm, these uncertainties can be described in the following affine form: In the formula, and These are the affine forms corresponding to wind power, photovoltaic power generation, and electricity, heat, and gas load demands during time period t; and They are respectively and The corresponding center value represents the predicted value of each uncertain factor; and They are respectively and The corresponding noise element coefficients represent the fluctuation limits of each uncertainty factor; and They are respectively and The corresponding noise element represents the deviation between the actual and predicted values ​​of each uncertain factor. (1.2) Affine form of decision variables The decision variables in the multi-energy microgrid affine optimization scheduling model will be affected by all uncertain factors, and its affine form will contain the noise elements corresponding to all uncertain factors, as shown in Equation (12): In the formula, For a decision variable X in a multi-energy microgrid t affine forms; Its central value represents the decision variable X when it is unaffected by uncertainties. t Optimized scheduling values; The noise element coefficients corresponding to each uncertainty factor represent the impact of each uncertainty factor on the decision variable X. t The degree of influence; i is the set of various uncertainty factor types; (2) Definition of Affine Constraint The constraints in multi-energy microgrids are classified into three types: equality constraints, inequality constraints, and time-transition constraints. (2.1) Equality constraints In multi-energy microgrids, equality constraints fall into two categories: energy conversion relationships and power balance. Their affine forms are shown in equation (13). Since the decision variables in the affine form of multi-energy microgrids have the same noise element, the affine form of equality constraints is defined as the central values ​​of the affine variables on both sides of the equation and the coefficients of each noise element being equal. Therefore, equation (13) is further expressed as equation (14): In the formula, λ represents the affine variable contained in the equality constraints of the multi-energy microgrid; A , λ B …λ C These are the corresponding coefficients of each affine variable; The central values ​​of each affine variable; These are the noise element coefficients for each affine variable; (2.2) Inequality constraints The inequality constraints in the multi-energy microgrid are the upper and lower limits of the output of energy conversion equipment and energy storage equipment, as well as the upper and lower limits of the energy interaction between the multi-energy microgrid and the external grid. Their affine form is shown in Equation (15). To meet the completeness requirement, that is, to ensure that the inequality constraints can still be satisfied under the influence of all possible uncertain factors, the minimum and maximum values ​​that they may take are constrained, as shown in Equation (16). In the formula, D represents the affine variables involved in the inequality constraints of multi-energy microgrids. min and D max Affine variables The minimum and maximum allowed values; and These are its center value and noise element coefficient, respectively; (2.3) Time constraints The time constraints in multi-energy microgrids can be categorized into two types: time-series variation constraints on energy storage capacity and ramp-up constraints on energy conversion equipment. For constraints on the time-series variation of energy storage device capacity, its affine form is: In the formula, and These are the affine forms of the energy storage device capacity during time period t and time period t-1, respectively. and These represent the charging and discharging power of the affine energy storage device during time period t. η c and η d These refer to the charging and discharging efficiencies of energy storage devices, respectively. It can be assumed that the capacity of the energy storage device is equal to the set initial capacity in the period before the start of the optimized scheduling cycle. Since this capacity is a fixed value, the noise element coefficients in its corresponding affine form are all zero. Furthermore, to ensure the periodic availability of energy storage devices at the end of the scheduling cycle, and to still allow for adjustments to cope with uncertainties, the capacity of energy storage devices at the end of the scheduling cycle is constrained only for its central value. Therefore, the affine form of the constraint on the time-series change of energy storage device capacity is expressed more specifically as follows: In the formula, They are respectively The center value and noise element coefficient; They are respectively The center value and noise element coefficient; E 0 This refers to the energy storage capacity for the period preceding the start of the scheduling cycle. They are respectively Center value and noise element coefficient; They are respectively The center value and noise element coefficient; For the ramp-up constraint of the energy conversion equipment, its affine form is shown in equation (19); to meet the completeness requirement, equation (19) is transformed into equation (20): In the formula, and These are the affine forms of the power output of the energy conversion equipment in a multi-energy microgrid during time period t and time period t-1, respectively; K down and K up These are the set lower and upper limits for climbing, respectively; They are respectively The center value and noise element coefficient; They are respectively The center value and noise element coefficient; (3) Definition of affine objective function With the goal of minimizing the operating cost of a multi-energy microgrid, the objective function of the affine optimization scheduling model for the multi-energy microgrid is: in: In the formula: and These are the operating costs of the affine multi-energy microgrid for time period t, including gas purchase costs, electricity purchase costs, electricity sales costs, and the operation and maintenance costs of the energy conversion equipment. and These are the affine forms of the gas purchase volume, electricity purchase volume, and electricity sales volume of the multi-energy microgrid during time period t; and The affine forms represent the power consumption of the combined heat and power unit, the electric-to-gas converter, the electric boiler, and the gas boiler during time period t, respectively; c buy,g and These represent the unit cost of gas purchase for multi-energy microgrids and the unit cost of electricity purchase and sale during time period t; c CHP c P2G c EB and c GB The unit maintenance costs are respectively for combined heat and power units, electric-to-gas conversion units, electric boilers, and gas-fired boilers; Based on the basic composition of affine variables, the objective function of the affine form is further defined and transformed, and divided into two parts: the central value of operating costs and the fluctuation range; therefore, equation (21) is further defined as: In the formula, and These are the central value of the multi-energy microgrid operating cost and the noise element coefficient for time period t, respectively. These are the center value and noise element coefficient of the gas purchase cost of the multi-energy microgrid during time period t; These are the center value of the electricity purchase cost of the multi-energy microgrid and the noise element coefficient, respectively, for time period t. These are the center value of the electricity sales cost of the multi-energy microgrid and the noise element coefficient, respectively, for time period t. , respectively, are the center value and noise element coefficient of the multi-energy microgrid operation and maintenance cost in time period t; w is the optimization weight, and its value range is [0,1]; Based on the construction of a multi-energy microgrid affine optimization scheduling model, the model predictive control (MPC) method is used to continuously obtain the daily updated wind and solar power output and load demand forecast information. Combined with the rolling optimization idea, the proposed multi-energy microgrid affine optimization scheduling model is solved in a rolling manner.

2. The affine optimization scheduling method for multi-energy microgrids based on model predictive control according to claim 1, characterized in that, The multi-energy microgrid consists of four parts: energy supply, energy conversion equipment, energy storage equipment, and loads, achieving synergistic support of electricity, heat, and gas. Energy supply includes wind and solar power generation, as well as energy interaction with the power grid and gas grid. Energy conversion equipment comprises four types of devices: combined heat and power (CHP) units, electricity-to-gas (EPG) converters, electric boilers, and gas-fired boilers. CHP units consist of micro gas turbines and waste heat recovery devices. Energy storage includes electrical, thermal, and gas storage. Loads include three types: electrical, thermal, and gas loads. The mathematical models for the energy conversion and storage equipment are as follows: (1) Cogeneration unit The mathematical model for a combined heat and power (CHP) unit is: In the formula, and These represent the electrical power and thermal power generated by the combined heat and power unit during time period t, respectively. η is the natural gas power consumed by the combined heat and power unit during time period t; GE and η GH These represent the power generation and heat generation efficiencies of the micro gas turbine, respectively; η HR The recovery efficiency of the waste heat recovery device; (2) Electric to gas conversion The mathematical model for electro-gas conversion is: In the formula, and η represents the power of natural gas produced and the power of electricity consumed during time period t, respectively, in the power-to-gas conversion process. P2G Energy conversion efficiency from electricity to gas; (3) Electric boiler The mathematical model of an electric boiler is: In the formula, and η represents the thermal power generated and the electrical power consumed by the electric boiler during time period t, respectively. EB The electrothermal conversion efficiency of the electric boiler; (4) Gas-fired boiler The mathematical model for a gas-fired boiler is as follows: In the formula, and η represents the thermal power produced by the gas-fired boiler and the natural gas power consumed during time period t, respectively. GB The heating efficiency of the gas-fired boiler; (5) Energy storage equipment The mathematical model for energy storage devices is as follows: In the formula, tp represents the type of energy storage device, tp∈[ES,HS,GS], where ES represents electrical energy storage, HS represents thermal energy storage, and GS represents gas energy storage; Let t be the capacity of the energy storage device during time period t; and These represent the charging and discharging power of the energy storage device during time period t; η c,tp and η d,tp Δt represents the charging and discharging efficiency of the energy storage device, respectively; Δt is the scheduling time interval.

3. The affine optimization scheduling method for multi-energy microgrids based on model predictive control according to claim 2, characterized in that, The operational constraints of multi-energy microgrids are as follows: (1) Power balance constraint Multi-energy microgrids need to satisfy the power balance constraints of electricity, heat, and gas energy sources respectively: In the formula, and These represent the electricity purchase and sales volume of the multi-energy microgrid during time period t; The gas purchase volume of the multi-energy microgrid during time period t; and These represent the power generation capacity of the wind turbine and the photovoltaic system during time period t, respectively. and These represent the charging and discharging power of the energy storage device during time period t; and These represent the charging and discharging power of the thermal energy storage device during time period t; and These represent the charging and discharging power of the gas storage device during time period t. and These represent the electricity, heat, and gas load demands during time period t; (2) External network interaction constraints The constraints that a multi-energy microgrid must meet to engage in electricity purchase and sale with the external power grid and to purchase gas from the external gas grid are as follows: In the formula, and These are the lower and upper limits of the power purchase capacity, respectively. and These are the lower and upper limits of the electricity sales capacity, respectively. binary variable v t This indicates the power purchase and sale status of the multi-energy microgrid during time period t. If v t =1 indicates that the multi-energy microgrid purchases electricity during time period t, and vice versa if v t =0 indicates that electricity is sold during time period t; and These are the lower and upper limits for the amount of gas that can be purchased, respectively. (3) Constraints on energy conversion equipment The output and ramping constraints of the energy conversion equipment are as follows: In the formula, These are the lower and upper limits of the output of a combined heat and power (CHP) unit, respectively. These are the lower and upper limits of the output power for the electro-gas conversion, respectively. These are the lower and upper limits of the output of the electric boiler, respectively. These are the lower and upper limits of the output of the gas-fired boiler, respectively. These are the lower and upper limits of ramp speed for combined heat and power (CHP) units, respectively. These are the lower and upper limits of the ramp speed limit for electric boilers, respectively. These are the lower and upper limits for ramping up gas-fired boilers, respectively. (4) Constraints of energy storage devices The constraints of energy storage devices are: In the formula, and These represent the lower and upper limits of the capacity of energy storage devices, respectively. and These are the lower and upper limits of the charging power for energy storage devices, respectively. and These represent the lower and upper limits of the power that energy storage devices can release, respectively. and These represent the capacity of the energy storage devices before and at the end of the scheduling cycle, respectively; T is the total number of time periods in the scheduling cycle. Through binary variables This indicates the energy storage and release status of the energy storage device during time period t. This indicates that the energy storage device stores energy during time period t, and vice versa. This indicates that the energy storage device releases energy during time period t, and the availability of the energy storage device is guaranteed by ensuring that its capacity remains consistent at the beginning and end of the scheduling cycle.

4. The affine optimization scheduling method for multi-energy microgrids based on model predictive control according to claim 1, characterized in that, Based on the construction of a multi-energy microgrid affine optimization scheduling model, the model predictive control (MPC) method is used to continuously obtain updated intraday wind and solar power output and load demand forecast information. Combined with the rolling optimization concept, the specific implementation of the proposed multi-energy microgrid affine optimization scheduling model is as follows: Based on the affine optimization objective function defined in equation (26), and combined with the rolling optimization idea in the model predictive control method, the objective function of the multi-energy microgrid affine optimization scheduling method based on model predictive control is defined as shown in equation (27): The absolute value of the noise element coefficients in Equation (27) related to the operating costs of multi-energy microgrids will increase the difficulty of solving the problem and reduce the efficiency of solving the problem. In order to shorten the solution time and meet the needs of online optimization, an auxiliary variable z is introduced. i,+ z i,- With constraint (28), the absolute value term in equation (27) is replaced by the equivalent substitution shown in equation (29); Finally, the affine optimization scheduling model M for a multi-energy microgrid based on model predictive control is constructed as follows: Before each optimization, the system status information that needs to be obtained includes the output of each energy conversion device in the multi-energy microgrid and the capacity of the energy storage device in the previous period. The forecast data that needs to be updated includes the power generation of wind and photovoltaic in the multi-energy microgrid, as well as the demand of the three types of loads: electricity, heat, and gas.

5. The affine optimization scheduling method for multi-energy microgrids based on model predictive control according to claim 4, characterized in that, The specific implementation steps of this method are as follows: S1. Initialize the start time period of the optimized scheduling, let t=1; S2. Obtain the energy storage status information of the multi-energy microgrid in the previous time period, i.e., the time period t-1, and the wind and solar power output and load demand forecast information of the multi-energy microgrid in the time period from t to t+N-1. S3. Define the prediction information obtained in S2 as an affine form; S4. Solve the affine optimization scheduling model of the multi-energy microgrid to obtain the optimal scheduling scheme of the multi-energy microgrid in the time period t to t+N-1. S5. Send the value of the first time period in the optimized scheduling scheme to the t time period for execution; S6. Determine whether the current optimized scheduling period t is less than the total number of scheduling periods T; S7. If t < T, then t = t + 1, and return to S2; if t ≥ T, then end the entire optimization scheduling process, and the optimization scheduling of T time periods is completed.

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