A method, device, medium, and product for intelligent connected multi-microgrid mutual assistance and collaborative scheduling.

Abstract

This application discloses a method, device, medium, and product for intelligent connected multi-microgrid mutual assistance and coordinated scheduling, relating to the field of power system optimization scheduling. The method includes: dividing intelligent connected loads into variable power operating equipment and constant power operating equipment; constructing a source-load output uncertainty set and obtaining wind and solar power output limit scenarios based on microgrid source-load historical data and intelligent connected load models; modeling microgrid energy storage modules and introducing energy storage unpredictability constraints based on the source-load output uncertainty set and wind and solar power output limit scenarios; constructing an intelligent connected multi-microgrid mutual assistance and coordinated scheduling architecture; and constructing a day-ahead multi-microgrid mutual assistance and scheduling model using energy routers for power interconnection, with the objective function of minimizing the multi-microgrid operating coefficients. This application can improve the ability of multi-microgrids to absorb source-load uncertainties, ensuring stable and efficient operation of each microgrid while improving the reliability of microgrid operation.

Description

Technical Field

[0001] This application relates to the field of power system optimization dispatching, and in particular to a method, equipment, medium, and product for intelligent interconnected multi-microgrid mutual assistance and coordinated dispatching. Background Technology

[0002] With the acceleration of urbanization, intelligent connected transportation self-consistent road systems have emerged. These systems utilize autonomous driving and IoT technologies to achieve intelligent traffic flow management, traffic light optimization, accident warnings, and optimal route guidance, effectively improving efficiency and safety, reducing carbon emissions, and laying the foundation for the widespread application of intelligent transportation.

[0003] The rapid development of intelligent connected vehicle self-consistent road systems has also brought about a series of energy-related problems. The spatiotemporal characteristics of traffic flow distribution, leading to variations in load demand, and the volatility of renewable energy output, have posed significant challenges to the stable operation of intelligent connected multi-microgrids. Researching their optimal scheduling problem is crucial for achieving energy self-consistency in intelligent connected roads. However, existing research has not fully considered the impact of source-load uncertainty and the unpredictable nature of energy storage system decisions on the energy management of intelligent connected multi-microgrids. This has resulted in models that are unsolvable or whose proposed scheduling strategies are unusable in real-world scenarios.

[0004] Therefore, based on the above problems, there is an urgent need to provide a new intelligent connected multi-microgrid mutual assistance and collaborative scheduling method. Summary of the Invention

[0005] The purpose of this application is to provide a method, device, medium and product for intelligent connected multi-microgrid mutual assistance and coordinated scheduling, which can improve the ability of multi-microgrid to absorb source load uncertainty, and improve the reliability of microgrid operation while ensuring the stable and efficient operation of each microgrid.

[0006] To achieve the above objectives, this application provides the following solution:

[0007] Firstly, this application provides a method for intelligent connected multi-microgrid mutual assistance and coordinated scheduling, the method comprising:

[0008] The intelligent connected load is divided into variable power operating devices and constant power operating devices; and modeling is performed separately to obtain corresponding intelligent connected load models; the intelligent connected load models are used to predict load demand; the variable power operating devices include: roadside units, 5G base stations and edge computing devices; the constant power operating devices include: radar, broadcasting, fisheye cameras, PTZ cameras, serial port servers and management and control release devices;

[0009] Based on the historical data of microgrid source and load and the intelligent connected load model, an uncertain set of source and load output is constructed and the extreme scenarios of wind and solar power output are obtained; the historical data of microgrid source and load includes: wind and solar power output at historical moments and intelligent connected load data at historical moments;

[0010] Based on the uncertainty set of source and load output and the extreme scenario of wind and solar power output, the microgrid energy storage module is modeled and energy storage unexpected constraints are introduced to obtain the energy storage model;

[0011] Based on the intelligent connected load model, the source-load output uncertainty set, the extreme scenarios of wind and solar power output, and the energy storage model, an intelligent connected multi-microgrid mutual assistance and collaborative scheduling architecture is constructed; and with minimizing the multi-microgrid operating coefficient as the objective function, an energy router is used for power interconnection to construct a day-ahead multi-microgrid mutual assistance and scheduling model.

[0012] Based on the day-ahead multi-microgrid mutual assistance scheduling model, the mutual assistance power between microgrids and the optimal day-ahead scheduling scheme are obtained.

[0013] Optionally, the step of dividing the intelligent connected load into variable power operating equipment and constant power operating equipment, and modeling them separately to obtain corresponding intelligent connected load models, specifically includes:

[0014] Using formula Construct a roadside RSU intelligent connected load model;

[0015] Using formula Construct a smart network load model for 5G base stations;

[0016] Using formula E edge =P edge ×T edge Construct an intelligent network load model for edge computing devices;

[0017] A smart network load model for constant power operating equipment is constructed using the formula P = const;

[0018] Among them, P t RSU Let P be the operating power of the RSU at time t. active P represents the operating power of the RSU in active mode. ps The operating power of the RSU in energy-saving mode. δ represents static power consumption, which is a fixed constant; δ is the energy efficiency coefficient of the base station. The power consumption is dynamic and related to traffic flow. g max The maximum traffic flow for the base station. Let E be the base station's transmission power to a single car at time t. edge The power consumption of edge computing devices is determined by the power P of the edge devices. edgeMultiply by execution time T edge Calculated; P edge For fixed constants, parameters l i Let fedge be the task load of task i, P be the task processing speed, and const be a fixed constant.

[0019] Optionally, the step of constructing an uncertain set of source-load output and obtaining the extreme scenarios of wind and solar power output based on historical microgrid source-load data and intelligent connected load models specifically includes:

[0020] Using formula Construct a box-shaped uncertain set U1 of load demand; where P load For load demand, P load,t Let t be the load demand. This is the load demand forecast. This represents the upward fluctuation value of load demand. This represents the downward fluctuation value of load demand. For 0-1 variables, This indicates that load demand is fluctuating upwards. This indicates a downward fluctuation in load demand; both cannot be 1 simultaneously. As a load uncertainty control factor, the degree of robustness conservatism is controlled by changing its size;

[0021] Using formula Construct a rectangular uncertainty set U2; where P i P contributes to wind and solar power. i,t The output of wind and solar power at time t; Forecast values ​​for wind and solar power output; The upward fluctuation value of the wind and solar power output; The downward fluctuation value of the wind and solar power output; For 0-1 variables, This indicates that the power output of wind and solar power is fluctuating upwards. This indicates that the power output of wind and solar power is fluctuating downwards; both cannot be 1 at the same time. Control factors for uncertainties in wind and solar power output; a, c, b + b - d + d - P represents the fitting coefficients for the relevant rectangular uncertain set. pv，t For the photovoltaic output at time t, P wt，t Let pv be the wind power output at time t, pv be the photovoltaic power output, wt be the wind power output, and i be the index, which can be either pv or wt.

[0022] Using formula Construct a historical power output matrix ω for wind and solar power; where N wN represents the number of wind turbine units. p N represents the number of photovoltaic power plants. s The number of days for collecting historical data. The m-th day is numbered N w The output of the wind turbine at time T. The m-th day is numbered N p The output of the photovoltaic array at time T, ω m Historical matrix of scenic contribution on day m;

[0023] Based on the historical power output matrix of wind and solar power, the minimum volume envelope ellipsoid algorithm is used to determine the minimum volume ellipsoid.

[0024] The extreme scenario is obtained by using rotational and inverse transformations on the ellipsoid with the smallest volume.

[0025] Optionally, based on the uncertainty set of source load output and the extreme scenario of wind and solar power output, the microgrid energy storage module is modeled and energy storage unpredictability constraints are introduced to obtain the energy storage model, which specifically includes:

[0026] Using formula and Constrain the upper and lower limits of energy storage capacity;

[0027] Using formula Determine the energy storage capacity at time t The safe zone;

[0028] in, The upper and lower bounds of the energy storage capacity at time t are considered for unexpected events. The upper and lower bounds of the energy storage capacity at time t-1 are considered for unexpected events. The upper and lower bounds of energy storage capacity are not considered under unforeseen circumstances; P i ch,max P i dis,max For the maximum charge and discharge power of energy storage, f -1 () is an auxiliary function. For the wind power output of microgrid i at time t under scenario s, For the photovoltaic output of microgrid i at time t under scenario s, Let Δt be the maximum electrical power transmitted from microgrid i to microgrid j through the energy router, and Δt be the time interval. This is the lower bound of the load fluctuation value. This is the upper limit of the load fluctuation value. J represents the upper limit of the power interaction with the power grid, and J is the set of microgrids interconnected with microgrid i.

[0029] Optionally, using the formula and formula Determine the auxiliary function;

[0030] Where, η bt,ch η bt,dis y is the energy storage charging and discharging efficiency coefficient, τ is the unit time interval, y is the dependent variable, and x is the independent variable.

[0031] Optionally, based on the intelligent connected load model, the source-load output uncertainty set, the extreme scenarios of wind and solar output, and the energy storage model, an intelligent connected multi-microgrid mutual assistance and collaborative scheduling architecture is constructed; and with minimizing the multi-microgrid operating coefficient as the objective function, an energy router is used for power interconnection to construct a day-ahead multi-microgrid mutual assistance and scheduling model, specifically including:

[0032] Using formula u s,i,j,t -v s,i,j,t =i s,i,j,t -i s,i,j,t-1 and u s,i,j,t +v s,i,j,t ≤1 determines the energy exchange model between microgrids; where i s,i,j,t Let i be the interconnection state between micronet i and micronet j at time t in scenario s. s,i,j,t-1 Let i be the interconnection state between microgrid i and microgrid j at time t-1 in scenario s. These represent the minimum and maximum electrical power transmitted from microgrid i to microgrid j via the energy router, respectively. Let be the interaction power between microgrid i and microgrid j at time t in scenario s. A positive value indicates that microgrid i transmits power to microgrid j, while a negative value indicates that microgrid j transmits power to microgrid i. All are 0-1 variables, when At this time, it signifies that the interconnection state of microgrids i and j changes from 0 to 1. At this time, it signifies that the interconnection status of microgrids i and j changes from 1 to 0;

[0033] Using formula Determine the electrical balance constraints; among which, For the wind power output of microgrid i at time t under scenario s, For the photovoltaic output of microgrid i at time t under scenario s, These represent the energy storage charging and discharging power of microgrid i at time t under scenario s. Let i be the load demand of microgrid i at time t under scenario s. Let be the power of interaction between microgrid i and the power grid at time t under scenario s. Let i be the wind and solar power curtailment of microgrid i at time t under scenario s;

[0034] Using formula Determine the interaction power between microgrid i and the power grid in, This represents the maximum interaction power between the microgrid i and the power grid.

[0035] Using formula Determine the constraints for wind and solar power curtailment;

[0036] Using formula Determine the objective function; where C ah For the multi-microgrid operation coefficient, β represents the interconnection coefficient, energy router operation coefficient, microgrid-grid interaction coefficient, wind and solar curtailment coefficient, and energy storage operation coefficient of microgrid i under the extreme wind and solar power output scenario s, obtained through a high-dimensional ellipsoid set; S and I are the representative scenario set and microgrid group set, respectively; s For the probabilities of each representative scenario, To account for the maximum energy storage capacity after considering unforeseen circumstances, The minimum capacity for energy storage after taking into account unforeseen circumstances.

[0037] Secondly, this application provides an intelligent connected multi-microgrid mutual assistance and collaborative scheduling device, the intelligent connected multi-microgrid mutual assistance and collaborative scheduling device comprising:

[0038] The intelligent connected load model construction module is used to divide intelligent connected loads into variable power operating devices and constant power operating devices; and to model them separately to obtain corresponding intelligent connected load models; the intelligent connected load models are used to predict load demand; the variable power operating devices include: roadside units, 5G base stations and edge computing devices; the constant power operating devices include: radar, broadcasting, fisheye cameras, dome cameras, serial port servers and management and control release devices;

[0039] The uncertainty set determination module is used to construct the uncertainty set of source and load output and obtain the extreme scenarios of wind and solar power output based on the historical data of microgrid source and load and the intelligent connected load model; the historical data of microgrid source and load includes: the wind and solar power output at historical moments and the intelligent connected load data at historical moments;

[0040] The energy storage model determination module is used to model the microgrid energy storage module based on the source load output uncertainty set and the wind and solar power output limit scenario, and introduce energy storage unpredictability constraints to obtain the energy storage model;

[0041] The module for constructing a multi-microgrid mutual assistance scheduling model is used to build a smart grid multi-microgrid mutual assistance and collaborative scheduling architecture based on the smart grid load model, the source-load output uncertainty set, the extreme scenarios of wind and solar output, and the energy storage model; and with minimizing the multi-microgrid operating coefficient as the objective function, it uses energy routers for power interconnection to construct a day-ahead multi-microgrid mutual assistance scheduling model.

[0042] The scheduling optimization module is used to obtain the mutual assistance power between microgrids and the optimal day-ahead scheduling scheme based on the day-ahead multi-microgrid mutual assistance scheduling model.

[0043] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the intelligent connected multi-microgrid mutual assistance and coordinated scheduling method described above.

[0044] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned intelligent connected multi-microgrid mutual assistance and cooperative scheduling method.

[0045] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned intelligent connected multi-microgrid mutual assistance and collaborative scheduling method.

[0046] According to the specific embodiments provided in this application, this application has the following technical effects:

[0047] This application provides a method, device, medium, and product for intelligent connected multi-microgrid mutual assistance and coordinated scheduling. First, intelligent connected loads are categorized into variable power operation and constant power operation. Considering the coupling relationship between the operating power of intelligent connected loads and traffic flow within the microgrid, the operating characteristics of each intelligent connected load are modeled in detail. This allows the load demand of the intelligent connected multi-microgrid to change with traffic flow, achieving a certain degree of coupling between the transportation network and the power grid, which is more conducive to the intelligent management of the intelligent connected system. Second, historical source-load data of each microgrid are collected to construct an uncertain set of source-load output and obtain the extreme scenarios of wind and solar power output. Then, the microgrid energy storage module is modeled, and unexpected constraints are introduced to ensure the solvability of the model. Finally, an intelligent connected multi-microgrid mutual assistance and coordinated scheduling architecture is constructed, and power interconnection between microgrids is achieved through energy routers, establishing a day-ahead multi-microgrid coordinated scheduling model. This application provides a detailed characterization of source-load uncertainty and uses the extreme scenario of wind and solar power output as a representative scenario. Combined with the unpredictable constraints of energy storage decision-making, it ensures the effectiveness of the established day-ahead multi-microgrid mutual assistance scheduling model, greatly improving the ability of multi-microgrids to absorb source-load uncertainty. While ensuring the stable and efficient operation of each microgrid, it also greatly improves the reliability of microgrid operation. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments 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.

[0049] Figure 1 This is a schematic diagram of a method for intelligent connected multi-microgrid mutual assistance and coordinated scheduling in one embodiment of this application;

[0050] Figure 2 Diagram of intelligent connected multi-micronet structure;

[0051] Figure 3 Flowchart for capturing extreme scenes of landscape photography. Detailed Implementation

[0052] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0053] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0054] In one exemplary embodiment, such as Figure 1 As shown, a method for intelligent connected multi-microgrid mutual assistance and coordinated scheduling is provided, which includes the following steps 201 to 208. Wherein:

[0055] S101, the intelligent connected load is divided into variable power operating devices and constant power operating devices; and modeling is performed separately to obtain the corresponding intelligent connected load models; the intelligent connected load models are used to predict load demand; the variable power operating devices include: roadside units (RSUs), 5G base stations and edge computing devices; the constant power operating devices include: radar, broadcasting, fisheye cameras, dome cameras, serial port servers and management and control release devices;

[0056] like Figure 2As shown, intelligent connected loads are diverse in type and function, and can be broadly categorized into four main modules: sensing, positioning, guidance, and communication. The sensing module is primarily responsible for collecting information about the surrounding environment, the positioning module provides real-time location information, the guidance module provides navigation and driving instructions to traffic participants, and the communication module enables information transmission between devices. Considering the close coupling between the operating power of some devices and traffic flow, to more precisely characterize intelligent connected loads, they are divided into variable power loads and constant power loads based on their operating power characteristics. Variable power loads correspond to variable power operating devices (such as RSUs, 5G base stations, and edge computing devices), while constant power loads correspond to constant power operating devices (such as radar, broadcasting, fisheye cameras, dome cameras, serial servers, and management and control publishing devices).

[0057] S101 specifically includes:

[0058] In order to reduce unnecessary power consumption during the operation of a large number of RSUs, the formula is used. Construct a roadside RSU intelligent connected load model;

[0059] Using formula Construct a smart network load model for 5G base stations;

[0060] Using formula E edge =P edge ×T edge Construct an intelligent network load model for edge computing devices;

[0061] Traffic flow has little impact on the operating power of radar, broadcasting, fisheye cameras, PTZ cameras, serial servers, and control and release equipment. Therefore, the formula P=const is used to construct an intelligent network load model for constant power operating equipment.

[0062] Among them, P t RSU Let P be the operating power of the RSU at time t. active P represents the operating power of the RSU in active mode. ps The operating power of the RSU in energy-saving mode. δ represents static power consumption, which is a fixed constant; δ is the energy efficiency coefficient of the base station. The power consumption is dynamic and related to traffic flow. g max The maximum traffic flow for the base station. Let E be the base station's transmission power to a single car at time t. edge The power consumption of edge computing devices is determined by the power P of the edge devices. edge Multiply by execution time T edge Calculated; P edge For fixed constants, parameters l i Let fedge be the task load of task i, P be the task processing speed, and const be a fixed constant.

[0063] S102, Based on the microgrid source-load historical data and the intelligent connected load model, construct the source-load output uncertainty set and obtain the wind and solar power output limit scenario; the microgrid source-load historical data includes: wind and solar power output at historical moments and intelligent connected load data at historical moments;

[0064] S102 specifically includes:

[0065] Using formula Construct a box-shaped uncertain set U1 of load demand; where P load For load demand, P load,t Let t be the load demand. This is the load demand forecast. This represents the upward fluctuation value of load demand. This represents the downward fluctuation value of load demand. For 0-1 variables, This indicates that load demand is fluctuating upwards. This indicates a downward fluctuation in load demand; both cannot be 1 simultaneously. As a load uncertainty control factor, the degree of robustness conservatism is controlled by changing its size;

[0066] Using formula A rectangular uncertainty set U2 is constructed, which takes into account the certain correlation between wind and solar power output and is constructed based on historical wind and solar data.

[0067] Among them, P i P contributes to wind and solar power. i,t The output of wind and solar power at time t; Forecast values ​​for wind and solar power output; The upward fluctuation value of the wind and solar power output; The downward fluctuation value of the wind and solar power output; For 0-1 variables, This indicates that the power output of wind and solar power is fluctuating upwards. This indicates that the power output of wind and solar power is fluctuating downwards; both cannot be 1 at the same time. Control factors for uncertainties in wind and solar power output; a, c, b + b - d + d - P represents the fitting coefficients for the relevant rectangular uncertain set. pv，t For the photovoltaic output at time t, P wt，tLet t represent the wind power output, pv represent photovoltaic power, wt represent wind power, and i represent the index, which can be either pv or wt.

[0068] To ensure the effectiveness of the constructed model, it must have a solution in all scenarios. Therefore, the extreme scenario of wind and solar power output is selected as a representative scenario to ensure that the obtained solution is feasible in all scenarios. Figure 3 As shown, the specific process is as follows:

[0069] S1, using the formula Construct a historical power output matrix ω for wind and solar power; where N w N represents the number of wind turbine units. p N represents the number of photovoltaic power plants. s The number of days for collecting historical data. The m-th day is numbered N w The output of the wind turbine at time T. The m-th day is numbered N p The output of the photovoltaic array at time T, ω m Historical matrix of scenic contribution on day m;

[0070] S2. Based on the historical wind and solar power output matrix, the minimum volume ellipsoid (MVEE) algorithm is used to determine the minimum volume ellipsoid.

[0071] Where Q is a symmetric positive definite matrix and q is the central vertex.

[0072] The expression for the minimum volume ellipsoid obtained by solving is:

[0073]

[0074] in, It is the set of real numbers.

[0075] S3, based on the ellipsoid with the smallest volume, uses rotational and inverse transformations to obtain the extreme scene.

[0076] By rotating the ellipsoid so that its axis of symmetry coincides with the coordinate axes, the coordinates of the ellipsoid's vertices can be obtained.

[0077] ω'=P×(ω-q);

[0078]

[0079] In the formula, P is a rotation matrix that satisfies Q = P T DP = P -1DP, D is a symmetric matrix composed of Q eigenvalues, ω' is the transformed wind and solar power output matrix; E'(·) is the transformed ellipsoidal expression; N represents the coordinates of the ellipsoid vertices after transformation; e Let λ be the number of vertices and λ1 be the first eigenvalue. For the (N) w +N p There are T eigenvalues, and diag is a diagonal matrix.

[0080] The extreme scenario is obtained through inverse transformation.

[0081]

[0082] S103. Based on the uncertainty set of source load output and the extreme scenario of wind and solar power output, the microgrid energy storage module is modeled and energy storage unpredictability constraints are introduced to obtain the energy storage model.

[0083] Unexpectedness refers to the fact that decisions at the current stage are only related to the realized values ​​of current and previous uncertainties. At present, most of the handling of uncertainties depends on the realization of future uncertainties for scheduling, which violates the timing logic of scheduling, thus making the model infeasible in some real-world scenarios. In order to consider the unexpectedness of energy storage decisions and ensure that the model has a solution, the conventional model of microgrid energy storage modules is established as follows:

[0084]

[0085] In the formula, Let η be the energy storage capacity at time t; bt,ch η bt,dis The energy storage charge / discharge efficiency coefficient; These represent the energy storage charging and discharging power; E i,init This represents the initial energy storage capacity. For energy storage charging and discharging state variables, If it indicates that the energy storage is in a charging state, then it indicates that the energy storage is in a discharging state. The upper and lower bounds of energy storage capacity are not considered under unforeseen circumstances; P i ch,max P i dis,max This represents the maximum charging and discharging power of the energy storage.

[0086] To address the problem of unsolvable issues caused by unpredictable energy storage decisions, an unpredictable constraint of adding energy storage is adopted. First, using the formula... and formula Determine the auxiliary function;

[0087] Where, ηbt,ch η bt,dis ρ represents the energy storage charging and discharging efficiency coefficient, y represents the unit time interval, and x represents the dependent variable.

[0088] Secondly, using the formula and Constrain the upper and lower limits of energy storage capacity;

[0089] Using formula Determine the energy storage capacity at time t The safe zone;

[0090] in, The upper and lower bounds of the energy storage capacity at time t are considered for unexpected events. The upper and lower bounds of the energy storage capacity at time t-1 are considered for unexpected events. The upper and lower bounds of energy storage capacity are not considered under unforeseen circumstances; P i ch,max P i dis,max For the maximum charge and discharge power of energy storage, f -1 () is an auxiliary function. For the wind power output of microgrid i at time t under scenario s, For the photovoltaic output of microgrid i at time t under scenario s, Let Δt be the maximum electrical power transmitted from microgrid i to microgrid j through the energy router, and Δt be the time interval. This is the lower bound of the load fluctuation value. This is the upper limit of the load fluctuation value. J represents the upper limit of the power interaction with the power grid, and J is the set of microgrids interconnected with microgrid i.

[0091] S104. Based on the intelligent connected load model, the source-load output uncertainty set, the extreme scenarios of wind and solar output, and the energy storage model, an intelligent connected multi-microgrid mutual assistance and collaborative scheduling architecture is constructed; and with minimizing the multi-microgrid operating coefficient as the objective function, an energy router is used for power interconnection to construct a day-ahead multi-microgrid mutual assistance and scheduling model.

[0092] Due to varying traffic volumes, the load demand characteristics of each sub-microgrid within the intelligent connected multi-microgrid differ. Furthermore, considering the different components of each microgrid, an energy router is used to achieve power interconnection between microgrids to ensure the stable operation of each microgrid.

[0093] Specifically, it includes:

[0094] Using formula u s,i,j,t -v s,i,j,t =i s,i,j,t -i s,i,j,t-1 and us,i,j,t +v s,i,j,t ≤1 determines the energy exchange model between microgrids; where i s,i,j,t Let i be the interconnection state between micronet i and micronet j at time t in scenario s. s,i,j,t-1 Let i be the interconnection state between microgrid i and microgrid j at time t-1 in scenario s. These are the minimum and maximum electrical power transmitted from microgrid i to microgrid j via the energy router, respectively. Let be the interaction power between microgrid i and microgrid j at time t in scenario s. A positive value indicates that microgrid i transmits power to microgrid j, while a negative value indicates that microgrid j transmits power to microgrid i. All are 0-1 variables, when At this time, it signifies that the interconnection state of microgrids i and j changes from 0 to 1. At this time, it signifies that the interconnection status of microgrids i and j changes from 1 to 0;

[0095] Using formula Determine the electrical balance constraints; among which, For the wind power output of microgrid i at time t under scenario s, For the photovoltaic output of microgrid i at time t under scenario s, These represent the energy storage charging and discharging power of microgrid i at time t under scenario s. Let i be the load demand of microgrid i at time t under scenario s. Let be the power of interaction between microgrid i and the power grid at time t under scenario s. Let i be the wind and solar power curtailment of microgrid i at time t under scenario s;

[0096] Using formula Determine the interaction power between microgrid i and the power grid in, This represents the maximum interaction power between the microgrid i and the power grid.

[0097] Using formula Determine the constraints for wind and solar power curtailment;

[0098] Using formula The objective function is determined; based on the establishment of the source-load uncertainty set and the acquisition of wind and solar power output limit scenarios, the day-ahead optimization scheduling comprehensively considers the microgrid interconnection coefficient, energy router operation coefficient, microgrid-grid interaction coefficient, wind and solar curtailment coefficient, and energy storage operation coefficient under representative scenarios, and takes the minimum multi-microgrid operation coefficient as the objective function for optimization scheduling.

[0099] Among them, C ah For the multi-microgrid operation coefficient, β represents the interconnection coefficient, energy router operation coefficient, microgrid-grid interaction coefficient, wind and solar curtailment coefficient, and energy storage operation coefficient of microgrid i under the extreme wind and solar power output scenario s, obtained through a high-dimensional ellipsoid set; S and I are the representative scenario set and microgrid group set, respectively; s For the probabilities of each representative scenario, To account for the maximum energy storage capacity after considering unforeseen circumstances, The minimum capacity for energy storage after taking into account unforeseen circumstances.

[0100] use Determine the microgrid interconnection coefficient;

[0101] In the formula, o ex is the state switching coefficient for a single microgrid interconnection; J and T are the set of microgrids interconnected with microgrid i and the total scheduling time, respectively.

[0102] Using formula Determine the operating coefficient of the energy router;

[0103] In the formula, o ER η is the unit operating coefficient of the energy router. i,ER This refers to the energy transmission efficiency of the energy router.

[0104] Using formula Determine the interaction coefficient between the microgrid and the power grid;

[0105] In the formula, o e The interaction coefficient is the unit power coefficient. Let be the interaction power between microgrid i and the power grid at time t under scenario s.

[0106] Using formula Determine the wind and solar curtailment coefficient;

[0107] In the formula, o cut This refers to the wind and solar curtailment coefficient. Let i be the wind and solar power curtailment of microgrid i at time t under scenario s;

[0108] Using formula Determine the energy storage operating coefficient;

[0109] In the formula, o bt This is the energy storage operation coefficient.

[0110] S105. Based on the day-ahead multi-microgrid mutual assistance scheduling model, the mutual assistance power between microgrids and the optimal day-ahead scheduling scheme are obtained.

[0111] This application considers the coupling relationship between the operating power of intelligent connected loads and traffic flow within a microgrid, and performs refined modeling of the operating characteristics of each intelligent connected load. This allows the load demand of the intelligent connected multi-microgrid to change with traffic flow, realizing the coupling between the transportation network and the power grid to a certain extent, which is more conducive to the intelligent management of the intelligent connected system. Secondly, it collects historical source-load data and corresponding parameters of the intelligent connected multi-microgrid, analyzes and processes the historical source-load data, and uses box-type uncertainty sets to describe load uncertainty and rectangular uncertainty sets to describe wind and solar uncertainty, thus performing relatively detailed analysis of source-load uncertainty. The model accurately depicts and considers the correlation between wind and solar power output. Solving for the minimum ellipsoid yields the extreme scenarios for wind and solar power output, ensuring the model's feasibility across all scenarios and reducing computational complexity. Next, energy storage within the microgrid is modeled. Considering uncertainties and unexpected issues arising from energy storage decision-making timing, unexpected constraints are introduced for energy storage. This, combined with the concept of extreme scenarios, ensures the model's feasibility across all scenarios. Finally, a smart connected multi-microgrid mutual assistance and collaborative scheduling framework is constructed. Power interconnection between microgrids is achieved through energy routers, and a day-ahead multi-microgrid mutual assistance and scheduling model is established. Based on day-ahead source-load forecasting, the mutual assistance and scheduling of day-ahead multi-microgrids fully considers the impact of its uncertainties and the unexpected nature of energy storage decisions, optimizing it globally. Power interconnection and energy storage charging / discharging strategies are implemented through energy routers, effectively ensuring the stable and reliable operation of the smart connected multi-microgrid.

[0112] Based on the same inventive concept, this application also provides an intelligent connected multi-microgrid mutual assistance and cooperative scheduling device for implementing the aforementioned intelligent connected multi-microgrid mutual assistance and cooperative scheduling method. The solution provided by this device is similar to the implementation scheme described in the above method. Therefore, the specific limitations of one or more intelligent connected multi-microgrid mutual assistance and cooperative scheduling device embodiments provided below can be found in the limitations of the intelligent connected multi-microgrid mutual assistance and cooperative scheduling method described above, and will not be repeated here.

[0113] In one exemplary embodiment, a smart connected multi-microgrid mutual assistance and collaborative scheduling device is provided, comprising:

[0114] The intelligent connected load model construction module is used to divide intelligent connected loads into variable power operating devices and constant power operating devices; and to model them separately to obtain corresponding intelligent connected load models; the intelligent connected load models are used to predict load demand; the variable power operating devices include: RSUs, 5G base stations and edge computing devices; the constant power operating devices include: radar, broadcast, fisheye cameras, dome cameras, serial port servers and management and control release devices;

[0115] The uncertainty set determination module is used to construct the uncertainty set of source and load output and obtain the extreme scenarios of wind and solar power output based on the historical data of microgrid source and load and the intelligent connected load model; the historical data of microgrid source and load includes: the wind and solar power output at historical moments and the intelligent connected load data at historical moments;

[0116] The energy storage model determination module is used to model the microgrid energy storage module based on the source load output uncertainty set and the wind and solar power output limit scenario, and introduce energy storage unpredictability constraints to obtain the energy storage model;

[0117] The module for constructing a multi-microgrid mutual assistance scheduling model is used to build a smart grid multi-microgrid mutual assistance and collaborative scheduling architecture based on the smart grid load model, the source-load output uncertainty set, the extreme scenarios of wind and solar output, and the energy storage model; and with minimizing the multi-microgrid operating coefficient as the objective function, it uses energy routers for power interconnection to construct a day-ahead multi-microgrid mutual assistance scheduling model.

[0118] The scheduling optimization module is used to obtain the mutual assistance power between microgrids and the optimal day-ahead scheduling scheme based on the day-ahead multi-microgrid mutual assistance scheduling model.

[0119] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is connected to the system bus via the I / O interfaces. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The I / O interfaces of the computer device are used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements an intelligent connected multi-microgrid mutual assistance and cooperative scheduling method.

[0120] Those skilled in the art will understand that the block diagrams are merely partial structural representations related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combinations of certain components, or different component arrangements. In one exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0121] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0122] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0123] 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, data stored, data displayed, 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 the relevant data must comply with relevant regulations.

[0124] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile 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. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0125] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0126] In this application, all actions to acquire signals, information, or data are carried out in compliance with the relevant data protection laws and policies of the country where the location is situated, and with the authorization granted by the owner of the relevant device.

[0127] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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 specification.

[0128] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for intelligent connected multi-microgrid mutual assistance and collaborative scheduling, characterized in that, The intelligent connected multi-microgrid mutual assistance and coordinated scheduling method includes: The intelligent connected load is divided into variable power operating devices and constant power operating devices; and modeling is performed separately to obtain corresponding intelligent connected load models; the intelligent connected load models are used to predict load demand; the variable power operating devices include: roadside units, 5G base stations and edge computing devices; the constant power operating devices include: radar, broadcasting, fisheye cameras, PTZ cameras, serial port servers and management and control release devices; Based on the historical data of microgrid source and load and the intelligent connected load model, an uncertain set of source and load output is constructed and the extreme scenarios of wind and solar power output are obtained; the historical data of microgrid source and load includes: wind and solar power output at historical moments and intelligent connected load data at historical moments; Based on the uncertainty set of source and load output and the extreme scenario of wind and solar power output, the microgrid energy storage module is modeled and energy storage unexpected constraints are introduced to obtain the energy storage model; Based on the intelligent connected load model, the source-load output uncertainty set, the extreme scenarios of wind and solar power output, and the energy storage model, an intelligent connected multi-microgrid mutual assistance and collaborative scheduling architecture is constructed; and with minimizing the multi-microgrid operating coefficient as the objective function, an energy router is used for power interconnection to construct a day-ahead multi-microgrid mutual assistance and scheduling model. Based on the day-ahead multi-microgrid mutual assistance scheduling model, the mutual assistance power between microgrids and the optimal day-ahead scheduling scheme are obtained.

2. The intelligent connected multi-microgrid mutual assistance and cooperative scheduling method according to claim 1, characterized in that, The intelligent connected load is divided into variable power operating equipment and constant power operating equipment. Each model is then created to obtain the corresponding intelligent connected load model, specifically including: Using formula Construct a roadside RSU intelligent connected load model; Using formula Construct a smart network load model for 5G base stations; Using formula E edge =P edge ×T edge Construct an intelligent network load model for edge computing devices; A smart network load model for constant power operating equipment is constructed using the formula P = const; Among them, P t RSU Let P be the operating power of the RSU at time t. active P represents the operating power of the RSU in active mode. ps The operating power of the RSU in energy-saving mode. δ represents static power consumption, which is a fixed constant; δ is the energy efficiency coefficient of the base station. The power consumption is dynamic and related to traffic flow. g max The maximum traffic flow for the base station. Let E be the base station's transmission power to a single car at time t. edge The power consumption of edge computing devices is determined by the power P of the edge devices. edge Multiply by execution time T edge Calculated; P edge For fixed constants, parameters l i For task i, f edge Where P is the task processing speed, P is the working power of the intelligent connected load, and const is a fixed constant.

3. The intelligent connected multi-microgrid mutual assistance and cooperative scheduling method according to claim 1, characterized in that, The process of constructing an uncertain set of source and load output and obtaining the extreme scenarios of wind and solar power output based on historical data of microgrid sources and loads and intelligent connected load models specifically includes: Using formula Construct a box-shaped uncertain set U1 of load demand; where P load For load demand, P load,t Let t be the load demand. This is the predicted load demand value. This represents the upward fluctuation value of load demand. This represents the downward fluctuation value of load demand. For 0-1 variables, This indicates that load demand is fluctuating upwards. This indicates a downward fluctuation in load demand; both cannot be 1 simultaneously. As a load uncertainty control factor, the degree of robustness conservatism is controlled by changing its size; Using formula Construct a rectangular uncertain set U2; where P i P contributes to wind and solar power. i,t The output of wind and solar power at time t; Forecast values ​​for wind and solar power output; The upward fluctuation value of the wind and solar power output; The downward fluctuation value of the wind and solar power output; For 0-1 variables, This indicates that the power output of wind and solar power is fluctuating upwards. This indicates that the power output of wind and solar power is fluctuating downwards; both cannot be 1 at the same time. Uncertainty control factors for wind and solar power output; a, c, b + b - d + d - P represents the fitting coefficients for the relevant rectangular uncertain set. pv，t For the photovoltaic output at time t, P pv，t Let pv be the wind power output at time t, pv be the photovoltaic power output, wt be the wind power output, and i be the index, which can be either pv or wt. Using formula Construct a historical power output matrix ω for wind and solar power; where N w N represents the number of wind turbine units. p N represents the number of photovoltaic power plants. s The number of days for collecting historical data, The m-th day is numbered N w The output of the wind turbine at time T. The m-th day is numbered N p The output of the photovoltaic array at time T, ω m Historical matrix of scenic contribution on day m; Based on the historical power output matrix of wind and solar power, the minimum volume envelope ellipsoid algorithm is used to determine the minimum volume ellipsoid. The extreme scenario is obtained by using rotational and inverse transformations on the ellipsoid with the smallest volume.

4. The intelligent connected multi-microgrid mutual assistance and cooperative scheduling method according to claim 1, characterized in that, Based on the uncertainty set of source and load output and the extreme scenario of wind and solar power output, the microgrid energy storage module is modeled and energy storage unpredictability constraints are introduced to obtain the energy storage model, which specifically includes: Using formula and Constrain the upper and lower limits of energy storage capacity; Using formula Determine the energy storage capacity at time t The safe zone; in, The upper and lower bounds of the energy storage capacity at time t are considered for unexpected events. The upper and lower bounds of the energy storage capacity at time t-1 are considered for unexpected events. The upper and lower bounds of energy storage capacity are not considered under unforeseen circumstances; P i ch,max P i dis,max For the maximum charge and discharge power of energy storage, f -1 () is an auxiliary function. For the wind power output of microgrid i at time t under scenario s, For the photovoltaic output of microgrid i at time t under scenario s, Let Δt be the maximum electrical power transmitted from microgrid i to microgrid j through the energy router, and Δt be the time interval. This is the lower bound of the load fluctuation value. This is the upper limit of the load fluctuation value. J represents the upper limit of the power interaction with the power grid, and J is the set of microgrids interconnected with microgrid i.

5. The intelligent connected multi-microgrid mutual assistance and cooperative scheduling method according to claim 4, characterized in that, Using formula and formula Determine the auxiliary function; Where, η bt,ch η bt,dis y is the energy storage charging and discharging efficiency coefficient, τ is the unit time interval, y is the dependent variable, and x is the independent variable.

6. The intelligent connected multi-microgrid mutual assistance and cooperative scheduling method according to claim 1, characterized in that, The above describes the construction of an intelligent connected multi-microgrid mutual assistance and collaborative scheduling architecture based on the intelligent connected load model, the source-load output uncertainty set, the extreme scenarios of wind and solar power output, and the energy storage model. With minimizing the operating coefficient of the multi-microgrid as the objective function, an energy router is used for power interconnection to construct a day-ahead multi-microgrid mutual assistance scheduling model, which specifically includes: Using formula u s,i,j,t -v s,i,j,t =i s,i,j,t -i s,i,j,t-1 and u s,i,j,t +v s,i,j,t ≤1 determines the energy exchange model between microgrids; where i s,i,j,t Let i be the interconnection state between micronet i and micronet j at time t in scenario s. s,i,j,t-1 Let i be the interconnection state between microgrid i and microgrid j at time t-1 in scenario s. These are the minimum and maximum electrical power transmitted from microgrid i to microgrid j via the energy router, respectively. Let be the interaction power between microgrid i and microgrid j at time t in scenario s. A positive value indicates that microgrid i transmits power to microgrid j, while a negative value indicates that microgrid j transmits power to microgrid i. All are 0-1 variables, when At this time, it signifies that the interconnection state of microgrids i and j changes from 0 to 1. At this time, it signifies that the interconnection status of microgrids i and j changes from 1 to 0; Using formula Determine the electrical balance constraints; among which, For the wind power output of microgrid i at time t under scenario s, For the photovoltaic output of microgrid i at time t under scenario s, These represent the energy storage charging and discharging power of microgrid i at time t under scenario s. Let i be the load demand of microgrid i at time t under scenario s. Let be the power of interaction between microgrid i and the power grid at time t under scenario s. Let i be the wind and solar power curtailment of microgrid i at time t under scenario s; Using formula Determine the interaction power between microgrid i and the power grid Among them, P i e，max This represents the maximum interaction power between the microgrid i and the power grid. Using formula Determine the constraints for wind and solar power curtailment; Using formula Determine the objective function; where C ah For the multi-microgrid operation coefficient, β represents the interconnection coefficient, energy router operation coefficient, microgrid-grid interaction coefficient, wind and solar curtailment coefficient, and energy storage operation coefficient of microgrid i under the extreme wind and solar power output scenario s, obtained through a high-dimensional ellipsoid set; S and I are the representative scenario set and microgrid group set, respectively; s For the probabilities of each representative scenario, To account for the maximum energy storage capacity after considering unforeseen circumstances, The minimum capacity for energy storage after taking into account unforeseen circumstances.

7. A smart connected multi-microgrid mutual assistance and collaborative scheduling device, characterized in that, The intelligent connected multi-microgrid mutual assistance and collaborative scheduling equipment includes: The intelligent connected load model construction module is used to divide intelligent connected loads into variable power operating devices and constant power operating devices; and to model them separately to obtain corresponding intelligent connected load models; the intelligent connected load models are used to predict load demand; the variable power operating devices include: roadside units, 5G base stations and edge computing devices; the constant power operating devices include: radar, broadcasting, fisheye cameras, dome cameras, serial port servers and management and control release devices; The uncertainty set determination module is used to construct the uncertainty set of source and load output and obtain the extreme scenarios of wind and solar power output based on the historical data of microgrid source and load and the intelligent connected load model; the historical data of microgrid source and load includes: the wind and solar power output at historical moments and the intelligent connected load data at historical moments; The energy storage model determination module is used to model the microgrid energy storage module based on the source load output uncertainty set and the wind and solar power output limit scenario, and introduce energy storage unpredictability constraints to obtain the energy storage model; The module for constructing a multi-microgrid mutual assistance scheduling model is used to build a smart grid multi-microgrid mutual assistance and collaborative scheduling architecture based on the smart grid load model, the source-load output uncertainty set, the extreme scenarios of wind and solar output, and the energy storage model; and with minimizing the multi-microgrid operating coefficient as the objective function, it uses energy routers for power interconnection to construct a day-ahead multi-microgrid mutual assistance scheduling model. The scheduling optimization module is used to obtain the mutual assistance power between microgrids and the optimal day-ahead scheduling scheme based on the day-ahead multi-microgrid mutual assistance scheduling model.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the intelligent connected multi-microgrid mutual assistance and cooperative scheduling method according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the intelligent connected multi-microgrid mutual assistance and coordinated scheduling method as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the intelligent connected multi-microgrid mutual assistance and coordinated scheduling method as described in any one of claims 1-6.

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