Intelligent network connection multi-microgrid mutual aid cooperative scheduling method, device, medium and product

Abstract

The invention discloses an intelligent network connection multi-microgrid mutual aid cooperative scheduling method and device, a medium and a product, and relates to the field of power system optimization scheduling, and the method comprises the steps: dividing an intelligent network connection load into a variable power operation device and a constant power operation device; according to the microgrid source load historical data and the intelligent network connection load model, constructing a source load output uncertainty set and obtaining a wind and light output limit scene; according to the source load output uncertainty set and the wind and light output limit scene, modeling is carried out on a microgrid energy storage module, and energy storage unpredictability constraints are introduced; constructing an intelligent networked multi-microgrid mutual aid cooperative scheduling architecture; taking the minimum multi-microgrid operation coefficient as a target function, and adopting an energy router to carry out power interconnection to construct a day-ahead multi-microgrid mutual aid scheduling model; according to the method, the capability of absorbing the uncertainty of the source load by the multiple micro-grids can be improved, and the running reliability of the micro-grids is improved while stable and efficient running of the micro-grids is ensured.

Description

Technical Field

[0001] The present application relates to the field of power system optimization and dispatching, and in particular to a method, equipment, medium and product for the coordinated dispatching of intelligent networked multi-microgrids. Background Art

[0002] With the acceleration of urbanization, the intelligent networked transportation self-consistent road system has emerged. Through autonomous driving and Internet of Things technology, the system realizes intelligent management of traffic flow, signal light optimization, accident warning and optimal path guidance, effectively improving efficiency and safety, reducing carbon emissions, and laying the foundation for the widespread application of intelligent transportation.

[0003] A series of energy problems have also emerged in the rapid development of intelligent networked transportation self-consistent road systems. The temporal and spatial characteristics of traffic flow distribution lead to changes in load demand and the volatility of renewable energy output, which have brought huge challenges to the smooth operation of intelligent networked multi-microgrids. Research on its optimization scheduling problem is of great significance to achieve energy self-consistency of intelligent networked roads. However, in the aspect of achieving energy self-consistency, existing research has not fully considered the impact of source-load uncertainty and the unpredictability of energy storage system decisions on the energy management of intelligent networked multi-microgrids, resulting in the established model having no solution in actual scenarios or the required scheduling strategy being unavailable.

[0004] Therefore, based on the above problems, there is an urgent need to provide a new method for the coordinated scheduling of multi-microgrids in an intelligent network. Summary of the invention

[0005] The purpose of this application is to provide a method, equipment, medium and product for the mutual assistance and coordinated scheduling of multiple microgrids in an intelligent network, which can enhance the ability of multiple microgrids to absorb source and load uncertainties, 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 solutions:

[0007] In a first aspect, the present application provides a method for coordinated scheduling of multi-microgrid mutual assistance in an intelligent network, and the method for coordinated scheduling of multi-microgrid mutual assistance in an intelligent network includes:

[0008] The intelligent network load is divided into variable power operation equipment and constant power operation equipment; and the models are respectively modeled to obtain the corresponding intelligent network load models; the intelligent network load model is used to predict the load demand; the variable power operation equipment includes: roadside unit, 5G base station and edge computing equipment; the constant power operation equipment includes: radar, broadcast, fisheye camera, ball camera, serial port server and control and release equipment;

[0009] According to the historical data of microgrid sources and loads and the intelligent network load model, the uncertain set of source and load output is constructed and the wind and solar output limit scenario is obtained; the historical data of microgrid sources and loads includes: wind and solar output at historical moments and intelligent network load data at historical moments;

[0010] According to the uncertain source-load output set and the wind-solar output limit scenario, the microgrid energy storage module is modeled and the unexpected energy storage constraints are introduced to obtain the energy storage model.

[0011] According to the intelligent network load model, the source-load output uncertainty set, the wind-solar output limit scenario and the energy storage model, a multi-microgrid mutual assistance and coordinated dispatching architecture for intelligent network is constructed; and the multi-microgrid mutual assistance dispatching model for the day-ahead is constructed by using energy routers for power interconnection and minimizing the multi-microgrid operation coefficient as the objective function;

[0012] According to the day-ahead multi-microgrid mutual assistance dispatch model, the mutual assistance power between microgrids and the optimal day-ahead dispatch plan are obtained.

[0013] Optionally, the intelligent network load is divided into variable power operation equipment and constant power operation equipment; and modeling is performed respectively to obtain corresponding intelligent network load models, specifically including:

[0014] Using the formula Construct roadside RSU intelligent network load model;

[0015] Using the formula Construct a 5G base station intelligent network load model;

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

[0017] The formula P = const is used to construct the intelligent network load model of constant power operation equipment;

[0018] Among them, P t RSU is the working power of RSU at time t, P active is the working power of RSU in active mode, P ps is the working power of RSU in energy-saving mode, is the static power consumption and is a fixed constant; δ is the energy efficiency coefficient of the base station, is the dynamic power consumption and is related to the traffic volume. g max is the maximum traffic flow of the base station, is the transmission power of the base station to a single car at time t, E edge is the execution energy consumption of the edge computing device, which is determined by the power P of the edge device. edgeMultiply by the execution time T edge Calculated; P edge is a fixed constant, parameter l i is the task load of task i, fedge is the task processing speed, P is the working power of the intelligent network load, and const is a fixed constant.

[0019] Optionally, the step of constructing a source-load output uncertainty set and obtaining a wind-solar output limit scenario based on the microgrid source-load historical data and the intelligent network load model specifically includes:

[0020] Using the formula Construct the box-type uncertainty set U1 of load demand; where P load is the load demand, P load,t is the load demand at time t, is the load demand forecast value, is the upward fluctuation value of load demand, is the downward fluctuation value of load demand, is a 0-1 variable, Indicates that the load demand fluctuates upward. This indicates that the load demand fluctuates downward. Both cannot be 1 at the same time. is the load uncertainty control factor, which controls the conservativeness of robustness by changing its size;

[0021] Using the formula Construct a rectangular uncertain set U2; where P i For wind power and photovoltaic power output, P i,t is the wind power and photovoltaic power output at time t; is the predicted value of wind and solar power output; It is the upward fluctuation value of wind and solar power output; It is the downward fluctuation value of wind and solar power output; is a 0-1 variable, It indicates that the wind and solar power output fluctuates upward. This means that the wind and solar power output fluctuates downward, and both cannot be 1 at the same time; is the uncertainty control factor of wind and solar power output; a, c, b + 、b - d + d - is the fitting coefficient of the related rectangular uncertainty set, P pv，t is the photovoltaic output at time t, P wt，t is the wind power output at time t, pv is photovoltaic, wt is wind power, i is the index, which can be pv or wt;

[0022] Using the formula Construct the historical matrix of wind and solar output ω; where N wis the number of wind turbines, N p is the number of photovoltaic power stations, N s is the number of days of historical data collected, The mth day is numbered N w The output of the wind turbine at time T, The mth day is numbered N p The output of the photovoltaic array at time T, ω m is the wind and solar power output history matrix for the mth day;

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

[0024] The extreme scene is obtained by using rotation transformation and inverse transformation according to the ellipsoid with the minimum volume.

[0025] Optionally, according to the uncertain source-load output set and the wind-solar output limit scenario, the microgrid energy storage module is modeled and unexpected energy storage constraints are introduced to obtain an energy storage model, which specifically includes:

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

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

[0028] in, The upper and lower bounds of the energy storage capacity that take into account unexpected events at time t are: The unexpected upper and lower bounds of energy storage capacity are considered at time t-1. are the upper and lower bounds of energy storage capacity without considering unexpected situations; P i ch,max , P i dis,max is the maximum charging and discharging power of energy storage, f -1 () is an auxiliary function, is the wind power output of microgrid i at time t in scenario s, is the photovoltaic output of microgrid i at time t in scenario s, is the maximum electric power transmitted from microgrid i to microgrid j through the energy router, Δt is the time interval, is the lower bound of load fluctuation, is the upper limit of load fluctuation, is the upper limit of the interaction power with the grid, and J is the set of microgrids interconnected with microgrid i.

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

[0030] Among them, η bt,ch , η bt,dis 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, the intelligent network load model, the source-load output uncertainty set, the wind-solar output limit scenario and the energy storage model are used to construct an intelligent network multi-microgrid mutual assistance coordinated dispatching architecture; and the minimization of the multi-microgrid operation coefficient is used as the objective function, and the energy router is used for power interconnection to construct a multi-microgrid mutual assistance dispatching model on the day before, which specifically includes:

[0032] Using the 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 is the interconnection status of microgrid i and microgrid j at time t under scenario s, i s,i,j,t-1 is the interconnection status of microgrid i and microgrid j at time t-1 under scenario s, are the minimum and maximum electric power transmitted from microgrid i to microgrid j through the energy router, respectively; is the interaction power between microgrid i and microgrid j at time t in scenario s. If it is positive, it means that microgrid i transmits power to microgrid j, and if it is negative, microgrid j transmits power to microgrid i. are all 0-1 variables. When , it indicates that the interconnection state of microgrid i and j changes from 0 to 1. When , it indicates that the interconnection status of microgrid i and j changes from 1 to 0;

[0033] Using the formula Determine the electrical balance constraints; where, is the wind power output of microgrid i at time t in scenario s, is the photovoltaic output of microgrid i at time t in scenario s, are the energy storage charging and discharging power of microgrid i at time t under scenario s, is the load demand of microgrid i at time t under scenario s, is the interaction power between microgrid i and the grid at time t under scenario s, is the abandoned wind and solar power of microgrid i at time t in scenario s;

[0034] Using the formula Determine the interaction power between microgrid i and the grid in, is the maximum interaction power between microgrid i and the grid;

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

[0036] Using the formula Determine the objective function; where C ah is the multi-microgrid operation coefficient, are 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 wind and solar power output limit scenario s obtained through the high-dimensional ellipsoid set; S and I are the representative scenario set and microgrid group set, respectively; β s is the probability of each representative scene, To consider the maximum capacity of energy storage after unexpected events, It is the minimum capacity of energy storage after considering unexpected events.

[0037] In a second aspect, the present application provides an intelligent network-connected multi-microgrid mutual assistance coordinated scheduling device, and the intelligent network-connected multi-microgrid mutual assistance coordinated scheduling device includes:

[0038] The intelligent network load model building module is used to divide the intelligent network load into variable power operation equipment and constant power operation equipment; and model them respectively to obtain the corresponding intelligent network load model; the intelligent network load model is used to predict the load demand; the variable power operation equipment includes: roadside unit, 5G base station and edge computing equipment; the constant power operation equipment includes: radar, broadcast, fisheye camera, ball camera, serial port server and control and release equipment;

[0039] The uncertain set determination module is used to construct the source-load output uncertainty set and obtain the wind-solar output limit scenario based on the microgrid source-load historical data and the intelligent network load model; the microgrid source-load historical data includes: wind-solar output at historical moments and intelligent network load data at historical moments;

[0040] The energy storage model determination module is used to model the microgrid energy storage module and introduce unexpected energy storage constraints according to the source-load output uncertainty set and the wind-solar output limit scenario to obtain the energy storage model;

[0041] The day-ahead multi-microgrid mutual assistance dispatch model construction module is used to construct a smart network multi-microgrid mutual assistance coordinated dispatch architecture based on the smart network load model, source-load output uncertainty set, wind-solar output limit scenario and energy storage model; and to minimize the multi-microgrid operation coefficient as the objective function, use energy routers for power interconnection, and construct a day-ahead multi-microgrid mutual assistance dispatch model;

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

[0043] In a third aspect, the present application provides a computer device, comprising: 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 method for cooperative scheduling of intelligent connected multi-microgrid mutual assistance.

[0044] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the intelligent networked multi-microgrid mutual assistance and coordinated scheduling method.

[0045] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the intelligent networked multi-microgrid mutual assistance and coordinated scheduling method.

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

[0047] The present application provides a method, device, medium and product for mutual assistance and coordinated dispatching of intelligent connected multi-microgrids. First, the intelligent connected loads are divided into two categories: variable power operation and constant power operation. Taking into account the coupling relationship between the working power of the intelligent connected loads in the microgrid and the vehicle flow, the operating characteristics of each intelligent connected load are finely modeled, which makes the load demand of the intelligent connected multi-microgrid change with the change of vehicle flow, realizes the coupling between the transportation network and the power grid to a certain extent, and is more conducive to the intelligent management of the intelligent connected system; secondly, the historical data of the source and load of each microgrid are collected, the uncertain set of source and load output is constructed, and the extreme scene of wind and solar output is obtained; then, the microgrid energy storage module is modeled and unexpected constraints are introduced to ensure the solvability of the model; finally, the mutual assistance and coordinated dispatching architecture of intelligent connected multi-microgrids is constructed, the power interconnection between the microgrids is realized through the energy router, and the multi-microgrid coordinated dispatching model of the day-ahead is established. This application describes the uncertainty of source and load in detail, and takes the wind and solar power output limit scenario as a representative scenario. Combined with the unexpected constraints of energy storage decision-making, it ensures the effectiveness of the established multi-microgrid mutual assistance scheduling model on the day before, greatly improves the ability of multiple microgrids to absorb source and load uncertainty, and greatly improves the reliability of microgrid operation while ensuring the stable and efficient operation of each microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be 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.

[0049] Figure 1 This is a flow chart of a method for cooperative scheduling of multi-microgrid mutual assistance in an intelligent network connection in one embodiment of the present application;

[0050] Figure 2 This is the structure diagram of the intelligent connected multi-microgrid;

[0051] Figure 3 To obtain the flow chart of the wind and solar power output limit scenario. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0053] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0054] In an exemplary embodiment, Figure 1 As shown, a method for cooperative scheduling of multi-microgrid mutual assistance in intelligent network connection is provided, and the method includes the following steps 201 to 208. Among them:

[0055] S101, the intelligent network load is divided into variable power operation equipment and constant power operation equipment; and modeling is performed respectively to obtain the corresponding intelligent network load model; the intelligent network load model is used to predict the load demand; the variable power operation equipment includes: Road Side Unit (RSU), 5G base station and edge computing equipment; the constant power operation equipment includes: radar, broadcast, fisheye camera, ball camera, serial port server and control and release equipment;

[0056] like Figure 2As shown in the figure, there are many types of intelligent network loads with different functions, which can be roughly divided into four main modules: perception, positioning, guidance and communication. The perception module is mainly responsible for collecting information about the surrounding environment, the positioning module provides real-time location information, the guidance module provides navigation and driving instructions for traffic participants, and the communication module realizes information transmission between devices. Considering that there is a close coupling relationship between the working power of some equipment and the traffic flow, in order to more carefully characterize the intelligent network load, it is divided into variable power load and constant power load according to its operating power characteristics. Among them, variable power load corresponds to variable power operation equipment (such as RSU, 5G base station, edge computing equipment), and constant power load corresponds to constant power operation equipment (such as radar, broadcast, fisheye camera, ball camera, serial port server and control and release equipment).

[0057] S101 specifically includes:

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

[0059] Using the formula Construct a 5G base station intelligent network load model;

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

[0061] The traffic flow has little effect on the operating power of radar, broadcast, fisheye camera, ball camera, serial port server, control and release equipment, and then the formula P = const is used to build the intelligent network load model of constant power operation equipment;

[0062] Among them, P t RSU is the working power of RSU at time t, P active is the working power of RSU in active mode, P ps is the working power of RSU in energy-saving mode, is the static power consumption and is a fixed constant; δ is the energy efficiency coefficient of the base station, is the dynamic power consumption and is related to the traffic volume. g max is the maximum traffic flow of the base station, is the transmission power of the base station to a single car at time t, E edge is the execution energy consumption of the edge computing device, which is determined by the power P of the edge device. edge Multiply by the execution time T edge Calculated; P edge is a fixed constant, parameter l i is the task load of task i, fedge is the task processing speed, P is the working power of the intelligent network load, and const is a fixed constant.

[0063] S102, constructing a source-load output uncertainty set and obtaining a wind-solar output limit scenario based on the microgrid source-load historical data and the smart grid load model; the microgrid source-load historical data includes: wind-solar output at historical moments and smart grid load data at historical moments;

[0064] S102 specifically includes:

[0065] Using the formula Construct the box-type uncertainty set U1 of load demand; where P load is the load demand, P load,t is the load demand at time t, is the load demand forecast value, is the upward fluctuation value of load demand, is the downward fluctuation value of load demand, is a 0-1 variable, Indicates that the load demand fluctuates upward. This indicates that the load demand fluctuates downward. Both cannot be 1 at the same time. is the load uncertainty control factor, which controls the conservativeness of robustness by changing its size;

[0066] Using the formula Construct a rectangular uncertainty set U2, which takes into account the certain correlation between wind and solar outputs and is constructed based on historical wind and solar data;

[0067] Among them, P i For wind power and photovoltaic power output, P i,t is the wind power and photovoltaic power output at time t; is the predicted value of wind and solar power output; It is the upward fluctuation value of wind and solar power output; It is the downward fluctuation value of wind and solar power output; is a 0-1 variable, It indicates that the wind and solar power output fluctuates upward. This means that the wind and solar power output fluctuates downward, and both cannot be 1 at the same time; is the uncertainty control factor of wind and solar power output; a, c, b + 、b - d + d - is the fitting coefficient of the related rectangular uncertainty set, P pv，t is the photovoltaic output at time t, P wt，tis the wind power output at time t, pv is photovoltaic power, wt is wind power, i is the index, which can be either pv or wt;

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

[0069] S1, using the formula Construct the historical matrix of wind and solar output ω; where N w is the number of wind turbines, N p is the number of photovoltaic power stations, N s is the number of days of historical data collected, The mth day is numbered N w The output of the wind turbine at time T, The mth day is numbered N p The output of the photovoltaic array at time T, ω m is the wind and solar power output history matrix for the mth day;

[0070] S2, according to the historical matrix of wind and solar power output, the minimum volume enveloping ellipsoid (MVEE) algorithm is used to determine the minimum volume ellipsoid;

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

[0072] The ellipsoid expression of the minimum volume obtained is:

[0073]

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

[0075] S3, based on the minimum volume ellipsoid, rotation transformation and inverse transformation are used to obtain the extreme scene.

[0076] The ellipsoid's symmetry axis is made to coincide with the coordinate axis through rotation transformation in order to obtain the coordinates of the ellipsoid's vertices:

[0077] ω'=P×(ω-q);

[0078]

[0079] Where P is the rotation matrix, satisfying Q = P T DP=P -1DP, D is the symmetric matrix composed of Q eigenvalues, ω' is the transformed wind and solar output matrix; E'(·) is the transformed ellipsoid expression; is the coordinate of the ellipsoid vertex after transformation; N e is the number of vertices, λ1 is the first eigenvalue, For the first (N w +N p )T eigenvalues, diag is a diagonal matrix.

[0080] Obtain the limit scenario by inverse transformation

[0081]

[0082] S103, based on the uncertain source-load output set and the wind-solar output limit scenario, model the microgrid energy storage module and introduce unexpected energy storage constraints to obtain an energy storage model;

[0083] Unexpectedness means that the decision at the current stage is only related to the current and previous realization values ​​of uncertainty. At present, most of the processing of uncertainty depends on the realization of future uncertainty for scheduling, which violates the timing logic of scheduling, resulting in the infeasibility of the model in some actual scenarios. In order to consider the unpredictability of energy storage decisions and ensure that the model has a solution, the conventional model of the microgrid energy storage module is established as follows:

[0084]

[0085] In the formula, is the energy storage capacity at time t; η bt,ch , η bt,dis is the energy storage charging and discharging efficiency coefficient; are the energy storage charging and discharging power respectively; E i,init is the initial capacity of energy storage; is the energy storage charging and discharging state variable, indicates that the energy storage is in a charging state, then it indicates that the energy storage is in a discharging state; are the upper and lower bounds of energy storage capacity without considering unexpected situations; P i ch,max , P i dis,max is the maximum charging and discharging power of energy storage.

[0086] In order to solve the problem that the unexpected energy storage decision makes the problem unsolvable, the unexpected energy storage constraint is added. First, using the formula and formula Determine the auxiliary function;

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

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

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

[0090] in, The upper and lower bounds of the energy storage capacity that take into account unexpected events at time t are: The unexpected upper and lower bounds of energy storage capacity are considered at time t-1. are the upper and lower bounds of energy storage capacity without considering unexpected situations; P i ch,max , P i dis,max is the maximum charging and discharging power of energy storage, f -1 () is an auxiliary function, is the wind power output of microgrid i at time t in scenario s, is the photovoltaic output of microgrid i at time t in scenario s, is the maximum electric power transmitted from microgrid i to microgrid j through the energy router, Δt is the time interval, is the lower bound of load fluctuation, is the upper limit of load fluctuation, is the upper limit of the interaction power with the grid, and J is the set of microgrids interconnected with microgrid i.

[0091] S104, based on the intelligent network load model, the source-load output uncertainty set, the wind-solar output limit scenario and the energy storage model, construct an intelligent network multi-microgrid mutual assistance coordinated dispatching architecture; and use energy routers for power interconnection and construct a day-ahead multi-microgrid mutual assistance dispatching model by minimizing the multi-microgrid operation coefficient as the objective function;

[0092] Due to the different traffic flows, there are differences in the load demand characteristics between the sub-microgrids in the intelligent connected multi-microgrid. Taking into account the differences in the components of each microgrid, energy routers are used to achieve power interconnection between microgrids to ensure the stable operation of each microgrid.

[0093] Specifically include:

[0094] Using the formula u s,i,j,t -vs,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 is the interconnection status of microgrid i and microgrid j at time t under scenario s, i s,i,j,t-1 is the interconnection status of microgrid i and microgrid j at time t-1 under scenario s, are the minimum and maximum electric power transmitted from microgrid i to microgrid j through the energy router, respectively; is the interaction power between microgrid i and microgrid j at time t in scenario s. If it is positive, it means that microgrid i transmits power to microgrid j, and if it is negative, microgrid j transmits power to microgrid i. are all 0-1 variables. When , it indicates that the interconnection state of microgrid i and j changes from 0 to 1. When , it indicates that the interconnection status of microgrid i and j changes from 1 to 0;

[0095] Using the formula Determine the electrical balance constraints; where, is the wind power output of microgrid i at time t in scenario s, is the photovoltaic output of microgrid i at time t in scenario s, are the energy storage charging and discharging power of microgrid i at time t under scenario s, is the load demand of microgrid i at time t under scenario s, is the interaction power between microgrid i and the grid at time t under scenario s, is the abandoned wind and solar power of microgrid i at time t in scenario s;

[0096] Using the formula Determine the interaction power between microgrid i and the grid in, is the maximum interaction power between microgrid i and the grid;

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

[0098] Using the formula Determine the objective function; based on the establishment of the source-load uncertainty set and the acquisition of the wind and solar output limit scenarios, the day-ahead optimal scheduling comprehensively considers the microgrid interconnection coefficient, energy router operation coefficient, microgrid and grid interaction coefficient, wind and solar power abandonment coefficient, and energy storage operation coefficient under representative scenarios, and optimizes the scheduling with the minimum multi-microgrid operation coefficient as the objective function.

[0099] Among them, C ah is the multi-microgrid operation coefficient, are 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 wind and solar power output limit scenario s obtained through the high-dimensional ellipsoid set; S and I are the representative scenario set and microgrid group set, respectively; β s is the probability of each representative scene, To consider the maximum capacity of energy storage after unexpected events, It is the minimum capacity of energy storage after considering unexpected events.

[0100] use Determine the microgrid interconnection coefficient;

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

[0102] Using the formula Determine the energy router operation coefficient;

[0103] In the formula, o ER is the unit operation coefficient of the energy router, η i,ER The energy transmission efficiency of the energy router.

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

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

[0106] Using the formula Determine the wind and solar power abandonment coefficients;

[0107] In the formula, o cut is the wind and solar power abandonment coefficient; is the abandoned wind and solar power of microgrid i at time t in scenario s;

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

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

[0110] S105, according to the multi-microgrid mutual assistance scheduling model of the day before, the mutual assistance power between microgrids and the optimal day-ahead scheduling plan are obtained.

[0111] This application takes into account the coupling relationship between the working power of the intelligent networked load in the microgrid and the vehicle flow, and conducts a refined modeling of the operating characteristics of each intelligent networked load, which enables the load demand of the intelligent networked multi-microgrid to change with the change of vehicle flow, and to a certain extent realizes the coupling between the transportation network and the power grid, which is more conducive to the intelligent management of the intelligent networked system; secondly, the source and load historical data and corresponding parameters of the intelligent networked multi-microgrid are collected, the source and load historical data are analyzed and processed, and the box uncertainty set is used to describe the load uncertainty and the rectangular uncertainty set is used to describe the wind and light uncertainty. The uncertainty of the source and load is analyzed in a relatively detailed manner. The author describes the model in detail and takes into account the correlation between wind and solar output. By solving the minimum ellipsoid, the extreme scenario of wind and solar output is obtained, which also guarantees the feasibility of the model in all scenarios and reduces the complexity of model solution calculation to a certain extent. Then, the energy storage in the microgrid is modeled. Taking into account the uncertainty and unexpected problems caused by the timing of energy storage decision-making, unexpected constraints are introduced to the energy storage. Combined with the idea of ​​extreme scenarios, the feasibility of the model in all scenarios is guaranteed. Finally, the intelligent networked multi-microgrid mutual assistance and coordinated dispatching framework is constructed, and the power interconnection between microgrids is realized through energy routers, and a multi-microgrid mutual assistance dispatching model is established. The mutual assistance dispatching of multi-microgrids on the day before is based on the day-ahead source and load forecast, fully considering the impact of its uncertainty, and taking into account the unexpectedness of energy storage decision-making, and optimizing it globally. The energy router is used to interconnect power and the charging and discharging strategy of energy storage is well guaranteed to ensure the stable and reliable operation of the intelligent networked multi-microgrid.

[0112] Based on the same inventive concept, the embodiment of the present application also provides an intelligent network-connected multi-microgrid mutual assistance coordinated scheduling device for implementing the intelligent network-connected multi-microgrid mutual assistance coordinated scheduling method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more intelligent network-connected multi-microgrid mutual assistance coordinated scheduling device embodiments provided below can refer to the limitations of the intelligent network-connected multi-microgrid mutual assistance coordinated scheduling method above, and will not be repeated here.

[0113] In an exemplary embodiment, a smart networked multi-microgrid mutual assistance coordinated scheduling device is provided, comprising:

[0114] The intelligent network load model building module is used to divide the intelligent network load into variable power operation equipment and constant power operation equipment; and model them respectively to obtain the corresponding intelligent network load model; the intelligent network load model is used to predict the load demand; the variable power operation equipment includes: RSU, 5G base station and edge computing equipment; the constant power operation equipment includes: radar, broadcast, fisheye camera, ball camera, serial port server and control and release equipment;

[0115] The uncertain set determination module is used to construct the source-load output uncertainty set and obtain the wind-solar output limit scenario based on the microgrid source-load historical data and the intelligent network load model; the microgrid source-load historical data includes: wind-solar output at historical moments and intelligent network load data at historical moments;

[0116] The energy storage model determination module is used to model the microgrid energy storage module and introduce unexpected energy storage constraints according to the source-load output uncertainty set and the wind-solar output limit scenario to obtain the energy storage model;

[0117] The day-ahead multi-microgrid mutual assistance dispatch model construction module is used to construct a smart network multi-microgrid mutual assistance coordinated dispatch architecture based on the smart network load model, source-load output uncertainty set, wind-solar output limit scenario and energy storage model; and to minimize the multi-microgrid operation coefficient as the objective function, use energy routers for power interconnection, and construct a day-ahead multi-microgrid mutual assistance dispatch model;

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

[0119] In an exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (I / O for short) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via 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 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 via a network connection. When the computer program is executed by the processor, a method for cooperative scheduling of multi-microgrid mutual assistance of intelligent network is implemented.

[0120] Those skilled in the art will appreciate that the block diagram is only a partial 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. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0121] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0122] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[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, 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.

[0124] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods 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 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 may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0125] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. 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, etc., but is not limited thereto.

[0126] In this application, all actions to obtain signals, information or data are carried out in compliance with the relevant data protection laws and policies of the country where they are located and with the authorization given by the owner of the corresponding device.

[0127] 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 specification.

[0128] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for cooperative dispatching of intelligent networked multi-microgrids, characterized in that: The intelligent network-connected multi-microgrid mutual assistance coordinated scheduling method comprises: The intelligent network load is divided into variable power operation equipment and constant power operation equipment; and the models are respectively modeled to obtain the corresponding intelligent network load models; the intelligent network load model is used to predict the load demand; the variable power operation equipment includes: roadside unit, 5G base station and edge computing equipment; the constant power operation equipment includes: radar, broadcast, fisheye camera, ball camera, serial port server and control and release equipment; According to the historical data of microgrid sources and loads and the intelligent network load model, the uncertain set of source and load output is constructed and the wind and solar output limit scenario is obtained; the historical data of microgrid sources and loads includes: wind and solar output at historical moments and intelligent network load data at historical moments; According to the uncertain source-load output set and the wind-solar output limit scenario, the microgrid energy storage module is modeled and the unexpected energy storage constraints are introduced to obtain the energy storage model. According to the intelligent network load model, the source-load output uncertainty set, the wind-solar output limit scenario and the energy storage model, a multi-microgrid mutual assistance and coordinated dispatching architecture for intelligent network is constructed; and the multi-microgrid mutual assistance dispatching model for the day-ahead is constructed by using energy routers for power interconnection and minimizing the multi-microgrid operation coefficient as the objective function; According to the day-ahead multi-microgrid mutual assistance dispatch model, the mutual assistance power between microgrids and the optimal day-ahead dispatch plan are obtained.

2. The method for cooperative dispatching of intelligent networked multi-microgrids according to claim 1 is characterized in that: The intelligent network load is divided into variable power operation equipment and constant power operation equipment; And model them respectively to obtain the corresponding intelligent network load model, including: Using the formula Construct roadside RSU intelligent network load model; Using the formula Construct a 5G base station intelligent network load model; Using Formula E edge =P edge ×T edge Build an intelligent network load model for edge computing devices; The formula P = const is used to construct the intelligent network load model of constant power operation equipment; Among them, P t RSU is the working power of RSU at time t, P active is the working power of RSU in active mode, P ps is the working power of RSU in energy-saving mode, is the static power consumption and is a fixed constant; δ is the energy efficiency coefficient of the base station, is the dynamic power consumption and is related to the traffic volume. g max is the maximum traffic flow of the base station, is the transmission power of the base station to a single car at time t, E edge is the execution energy consumption of the edge computing device, which is determined by the power P of the edge device. edge Multiply by the execution time T edge Calculated; P edge is a fixed constant, parameter l i is the task load of task i, fedge is the task processing speed, P is the working power of the intelligent network load, and const is a fixed constant.

3. The method for cooperative dispatching of intelligent networked multi-microgrids according to claim 1, characterized in that: According to the microgrid source-load historical data and the intelligent network load model, the source-load output uncertainty set is constructed and the wind-solar output limit scenario is obtained, which specifically includes: Using the formula Construct the box-type uncertainty set U1 of load demand; where P load is the load demand, P load,t is the load demand at time t, is the load demand forecast value, is the upward fluctuation value of load demand, is the downward fluctuation value of load demand, is a 0-1 variable, Indicates that the load demand fluctuates upward. This indicates that the load demand fluctuates downward. Both cannot be 1 at the same time. is the load uncertainty control factor, which controls the conservativeness of robustness by changing its size; Using the formula Construct a rectangular uncertain set U2; where P i For wind power and photovoltaic power output, P i,t is the wind power and photovoltaic power output at time t; is the predicted value of wind and solar power output; It is the upward fluctuation value of wind and solar power output; It is the downward fluctuation value of wind and solar power output; is a 0-1 variable, It indicates that the wind and solar power output fluctuates upward. This means that the wind and solar power output fluctuates downward, and both cannot be 1 at the same time; is the uncertainty control factor of wind and solar power output; a, c, b + , b - ,d + ,d - is the fitting coefficient of the related rectangular uncertainty set, P pv，t is the photovoltaic output at time t, P pv，t is the wind power output at time t, pv is photovoltaic, wt is wind power, i is the index, which can be pv or wt; Using the formula Construct the historical matrix of wind and solar output ω; where N w is the number of wind turbines, N p is the number of photovoltaic power stations, N s is the number of days of historical data collected, The mth day is numbered N w The output of the wind turbine at time T, The mth day is numbered N p The output of the photovoltaic array at time T, ω m is the wind and solar power output history matrix for the mth day; According to the historical matrix of wind and solar power output, the minimum volume envelope ellipsoid algorithm is used to determine the minimum volume ellipsoid; The extreme scene is obtained by using rotation transformation and inverse transformation according to the ellipsoid with the minimum volume.

4. The method for cooperative scheduling of intelligent network-connected multi-microgrid mutual assistance according to claim 1 is characterized in that: According to the uncertain source-load output set and the wind-solar output limit scenario, the microgrid energy storage module is modeled and the unexpected energy storage constraints are introduced to obtain the energy storage model, which specifically includes: Using the formula and Constrain the upper and lower bounds of energy storage capacity; Using the formula Determine the energy storage capacity at time t safe area; in, The upper and lower bounds of the energy storage capacity that take into account unexpected events at time t are: The unexpected upper and lower bounds of energy storage capacity are considered at time t-1. are the upper and lower bounds of energy storage capacity without considering unexpected situations; P i ch,max , P i dis,max is the maximum charging and discharging power of energy storage, f -1 () is an auxiliary function, is the wind power output of microgrid i at time t in scenario s, is the photovoltaic output of microgrid i at time t in scenario s, is the maximum electric power transmitted from microgrid i to microgrid j through the energy router, Δt is the time interval, is the lower bound of load fluctuation, is the upper limit of load fluctuation, is the upper limit of the interaction power with the grid, and J is the set of microgrids interconnected with microgrid i.

5. The method for cooperative dispatching of intelligent network-connected multi-microgrids according to claim 4 is characterized in that: Using the formula and formula Determine the auxiliary function; Among them, η bt,ch , η bt,dis 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 method for cooperative dispatching of intelligent networked multi-microgrids according to claim 1, characterized in that: According to the intelligent network load model, the source-load output uncertainty set, the wind-solar output limit scenario and the energy storage model, an intelligent network multi-microgrid mutual assistance coordinated dispatching architecture is constructed; Taking minimizing the multi-microgrid operation coefficient as the objective function, energy routers are used for power interconnection, and a multi-microgrid mutual assistance dispatch model is constructed, which includes: Using the 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 is the interconnection status of microgrid i and microgrid j at time t under scenario s, i s,i,j,t-1 is the interconnection status of microgrid i and microgrid j at time t-1 under scenario s, are the minimum and maximum electric power transmitted from microgrid i to microgrid j through the energy router, respectively; is the interaction power between microgrid i and microgrid j at time t in scenario s. If it is positive, it means that microgrid i transmits power to microgrid j, and if it is negative, microgrid j transmits power to microgrid i. are all 0-1 variables. When , it indicates that the interconnection state of microgrid i and j changes from 0 to 1. When , it indicates that the interconnection status of microgrid i and j changes from 1 to 0; Using the formula Determine the electrical balance constraints; where, is the wind power output of microgrid i at time t in scenario s, is the photovoltaic output of microgrid i at time t in scenario s, are the energy storage charging and discharging power of microgrid i at time t under scenario s, is the load demand of microgrid i at time t under scenario s, is the interaction power between microgrid i and the grid at time t under scenario s, is the abandoned wind and solar power of microgrid i at time t in scenario s; Using the formula Determine the interaction power between microgrid i and the grid Among them, P i e，max is the maximum interaction power between microgrid i and the grid; Using the formula Determine the constraints on wind and solar power curtailment; Using the formula Determine the objective function; where C ah is the multi-microgrid operation coefficient, are 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 wind and solar power output limit scenario s obtained through the high-dimensional ellipsoid set; S and I are the representative scenario set and microgrid group set, respectively; β s is the probability of each representative scene, To consider the maximum capacity of energy storage after unexpected events, It is the minimum capacity of energy storage after considering unexpected events.

7. An intelligent network-connected multi-microgrid mutual assistance and coordinated dispatching device, characterized in that: The intelligent networked multi-microgrid mutual assistance coordinated dispatching equipment includes: The intelligent network load model building module is used to divide the intelligent network load into variable power operation equipment and constant power operation equipment; and model them respectively to obtain the corresponding intelligent network load model; the intelligent network load model is used to predict the load demand; the variable power operation equipment includes: roadside unit, 5G base station and edge computing equipment; the constant power operation equipment includes: radar, broadcast, fisheye camera, ball camera, serial port server and control and release equipment; The uncertain set determination module is used to construct the source-load output uncertainty set and obtain the wind-solar output limit scenario based on the microgrid source-load historical data and the intelligent network load model; the microgrid source-load historical data includes: wind-solar output at historical moments and intelligent network load data at historical moments; The energy storage model determination module is used to model the microgrid energy storage module and introduce unexpected energy storage constraints according to the source-load output uncertainty set and the wind-solar output limit scenario to obtain the energy storage model; The day-ahead multi-microgrid mutual assistance dispatch model construction module is used to construct a smart network multi-microgrid mutual assistance coordinated dispatch architecture based on the smart network load model, source-load output uncertainty set, wind-solar output limit scenario and energy storage model; and to minimize the multi-microgrid operation coefficient as the objective function, use energy routers for power interconnection, and construct a day-ahead multi-microgrid mutual assistance dispatch model; The dispatch optimization module is used to obtain the mutual aid power between microgrids and the optimal day-ahead dispatch plan based on the day-ahead multi-microgrid mutual aid dispatch model.

8. A computer device comprising: 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 method for mutual assistance and coordinated scheduling of intelligent connected multi-microgrids according to any one of claims 1 to 6.

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 for mutual assistance and coordinated scheduling of intelligent connected multi-microgrids as described in 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 for mutual assistance and coordinated scheduling of intelligent connected multi-microgrids as described in any one of claims 1 to 6 is implemented.

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