CN117728421A - Micro-grid cluster coordinated scheduling method, system, computer equipment and storage medium - Google Patents
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Abstract
The invention discloses a micro-grid cluster coordination scheduling method, a micro-grid cluster coordination scheduling system, computer equipment and a storage medium, which relate to the technical field of micro-grid cluster coordination scheduling and comprise the following steps: constructing a micro-grid electrical model, constructing a toughness analysis model through static power flow analysis and dynamic power flow analysis, and performing toughness assessment; setting an optimization target according to the evaluation result, and automatically generating a scheduling strategy by combining a multi-target optimization algorithm; deploying a sensor and monitoring equipment, collecting data of power grid operation in real time, and detecting the data of the power grid operation by using time sequence analysis; and (3) applying a reinforcement learning algorithm, and optimizing the operation of the power grid in real time according to the data and the scheduling strategy of the operation of the power grid. The micro-grid cluster coordination scheduling method provided by the invention improves the running efficiency and stability of the power grid and maximizes the utilization of renewable energy sources. According to the real-time state of the power grid, power generation and load distribution are dynamically adjusted, and energy waste and overload risks are reduced. The response capability to the abnormal condition of the power grid and the overall toughness of the system are improved.
Description
Technical Field
The invention relates to the technical field of micro-grid cluster coordinated scheduling, in particular to a micro-grid cluster coordinated scheduling method, a micro-grid cluster coordinated scheduling system, computer equipment and a storage medium.
Background
Micro-grids are an important component of modern power systems, providing new solutions for improving reliability, stability and efficiency of the grid. Traditional power grid management methods focus on centralized control, and tend to ignore dynamic changes of local networks and optimal configuration of energy sources. With the widespread use of renewable energy sources and the increasing complexity of grid loads, traditional grid scheduling methods face new challenges.
The traditional grid management method is inefficient in handling complex, dynamically changing micro grid systems. These methods often lack the ability to analyze real-time data deeply, and are not effective in predicting and coping with rapidly changing grid conditions such as load fluctuations, power flow changes, and grid stability problems.
Therefore, there is a need for a micro-grid cluster coordination scheduling method for monitoring and analyzing grid data in real time, predicting future grid states, identifying potential risks, and formulating a corresponding optimization scheduling strategy.
Disclosure of Invention
The present invention has been made in view of the above-described problems.
Therefore, the technical problems solved by the invention are as follows: the existing power grid management method has the problems of low efficiency and insufficient response, and the problem of how to effectively utilize real-time data to perform optimal scheduling of the micro-grid clusters.
In order to solve the technical problems, the invention provides the following technical scheme: a micro-grid cluster coordination scheduling method comprises the following steps:
deploying a sensor and monitoring equipment, constructing a micro-grid electrical model, constructing a toughness analysis model through static power flow analysis and dynamic power flow analysis, and performing toughness assessment; setting an optimization target according to the evaluation result, and automatically generating a scheduling strategy by combining a multi-target optimization algorithm; detecting power grid operation data after the scheduling strategy is executed by utilizing time sequence analysis; and applying a reinforcement learning algorithm according to the power grid operation data to optimize the power grid operation in real time.
As a preferable scheme of the micro-grid cluster coordination scheduling method, the invention comprises the following steps: the method comprises the steps of constructing an electric model of the micro-grid, identifying and recording key components in each micro-grid, wherein the key components comprise a generator set, a load point, an energy storage unit and an interconnection interface, defining electric properties for each key component, wherein the electric properties comprise rated voltage, maximum power output, impedance parameters and efficiency characteristics, establishing an interconnection topological structure among the micro-grids, covering interconnection lines, transformers and switching equipment, and integrating key environment and operation data.
The static power flow analysis is expressed as:
；
wherein,indicating line->Power flow on, power slave node in power grid ∈>Transmitting to node->Quantity of->Representing node->Voltage of>Representing node->Voltage of>Indicating line->Is (are) conductive part of->Indicating line->Susceptance portion of->Representing node->And->Voltage phase angle difference between them.
The dynamic power flow analysis is expressed as:
；
wherein,representing node->Phase angle change rate, < >>Representing node->Is>Representing node->And->Admittance between->Representing node->Voltage of>Representing node->Voltage of>Representing node->Is used for the voltage phase angle of (a),representing node->Voltage phase of (2)And (5) corners.
The toughness analysis model is expressed as:
；
wherein,indicating system toughness index,/->Indicating adjustment circuit->Upper power flow->Coefficients of weights in the toughness index, +.>Representing adjustment node->Phase angle change rate>Coefficients of weights in the toughness index, +.>Indicating adjustment circuitImpedance +.>Coefficients of weights in the toughness index, +.>Indicating line->Impedance of->Representing adjustment node->Load power of->Coefficients of weights in the toughness index, +.>Representing node->Is set in the above-described range).
As a preferable scheme of the micro-grid cluster coordination scheduling method, the invention comprises the following steps: the toughness assessment includes obtaining a system toughness index via the toughness analysis modelWill->Comparing with the evaluation threshold, if->Judging that the system has potential safety risk when the power flow is lower than an evaluation threshold value, starting a risk identification process, and utilizing the power flow +.>Data identifying lines facing overload using node phase angle change rate +.>Dynamic stability of the electrical network is evaluated, combined +.>And voltage->Data identifying a short circuit condition in the power grid.
As a preferable scheme of the micro-grid cluster coordination scheduling method, the invention comprises the following steps: the scheduling strategy comprises the steps of constructing an optimization model comprising overload risk minimization, dynamic stability maximization and short circuit probability minimization based on the optimization targets, incorporating constraint conditions of power grid operation into the model, inputting the optimization model and the electrical attributes into a multi-target optimization algorithm, and balancing optimal solutions of all the optimization targets to solve the optimal scheduling strategy under the condition of meeting all the constraint conditions.
The minimized optimization model is expressed as:
；
wherein,representing the maximum capacity of the line, +.>For the first weight factor, represent line importance, < +.>For the second weight factor, represent the importance of node stability,/->Representing a third weight factor,/->Representation function, measure line->And (5) the probability of short circuit.
The constraint is expressed as:
；
wherein,representing node->Is>Representing a set of nodes in the grid, +.>Representing a set of lines in the grid, +.>Representing node->Is provided.
As a preferable scheme of the micro-grid cluster coordination scheduling method, the invention comprises the following steps: the optimal scheduling strategy solving comprises an optimal solution set provided by an analysis algorithm, and output adjustment is carried out on the power station, so that the power generation amount of the renewable energy power station is increased, and the output of the traditional power station is reduced; the power grid load is redistributed, the power station output adjustment result is combined, the area needing load redistribution is identified, the demand response measures are started in the high load area, the load distribution is adjusted, the power grid pressure is reduced, the load management strategy is adjusted according to the real-time monitoring data, and the overload risk is reduced; changing the line configuration, comprehensively considering the results of power generation adjustment and load redistribution, analyzing the requirement of the line configuration adjustment, reconfiguring the power grid line, adjusting the transformer setting, and optimizing the power flow.
As a preferable scheme of the micro-grid cluster coordination scheduling method, the invention comprises the following steps: the power grid operation data after the dispatching strategy is executed comprises real-time power flow data, line load level and power flow direction, real-time measurement values of voltage and current on power grid nodes and lines, recording phase differences among power grid frequencies and nodes, real-time load demand data of the nodes, carrying out load prediction by combining historical data, and monitoring real-time power generation capacity and operation states of different types of power stations and environmental factors influencing the power generation efficiency of renewable energy sources; the step of detecting the power grid operation data after the scheduling strategy is executed by using time sequence analysis comprises the steps of cleaning and standardizing the power grid operation data, and analyzing standardized historical and real-time load data by using an ARIMA model to predict short-term and long-term load fluctuation trend; according to the load fluctuation trend, analyzing real-time power flow data on power grid nodes and lines, and evaluating time change trend and influence; predicting future power flow changes and potential influences on a power grid by combining analysis results of the load fluctuation trend, the time change trend and the influences by using a neural network model; and analyzing the frequency data and the phase difference of the power grid by utilizing Fourier transformation, and evaluating the stability of the power grid by combining the analysis results of predicting future power flow changes and potential influences on the power grid.
As a preferable scheme of the micro-grid cluster coordination scheduling method, the invention comprises the following steps: the real-time optimizing power grid operation comprises the steps of carrying out cluster analysis on the time sequence analysis and detection results, and identifying the normal state and the abnormal state in the power grid; if the power grid stability index shows that the power grid runs in a safe and stable range, judging the power grid to be in a normal state, continuously maintaining the existing power grid running strategy and scheduling plan, and continuously monitoring key indexes including power flow, load fluctuation and frequency stability; if the power grid stability index shows that the power grid has potential risks, judging the power grid to be in an abnormal state, and analyzing specific types, wherein the specific types comprise overload risks, dynamic instability and load fluctuation; if the overload risk is identified, predicting and adjusting an overload line or node by using a reinforcement learning algorithm, increasing the output of a nearby renewable energy power station, temporarily starting energy storage equipment, and reconfiguring a power grid line to disperse the load; if the dynamic instability is identified, the frequency response and the load balance are optimized by using a reinforcement learning algorithm, load transfer is executed, energy storage equipment measures are started, and the response capacity and the stability of the power grid are enhanced; if the load fluctuation is identified, the reinforcement learning algorithm predicts future load fluctuation and puts forward an adjustment strategy, and power generation and load distribution are optimized according to the demand side response and the power grid dispatching plan, so that the power grid load fluctuation is reduced.
Another object of the present invention is to provide a micro-grid cluster coordinated scheduling system, which can optimize the operation of the micro-grid by using advanced data analysis technology and algorithm, ensure the stability and high efficiency of the grid, and simultaneously fully utilize renewable energy sources to improve the overall sustainability of the system.
In order to solve the technical problems, the invention provides the following technical scheme: a micro-grid cluster coordinated scheduling system, comprising: the system comprises a power grid modeling module, a multi-target scheduling module, a data intelligent analysis module and a real-time power grid adjustment module; the power grid modeling module builds a micro-grid electric model, builds a toughness analysis model through static power flow analysis and dynamic power flow analysis, and performs protection device configuration analysis and system stability analysis; the multi-objective scheduling module sets an optimization objective according to the evaluation result and automatically generates a scheduling strategy by combining a multi-objective optimization algorithm; the data intelligent analysis module deploys a sensor and monitoring equipment, collects the data of the power grid operation in real time, and detects the data of the power grid operation by using time sequence analysis; and the real-time power grid adjustment module applies a reinforcement learning algorithm to optimize the power grid operation in real time according to the power grid operation data and the scheduling strategy.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the microgrid cluster co-ordination scheduling method as described above when the computer program is executed.
A computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of a microgrid cluster coordination scheduling method as described above.
The invention has the beneficial effects that: the micro-grid cluster coordination scheduling method provided by the invention improves the running efficiency and stability of the power grid, and maximizes the utilization of renewable energy sources. According to the real-time state of the power grid, power generation and load distribution are dynamically adjusted, so that energy waste and overload risks are reduced. Through intelligent scheduling, the response capability to the abnormal condition of the power grid and the overall toughness of the system are improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is an overall flowchart of a micro grid cluster coordination scheduling method according to an embodiment of the present invention.
Fig. 2 is an overall structure diagram of a micro-grid cluster coordination and scheduling system according to a second embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Example 1
Referring to fig. 1, for one embodiment of the present invention, there is provided a micro grid cluster coordinated scheduling method, including:
s1: and deploying a sensor and monitoring equipment, constructing a micro-grid electrical model, constructing a toughness analysis model through static power flow analysis and dynamic power flow analysis, and performing toughness assessment.
S2: and setting an optimization target according to the evaluation result, and automatically generating a scheduling strategy by combining a multi-target optimization algorithm.
S3: and detecting the power grid operation data after the scheduling strategy is executed by using time sequence analysis.
S4: and applying a reinforcement learning algorithm according to the power grid operation data to optimize the power grid operation in real time.
The method comprises the steps of constructing an electric model of the micro-grid, identifying and recording key components in each micro-grid, wherein the key components comprise a generator set, load points, an energy storage unit and an interconnection interface, defining electric properties for each key component, wherein the electric properties comprise rated voltage, maximum power output, impedance parameters and efficiency characteristics, establishing an interconnection topological structure among the micro-grids, covering interconnection lines, transformers and switching equipment, and integrating key environment and operation data.
The static power flow analysis is expressed as:
；
wherein,indicating line->Power flow on, power slave node in power grid ∈>Transmitting to node->Quantity of->Representing node->Voltage of>Representing node->Voltage of>Indicating line->Is (are) conductive part of->Indicating line->Susceptance portion of->Representing node->And->Voltage phase angle difference between them.
The dynamic power flow analysis is expressed as:
；
wherein,representing node->Phase angle change rate, < >>Representing node->Is>Representing node->And->The admittance between them,/>representing node->Voltage of>Representing node->Voltage of>Representing node->Is used for the voltage phase angle of (a),representing node->Is a voltage phase angle of (c).
The toughness analysis model is expressed as:
；
wherein,representing system toughness index, reflecting comprehensive performance of micro-grid under specific condition,/and/or>Indicating adjustment circuit->Upper power flow->Coefficients of weights in the toughness index, +.>Indicating line->Power flow from static tide analysis, +.>Representing adjustment node->Phase angle change rate>Coefficients of weights in the toughness index, +.>Representing node->From dynamic tide analysis, +.>Indicating adjustment circuit->Impedance +.>Coefficients of weights in the toughness index, +.>Indicating line->The impedance, typically including the influence of resistance and reactance, of the voltage and current relationship in the power system is a critical parameter,/->Representing adjustment node->Load power of->Coefficients of weights in the toughness index, +.>Representing node->Is set in the above-described range).
The toughness assessment includes obtaining a system toughness index through a toughness analysis modelWill->Comparing with the evaluation threshold, if->Judging that the system has potential safety risk when the power flow is lower than an evaluation threshold value, starting a risk identification process, and utilizing the power flow +.>Data identifying lines facing overload using node phase angle change rate +.>Dynamic stability of the electrical network is evaluated, combined +.>And voltage->Data identifying a short circuit condition in the power grid.
The scheduling strategy comprises the steps of constructing an optimization model comprising overload risk minimization, dynamic stability maximization and short circuit probability minimization based on optimization targets, taking constraint conditions of power grid operation into the model, inputting the optimization model and electrical properties into a multi-target optimization algorithm, and balancing optimal solutions of all the optimization targets to solve the optimal scheduling strategy under the condition of meeting all the constraint conditions.
The minimized optimization model is expressed as:
；
wherein,indicating line->Power flow on->Representing the maximum capacity of the line, +.>For the first weight factor, represent line importance, < +.>For the second weight factor, represent the importance of node stability,/->Representing a third weight factor,/->Representation function, measure line->The probability of short circuit is increased;
the constraint is expressed as:
；
wherein,and->Respectively represent node->Load and power generation of>Representing a set of nodes in the grid, +.>Representing a set of lines in the grid, +.>Representing node->Load of->Representing node->Is>Representing node->Voltage of>Representing node->Is provided.
Solving an optimal scheduling strategy comprises analyzing an optimal solution set provided by an algorithm, carrying out output adjustment on a power station, increasing the power generation amount of a renewable energy power station and reducing the output of a traditional power station; the power grid load is redistributed, the power station output adjustment result is combined, the area needing load redistribution is identified, the demand response measures are started in the high load area, the load distribution is adjusted, the power grid pressure is reduced, the load management strategy is adjusted according to the real-time monitoring data, and the overload risk is reduced; changing the line configuration, comprehensively considering the results of power generation adjustment and load redistribution, analyzing the requirement of the line configuration adjustment, reconfiguring the power grid line, adjusting the transformer setting, and optimizing the power flow.
The power grid operation data after the dispatching strategy is executed comprises real-time power flow data, line load level and power flow direction, real-time measured values of voltage and current, real-time load demand data of the nodes, and real-time power generation capacity and operation state of different types of power stations, wherein the real-time power flow data, the line load level and the power flow direction, the real-time measured values of voltage and current, the real-time load demand data of the nodes, the phase difference of the power grid frequency and each node are recorded, the load prediction is carried out by combining the historical data, and the real-time power generation capacity and the operation state of the power stations with different types and the environmental factors influencing the power generation efficiency of renewable energy sources are monitored; the method comprises the steps of performing time sequence analysis, detecting and scheduling strategy execution on power grid operation data, including cleaning and standardizing the power grid operation data, and performing analysis on standardized historical and real-time load data by using an ARIMA model to predict short-term and long-term load fluctuation trend; according to the load fluctuation trend, analyzing real-time power flow data on power grid nodes and lines, and evaluating time change trend and influence; predicting future power flow changes and potential influences on a power grid by using a neural network model and combining analysis results of load fluctuation trend, time change trend and influence; and analyzing the frequency data and the phase difference of the power grid by utilizing Fourier transformation, and evaluating the stability of the power grid by combining the analysis result of predicting future power flow changes and potential influences on the power grid.
Optimizing the operation of the power grid in real time comprises performing cluster analysis on the results of time sequence analysis and detection, and identifying the normal state and the abnormal state in the power grid; if the power grid stability index shows that the power grid runs in a safe and stable range, judging the power grid to be in a normal state, continuously maintaining the existing power grid running strategy and scheduling plan, and continuously monitoring key indexes including power flow, load fluctuation and frequency stability; if the power grid stability index shows that the power grid has potential risks, judging the power grid to be in an abnormal state, and analyzing specific types, wherein the specific types comprise overload risks, dynamic instability and load fluctuation; if the overload risk is identified, predicting and adjusting an overload line or node by using a reinforcement learning algorithm, increasing the output of a nearby renewable energy power station, temporarily starting energy storage equipment, and reconfiguring a power grid line to disperse the load; if the dynamic instability is identified, the frequency response and the load balance are optimized by using a reinforcement learning algorithm, load transfer is executed, energy storage equipment measures are started, and the response capacity and the stability of the power grid are enhanced; if the load fluctuation is identified, the reinforcement learning algorithm predicts future load fluctuation and puts forward an adjustment strategy, and power generation and load distribution are optimized according to the demand side response and the power grid dispatching plan, so that the power grid load fluctuation is reduced.
Example 2
Referring to fig. 2, for one embodiment of the present invention, there is provided a micro grid cluster coordinated scheduling system, including:
the system comprises a power grid modeling module, a multi-target scheduling module, a data intelligent analysis module and a real-time power grid adjustment module.
The power grid modeling module builds a micro-grid electric model, builds a toughness analysis model through static power flow analysis and dynamic power flow analysis, and performs protection device configuration analysis and system stability analysis.
And the multi-target scheduling module sets an optimization target according to the evaluation result and automatically generates a scheduling strategy by combining a multi-target optimization algorithm.
The intelligent data analysis module deploys sensors and monitoring equipment, collects the running data of the power grid in real time, and detects the running data of the power grid by using time sequence analysis.
And the real-time power grid adjustment module applies a reinforcement learning algorithm to optimize the power grid operation in real time according to the power grid operation data and the scheduling strategy.
Example 3
One embodiment of the present invention, which is different from the first two embodiments, is:
the functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, randomAccess Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Example 4
For one embodiment of the invention, a micro-grid cluster coordination scheduling method is provided, and for verifying the effectiveness of the micro-grid cluster coordination scheduling method provided by the invention, the following experiment is designed. The experimental aim is to demonstrate the advantages of the method of the invention over conventional methods in terms of improving the operating efficiency, stability and renewable energy utilization of the power grid.
The experiment is divided into two parts: control group (traditional method) and experimental group (method of the invention). Three micro-grids with similar conditions were selected for testing per group. The control group adopts a static scheduling method based on rules, and the experimental group adopts a dynamic scheduling method based on the data driving and reinforcement learning algorithm.
In order to ensure the accuracy of the data, detailed equipment inspection and system calibration are performed on all the test micro-grids before the experiment. During the experiment, the operational data (including power flow, load fluctuations, frequency and phase differences) for each microgrid was recorded in real time. The experiment was continued for 30 days during which a significant amount of grid operation data was collected, including but not limited to energy consumption, load satisfaction, renewable energy utilization, and system stability metrics.
The experimental group uses the technical scheme of my invention to conduct deep analysis on the real-time power grid data. Based on the analysis result, the experimental group dynamically adjusts the power grid operation strategy through a reinforcement learning algorithm, and optimizes power generation and load distribution. Meanwhile, the experimental group responds to the change of the power grid in real time, and the strategy is quickly adjusted to cope with potential overload risks and instability problems. The experimental results are shown in table 1.
Table 1 comparison of experimental results
From the above table, the experimental group showed significant advantages in terms of energy consumption, load satisfaction rate, renewable energy utilization rate, and system stability index, compared to the control group.
Firstly, the average energy consumption of the micro-grid of the experimental group is lower than that of the control group, which shows that the method of the invention more effectively manages the energy distribution of the grid and reduces unnecessary energy waste. Secondly, the load satisfaction rate of the experimental group is generally higher than that of the control group, so that the micro-grid of the experimental group can better meet the power requirements of users, and the operation efficiency of the grid is improved.
The experimental group performed better than the control group in terms of renewable energy utilization. This result reflects the ability of the inventive method to optimize renewable energy integration, particularly in terms of dynamic adjustment of power generation strategies and load distribution.
Finally, from the aspect of system stability index, the micro-grid of the experimental group performs better in terms of stability maintenance, and the high efficiency of the method in the aspects of identifying and coping with the grid instability factors is highlighted.
In summary of the data analysis, the micro-grid cluster coordinated scheduling method provided by the invention has remarkable advantages compared with the traditional method in the aspects of improving energy efficiency, meeting load demands, optimizing renewable energy utilization and maintaining system stability. The method not only solves the defects of the traditional power grid management method, but also introduces an innovative technical scheme, thereby realizing optimization and innovation of power grid management.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (10)
1. The micro-grid cluster coordination scheduling method is characterized by comprising the following steps of:
deploying a sensor and monitoring equipment, constructing a micro-grid electrical model, constructing a toughness analysis model through static power flow analysis and dynamic power flow analysis, and performing toughness assessment;
setting an optimization target according to the evaluation result, and automatically generating a scheduling strategy by combining a multi-target optimization algorithm;
detecting power grid operation data after the scheduling strategy is executed by utilizing time sequence analysis;
and applying a reinforcement learning algorithm according to the power grid operation data to optimize the power grid operation in real time.
2. The micro-grid cluster coordination scheduling method as claimed in claim 1, wherein: the method comprises the steps that a micro-grid electrical model is built, key components in each micro-grid are identified and recorded, the key components comprise a generator set, a load point, an energy storage unit and an interconnection interface, electrical attributes are defined for each key component, the electrical attributes comprise rated voltage, maximum power output, impedance parameters and efficiency characteristics, an interconnection topological structure among the micro-grids is built, interconnection lines, transformers and switching equipment are covered, and key environment and operation data are integrated;
the static power flow analysis is expressed as,
；
wherein,indicating line->Power flow on, power slave node in power grid ∈>Transmitting to node->Quantity of->Representing nodesVoltage of>Representing node->Voltage of>Indicating line->Is (are) conductive part of->Indicating line->Is provided with a susceptance portion of (c),representing node->And->A voltage phase angle difference therebetween;
the dynamic power flow analysis is expressed as,
；
wherein,representing node->Phase angle change rate, < >>Representing node->Is>Representing node->And->Admittance between->Representing node->Voltage of>Representing node->Voltage of>Representing node->Voltage phase angle,/v>Representing node->Voltage phase angle of (2);
the toughness analysis model is expressed as,
；
wherein,indicating system toughness index,/->Indicating adjustment circuit->Upper power flow->Coefficients of weights in the toughness index, +.>Representing adjustment node->Phase angle change rate>Coefficients of weights in the toughness index, +.>Indicating adjustment circuit->Impedance +.>Coefficients of weights in the toughness index, +.>Indicating line->Impedance of->Representing adjustment node->Load power of->Coefficients of weights in the toughness index, +.>Representing node->Is set in the above-described range).
3. The micro-grid cluster coordination scheduling method as claimed in claim 2, wherein: the toughness assessment includes obtaining a system toughness index via the toughness analysis modelWill->Comparing with the evaluation threshold, if->Judging that the system has potential safety risk when the power flow is lower than an evaluation threshold value, starting a risk identification process, and utilizing the power flow +.>Data identifying lines facing overload using node phase angle change rate +.>Dynamic stability of the electrical network is evaluated, combined +.>And voltage->Data identifying a short circuit condition in the power grid.
4. The micro-grid cluster coordination scheduling method as set forth in claim 3, wherein: the scheduling strategy comprises the steps of constructing an optimization model comprising overload risk minimization, dynamic stability maximization and short circuit probability minimization based on the optimization targets, incorporating constraint conditions of power grid operation into the model, inputting the optimization model and the electrical attributes into a multi-target optimization algorithm, and balancing optimal solutions of all the optimization targets to solve the optimal scheduling strategy under the condition of meeting all the constraint conditions;
the minimized optimization model is expressed as,
；
wherein,representing the maximum capacity of the line, +.>For the first weight factor, represent line importance, < +.>For the second weight factor, represent the importance of node stability,/->Representing a third weight factor,/->Representing a function, measuring a lineThe probability of short circuit is increased;
the constraint condition is expressed as that,
；
wherein,representing node->Is>Representing a set of nodes in the grid, +.>Representing a set of lines in the grid, +.>Representing node->Is provided.
5. The micro-grid cluster coordination scheduling method according to claim 4, wherein: the optimal scheduling strategy solving comprises an optimal solution set provided by an analysis algorithm, and output adjustment is carried out on the power station, so that the power generation amount of the renewable energy power station is increased, and the output of the traditional power station is reduced; the power grid load is redistributed, the power station output adjustment result is combined, the area needing load redistribution is identified, the demand response measures are started in the high load area, the load distribution is adjusted, the power grid pressure is reduced, the load management strategy is adjusted according to the real-time monitoring data, and the overload risk is reduced; changing the line configuration, comprehensively considering the results of power generation adjustment and load redistribution, analyzing the requirement of the line configuration adjustment, reconfiguring the power grid line, adjusting the transformer setting, and optimizing the power flow.
6. The micro-grid cluster coordination scheduling method according to claim 5, wherein: the power grid operation data after the dispatching strategy is executed comprises real-time power flow data, line load level and power flow direction, real-time measurement values of voltage and current on power grid nodes and lines, recording phase differences among power grid frequencies and nodes, real-time load demand data of the nodes, carrying out load prediction by combining historical data, and monitoring real-time power generation capacity and operation states of different types of power stations and environmental factors influencing the power generation efficiency of renewable energy sources;
the step of detecting the power grid operation data after the scheduling strategy is executed by using time sequence analysis comprises the steps of cleaning and standardizing the power grid operation data, and analyzing standardized historical and real-time load data by using an ARIMA model to predict short-term and long-term load fluctuation trend;
according to the load fluctuation trend, analyzing real-time power flow data on power grid nodes and lines, and evaluating time change trend and influence;
predicting future power flow changes and potential influences on a power grid by combining analysis results of the load fluctuation trend, the time change trend and the influences by using a neural network model;
and analyzing the frequency data and the phase difference of the power grid by utilizing Fourier transformation, and evaluating the stability of the power grid by combining the analysis results of predicting future power flow changes and potential influences on the power grid.
7. The micro-grid cluster coordination scheduling method according to claim 6, wherein: the real-time optimizing power grid operation comprises the steps of carrying out cluster analysis on the time sequence analysis and detection results, and identifying the normal state and the abnormal state in the power grid;
if the power grid stability index shows that the power grid runs in a safe and stable range, judging the power grid to be in a normal state, continuously maintaining the existing power grid running strategy and scheduling plan, and continuously monitoring key indexes including power flow, load fluctuation and frequency stability;
if the power grid stability index shows that the power grid has potential risks, judging the power grid to be in an abnormal state, and analyzing specific types, wherein the specific types comprise overload risks, dynamic instability and load fluctuation;
if the overload risk is identified, predicting and adjusting an overload line or node by using a reinforcement learning algorithm, increasing the output of a nearby renewable energy power station, temporarily starting energy storage equipment, and reconfiguring a power grid line to disperse the load;
if the dynamic instability is identified, the frequency response and the load balance are optimized by using a reinforcement learning algorithm, load transfer is executed, energy storage equipment measures are started, and the response capacity and the stability of the power grid are enhanced;
if the load fluctuation is identified, the reinforcement learning algorithm predicts future load fluctuation and puts forward an adjustment strategy, and power generation and load distribution are optimized according to the demand side response and the power grid dispatching plan, so that the power grid load fluctuation is reduced.
8. A system employing the micro grid cluster coordination scheduling method as claimed in any one of claims 1 to 7, comprising: the system comprises a power grid modeling module, a multi-target scheduling module, a data intelligent analysis module and a real-time power grid adjustment module;
the power grid modeling module builds a micro-grid electric model, builds a toughness analysis model through static power flow analysis and dynamic power flow analysis, and performs protection device configuration analysis and system stability analysis;
the multi-objective scheduling module sets an optimization objective according to the evaluation result and automatically generates a scheduling strategy by combining a multi-objective optimization algorithm;
the data intelligent analysis module deploys a sensor and monitoring equipment, collects the data of the power grid operation in real time, and detects the data of the power grid operation by using time sequence analysis;
and the real-time power grid adjustment module applies a reinforcement learning algorithm to optimize the power grid operation in real time according to the power grid operation data and the scheduling strategy.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the micro grid cluster coordinated scheduling method of any one of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor implements the steps of the micro grid cluster coordinated scheduling method of any of claims 1 to 7.
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