Multi-energy system day-ahead scheduling method and device considering comprehensive demand response, equipment, storage medium and program product

Abstract

The invention relates to the technical field of power grid dispatching, and provides a multi-energy system day-ahead dispatching method and device considering comprehensive demand response, equipment, a storage medium and a program product, which can reflect real physical network constraints in a multi-energy scene, so that a dispatching strategy is more practical. The method comprises the following steps: acquiring a power-related mathematical model and a voltage-related mathematical model of a multi-energy micro-grid system; acquiring a heat supply network constraint mathematical model; based on the high-dimensional linear polyhedron uncertainty set, the power correlation mathematical model, the voltage correlation mathematical model and the heat supply network constraint mathematical model, obtaining a robust optimization scheduling model of the multi-energy micro-grid system; and based on historical data and parameters of the multi-energy micro-grid system, solving the robust optimization scheduling model to obtain a day-ahead scheduling result of the multi-energy micro-grid system.

Description

Technical Field

[0001] The present application relates to the technical field of power grid dispatching, and in particular to a method, apparatus, computer equipment, storage medium and computer program product for day-ahead dispatching of a multi-energy system taking into account comprehensive demand response. Background Art

[0002] Driven by energy transformation and the "dual carbon" goal, the proportion of renewable energy in the energy structure has increased year by year, becoming an important means to alleviate the energy crisis and reduce carbon emissions. As a new energy management structure that integrates distributed generation and renewable energy, microgrids play an increasingly important role in the transformation from traditional power grids to smart grids. Multi-energy microgrid systems can further improve the utilization rate of renewable energy and the economy of the system by integrating multiple energy forms such as electricity and heat, and implementing comprehensive demand response and hierarchical energy utilization. However, the uncertainty of renewable energy and load has a negative impact on the economy and low-carbon optimization operation of multi-energy microgrid systems. In order to meet this challenge, it is of great theoretical and practical significance to study the optimal scheduling strategy of multi-energy microgrid systems and improve the flexibility and robustness of the system.

[0003] The optimal dispatching strategy of microgrids has become a research hotspot, and various optimization methods have been proposed to cope with the uncertainty of renewable energy and load. However, most of the existing dispatching strategies ignore the physical network constraints, such as the operating limitations of the power grid and heat network, resulting in low practicality. Summary of the invention

[0004] Based on this, it is necessary to provide a multi-energy system day-ahead scheduling method, device, computer equipment, storage medium and computer program product that considers comprehensive demand response to address the above technical issues.

[0005] The present application provides a multi-energy system day-ahead scheduling method considering comprehensive demand response, the method comprising:

[0006] Obtain power-related mathematical models and voltage-related mathematical models of multi-energy microgrid systems;

[0007] Obtaining the mathematical model of heat network constraints;

[0008] Based on the high-dimensional linear polyhedron uncertainty set, the power-related mathematical model, the voltage-related mathematical model and the heat network constraint mathematical model, a robust optimization scheduling model of the multi-energy microgrid system is obtained;

[0009] Based on the historical data and parameters of the multi-energy microgrid system, the robust optimization scheduling model is solved to obtain the day-ahead scheduling result of the multi-energy microgrid system.

[0010] In one embodiment, based on the historical data and parameters of the multi-energy microgrid system, the robust optimization scheduling model is solved to obtain the day-ahead scheduling result of the multi-energy microgrid system, including:

[0011] According to the duality theory, the robust optimization scheduling model is transformed from a double-layer form to a single-layer form;

[0012] Based on the historical data and parameters of the multi-energy microgrid system, a single-layer robust optimization scheduling model is solved to obtain the day-ahead scheduling result of the multi-energy microgrid system.

[0013] In one embodiment, based on the historical data and parameters of the multi-energy microgrid system, a single-layer robust optimization scheduling model is solved to obtain a day-ahead scheduling result of the multi-energy microgrid system, including:

[0014] Based on the historical data and parameters of the multi-energy microgrid system, a single-layer robust optimization scheduling model is solved using a solver to obtain a day-ahead scheduling result of the multi-energy microgrid system.

[0015] In one embodiment, the power-related mathematical model includes: a bus power balance formula, a line power relationship formula, a bus voltage difference formula, and a bus voltage constraint formula of a multi-energy microgrid system.

[0016] In one embodiment, the bus power balance formula includes a bus active power balance formula and a bus reactive power balance formula;

[0017] The line power relationship formula includes a line active power relationship formula and a line reactive power relationship formula.

[0018] In one embodiment, the heat network constraint mathematical model includes: a heat network pipe temperature range constraint formula, a heat load calculation formula, a heat balance formula, a pipe temperature correlation formula, a flow balance formula and a temperature change formula.

[0019] The present application provides a multi-energy system day-ahead scheduling device considering comprehensive demand response, the device comprising:

[0020] A microgrid model acquisition module is used to acquire a power-related mathematical model and a voltage-related mathematical model of a multi-energy microgrid system;

[0021] A heat network model acquisition module is used to obtain a heat network constraint mathematical model;

[0022] A scheduling model acquisition module, used to obtain a robust optimization scheduling model of the multi-energy microgrid system based on a high-dimensional linear polyhedron uncertainty set, the power-related mathematical model, the voltage-related mathematical model and the heat network constraint mathematical model;

[0023] A solution module is used to solve the robust optimization scheduling model based on the historical data and parameters of the multi-energy microgrid system to obtain the day-ahead scheduling result of the multi-energy microgrid system.

[0024] The present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the above method.

[0025] The present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to execute the above method.

[0026] The present application provides a computer program product, on which a computer program is stored, and the computer program is executed by a processor to perform the above method.

[0027] The above-mentioned multi-energy system day-ahead scheduling method, device, computer equipment, storage medium and computer program product considering comprehensive demand response obtains the power-related mathematical model and voltage-related mathematical model of the multi-energy microgrid system; obtains the heat network constraint mathematical model; based on the high-dimensional linear polyhedron uncertainty set, the power-related mathematical model, the voltage-related mathematical model and the heat network constraint mathematical model, obtains the robust optimization scheduling model of the multi-energy microgrid system; based on the historical data and parameters of the multi-energy microgrid system, the robust optimization scheduling model is solved to obtain the day-ahead scheduling result of the multi-energy microgrid system. The present application provides a robust optimization scheduling model for a multi-energy microgrid system based on an actual multi-energy microgrid system and heat network constraints, which can reflect the real physical network constraints in a multi-energy scenario, making the scheduling strategy more practical; and the present application provides a more sophisticated high-dimensional linear polyhedron uncertainty set to characterize the uncertainty of renewable energy and load, which can effectively improve the robustness and cost of the microgrid scheduling strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0029] Figure 1 A schematic flow chart of a method for day-ahead scheduling of a multi-energy system considering comprehensive demand response in one embodiment;

[0030] Figure 2 A schematic diagram of the structure of a heat network in an embodiment;

[0031] Figure 3 It is a flowchart of a multi-energy system day-ahead scheduling method considering comprehensive demand response in another embodiment;

[0032] Figure 4 It is a structural block diagram of a multi-energy system day-ahead dispatching device considering comprehensive demand response in one embodiment;

[0033] Figure 5 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0035] The multi-energy system day-ahead dispatching method considering comprehensive demand response provided in the present application can be executed by a computer device, including Figure 1 Steps shown.

[0036] Step S101, obtaining a power-related mathematical model and a voltage-related mathematical model of a multi-energy microgrid system.

[0037] This application takes into account the constraints of the multi-energy microgrid system and the heat network, and uses linear power flow constraints to model the multi-energy microgrid system, so as to obtain the power-related mathematical model and voltage-related mathematical model of the multi-energy microgrid system.

[0038] Among them, the power-related mathematical models include: the bus power balance formula of the multi-energy microgrid system, the line power relationship formula, the bus voltage difference formula and the bus voltage constraint formula.

[0039] Furthermore, the bus power balance formula includes a bus active power balance formula and a bus reactive power balance formula; the line power relationship formula includes a line active power relationship formula and a line reactive power relationship formula.

[0040] The power-related mathematical models specifically include:

[0041]

[0042]

[0043]

[0044]

[0045] The voltage-related mathematical model specifically includes:

[0046]

[0047]

[0048] Among them, formula (1) is the bus active power balance formula, formula (2) is the bus reactive power balance formula, formula (3) is the line active power relationship formula, formula (4) is the line reactive power relationship formula, formula (5) is the bus voltage difference formula, and formula (6) is the bus voltage constraint formula.

[0049] Regarding formula (1), It represents the active power of bus m of the multi-energy microgrid system at time t. Represents the active load of bus m of the multi-energy microgrid system at time t. It represents the convertible power of bus m of the multi-energy microgrid system at time t. Represents the convertible load of bus m of the multi-energy microgrid system at time t. It represents the power generated by the combined heat and power (CHP) equipment at the bus m of the multi-energy microgrid system at time t. It represents the power generated by the solar photovoltaic (PV) device at the bus m of the multi-energy microgrid system at time t. It represents the power generated by the wind turbine (WT) device at the bus m of the multi-energy microgrid system at time t. It represents the active power exchanged between the multi-energy microgrid system and the main grid at time t.

[0050] Formula (1) is the active power balance formula of the bus at time t. When m≠0, the active power of the bus at time t is By active load Plus convertible electric and convertible load , minus the combined heat and power , Photovoltaic power and wind power When m = 0, the active power at time t is It is equal to the negative value of the active power exchanged between the multi-energy microgrid system and the main grid.

[0051] Regarding formula (2), It represents the reactive power of bus m of the multi-energy microgrid system at time t. Represents the reactive load of bus m of the multi-energy microgrid system at time t. It represents the reactive power exchanged between the multi-energy microgrid system and the main grid at time t.

[0052] Formula (2) is the reactive power balance formula of the bus at time t. When m≠0, the reactive power of the bus at time t is Equal to reactive load When m = 0, the reactive power of the bus at time t is It is equal to the negative value of the reactive power exchanged between the multi-energy microgrid system and the main grid.

[0053] Regarding formula (3), It represents the sum of the active powers of all lines flowing from bus m to bus n at time t. The summation symbol here accumulates the active powers of all lines connected to bus m and whose power flows from m to n. It represents the active power flowing from bus m to bus n at time t. It represents the active power injected into bus m at time t, that is, the external active power source flowing into bus m. Represents the active power of bus m at time t.

[0054] Formula (3) reflects the balance relationship of the line active power, that is, the external active power flowing into the bus minus the active power on the bus is equal to the total active power flowing out of the bus to other connected buses.

[0055] Regarding formula (4), It represents the sum of the reactive powers of all lines flowing from bus m to bus n at time t. The summation symbol here accumulates the reactive powers of all lines connected to bus m and whose power flows from m to n. It represents the reactive power flowing from bus m to bus n at time t. It represents the reactive power injected into bus m at time t, that is, the external reactive power source flowing into bus m. Represents the reactive power of bus m at time t.

[0056] Formula (4) reflects the balance relationship of the line reactive power, that is, the external reactive power flowing into the bus minus the reactive power on the bus is equal to the sum of the reactive power flowing out of the bus to other connected buses.

[0057] Regarding formula (5), Represents the voltage of bus h at time t. Represents the voltage of bus m at time t. Represents the line resistance between busbar h and busbar m. It represents the active power flowing from bus h to bus m at time t. Represents the line reactance between busbar h and busbar m. It represents the reactive power flowing from bus h to bus m at time t. Indicates standard voltage.

[0058] Formula (5) is the voltage difference formula between the two buses, which reflects the relationship between the voltage difference between the two buses and the line resistance, reactance, and the transmitted active and reactive power.

[0059] Regarding formula (6), Indicates the lower limit of bus m voltage. It represents the voltage of bus m at time t, which is the actual voltage value. Indicates the upper limit value of bus m voltage.

[0060] Formula (6) is the bus voltage constraint formula, which means that the actual voltage of bus m must be kept within the specified voltage upper and lower limits to ensure the safe and stable operation of the power grid.

[0061] Step S102, obtaining a heat network constraint mathematical model.

[0062] The heat network includes heat sources, loads, water supply pipes and return pipes, such as Figure 2 As shown in the figure. Based on the structure of the heat network, a heat network constraint mathematical model can be constructed. The heat network constraint mathematical model is mainly used to describe and analyze the mathematical expressions of the heat network system operation characteristics and restriction conditions.

[0063] Among them, the heat network constraint mathematical model includes: the heat network pipeline temperature range constraint formula, heat load calculation formula, heat balance formula, pipeline temperature correlation formula, flow balance formula and temperature change formula.

[0064]

[0065]

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[0068]

[0069]

[0070]

[0071]

[0072]

[0073]

[0074]

[0075] Formulas (7) and (8) are the pipe temperature range constraint formulas for the heat network. Formula (9) is the heat load calculation formula. Formula (10) is the heat balance formula. Formulas (11) to (14) are pipe temperature correlation formulas. Formula (15) is the flow balance formula. Formulas (16) and (17) are temperature change formulas.

[0076] Regarding formula (7), Represents the minimum temperature of the water supply pipe s at position i and time t. Represents the temperature of the water supply pipe s at position i and time t. Represents the maximum temperature of the water supply pipe s at position i and time t.

[0077] Formula (7) indicates that at position i and time t, the actual temperature of the water supply pipe s should be between the specified minimum and maximum temperatures.

[0078] Regarding formula (8), It represents the minimum temperature of the return pipe r at position i and time t. Represents the temperature of the return pipe r at position i and time t. Represents the maximum temperature of the return pipe r at position i and time t.

[0079] Formula (8) indicates that at position i and time t, the actual temperature of the return pipe r should be between the specified minimum and maximum temperatures.

[0080] Regarding formula (9), It represents the heat load at position i and time t, that is, the heat required or transferred by the heating network at position i and time t. Represents the specific heat capacity of water. represents the mass of water at position i. Represents the temperature of the water supply pipe s at position i and time t. Represents the temperature of the return pipe r at position i and time t.

[0081] Formula (9) calculates the heat load using the water mass, specific heat capacity, and the temperature difference between supply and return water.

[0082] Regarding formula (10), Represents the heat generated by the heat pump (HP) at time t. A heat pump is a device that converts low-temperature heat energy into high-temperature heat energy. Represents the heat generated by combined heat and power (CHP) at time t. Combined heat and power is an energy utilization method that produces both electricity and heat. It represents the heat load at position 1 and time t, that is, the heat required by the heating network at that position and time. Represents the convertible heat at time t. This part of heat may be energy that can be flexibly deployed or converted.

[0083] Formula (10) reflects the energy balance relationship between the total heat generated by the heat pump and the combined heat and power generation, the heat load at a specific location, and the convertible heat at time t.

[0084] Regarding formula (11), It represents the temperature at the entrance of the water supply pipe s when it flows from position i to position j at time t. Represents the temperature of the water supply pipe s at position i and time t.

[0085] Regarding formula (12), It represents the temperature at the outlet of the water supply pipe s when it flows from position i to position j at time t. Represents the temperature of the water supply pipe s at position j and time t.

[0086] Regarding formula (13), It represents the temperature at the entrance of the return pipe r when it flows from position i to position j at time t. Represents the temperature of the return pipe r at position j and time t.

[0087] Regarding formula (14), It represents the temperature at the outlet of the return pipe r when it flows from position i to position j at time t. Represents the temperature of the return pipe r at position i and time t.

[0088] Regarding formula (15), It is a summation symbol, which means summing all the situations where the flow goes from position i to position j. It represents the temperature at the outlet of the return pipe r when it flows from position i to position j at time t. Represents the amount of water flowing through the pipe from position i to position j at time t. Represents the temperature of the return pipe r at position i and time t.

[0089] Formula (15) describes the relationship between the sum of the product of the return water pipe outlet temperature and the corresponding water volume flowing from position i to position j at time t, and the product of the return water temperature at position i and the total water volume flowing out of position i, reflecting the heat balance or the relationship between flow and temperature.

[0090] Regarding formula (16), It represents the outlet temperature of the water supply pipe s when it flows from position i to position j at time t. It represents the temperature at the entrance of the water supply pipe s when it flows from position i to position j at time t. represents the ambient temperature at time t. e is a constant, approximately equal to 2.71828. λ represents the loss coefficient, reflecting the characteristics of heat loss in the pipeline. Represents the length of the pipe from position i to position j. Represents the specific heat capacity of water. Represents the amount of water flowing through the pipe from position i to position j at time t.

[0091] Formula (16) is used to calculate the outlet temperature of the water supply pipeline after considering the heat loss during the flow process. It is calculated based on the inlet temperature, ambient temperature, pipeline characteristic parameters (length, loss coefficient), and physical parameters of water (specific heat capacity) and flow rate.

[0092] Regarding formula (17), It represents the temperature at the outlet of the return pipe r when it flows from position i to position j at time t. It represents the temperature at the entrance of the return pipe r when it flows from position i to position j at time t. represents the ambient temperature at time t. e is a constant, approximately equal to 2.71828. λ represents the loss coefficient, reflecting the characteristics of heat loss in the pipeline. Represents the length of the pipe from position i to position j. Represents the specific heat capacity of water. Represents the amount of water flowing through the pipe from position i to position j at time t.

[0093] In addition, this application considers two types of comprehensive demand response: electric heat demand conversion and transferable load, as shown below:

[0094]

[0095]

[0096]

[0097]

[0098] It represents the convertible power of bus m of the multi-energy microgrid system at time t. It represents the convertible heat at time t, which may be energy that can be flexibly deployed or converted. γ is the conversion coefficient between electricity and heat demand.

[0099] Represents the active load of bus m of the multi-energy microgrid system at time t. It is the maximum convertible ratio of electric power.

[0100] Represents the convertible load of bus m of the multi-energy microgrid system at time t. is the transferable ratio of electricity. Represents the active load of bus m of the multi-energy microgrid system at time t.

[0101] Step S103, based on the high-dimensional linear polyhedron uncertainty set, the power-related mathematical model, the voltage-related mathematical model and the heat network constraint mathematical model, a robust optimization scheduling model of the multi-energy microgrid system is obtained.

[0102] This embodiment constructs a robust optimization scheduling model for a multi-energy microgrid system based on a high-dimensional linear polyhedron uncertainty set. In traditional robust optimization methods, the correlation between different uncertain variables is often ignored, which affects the effectiveness of the model. The minimum volume enclosing ellipsoid (MVEE) algorithm can construct a high-dimensional ellipsoid uncertainty set based on historical data and effectively characterize the correlation between uncertain variables. The high-dimensional ellipsoid uncertainty set can be obtained by solving the following equation:

[0103]

[0104] and Represent the central values ​​of the high-dimensional ellipsoid uncertainty set and uncertainty variable, is a constant, represents the volume of a high-dimensional sphere with unit radius, represents the uncertainty variable, Represents the matrix that determines the shape of a high-dimensional ellipsoid.

[0105] To simplify the computation and make the size of the uncertainty set easily adjustable, it can be converted into a high-dimensional linear polyhedral uncertainty set as follows:

[0106]

[0107] represents a high-dimensional linear polyhedral uncertainty set, represents the dimension of uncertain variables, express The eigenvalue variables of represents the scaling factor that controls the size of the uncertainty set, is an auxiliary variable, represents an orthogonal matrix, Indicated by is a diagonal matrix composed of the eigenvalues ​​of .

[0108] The robust optimization scheduling model of the multi-energy microgrid system is as follows:

[0109]

[0110] Represents a controllable variable.

[0111] The objective functions of the robust optimization scheduling model of the multi-energy microgrid system include: The objective function represents a two-level optimization problem, the outer layer is in the uncertainty set Inner maximization, the inner layer is the controllable variable Minimize the overall cost of time t from 1 to T Perform sum optimization.

[0112] The constraints of the robust optimization dispatch model of the multi-energy microgrid system include: This constraint defines the controllable variables Including combined heat and power , Convertible Electricity and transferable load .

[0113] The constraints of the robust optimization scheduling model of the multi-energy microgrid system also include: This constraint represents a set of uncertain variables, including active load , reactive load , Photovoltaic power , Wind power and heat load .

[0114] The constraints of the robust optimization scheduling model of the multi-energy microgrid system also include the aforementioned formulas (1) to (23).

[0115] Step S104, based on the historical data and parameters of the multi-energy microgrid system, the robust optimization scheduling model is solved to obtain the day-ahead scheduling result of the multi-energy microgrid system.

[0116] After constructing the aforementioned robust optimization scheduling model, the robust optimization scheduling model can be solved based on the historical data and parameters of the multi-energy microgrid system, and the day-ahead scheduling result of the multi-energy microgrid system can be obtained according to the solution result.

[0117] In the above-mentioned multi-energy system day-ahead scheduling method considering comprehensive demand response, the power-related mathematical model and voltage-related mathematical model of the multi-energy microgrid system are obtained; the heat network constraint mathematical model is obtained; based on the high-dimensional linear polyhedron uncertainty set, the power-related mathematical model, the voltage-related mathematical model and the heat network constraint mathematical model, the robust optimization scheduling model of the multi-energy microgrid system is obtained; based on the historical data and parameters of the multi-energy microgrid system, the robust optimization scheduling model is solved to obtain the day-ahead scheduling result of the multi-energy microgrid system. The present application provides a robust optimization scheduling model for a multi-energy microgrid system based on an actual multi-energy microgrid system and heat network constraints, which can reflect the real physical network constraints in a multi-energy scenario, making the scheduling strategy more practical; and, the present application provides a more sophisticated high-dimensional linear polyhedron uncertainty set to characterize the uncertainty of renewable energy and load, which can effectively improve the robustness and cost of the microgrid scheduling strategy.

[0118] In one embodiment, based on the historical data and parameters of the multi-energy microgrid system, the robust optimization scheduling model is solved to obtain the day-ahead scheduling results of the multi-energy microgrid system, including:

[0119] According to the duality theory, the robust optimization scheduling model is transformed from a two-layer form to a single-layer form. Based on the historical data and parameters of the multi-energy microgrid system, the single-layer robust optimization scheduling model is solved to obtain the day-ahead scheduling result of the multi-energy microgrid system.

[0120] The robust optimization scheduling model constructed above is a two-layer form. In some embodiments, the two-layer robust optimization scheduling model can be directly solved to obtain the day-ahead scheduling result of the multi-energy microgrid system. The day-ahead scheduling result of the multi-energy microgrid system includes the day-ahead scheduling plan of cogeneration and the results of the comprehensive demand response. Considering that it is relatively complicated to directly solve the two-layer robust optimization scheduling model. Therefore, this embodiment uses the duality theory to convert the robust optimization scheduling model from a two-layer form to a single-layer form, and solves the single-layer robust optimization scheduling model to reduce the computational complexity.

[0121] The single-layer robust optimization scheduling model is as follows:

[0122]

[0123] L represents the Lagrangian function. , , , , , , , and is the dual variable. , and is a real number, and the other dual variables are not less than 0.

[0124] Based on the unconstrained variables , , , , and Perform the merge as follows:

[0125]

[0126] In order to make L bounded, the following system of equations must hold:

[0127]

[0128] At this point, the two-layer robust optimization scheduling model can be converted into the following single-layer form:

[0129]

[0130] This embodiment uses the duality theory to transform the robust optimization scheduling model from a double-layer form to a single-layer form, solves the single-layer robust optimization scheduling model, and reduces the computational complexity.

[0131] In one embodiment, based on the historical data and parameters of the multi-energy microgrid system, a single-layer robust optimization scheduling model is solved to obtain the day-ahead scheduling results of the multi-energy microgrid system, including:

[0132] Based on the historical data and parameters of the multi-energy microgrid system, the single-layer robust optimization scheduling model is solved by the solver to obtain the day-ahead scheduling result of the multi-energy microgrid system.

[0133] After converting the robust optimization scheduling model into a single-layer form, the solver can be used to solve the single-layer robust optimization scheduling model to obtain the day-ahead scheduling result of the multi-energy microgrid system.

[0134] In order to better understand the above method, an application example of the multi-energy system day-ahead scheduling method considering comprehensive demand response in the present application is elaborated in detail below.

[0135] Traditional solutions have made some progress in dealing with the uncertainty of renewable energy and load. Among them, robust optimization (RO), stochastic optimization (SO) and distributed robust optimization (DRO) are the three main methods. Robust optimization seeks the optimal solution in the worst case, but the result is often too conservative, affecting the economy of the system. Stochastic optimization relies heavily on the probabilistic information of uncertain variables and calculates the optimal solution based on expectations, but this method is affected by the accuracy of predictions and brings high risks. Distributed robust optimization balances the advantages and disadvantages of robust optimization and stochastic optimization, but the computational complexity is high.

[0136] Traditional technologies usually oversimplify physical network constraints (such as power grid flow, heat network transmission, etc.) when modeling microgrids, causing the scheduling scheme to deviate from the actual working conditions. To address this problem, this application proposes a robust optimization scheduling model for multi-energy microgrid systems based on actual power grid and heat network constraints, which can reflect the real physical network constraints in multi-energy scenarios, making the scheduling strategy more practical.

[0137] Traditional uncertainty modeling methods are conservative or computationally complex. Common box uncertainty sets cannot reflect the correlation between uncertain variables and are difficult to control conservatism. To address this problem, this application designs a more sophisticated high-dimensional linear polyhedron uncertainty set to characterize the uncertainty of renewable energy and loads, which can effectively improve the robustness and economy of microgrid scheduling strategies.

[0138] Although traditional microgrid dispatching strategies take into account the uncertainty of renewable energy and load, they usually do not fully exploit the synergy of multiple demand response mechanisms, limiting the optimization potential of dispatching strategies. To address this problem, this application designs a robust optimization dispatching model for a multi-energy microgrid system that coordinates multiple demand response mechanisms, reduces operating costs through shared energy storage, and taps into the complementary potential of multiple energy sources.

[0139] This application example mainly includes Figure 3 The following is a detailed description of the implementation process of this application example.

[0140] First, this application takes into account the constraints of the multi-energy microgrid system and the heat network, and uses linear power flow constraints to model the multi-energy microgrid system, so as to obtain the power-related mathematical model and voltage-related mathematical model of the multi-energy microgrid system.

[0141] The power-related mathematical models specifically include:

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[0145]

[0146] The voltage-related mathematical model specifically includes:

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[0149] The heat network includes heat sources, loads, water supply pipes and return pipes, such as Figure 2 The heat network constraint mathematical model includes the following formula:

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[0161] Second, this application considers two types of comprehensive demand response: electric heat demand conversion and transferable load, as shown below:

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[0166] Third, this embodiment constructs a robust optimization scheduling model for a multi-energy microgrid system based on a high-dimensional linear polyhedron uncertainty set. In traditional robust optimization methods, the correlation between different uncertain variables is often ignored, which affects the effectiveness of the model. The minimum volume enclosing ellipsoid (MVEE) algorithm can construct a high-dimensional ellipsoid uncertainty set based on historical data and effectively characterize the correlation between uncertain variables. The high-dimensional ellipsoid uncertainty set can be obtained by solving the following equation:

[0167]

[0168] and Represent the central values ​​of the high-dimensional ellipsoid uncertainty set and uncertainty variable, is a constant, represents the volume of a high-dimensional sphere with unit radius, represents the uncertainty variable, Represents the matrix that determines the shape of a high-dimensional ellipsoid.

[0169] To simplify the computation and make the size of the uncertainty set easily adjustable, it can be converted into a high-dimensional linear polyhedral uncertainty set as follows:

[0170]

[0171] represents a high-dimensional linear polyhedral uncertainty set, represents the dimension of uncertain variables, express The eigenvalue variables of represents the scaling factor that controls the size of the uncertainty set, is an auxiliary variable, represents an orthogonal matrix, Indicated by is a diagonal matrix composed of the eigenvalues ​​of .

[0172] The robust optimization scheduling model of the multi-energy microgrid system is as follows:

[0173]

[0174] Represents a controllable variable.

[0175] The objective functions of the robust optimization scheduling model of the multi-energy microgrid system include: The objective function represents a two-level optimization problem, the outer layer is in the uncertainty set Inner maximization, the inner layer is the controllable variable Minimize the overall cost of time t from 1 to T Perform sum optimization.

[0176] The constraints of the robust optimization dispatch model of the multi-energy microgrid system include: This constraint defines the controllable variables Including combined heat and power , Convertible Electricity and transferable load .

[0177] The constraints of the robust optimization scheduling model of the multi-energy microgrid system also include: This constraint represents a set of uncertain variables, including active load , reactive load , Photovoltaic power , Wind power and heat load .

[0178] The constraints of the robust optimization scheduling model of the multi-energy microgrid system also include the aforementioned formulas (1) to (23).

[0179] Fourth, considering that it is relatively complicated to directly solve the two-layer robust optimization scheduling model, this embodiment uses the duality theory to transform the robust optimization scheduling model from a two-layer form to a single-layer form, and solves the single-layer robust optimization scheduling model to reduce the computational complexity.

[0180] The single-layer robust optimization scheduling model is as follows:

[0181]

[0182] L represents the Lagrangian function. , , , , , , , and is the dual variable. , and is a real number, and the other dual variables are not less than 0.

[0183] Based on the unconstrained variables , , , , and Perform the merge as follows:

[0184]

[0185] In order to make L bounded, the following system of equations must hold:

[0186]

[0187] At this point, the two-layer robust optimization scheduling model can be converted into the following single-layer form:

[0188]

[0189] After converting the robust optimization scheduling model into a single-layer form, the solver can be used to solve the single-layer robust optimization scheduling model to obtain the day-ahead scheduling result of the multi-energy microgrid system.

[0190] This application proposes a robust optimization scheduling strategy for microgrids that takes into account the actual power grid and heat network to improve the ability of multi-energy microgrid systems to cope with renewable energy and load uncertainties. This application provides a high-dimensional linear polyhedron uncertainty set to reflect the correlation between uncertain variables, reduce unnecessary conservatism in scheduling strategies, and improve decision-making quality. This application is based on the comprehensive demand response of multiple energy sources to further reduce the cost and carbon emissions of microgrid operation.

[0191] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0192] Based on the same inventive concept, the embodiment of the present application also provides a multi-energy system day-ahead scheduling device considering comprehensive demand response for implementing the multi-energy system day-ahead scheduling method considering comprehensive demand response involved in the above-mentioned. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the multi-energy system day-ahead scheduling device considering comprehensive demand response provided below can refer to the limitations of the multi-energy system day-ahead scheduling method considering comprehensive demand response above, and will not be repeated here.

[0193] In one embodiment, Figure 4As shown, a multi-energy system day-ahead dispatching device considering comprehensive demand response is provided, comprising:

[0194] A microgrid model acquisition module 401 is used to acquire a power-related mathematical model and a voltage-related mathematical model of a multi-energy microgrid system;

[0195] A heat network model acquisition module 402 is used to acquire a heat network constraint mathematical model;

[0196] A scheduling model acquisition module 403 is used to obtain a robust optimization scheduling model of the multi-energy microgrid system based on a high-dimensional linear polyhedron uncertainty set, the power-related mathematical model, the voltage-related mathematical model and the heat network constraint mathematical model;

[0197] The solving module 404 is used to solve the robust optimization scheduling model based on the historical data and parameters of the multi-energy microgrid system to obtain the day-ahead scheduling result of the multi-energy microgrid system.

[0198] In one embodiment, the solution module 404 is further configured to:

[0199] According to the duality theory, the robust optimization scheduling model is transformed from a two-layer form to a single-layer form; based on the historical data and parameters of the multi-energy microgrid system, the single-layer robust optimization scheduling model is solved to obtain the day-ahead scheduling result of the multi-energy microgrid system.

[0200] In one embodiment, the solution module 404 is further configured to:

[0201] Based on the historical data and parameters of the multi-energy microgrid system, a single-layer robust optimization scheduling model is solved by using a solver to obtain a day-ahead scheduling result of the multi-energy microgrid system.

[0202] In one embodiment, the power-related mathematical model includes: a bus power balance formula, a line power relationship formula, a bus voltage difference formula, and a bus voltage constraint formula of a multi-energy microgrid system.

[0203] In one embodiment, the bus power balance formula includes a bus active power balance formula and a bus reactive power balance formula;

[0204] The line power relationship formula includes a line active power relationship formula and a line reactive power relationship formula.

[0205] In one embodiment, the heat network constraint mathematical model includes: a heat network pipe temperature range constraint formula, a heat load calculation formula, a heat balance formula, a pipe temperature correlation formula, a flow balance formula and a temperature change formula.

[0206] Each module in the multi-energy system day-ahead dispatching device considering comprehensive demand response can be implemented in whole or in part by software, hardware and a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0207] In an exemplary embodiment, a computer device is provided, the internal structure of which can be shown as follows: Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data involved in the above method. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a multi-energy system day-ahead scheduling method considering comprehensive demand response is implemented.

[0208] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0209] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps in the above-mentioned various method embodiments when executing the computer program.

[0210] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0211] In one embodiment, a computer program product is provided, on which a computer program is stored, and the computer program is used by a processor to execute the steps in the above-mentioned various method embodiments.

[0212] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0213] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0214] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0215] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A multi-energy system day-ahead scheduling method considering comprehensive demand response, characterized in that: The method comprises: Obtain power-related mathematical models and voltage-related mathematical models of multi-energy microgrid systems; Obtaining the mathematical model of heat network constraints; Based on the high-dimensional linear polyhedron uncertainty set, the power-related mathematical model, the voltage-related mathematical model and the heat network constraint mathematical model, a robust optimization scheduling model of the multi-energy microgrid system is obtained; Based on the historical data and parameters of the multi-energy microgrid system, the robust optimization scheduling model is solved to obtain the day-ahead scheduling result of the multi-energy microgrid system.

2. The method according to claim 1, characterized in that Based on the historical data and parameters of the multi-energy microgrid system, the robust optimization scheduling model is solved to obtain the day-ahead scheduling result of the multi-energy microgrid system, including: According to the duality theory, the robust optimization scheduling model is transformed from a double-layer form to a single-layer form; Based on the historical data and parameters of the multi-energy microgrid system, a single-layer robust optimization scheduling model is solved to obtain the day-ahead scheduling result of the multi-energy microgrid system.

3. The method according to claim 2, characterized in that Based on the historical data and parameters of the multi-energy microgrid system, a single-layer robust optimization scheduling model is solved to obtain the day-ahead scheduling result of the multi-energy microgrid system, including: Based on the historical data and parameters of the multi-energy microgrid system, a single-layer robust optimization scheduling model is solved by using a solver to obtain a day-ahead scheduling result of the multi-energy microgrid system.

4. The method according to claim 1, characterized in that: The power-related mathematical model includes: a bus power balance formula, a line power relationship formula, a bus voltage difference formula and a bus voltage constraint formula of a multi-energy microgrid system.

5. The method according to claim 4, characterized in that The bus power balance formula includes a bus active power balance formula and a bus reactive power balance formula; The line power relationship formula includes a line active power relationship formula and a line reactive power relationship formula.

6. The method according to claim 1, characterized in that The heat network constraint mathematical model includes: a heat network pipeline temperature range constraint formula, a heat load calculation formula, a heat balance formula, a pipeline temperature correlation formula, a flow balance formula and a temperature change formula.

7. A multi-energy system day-ahead dispatching device considering comprehensive demand response, characterized in that: The device comprises: A microgrid model acquisition module is used to acquire a power-related mathematical model and a voltage-related mathematical model of a multi-energy microgrid system; A heat network model acquisition module is used to obtain a heat network constraint mathematical model; A scheduling model acquisition module, used to obtain a robust optimization scheduling model of the multi-energy microgrid system based on a high-dimensional linear polyhedron uncertainty set, the power-related mathematical model, the voltage-related mathematical model and the heat network constraint mathematical model; A solution module is used to solve the robust optimization scheduling model based on the historical data and parameters of the multi-energy microgrid system to obtain the day-ahead scheduling result of the multi-energy microgrid system.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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