Multi-energy micro-grid real-time optimization scheduling method and device considering source-load correlation

Abstract

The invention provides a multi-energy micro-grid real-time optimization scheduling method and device considering source-load correlation, and relates to the field of multi-energy micro-grid real-time scheduling. The method comprises the following steps: under the constraint of a first constraint condition, constructing a multi-energy microgrid deterministic scheduling model, and obtaining real-time sensing data of a target variable; determining a source load affine form and a source load noise element coefficient, and constructing a multi-energy microgrid affine optimization model under the constraint of an affine form constraint condition to obtain an affine optimization interval of a target variable; and calculating a target noise element corresponding to the target variable by using the real-time sensing data of the target variable in the target scheduling time, and calculating a decision variable affine form in the target scheduling time based on the affine optimization interval and the target noise element to obtain a real-time scheduling trajectory of the multi-energy microgrid. According to the method and the device, the resistance of a scheduling system to uncertainty can be enhanced, and the scheduling robustness is improved.

Description

Technical Field

[0001] This application relates to the field of real-time scheduling of multi-energy microgrids, and in particular to a real-time optimized scheduling method and apparatus for multi-energy microgrids that considers source-load correlation. Background Technology

[0002] Multi-energy microgrids (MEMGs) integrate and coordinate multiple energy sources such as electricity, heat, and gas to achieve complementarity and efficient scheduling of different energy forms, helping to improve overall energy utilization efficiency and reduce environmental pollution from fossil fuel consumption. However, MEMGs not only suffer from complex source-load uncertainties but also exhibit significant source-load correlations. Source-load uncertainty makes it difficult to precisely match energy supply and demand, while source-load correlations further exacerbate the impact of this uncertainty, leading to higher safety risks and economic costs during microgrid operation. Therefore, there is an urgent need for effective uncertainty optimization methods that can simultaneously consider source-load uncertainty and source-load correlations to improve the safety and economic efficiency of multi-energy microgrids.

[0003] Currently, common uncertainty optimization methods include stochastic optimization, fuzzy optimization, robust optimization, and interval optimization. Stochastic optimization seeks the expected optimal solution by generating random scenarios based on the probability distribution of uncertain parameters; therefore, it relies on accurate probability information, and generating a large number of scenarios significantly increases computation time. Although scenario compression techniques can improve computational efficiency, they may lead to the loss of some scenarios, thus reducing optimization accuracy. Fuzzy optimization uses membership functions to describe uncertainty, but its result accuracy and the objectivity of the membership functions have limitations. Robust optimization ensures the robustness of decisions by considering the worst-case scenario of parameters, but its model solution is complex and the results may be overly conservative. Interval optimization has advantages when the upper and lower bounds of uncertain variables are readily available, but it cannot effectively reflect the correlation between multiple uncertain variables, leading to overly conservative optimization results. In summary, current common uncertainty optimization methods still face challenges in balancing computational efficiency, accuracy, and conservatism.

[0004] Affine arithmetic (AA), as an emerging and effective technique for handling uncertainty, offers a new approach to solving the aforementioned problems and has received widespread attention in recent years. Compared with traditional interval methods, affine arithmetic not only retains the advantage of ease of modeling but also provides more compact boundaries, while tracking uncertainty sources throughout the computation process. Compared with stochastic optimization and fuzzy optimization, affine arithmetic only requires information on the range of uncertain parameters, without needing precise probability information. Compared with robust optimization, affine arithmetic avoids the problem of overly conservative computational results. Based on these advantages, affine arithmetic theory has been applied to power flow calculation and analysis and reactive power optimization, proving its effectiveness. Currently, while existing affine optimization methods have significant advantages in handling uncertainty, they all fail to consider the correlation between source loads. This leads to an overestimation of the uncertainty range of source loads, resulting in excessive redundancy in the optimization scheme and making the scheme overly conservative.

[0005] To address real-time fluctuations in source loads within multi-energy microgrids, scheduling plans need to be adjusted in real time. Model Predictive Control (MPC), employing a rolling optimization strategy and utilizing the latest source load forecast data for feedback correction, can mitigate the impact of real-time source load fluctuations on the operation of multi-energy microgrids to some extent. However, these deterministic methods based on MPC are sensitive to source load forecast errors because they cannot accurately simulate uncertainties, leading to energy supply-demand mismatches. Therefore, some researchers have combined uncertainty optimization methods with MPC methods to ensure optimal operation of multi-energy microgrids under uncertainty. However, this method is still inevitably affected by the inherent limitations of uncertainty optimization methods. Summary of the Invention

[0006] This application provides a real-time optimization scheduling method and apparatus for multi-energy microgrids that considers source-load correlation, in order to solve the problem that the existing technology cannot coordinate the correlation and uncertainty between source and load, resulting in inaccurate source-load scheduling.

[0007] Firstly, this application provides a real-time optimization scheduling method for multi-energy microgrids that considers source-load correlation, including: Under the constraints of the first constraint, a deterministic scheduling model for a multi-energy microgrid is constructed to obtain real-time sensing data of the target variables. The deterministic scheduling model for the multi-energy microgrid includes power load and heat load, and the target variables include electricity demand, heat demand, photovoltaic output, ambient temperature and irradiance. The affine form of the source load and the noise element coefficients of the source load are determined, and under the constraint of the affine form, a multi-energy microgrid affine optimization model is constructed to obtain the affine optimization interval of the target variable. The affine form of the source load includes the affine form considering the nonlinear correlation of the source load and the affine form of the decision variable. Using real-time sensing data of the target variable within the target scheduling time, the target noise element corresponding to the target variable is calculated. Based on the affine optimization interval and the target noise element, the affine form of the decision variable within the target scheduling time is calculated to obtain the real-time scheduling trajectory of the multi-energy microgrid.

[0008] Secondly, this application provides a real-time optimized scheduling device for multi-energy microgrids that considers source-load correlation, comprising: The data acquisition module is used to construct a deterministic scheduling model for a multi-energy microgrid under the constraints of the first constraint condition, and to acquire real-time sensing data of the target variables. The deterministic scheduling model for the multi-energy microgrid includes power load and heat load, and the target variables include electricity demand, heat demand, photovoltaic output, ambient temperature and irradiance. The interval determination module is used to determine the affine form of the source load and the noise element coefficient of the source load, and under the constraint of the affine form constraint, construct a multi-energy microgrid affine optimization model to obtain the affine optimization interval of the target variable. The source load affine form includes the affine form considering the nonlinear correlation of the source load and the affine form of the decision variable. The trajectory generation module is used to calculate the target noise element corresponding to the target variable using real-time sensing data of the target variable within the target scheduling time, and calculate the affine form of the decision variable within the target scheduling time based on the affine optimization interval and the target noise element, so as to obtain the real-time scheduling trajectory of the multi-energy microgrid.

[0009] This application provides a real-time optimization scheduling method and apparatus for multi-energy microgrids considering source-load correlation. Under the constraint of a first constraint, a deterministic scheduling model for the multi-energy microgrid is constructed to obtain real-time sensing data of the target variables. The deterministic scheduling model includes electricity load and heat load, and the target variables include electricity demand, heat demand, photovoltaic output, ambient temperature, and irradiance. The affine form of the source-load and the source-load noise element coefficients are determined. Under the constraint of the affine form constraint, an affine optimization model for the multi-energy microgrid is constructed to obtain the affine optimization interval of the target variables. The source-load affine form includes an affine form considering the nonlinear correlation of source-loads and an affine form of the decision variables. Using the real-time sensing data of the target variables within the target scheduling time, the target noise element corresponding to the target variable is calculated. Based on the affine optimization interval and the target noise element, the affine form of the decision variables within the target scheduling time is calculated to obtain the real-time scheduling trajectory of the multi-energy microgrid. This application, by determining the affine form of source-load, including the affine form considering the nonlinear correlation between source and load, can deeply capture the complex relationship between sources (such as energy supply like photovoltaic power output) and loads (energy consumption like electricity and heat demand). Traditional scheduling methods often ignore this correlation, but this application incorporates it, enabling the scheduling model to more accurately predict and respond to changes in source and load, thus improving the model's fit to actual operating conditions. Furthermore, by constructing a multi-energy microgrid affine optimization model and obtaining the affine optimization interval of the target variable, a flexible range is provided for scheduling. In actual operation, due to the existence of various uncertainties, the target variable tends to... The variables will fluctuate within a certain range, and the affine optimization interval can accommodate these uncertainties, enabling scheduling decisions to remain reasonable and effective in the face of variable fluctuations. This enhances the scheduling system's ability to withstand uncertainties and improves scheduling robustness. At the same time, by using real-time sensing data within the target scheduling time to calculate the target noise element, and by calculating the affine form of the decision variables based on the affine optimization interval and the target noise element, the real-time scheduling trajectory can be obtained. This allows for dynamic adjustment of the scheduling strategy according to real-time changes during actual operation, timely response to various emergencies and uncertainties, and ensures stable and reliable operation of the multi-energy microgrid under different operating conditions. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart illustrating the real-time optimization scheduling method for multi-energy microgrids that considers source-load correlation, provided in an embodiment of this application. Figure 2This is a schematic diagram of the structure of the multi-energy microgrid system provided in the embodiments of this application; Figure 3 This is a flowchart illustrating the implementation of the affine optimization stage provided in an embodiment of this application; Figure 4 This is a flowchart illustrating the implementation of affine real-time scheduling for multi-energy microgrids provided in this application embodiment; Figure 5 This is a schematic diagram of the structure of a real-time optimization scheduling device for a multi-energy microgrid that considers source-load correlation, provided in an embodiment of this application. Detailed Implementation

[0012] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0013] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0014] To address the problem of inaccurate source-load scheduling caused by the inability of existing technologies to coordinate the correlation and uncertainty between source and load, this application provides a real-time optimization scheduling method for multi-energy microgrids that considers source-load correlation. This method obtains the real-time affine scheduling trajectory of the multi-energy microgrid by constructing and solving an affine optimization model. Specifically: First, a deterministic scheduling model for the multi-energy microgrid is constructed, integrating various energy devices such as gas turbines, photovoltaics, batteries, and waste heat recovery devices, supporting the storage of electrical and thermal energy and bidirectional interaction with the main grid. Then, based on the deterministic scheduling model, affine arithmetic theory is introduced to handle source-load uncertainty and correlation. The dependency relationship between photovoltaic output and electricity and heat demand is characterized by shared noise elements, avoiding the overestimation problem caused by neglecting correlation in traditional methods. Finally, an affine real-time scheduling method for multi-energy microgrids is proposed, appropriately reducing the scheduling time interval. Then, based on the real-time sensing data of each uncertain variable, the noise element values ​​are dynamically inferred to generate real-time scheduling instructions for equipment output.

[0015] Figure 1 The implementation flowchart of the real-time optimization scheduling method for multi-energy microgrids considering source-load correlation provided in the embodiments of this application is described in detail below: In step 101, under the constraints of the first constraint, a deterministic scheduling model for a multi-energy microgrid is constructed, and real-time sensing data of the target variables are obtained. The deterministic scheduling model for a multi-energy microgrid includes power load and heat load, and the target variables include electricity demand, heat demand, photovoltaic output, ambient temperature and irradiance.

[0016] In this embodiment, a deterministic scheduling model for a multi-energy microgrid is constructed, encompassing the supply and demand balance of electrical and thermal loads, equipment operating characteristics, and cost optimization objectives. The multi-energy microgrid system integrates various energy devices such as gas turbines, photovoltaics, batteries, and waste heat recovery devices, supporting the storage of electrical and thermal energy and bidirectional interaction with the main grid. The multi-energy microgrid deterministic scheduling model prioritizes minimizing fuel and electricity purchase costs, and sets strict first constraints to ensure operational safety. This model provides an optimization framework for subsequent uncertainty handling and real-time scheduling. Then, based on the constructed multi-energy microgrid deterministic scheduling model, real-time sensing data of the target variables (i.e., electricity demand, heat demand, photovoltaic output, ambient temperature, and irradiance) are obtained.

[0017] The structural schematic diagram of the multi-energy microgrid system proposed in this application is shown below. Figure 2 As shown, the multi-energy microgrid system includes both electrical and thermal loads. Electrical energy is supplied by the external power grid, gas turbines, photovoltaics, and batteries, while thermal energy is supplied by waste heat recovery devices, gas boilers, and hot water storage tanks. Excess electrical energy can be sold back to the external power grid or used to charge batteries, and excess thermal energy can be stored in the hot water storage tanks.

[0018] The mathematical model for the waste heat recovery device is as follows:

[0019] in, for The heat power of the waste heat recovery device during the time period for The electrical power produced by the gas turbine during the period For the power generation efficiency of gas turbines, This refers to the heat loss rate of the gas turbine. This refers to the recovery efficiency of the waste heat recovery device.

[0020] The mathematical model of a heat exchanger is:

[0021] in, for The thermal power of the time-limited heat exchanger for Hot demand during a certain period This refers to the efficiency of the heat exchanger.

[0022] The multi-energy microgrid deterministic scheduling model constructed in this embodiment can be expressed as a mixed-integer linear programming (MILP) algorithm, with the objective function being:

[0023] in, It is a minimum value function. For unit fuel cost, for The electrical power produced by the gas turbine during the period For the power generation efficiency of gas turbines, for The heat output of the gas-fired boiler during the period For the heat production efficiency of gas-fired boilers, To optimize the scheduling time interval, The unit price of electricity purchased. for Electricity purchases for multi-energy microgrids during different time periods This refers to the unit price of electricity sold. for Electricity sales of multi-energy microgrids during different time periods This refers to the total time period.

[0024] This application embodiment assumes that the energy storage device has no cost.

[0025] The first set of constraints may include power balance constraints, energy storage device constraints, device power constraints, and power interaction constraints. Energy storage device constraints include operational constraints for electrical energy storage and operational constraints for thermal energy storage. Device power constraints include power generation constraints for gas turbines and power generation constraints for gas boilers.

[0026] 1) Power balance constraints Multi-energy microgrids need to maintain a balance between electrical and thermal power during operation, that is:

[0027] in, for The electrical power produced by the gas turbine during the period for The amount of electricity generated by photovoltaic power during a given period. for The discharge power of the battery during the period for Battery charging power during the period for Electricity purchases for multi-energy microgrids during different time periods for Electricity sales of multi-energy microgrids during different time periods for Electricity demand during different time periods for The heat power of the waste heat recovery device during the time period for The heat output of the gas-fired boiler during the period for The discharge power of the hot water storage tank during the time period. for The thermal storage capacity of the hot water storage tank during a specific time period. for The thermal power of the time-limited heat exchanger for Waste heat power during a given time period.

[0028] 2) Constraints of energy storage devices The operational constraints of electric energy storage are:

[0029] in, for Time-of-use energy storage capacity, The energy self-loss rate of the energy storage device. for Time-of-use energy storage capacity, for Battery charging efficiency over time. for The discharge efficiency of the battery over a given period of time. To optimize the scheduling time interval, This represents the lower limit of electrical energy storage capacity. This is the upper limit of electrical energy storage capacity. This is the lower limit of the battery's charging power. It is a binary variable, that is The charging status of the battery during the period. This refers to the upper limit of the battery's charging power. This represents the lower limit of the battery's discharge power. It is a binary variable, that is The state of battery discharge during a given period. This represents the upper limit of the battery's discharge power.

[0030] Formula (6) is the energy balance constraint for electric energy storage, Formula (7) specifies the minimum and maximum limits of electric energy storage capacity, Formulas (8) and (9) specify the minimum and maximum charging and discharging power limits of electric energy storage, respectively, and Formula (10) ensures that the battery will not be charged and discharged at the same time.

[0031] The operational constraints for thermal energy storage are:

[0032] in, for Thermal energy storage capacity during specific time periods for Thermal energy storage capacity during specific time periods The energy self-loss rate of the energy storage device. To improve the heat storage efficiency of the hot water tank, To improve the heat release efficiency of the hot water storage tank, This represents the lower limit of thermal energy storage capacity. This is the upper limit of thermal energy storage capacity. This represents the lower limit of the thermal storage capacity of the hot water storage tank. This represents the upper limit of the thermal storage capacity of the hot water storage tank. This is the lower limit of the heat release capacity of the hot water storage tank. This is the upper limit of the heat dissipation power of the hot water storage tank. It is a binary variable, that is The thermal storage status of the hot water storage tank during a given time period. It is a binary variable, that is The heat release status of the hot water storage tank during a certain period.

[0033] Formula (11) is the energy balance constraint for thermal energy storage, and Formula (12) specifies the minimum and maximum limits for thermal energy storage capacity. Formulas (13) and (14) specify the minimum and maximum limits for thermal energy storage and release power. Formula (15) ensures that the hot water storage tank does not simultaneously store and release heat.

[0034] 3) Equipment power constraints The power generation limit of the gas turbine is:

[0035] in, This represents the lower limit of the gas turbine's output. This is the upper limit of the gas turbine's output. for Operating status of the gas turbine during a given period.

[0036] The heat production capacity constraint of a gas-fired boiler is:

[0037] in, This represents the lower limit of the output of the gas-fired boiler. This is the upper limit of the output of the gas-fired boiler. for Operating status of the gas-fired boiler during a given time period.

[0038] 4) Power interaction constraints Multi-energy microgrids can exchange electricity with the main grid through power purchase and sale, and their power is mutually constrained, that is:

[0039] in, This is the lower limit for the amount of electricity that can be purchased. This is the upper limit for the amount of electricity that can be purchased. This is the lower limit for electricity sales. This is the upper limit for electricity sales. for Electricity purchase status during specific time periods for Electricity sales status during specific time periods.

[0040] Formulas (18) and (19) specify the minimum and maximum limits for the power exchange between the multi-energy microgrid and the main grid. Formula (20) ensures that the multi-energy microgrid does not simultaneously purchase and sell electricity to the main grid.

[0041] In step 102, the affine form of the source load and the source load noise element coefficients are determined, and under the constraints of the affine form, a multi-energy microgrid affine optimization model is constructed to obtain the affine optimization interval of the objective variable. The affine form of the source load includes the affine form considering the nonlinear correlation of the source load and the affine form of the decision variable.

[0042] In this embodiment, based on the deterministic scheduling model of the multi-energy microgrid constructed in step 1, affine arithmetic theory is introduced to handle source-load uncertainty and correlation, thus constructing an affine optimization model for the multi-energy microgrid. By leveraging the dependency relationship between photovoltaic output and electricity and heat demand, the overestimation problem caused by neglecting correlation in traditional methods is avoided. The optimization objective takes into account both the expected value and fluctuation range of operating costs. By adjusting the weighting coefficients to balance economic efficiency and conservatism, the affine optimization interval of the objective variable is obtained.

[0043] Affine arithmetic (AA), as an improved interval algorithm, can track the first-order linear relationship between input and output. It can also reflect the correlation between different variables by sharing the same noise element among different affine variables, thus solving the dependency problem between variables in interval calculations. In affine arithmetic, uncertain variables... Affine Represented as:

[0044] in, The central value of the affine form, For the first noise element, Different noise elements represent different independent uncertainties; For the first The noise element coefficients corresponding to each noise element represent the degree of influence of that noise element on the affine variable. The number of noise elements.

[0045] Affine The upper and lower limits can be calculated using formula (22), that is:

[0046] in, The upper bound of the affine variable, Let be the lower bound of the affine variable. Let be the radius of the affine variable.

[0047] Affine It not only describes variables that are uncertain The smallest interval of all possible values ​​also contains uncertain variables. The inherent connection with various uncertain factors.

[0048] Reference Figure 3 As shown, the affine optimization stage process is as follows: Step 3.1: Obtain the source-load prediction range of the energy microgrid; Step 3.2, define the affine form of the source charge; Step 3.3, define the source load noise element coefficients; Step 3.4: Construct an affine optimization model for a multi-energy microgrid; Step 3.5: Solve the affine optimization model for the multi-energy microgrid; Step 3.6: Obtain the affine optimization scheme for the multi-energy microgrid, i.e., the affine optimization interval of the objective variable.

[0049] In one possible implementation, the affine form of the source charge and the source charge noise element coefficients are determined, specifically as follows: (1) Considering the affine form variable definition of source-charge non-correlation 1) The affine form of ambient temperature and irradiance is:

[0050] in, for Affine form of ambient temperature over a period of time. for Affine form of time-limited irradiance, The center value of the ambient temperature. The center value of irradiance, The noise element corresponds to the uncertainty of ambient temperature. For the noise element corresponding to the irradiance uncertainty, The noise element coefficient corresponding to the ambient temperature. This represents the noise element coefficient corresponding to the irradiance.

[0051] 2) The affine forms of electricity demand, heat demand, and photovoltaic output are:

[0052] in, for Affine form of time-of-use electricity demand. for Affine form of time-limited heat demand, for Affine form of photovoltaic power output during a given period The central value of electricity demand, The center value of heat demand, The center value of photovoltaic power output, The noise element coefficient corresponding to the electricity demand. The noise element coefficient corresponding to the heat demand. The noise element coefficient corresponding to photovoltaic power output. For noise elements corresponding to the uncertainty of electricity demand, For noise elements corresponding to the uncertainty of heat demand, This refers to the noise element corresponding to the uncertainty of photovoltaic power output.

[0053] Electricity demand, heat demand, and photovoltaic output all exhibit inherent uncertainties and strong inter-correlation relationships. The dependencies among these three factors possess complex nonlinear characteristics, making them difficult to characterize directly using traditional linear relationships. Since all three are affected by ambient temperature, and photovoltaic output is primarily influenced by irradiance, a noise element corresponding to the uncertainty of ambient temperature can be used to describe the complex coupling relationships among them. To represent by a function and , using about and the noise element corresponding to the irradiance uncertainty function representation Copula theory can be used to transform the joint distribution function of multiple random variables into the product of their marginal distribution functions. The principle is as follows:

[0054] in, for For random variables The joint distribution function, For Copula functions, For about The marginal distribution function.

[0055] Assume that the noise elements of ambient temperature, irradiance, electricity demand, heat demand, and photovoltaic output all follow a Gaussian distribution: , , , , .

[0056] Copula theory transforms the joint distribution function of multiple random variables into the product of their marginal distribution functions, i.e.:

[0057] in, For random variables The joint distribution function, For random variables The corresponding Copula function; For random variables The joint distribution function, For random variables The corresponding Copula function; For random variables The joint distribution function, For random variables The corresponding Copula function.

[0058] Through calculation , and exist and Given the conditional expectation, it can be expressed as... To represent by a function and , using about and function representation ,Right now:

[0059] in, For random variables The joint probability density function; For random variables The joint probability density function; For random variables The joint probability density function; For random variables The joint probability density function can be derived from... Calculated.

[0060] According to formulas (32)-(34), the source-load affine form considering source-load non-correlation is:

[0061] in, , , These are all new noise elements generated by nonlinear operations. , , All of these are noise element coefficients corresponding to the new noise element.

[0062] (2) Definition of decision variables in radial form Based on formulas (35)-(37), the radial form of continuous variables in the multi-energy microgrid scheduling problem is as follows:

[0063] in, For the affine form of the corresponding variable, the first term of the affine form of each variable (e.g.) ) is the center value of the corresponding variable, and the second term (such as The third term (e.g., the deviation of this variable due to electricity demand forecasting errors) is the deviation of this variable. The fourth item (e.g., the deviation caused by the error in heat demand forecasting) represents the bias of this variable. The fifth item (e.g., the deviation caused by the photovoltaic power output prediction error) represents the bias of this variable. ) to the seventh item (such as The deviations of this variable from the error compensation terms for electricity demand, heat demand, and photovoltaic output are respectively.

[0064] In the definition of the minimization operator in affine arithmetic theory, the affine objective function can be expressed as a multi-objective optimization problem that minimizes the central values ​​and affine radii of the affine variables. Therefore, the objective function is expressed as a weighted function of the central values ​​and the affine radius. The objective function of the multi-energy microgrid affine optimization model is:

[0065] in, It is a minimum value function. These are the weighting coefficients. For unit fuel cost, From 0 to The electrical power produced by the gas turbine during the period For the power generation efficiency of gas turbines, From 0 to The heat output of the gas-fired boiler during the period For the heat production efficiency of gas-fired boilers, To optimize the scheduling time interval, The unit price of electricity purchased. From 0 to Electricity purchases for multi-energy microgrids during different time periods This refers to the unit price of electricity sold. From 0 to Electricity sales of multi-energy microgrids during different time periods for arrive The electrical power produced by the gas turbine during the period for arrive The heat output of the gas-fired boiler during the period for arrive Electricity purchases for multi-energy microgrids during different time periods for arrive Electricity sales of multi-energy microgrids during different time periods Total time period This is a type of uncertainty. .

[0066] in, The choice can be made based on the degree of conservatism of the desired optimization scheme in terms of uncertainty. When the weight coefficient approaches 1, the optimization objective focuses on the central value of the operating cost, the expected operating cost is small, but it is sensitive to uncertainty. When the weight coefficient approaches 0, the optimization objective focuses on the affine radius of the operating cost, the cost range of the operating scheme is small, but the operating cost is high.

[0067] The corresponding affine form constraints are: When using affine arithmetic to solve the economic scheduling problem of multi-energy microgrids, the constraints can be divided into non-time-transitional equality constraints, non-time-transitional inequality constraints, and time-transitional equality constraints.

[0068] (1) For non-time-interval equality constraints, taking the power balance constraint as an example, the affine form corresponding to formula (4) is:

[0069] The affine form of formula (5) is similar. The power balance constraint in the deterministic scheduling model of multi-energy microgrids is divided into two parts: the first part is the central value, and the second part is the partial deviation related to the uncertainty factors.

[0070] (2) For non-time-interval inequality constraints, taking the power generation constraint of the gas turbine as an example, the affine form of formula (16) is:

[0071] The affine forms of formulas (7)-(9), (12)-(14), (18), and (19) are similar. Formulas (10), (15), and (20) only involve binary variables and are therefore unaffected by continuous variables in the affine form. Therefore, these constraints can be included in the multi-energy microgrid affine optimization model without modification.

[0072] (3) For time-series equality constraints, their affine form can be redefined by balancing the central value and the affine radius. Taking formula (6) as an example, the affine form of the time-series equality constraint is:

[0073] The affine form of formula (11) is similar to it.

[0074] In addition, apart from binary variables, the optimization schemes obtained by solving the affine optimization model of multi-energy microgrid are all represented in affine form. In essence, it is a range of changes and adjustments rather than specific real-time scheduling instructions. This results in a lack of clear scheduling trajectory in real-time applications, making it difficult to apply directly to the real-time scheduling stage.

[0075] In this embodiment, the prediction interval based on the uncertain variable is first affine optimized to obtain the center value and affine radius of the affine variable, and then transformed into the affine optimization interval of the target variable according to formula (22).

[0076] Considering the influence of uncertainty, the absolute value of the noise element coefficient of an affine variable can reflect the sensitivity of the variable to changes in the corresponding uncertain factors. The noise element coefficient of an affine variable can be defined as the fluctuation limit of the variable under the influence of the corresponding uncertain factors, and the source load noise element coefficient can be determined through sensitivity analysis. Furthermore, the large number of nonlinear absolute value terms introduced by the proposed affine optimization model complicates the problem and significantly increases the computation time. To avoid this, this application can also linearize the absolute value terms using the following method:

[0077] in, These are variables to be processed. and These are auxiliary variables used for the equivalent absolute value term. The processed optimization problem is a mixed-integer linear programming problem, which can be solved using a commercial solver.

[0078] In step 103, using real-time sensing data of target variables within the target scheduling time, the target noise element corresponding to the target variable is calculated. Based on the affine optimization interval and the target noise element, the affine form of the decision variable within the target scheduling time is calculated to obtain the real-time scheduling trajectory of the multi-energy microgrid.

[0079] In this embodiment, nonlinear constraints are transformed into a mixed-integer linear programming problem, reducing computational complexity and providing an efficient solution for real-time scheduling. The noise element values ​​are dynamically inferred from real-time sensing data of various target variables (such as temperature and irradiance), generating real-time scheduling instructions for equipment output. Data is updated every 5 minutes, and the noise elements are substituted into the multi-energy microgrid affine optimization model to output adjustment strategies, quickly responding to source-load fluctuations. For extreme scenarios (such as noise elements exceeding preset ranges), supply-demand balance is maintained through curtailment or load reduction, significantly reducing compensation costs.

[0080] In one possible implementation, real-time sensing data of the target variable within the target scheduling time is used to calculate the target noise element corresponding to the target variable. Based on the affine optimization interval and the target noise element, the affine form of the decision variable within the target scheduling time is calculated to obtain the real-time scheduling trajectory of the multi-energy microgrid. This can include: The initial time of the target scheduling time is taken as the current time; Acquire real-time sensing data of the target variable at the current moment, and calculate the target noise element of the target variable at the current moment; Determine whether the target noise element at the current moment is within the affine optimization interval; If the target noise element at the current moment is within the affine optimization interval, then the affine form of the decision variable at the current moment is calculated using the target noise element at the current moment. Determine if the current time is less than the target scheduling time; If the current time is less than the target scheduling time, then update the current time by adding 1 to the current time, and return to obtain the real-time perception data of the target variable at the current time, and calculate the target noise element of the target variable at the current time and continue to execute the step. If the current time is not less than the target scheduling time, then the real-time scheduling trajectory of the multi-energy microgrid is generated using the affine form of the decision variables at the current time.

[0081] Optionally, in order to obtain the real-time scheduling trajectory, this embodiment of the application needs to transform the obtained affine form of the decision variables into a particular solution under specific conditions without missing rated parameters.

[0082] Reference Figure 4 As shown, the specific execution process is as follows: Step 4.1, with the target scheduling time The initial time is taken as the current time, and the current time is initialized to 0. .

[0083] Step 4.2, obtain the current time. Real-time sensing data of target variables, including real-time sensing data of electricity demand, heat demand, photovoltaic output, ambient temperature, and irradiance, and utilizing the current moment... Real-time sensing data of the target variable, calculating the target variable at the current time. Target noise element .

[0084] Step 4.3, assume the affine optimization interval is Then determine the current time. Target noise element Is it within the affine optimization range? Then proceed to step 4.4.

[0085] Step 4.4, set the current time Target noise element Substituting into formulas (38)-(45), the current time is calculated. The decision variables are derived in a radial form, and the real-time scheduling trajectory of the multi-energy microgrid is generated.

[0086] Step 4.5, determine the current time. Is it less than the target scheduling time? If it is less than, proceed to step 4.6; if it is not less than, proceed to step 4.7.

[0087] Step 4.6: Update the current time by incrementing the current time by 1, i.e. Then return to step 4.2 to continue execution.

[0088] Step 4.7, End.

[0089] In one possible implementation, acquiring real-time sensing data of the target variable at the current moment and calculating the target noise element of the target variable at the current moment may include: Real-time sensing data of current electricity demand, heat demand, photovoltaic output, ambient temperature and irradiance are obtained respectively. Using real-time sensing data of the ambient temperature at the current moment, the first noise element is calculated. The first noise element is the noise element corresponding to the uncertainty of the ambient temperature. Using real-time sensing data of irradiance at the current moment, the second noise element is calculated. The second noise element is the noise element corresponding to the uncertainty of irradiance. Using the first and second noise elements, the third, fourth, and fifth noise elements are calculated. The third noise element is the noise element corresponding to the uncertainty of electricity demand; the fourth noise element is the noise element corresponding to the uncertainty of heat demand; and the fifth noise element is the noise element corresponding to the uncertainty of photovoltaic output. Using the first noise element, the second noise element, the third noise element, the fourth noise element, the fifth noise element, real-time sensing data of electricity demand, real-time sensing data of heat demand, and real-time sensing data of photovoltaic output, a new noise element is calculated by reverse deduction.

[0090] Optionally, the scheduling time interval is appropriately reduced, and then the specific value of the noise element corresponding to each uncertain factor at the current moment is calculated based on the real-time sensing data of each uncertain variable. The real-time sensing data of the ambient temperature at the current moment is input into formula (23) to calculate the first noise element; the real-time sensing data of the irradiance at the current moment is input into formula (24) to calculate the second noise element. The first noise element and the second noise element are input into formulas (32)-(34) to calculate the third noise element. Fourth noise element and the fifth noise element Then, the first, second, third, fourth, and fifth noise elements, along with real-time sensing data of electricity demand, heat demand, and photovoltaic output, are input into the inverse calculation formula to obtain the new noise element. , and That is, the inverse formula is:

[0091] in, , , All New noise elements during the time period for Real-time sensing data of electricity demand during different time periods. for, for, for The third noise element in the time period, for Real-time sensing data of time-of-day heat demand, for, for, for This is the fourth noise element in the time period. for Real-time sensing data of photovoltaic output during different time periods for, for, for The fifth noise element in the time period, , , All New noise element coefficients for the time period This refers to the current moment.

[0092] Furthermore, in extreme cases, the real-time value of the source charge may exceed the fluctuation range of the preset affine optimization interval, causing the calculated real-time value of the noise element to exceed the limit. Two scenarios exist: First scenario: If the target noise element at the current moment is not within the affine optimization interval, then determine whether the target noise element at the current moment is greater than the upper limit of the affine optimization interval. If the target noise element at the current moment is greater than the upper limit of the affine optimization interval, then update the target noise element at the current moment using unit 1, and use the updated target noise element to calculate the affine form of the decision variable at the current moment.

[0093] For example, if Then let Then proceed to step 4.4 to continue execution.

[0094] Second scenario: If the target noise element at the current moment is not within the affine optimization interval, then determine whether the target noise element at the current moment is less than the lower limit of the affine optimization interval. If the target noise element at the current moment is less than the lower limit of the affine optimization interval, then the target noise element at the current moment is updated with a negative value of unit 1, and the affine form of the decision variable at the current moment is calculated using the updated target noise element.

[0095] For example, if Then let Then proceed to step 4.4 to continue execution.

[0096] This application provides a real-time optimization scheduling method for multi-energy microgrids considering source-load correlation. Under a first constraint, a deterministic scheduling model for the multi-energy microgrid is constructed to obtain real-time sensing data of the target variables. The deterministic scheduling model includes electricity load and heat load, and the target variables include electricity demand, heat demand, photovoltaic output, ambient temperature, and irradiance. The affine form of the source-load and the source-load noise element coefficients are determined. Under the constraint of the affine form constraint, an affine optimization model for the multi-energy microgrid is constructed to obtain the affine optimization interval of the target variables. The source-load affine form includes an affine form considering the nonlinear correlation of source-load and an affine form of the decision variables. Using the real-time sensing data of the target variables within the target scheduling time, the target noise element corresponding to the target variable is calculated. Based on the affine optimization interval and the target noise element, the affine form of the decision variables within the target scheduling time is calculated to obtain the real-time scheduling trajectory of the multi-energy microgrid. This application, by determining the affine form of source-load, including the affine form considering the nonlinear correlation between source and load, can deeply capture the complex relationship between sources (such as energy supply like photovoltaic power output) and loads (energy consumption like electricity and heat demand). Traditional scheduling methods often ignore this correlation, but this application incorporates it, enabling the scheduling model to more accurately predict and respond to changes in source and load, thus improving the model's fit to actual operating conditions. Furthermore, by constructing a multi-energy microgrid affine optimization model and obtaining the affine optimization interval of the target variable, a flexible range is provided for scheduling. In actual operation, due to the existence of various uncertainties, the target variable tends to... The variables will fluctuate within a certain range, and the affine optimization interval can accommodate these uncertainties, enabling scheduling decisions to remain reasonable and effective in the face of variable fluctuations. This enhances the scheduling system's ability to withstand uncertainties and improves scheduling robustness. At the same time, by using real-time sensing data within the target scheduling time to calculate the target noise element, and by calculating the affine form of the decision variables based on the affine optimization interval and the target noise element, the real-time scheduling trajectory can be obtained. This allows for dynamic adjustment of the scheduling strategy according to real-time changes during actual operation, timely response to various emergencies and uncertainties, and ensures stable and reliable operation of the multi-energy microgrid under different operating conditions.

[0097] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0098] The following are device embodiments of this application. For details not described in detail, please refer to the corresponding method embodiments described above.

[0099] Figure 5A schematic diagram of the structure of a real-time optimization scheduling device for multi-energy microgrids considering source-load correlation, provided in an embodiment of this application, is shown. For ease of explanation, only the parts relevant to the embodiment of this application are shown, and are described in detail below: like Figure 5 As shown, the multi-energy microgrid real-time optimization scheduling device 5, which considers source-load correlation, includes: The data acquisition module 51 is used to construct a deterministic scheduling model for a multi-energy microgrid under the constraints of the first constraint condition, and to acquire real-time sensing data of the target variables. The deterministic scheduling model for the multi-energy microgrid includes power load and heat load, and the target variables include electricity demand, heat demand, photovoltaic output, ambient temperature and irradiance. The interval determination module 52 is used to determine the affine form of the source load and the noise element coefficient of the source load, and under the constraint of the affine form constraint, construct the affine optimization model of the multi-energy microgrid to obtain the affine optimization interval of the target variable. The affine form of the source load includes the affine form considering the nonlinear correlation of the source load and the affine form of the decision variable. The trajectory generation module 53 is used to calculate the target noise element corresponding to the target variable using real-time sensing data of the target variable within the target scheduling time, and calculate the affine form of the decision variable within the target scheduling time based on the affine optimization interval and the target noise element, so as to obtain the real-time scheduling trajectory of the multi-energy microgrid.

[0100] This application provides a real-time optimization scheduling device for a multi-energy microgrid considering source-load correlation. Under a first constraint, a deterministic scheduling model for the multi-energy microgrid is constructed to obtain real-time sensing data of the target variables. The deterministic scheduling model includes electricity load and heat load, and the target variables include electricity demand, heat demand, photovoltaic output, ambient temperature, and irradiance. The affine form of the source-load and the source-load noise element coefficients are determined. Under the constraint of the affine form constraint, an affine optimization model for the multi-energy microgrid is constructed to obtain the affine optimization interval of the target variables. The source-load affine form includes an affine form considering the nonlinear correlation of source-load and an affine form of the decision variables. Using the real-time sensing data of the target variables within the target scheduling time, the target noise element corresponding to the target variable is calculated. Based on the affine optimization interval and the target noise element, the affine form of the decision variables within the target scheduling time is calculated to obtain the real-time scheduling trajectory of the multi-energy microgrid. This application, by determining the affine form of source-load, including the affine form considering the nonlinear correlation between source and load, can deeply capture the complex relationship between sources (such as energy supply like photovoltaic power output) and loads (energy consumption like electricity and heat demand). Traditional scheduling methods often ignore this correlation, but this application incorporates it, enabling the scheduling model to more accurately predict and respond to changes in source and load, thus improving the model's fit to actual operating conditions. Furthermore, by constructing a multi-energy microgrid affine optimization model and obtaining the affine optimization interval of the target variable, a flexible range is provided for scheduling. In actual operation, due to the existence of various uncertainties, the target variable tends to... The variables will fluctuate within a certain range, and the affine optimization interval can accommodate these uncertainties, enabling scheduling decisions to remain reasonable and effective in the face of variable fluctuations. This enhances the scheduling system's ability to withstand uncertainties and improves scheduling robustness. At the same time, by using real-time sensing data within the target scheduling time to calculate the target noise element, and by calculating the affine form of the decision variables based on the affine optimization interval and the target noise element, the real-time scheduling trajectory can be obtained. This allows for dynamic adjustment of the scheduling strategy according to real-time changes during actual operation, timely response to various emergencies and uncertainties, and ensures stable and reliable operation of the multi-energy microgrid under different operating conditions.

[0101] In one possible implementation, the objective function of the multi-energy microgrid deterministic scheduling model is:

[0102] in, It is a minimum value function. For unit fuel cost, for The electrical power produced by the gas turbine during the period For the power generation efficiency of gas turbines, for The heat output of the gas-fired boiler during the period For the heat production efficiency of gas-fired boilers, To optimize the scheduling time interval, The unit price of electricity purchased. for Electricity purchases for multi-energy microgrids during different time periods This refers to the unit price of electricity sold. for Electricity sales of multi-energy microgrids during different time periods This refers to the total time period.

[0103] In one possible implementation, the objective function of the multi-energy microgrid affine optimization model is:

[0104] in, It is a minimum value function. These are the weighting coefficients. For unit fuel cost, From 0 to The electrical power produced by the gas turbine during the period For the power generation efficiency of gas turbines, From 0 to The heat output of the gas-fired boiler during the period For the heat production efficiency of gas-fired boilers, To optimize the scheduling time interval, The unit price of electricity purchased. From 0 to Electricity purchases for multi-energy microgrids during different time periods This refers to the unit price of electricity sold. From 0 to Electricity sales of multi-energy microgrids during different time periods for arrive The electrical power produced by the gas turbine during the period for arrive The heat output of the gas-fired boiler during the period for arrive Electricity purchases for multi-energy microgrids during different time periods for arrive Electricity sales of multi-energy microgrids during different time periods Total time period This is a type of uncertainty.

[0105] In one possible implementation, the interval determination module can be used for: The affine optimization interval of the target variable is calculated using the formula for calculating the upper and lower limits of the affine variable. The formula for calculating the upper and lower limits of the affine variable is as follows:

[0106] in, The upper bound of the affine variable, Let be the lower bound of the affine variable. Let be the radius of the affine variable.

[0107] In one possible implementation, the trajectory generation module can specifically be used for: The initial time of the target scheduling time is taken as the current time; Acquire real-time sensing data of the target variable at the current moment, and calculate the target noise element of the target variable at the current moment; Determine whether the target noise element at the current moment is within the affine optimization interval; If the target noise element at the current moment is within the affine optimization interval, then the affine form of the decision variable at the current moment is calculated using the target noise element at the current moment. Determine if the current time is less than the target scheduling time; If the current time is less than the target scheduling time, then update the current time by adding 1 to the current time, return to obtain the real-time perception data of the target variable at the current time, and calculate the target noise element of the target variable at the current time and continue to execute the step. If the current time is not less than the target scheduling time, then the real-time scheduling trajectory of the multi-energy microgrid is generated using the affine form of the decision variables at the current time.

[0108] In one possible implementation, the trajectory generation module can also be used for: Real-time sensing data of current electricity demand, heat demand, photovoltaic output, ambient temperature and irradiance are obtained respectively. Using real-time sensing data of the ambient temperature at the current moment, the first noise element is calculated. The first noise element is the noise element corresponding to the uncertainty of the ambient temperature. Using real-time sensing data of irradiance at the current moment, the second noise element is calculated. The second noise element is the noise element corresponding to the uncertainty of irradiance. Using the first and second noise elements, the third, fourth, and fifth noise elements are calculated. The third noise element is the noise element corresponding to the uncertainty of electricity demand; the fourth noise element is the noise element corresponding to the uncertainty of heat demand; and the fifth noise element is the noise element corresponding to the uncertainty of photovoltaic output. Using the first noise element, the second noise element, the third noise element, the fourth noise element, the fifth noise element, real-time sensing data of electricity demand, real-time sensing data of heat demand, and real-time sensing data of photovoltaic output, a new noise element is calculated by reverse deduction.

[0109] In one possible implementation, the trajectory generation module can also be used for: The new noise element is calculated using a reverse formula, which is:

[0110] in, , , All New noise elements during the time period for Real-time sensing data of electricity demand during different time periods. for, for, for The third noise element in the time period, for Real-time sensing data of time-of-day heat demand, for, for, for This is the fourth noise element in the time period. for Real-time sensing data of photovoltaic output during different time periods for, for, for The fifth noise element in the time period, , , All New noise element coefficients for the time period This refers to the current moment.

[0111] In one possible implementation, the device may further include a first determination module, which may be used to: If the target noise element at the current moment is not within the affine optimization interval, then determine whether the target noise element at the current moment is greater than the upper limit of the affine optimization interval. If the target noise element at the current moment is greater than the upper limit of the affine optimization interval, then update the target noise element at the current moment using unit 1, and use the updated target noise element to calculate the affine form of the decision variable at the current moment.

[0112] In one possible implementation, the device may further include a second determining module, which may be used to: If the target noise element at the current moment is not within the affine optimization interval, then determine whether the target noise element at the current moment is less than the lower limit of the affine optimization interval. If the target noise element at the current moment is less than the lower limit of the affine optimization interval, then the target noise element at the current moment is updated with a negative value of unit 1, and the affine form of the decision variable at the current moment is calculated using the updated target noise element.

[0113] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0114] Those skilled in the art will recognize that the templates, units, and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0115] If the module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the above embodiments of the multi-energy microgrid real-time optimization scheduling method considering source-load correlation. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0116] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A real-time optimization scheduling method for multi-energy microgrids considering source-load correlation, characterized in that, include: Under the constraints of the first constraint, a deterministic scheduling model for a multi-energy microgrid is constructed to obtain real-time sensing data of the target variables. The deterministic scheduling model for the multi-energy microgrid includes power load and heat load, and the target variables include electricity demand, heat demand, photovoltaic output, ambient temperature and irradiance. The affine form of the source load and the noise element coefficients of the source load are determined, and under the constraint of the affine form, a multi-energy microgrid affine optimization model is constructed to obtain the affine optimization interval of the target variable. The affine form of the source load includes the affine form considering the nonlinear correlation of the source load and the affine form of the decision variable. Using real-time sensing data of the target variable within the target scheduling time, the target noise element corresponding to the target variable is calculated. Based on the affine optimization interval and the target noise element, the affine form of the decision variable within the target scheduling time is calculated to obtain the real-time scheduling trajectory of the multi-energy microgrid.

2. The real-time optimization scheduling method for multi-energy microgrids considering source-load correlation according to claim 1, characterized in that, The objective function of the multi-energy microgrid deterministic scheduling model is: in, It is a minimum value function. For unit fuel cost, for The electrical power produced by the gas turbine during the period For the power generation efficiency of gas turbines, for The heat output of the gas-fired boiler during the period For the heat production efficiency of gas-fired boilers, To optimize the scheduling time interval, The unit price of electricity purchased. for Electricity purchases for multi-energy microgrids during different time periods This refers to the unit price of electricity sold. for Electricity sales of multi-energy microgrids during different time periods This refers to the total time period.

3. The real-time optimization scheduling method for multi-energy microgrids considering source-load correlation according to claim 1, characterized in that, The objective function of the multi-energy microgrid affine optimization model is: in, It is a minimum value function. These are the weighting coefficients. For unit fuel cost, From 0 to The electrical power produced by the gas turbine during the period For the power generation efficiency of gas turbines, From 0 to The heat output of the gas-fired boiler during the period For the heat production efficiency of gas-fired boilers, To optimize the scheduling time interval, The unit price of electricity purchased. From 0 to Electricity purchases for multi-energy microgrids during different time periods This refers to the unit price of electricity sold. From 0 to Electricity sales of multi-energy microgrids during different time periods for arrive The electrical power produced by the gas turbine during the period for arrive The heat output of the gas-fired boiler during the period for arrive Electricity purchases for multi-energy microgrids during different time periods for arrive Electricity sales of multi-energy microgrids during different time periods Total time period This is a type of uncertainty.

4. The real-time optimization scheduling method for multi-energy microgrids considering source-load correlation according to claim 1, characterized in that, The process of obtaining the affine optimization interval of the target variable includes: The affine optimization interval of the target variable is calculated using the formula for calculating the upper and lower limits of the affine variable. in, The upper bound of the affine variable, Let be the lower bound of the affine variable. Let be the radius of the affine variable.

5. The real-time optimization scheduling method for multi-energy microgrids considering source-load correlation according to claim 1, characterized in that, The process involves using real-time sensing data of the target variable within the target scheduling time to calculate the target noise element corresponding to the target variable, and based on the affine optimization interval and the target noise element, calculating the affine form of the decision variable within the target scheduling time to obtain the real-time scheduling trajectory of the multi-energy microgrid, including: The initial time of the target scheduling time is taken as the current time; Obtain the real-time sensing data of the target variable at the current moment, and calculate the target noise element of the target variable at the current moment; Determine whether the target noise element at the current moment is within the affine optimization interval; If the target noise element at the current moment is within the affine optimization interval, then the affine form of the decision variable at the current moment is calculated using the target noise element at the current moment. Determine whether the current time is less than the target scheduling time; If the current time is less than the target scheduling time, then update the current time by adding 1 to the current time, and return to the step of obtaining the real-time sensing data of the target variable at the current time and calculating the target noise element of the target variable at the current time to continue execution; If the current time is not less than the target scheduling time, then the real-time scheduling trajectory of the multi-energy microgrid is generated using the affine form of the decision variables at the current time.

6. The real-time optimization scheduling method for multi-energy microgrids considering source-load correlation according to claim 5, characterized in that, The step of acquiring the real-time sensing data of the target variable at the current moment and calculating the target noise element of the target variable at the current moment includes: Real-time sensing data of current electricity demand, heat demand, photovoltaic output, ambient temperature and irradiance are obtained respectively. Using real-time sensing data of the ambient temperature at the current moment, a first noise element is calculated, which is the noise element corresponding to the uncertainty of the ambient temperature. Using real-time sensing data of irradiance at the current moment, a second noise element is calculated. The second noise element is the noise element corresponding to the uncertainty of irradiance. Using the first noise element and the second noise element, calculate the third noise element, the fourth noise element and the fifth noise element. The third noise element is the noise element corresponding to the uncertainty of electricity demand; the fourth noise element is the noise element corresponding to the uncertainty of heat demand; and the fifth noise element is the noise element corresponding to the uncertainty of photovoltaic output. A new noise element is calculated by reverse engineering using the first noise element, the second noise element, the third noise element, the fourth noise element, the fifth noise element, the real-time sensing data of electricity demand, the real-time sensing data of heat demand, and the real-time sensing data of photovoltaic output.

7. The real-time optimization scheduling method for multi-energy microgrids considering source-load correlation according to claim 6, characterized in that, The process of using the first noise element, the second noise element, the third noise element, the fourth noise element, the fifth noise element, the real-time sensing data of electricity demand, the real-time sensing data of heat demand, and the real-time sensing data of photovoltaic output to calculate a new noise element includes: The new noise element is calculated using a reverse calculation formula, which is: in, , , All New noise elements during the time period for Real-time sensing data of electricity demand during different time periods. for, for, for The third noise element in the time period, for Real-time sensing data of time-of-day heat demand, for, for, for This is the fourth noise element in the time period. for Real-time sensing data of photovoltaic output during different time periods for, for, for The fifth noise element in the time period, , , All New noise element coefficients for the time period This refers to the current moment.

8. The real-time optimization scheduling method for multi-energy microgrids considering source-load correlation according to claim 5, characterized in that, After determining whether the target noise element at the current moment is within the affine optimization interval, the method further includes: If the target noise element at the current moment is not within the affine optimization interval, then determine whether the target noise element at the current moment is greater than the upper limit of the affine optimization interval. If the target noise element at the current moment is greater than the upper limit of the affine optimization interval, then the target noise element at the current moment is updated using unit 1, and the affine form of the decision variable at the current moment is calculated using the updated target noise element.

9. The real-time optimization scheduling method for multi-energy microgrids considering source-load correlation according to claim 5, characterized in that, After determining whether the target noise element at the current moment is within the affine optimization interval, the method further includes: If the target noise element at the current moment is not within the affine optimization interval, then determine whether the target noise element at the current moment is less than the lower limit of the affine optimization interval. If the target noise element at the current moment is less than the lower limit of the affine optimization interval, then the target noise element at the current moment is updated with a negative value of unit 1, and the affine form of the decision variable at the current moment is calculated using the updated target noise element.

10. A real-time optimization scheduling device for multi-energy microgrids considering source-load correlation, characterized in that, include: The data acquisition module is used to construct a deterministic scheduling model for a multi-energy microgrid under the constraints of the first constraint condition, and to acquire real-time sensing data of the target variables. The deterministic scheduling model for the multi-energy microgrid includes power load and heat load, and the target variables include electricity demand, heat demand, photovoltaic output, ambient temperature and irradiance. The interval determination module is used to determine the affine form of the source load and the noise element coefficient of the source load, and under the constraint of the affine form constraint, construct a multi-energy microgrid affine optimization model to obtain the affine optimization interval of the target variable. The source load affine form includes the affine form considering the nonlinear correlation of the source load and the affine form of the decision variable. The trajectory generation module is used to calculate the target noise element corresponding to the target variable using real-time sensing data of the target variable within the target scheduling time, and calculate the affine form of the decision variable within the target scheduling time based on the affine optimization interval and the target noise element, so as to obtain the real-time scheduling trajectory of the multi-energy microgrid.

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