Flexible load adjusting method and system applied to micro-grid

Abstract

The invention belongs to the field of flexible load regulation, and discloses a flexible load regulation method and system applied to a micro-grid, and the method comprises the steps: employing a data fusion algorithm to comprehensively analyze load power and power output multi-source heterogeneous data according to the real-time operation state data of the micro-grid, and obtaining a comprehensive operation state evaluation result of the micro-grid; based on the comprehensive operation state evaluation result, various loads in the micro-grid are identified and classified through a load characteristic identification algorithm, and power utilization characteristic parameters, including load priorities, power utilization habits and adjustment characteristics, of different types of loads are obtained; and the load allocation strategy quick response mechanism quickly calculates the load power adjustment amount according to the power supply output fluctuation amplitude and the load priority based on a preset response rule, and issues an adjustment instruction in real time through the energy management system to ensure the power balance and stable operation of the microgrid.

Description

Technical Field

[0001] The present invention relates to the technical field of flexible load regulation, and in particular to a flexible load regulation method and system applied to a microgrid. Background Art

[0002] There are many types of loads in microgrids, such as critical loads, controllable loads, interruptible loads, etc. Different types of loads have different requirements for power quality and power supply reliability. In the real-time operation of microgrids, load power and power output are changing dynamically. In order to ensure the stable operation and economy of microgrids, it is necessary to optimize the load allocation according to load characteristics and power output. However, the regulation characteristics of different types of loads are quite different. For example, critical loads need to ensure continuous power supply, while interruptible loads are allowed to interrupt power supply within a specific period of time. In addition, load allocation also needs to consider multiple factors such as load priority, power consumption habits, and electricity price policies. Therefore, how to formulate a reasonable load allocation strategy based on the real-time operation status of the microgrid, comprehensively consider multiple constraints, coordinate the operation of different types of loads, achieve a dynamic balance between loads and power output, improve the energy utilization efficiency and operation economy of the microgrid, and take into account the power quality and reliability requirements of the loads at the same time is a technical problem that needs to be solved urgently. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides a flexible load regulation method and system applied to a microgrid, which realizes intelligent and precise regulation of the microgrid load and improves the system operation efficiency and reliability.

[0004] The present invention provides a flexible load regulation method applied to a microgrid, the method comprising:

[0005] According to the real-time operation status data of the microgrid, a data fusion algorithm is used to comprehensively analyze the multi-source heterogeneous data of load power and power output to obtain the comprehensive operation status evaluation results of the microgrid;

[0006] Based on the comprehensive operation status evaluation results, the various types of loads in the microgrid are identified and classified through the load characteristics identification algorithm to obtain the power consumption characteristic parameters of different types of loads;

[0007] Combined with the load's power consumption characteristic parameters, the power quality analysis algorithm is used to conduct real-time evaluation of the microgrid's power quality indicators;

[0008] According to the power quality assessment results, combined with load priority and electricity price policy factors, a load dispatch strategy optimization model is constructed to optimize the power allocation of controllable loads and interruptible loads while ensuring continuous power supply to key loads;

[0009] Based on the optimization results of the load dispatching strategy, power dispatching instructions for various loads are generated to regulate the load power in real time. During the regulation process, the power output changes are continuously monitored. When the power output fluctuates, the load dispatching strategy rapid response mechanism is activated.

[0010] The load dispatch strategy quick response mechanism is based on preset response rules. It calculates the load power adjustment amount according to the power output fluctuation amplitude and load priority to ensure the power balance and stable operation of the microgrid.

[0011] Preferably, according to the real-time operation status data of the microgrid, a data fusion algorithm is used to comprehensively analyze the multi-source heterogeneous data of load power and power output, and the comprehensive operation status evaluation results of the microgrid are obtained, including:

[0012] Obtain real-time operating status data of each component of the microgrid;

[0013] Preprocess the acquired multi-source heterogeneous data and convert the data into a unified format;

[0014] The Kalman filter algorithm is used to fuse the preprocessed data to obtain an estimated value of the overall operating status of the microgrid;

[0015] According to the fused estimated values ​​of the microgrid operation status, key indicators reflecting the operation characteristics of the microgrid are extracted;

[0016] Compare the extracted key indicators with the preset thresholds to evaluate the operating status of the microgrid;

[0017] If the key indicators exceed the normal range, the early warning mechanism will be triggered, the early warning level will be determined according to the degree of deviation, and an operation status assessment report will be generated;

[0018] In the operation status assessment report, the specific values, normal ranges and deviations of each key indicator of the microgrid are described in detail to provide a basis for decision-making for dispatchers.

[0019] Preferably, based on the comprehensive operation status evaluation result, various types of loads in the microgrid are identified and classified through a load characteristic identification algorithm, and the power consumption characteristic parameters of different types of loads are obtained, including:

[0020] Obtain real-time operation data of the microgrid by comprehensively evaluating the operation status of the microgrid;

[0021] According to the acquired microgrid operation status data, a load characteristic recognition algorithm based on machine learning is used to identify and classify various loads in the microgrid, and obtain labels for different types of loads;

[0022] For the different types of loads identified, the power consumption characteristic parameters of each type of load are obtained by analyzing the historical power consumption data.

[0023] Preferably, combining the power consumption characteristic parameters of the load and using the power quality analysis algorithm, the power quality indicators of the microgrid are evaluated in real time, including:

[0024] Obtain real-time operation data of the microgrid and power demand and characteristic parameters of different types of loads;

[0025] The acquired data is input into a pre-built power quality assessment model, which uses a support vector machine algorithm and is obtained by training historical data;

[0026] The model outputs real-time evaluation values ​​of various power quality indicators;

[0027] The evaluation value of the power quality index is compared with the preset quality threshold to determine whether the current power quality meets the power supply requirements of different loads.

[0028] Preferably, based on the power quality assessment results, combined with load priority and electricity price policy factors, a load dispatch strategy optimization model is constructed to optimize the power distribution of controllable loads and interruptible loads under the premise of ensuring continuous power supply to key loads, including:

[0029] Obtain power quality assessment results, load priority data and electricity price policy information, and determine critical loads, controllable loads and interruptible loads based on power quality assessment results;

[0030] According to the load priority data, a clustering algorithm is used to classify the loads and obtain a set of loads with different priorities;

[0031] According to the electricity price policy information, a load dispatch optimization model considering electricity price factors is constructed, and load priority and electricity price factors are used as model constraints;

[0032] Taking maximizing energy efficiency and minimizing operating costs as optimization goals, a multi-objective optimization model is established, and power balance and voltage limit constraints are set;

[0033] The particle swarm optimization algorithm is used to solve the multi-objective optimization model, and the Pareto optimal solution set is obtained through iterative search;

[0034] Select the optimal allocation scheme that balances energy utilization efficiency and operating cost from the Pareto optimal solution set and determine the power allocation of each load;

[0035] Generate load dispatch control instructions, perform power control on controllable loads and interruptible loads according to the optimization results, and monitor load responses to ensure continuous power supply to critical loads.

[0036] Preferably, based on the optimization result of the load dispatching strategy, power dispatching instructions for various loads are generated, and the load power is regulated in real time. During the regulation process, the power output change is continuously monitored. When the power output fluctuates, the load dispatching strategy rapid response mechanism is started, including:

[0037] After receiving the power dispatch instruction, each load control unit uses the PID control algorithm to adjust the actual operating power of each load according to the power value in the instruction, thus realizing real-time control of the load power;

[0038] During the load power regulation process, the power output data is continuously collected, and the data analysis algorithm is used to determine whether the power output fluctuates;

[0039] If the power output fluctuation exceeds the preset threshold, the load allocation strategy rapid response mechanism is triggered;

[0040] After the rapid response mechanism is activated, the heuristic optimization algorithm is used to quickly calculate the load power adjustment plan and generate new load power dispatch instructions based on the power output fluctuation and load priority;

[0041] The newly generated load power dispatching instructions are sent to the corresponding load control unit through the microgrid energy management system. The load control unit quickly adjusts the operating power of each load according to the new instructions to achieve dynamic balance of load power;

[0042] During the dynamic balancing process of load power, the power output and load power data are continuously monitored, and an adaptive control algorithm is used to dynamically optimize the load power control parameters;

[0043] According to the evaluation results of load regulation effect, the reinforcement learning algorithm is used to perform self-learning optimization on the load dispatching strategy to realize adaptive optimization control of microgrid energy management.

[0044] Preferably, the load dispatching strategy quick response mechanism is based on a preset response rule, calculates the load power adjustment amount according to the power output fluctuation amplitude and load priority, and ensures the power balance and stable operation of the microgrid, including:

[0045] Obtain real-time data on power output, and calculate and judge the fluctuation range of power output;

[0046] Determine the priority adjustment order of each load according to the preset load priority rules;

[0047] Through the rapid response mechanism, the power adjustment amount of each load is calculated according to the power output fluctuation amplitude and load priority;

[0048] The calculated load power adjustment amount is sent to each load control unit in real time through the energy management system;

[0049] After receiving the adjustment instruction, each load control unit quickly performs power adjustment to achieve dynamic allocation of load power.

[0050] The present invention also provides a flexible load regulation system for microgrids, the system is used to implement any one of the methods described, including: a comprehensive operation status evaluation module, a load characteristic identification module, a power quality analysis module, a load dispatch strategy optimization module, a load power scheduling module, and a rapid response mechanism module;

[0051] The comprehensive operation status evaluation module is used to comprehensively analyze the multi-source heterogeneous data of load power and power output according to the real-time operation status data of the microgrid by using a data fusion algorithm to obtain a comprehensive operation status evaluation result of the microgrid;

[0052] The load characteristic identification module is used to identify and classify various types of loads in the microgrid based on the comprehensive operation status evaluation results and through the load characteristic identification algorithm to obtain power consumption characteristic parameters of different types of loads;

[0053] The power quality analysis module is used to combine the power consumption characteristic parameters of the load and adopt the power quality analysis algorithm to perform real-time evaluation on the power quality indicators of the microgrid;

[0054] The load dispatch strategy optimization module is used to construct a load dispatch strategy optimization model based on the power quality evaluation results, combined with load priority and electricity price policy factors, to optimize the power distribution of controllable loads and interruptible loads while ensuring continuous power supply to key loads;

[0055] The load power dispatch module is used to generate power dispatch instructions for various loads according to the optimization results of the load dispatch strategy, and to perform real-time control on the load power. During the control process, the power output changes are continuously monitored. When the power output fluctuates, the load dispatch strategy rapid response mechanism is activated;

[0056] The quick response mechanism module is used for the load dispatching strategy quick response mechanism. Based on the preset response rules, it calculates the load power adjustment amount according to the power output fluctuation amplitude and load priority to ensure the power balance and stable operation of the microgrid.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] The present invention discloses a microgrid intelligent control method based on load characteristics. The method comprehensively evaluates the operating status of the microgrid through a data fusion algorithm, and classifies various types of loads using a load characteristic identification algorithm. In combination with load characteristic parameters, the present invention uses a power quality analysis algorithm to evaluate the power supply quality in real time, and triggers load allocation strategy optimization when the requirements are not met. The optimization process takes into account factors such as load priority and electricity price policy, and uses a multi-objective optimization algorithm to generate a load allocation plan to maximize energy utilization efficiency and minimize operating costs while ensuring power supply to key loads. The present invention also includes a fast response mechanism that can adjust the load power in real time according to power output fluctuations to ensure power balance and stable operation of the microgrid. The method realizes intelligent and precise control of microgrid loads and improves system operation efficiency and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0060] Figure 1 A schematic flow chart of a flexible load regulation method applied to a microgrid according to an embodiment of the present invention; DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should be understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the embodiments of the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0063] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0064] Embodiment 1

[0065] like Figure 1 As shown, an embodiment of the present invention provides a flexible load regulation method applied to a microgrid, the method comprising:

[0066] According to the real-time operation status data of the microgrid, a data fusion algorithm is used to comprehensively analyze the multi-source heterogeneous data of load power and power output to obtain the comprehensive operation status evaluation results of the microgrid;

[0067] Based on the comprehensive operation status evaluation results, the various types of loads in the microgrid are identified and classified through the load characteristics identification algorithm to obtain the power consumption characteristic parameters of different types of loads;

[0068] Combined with the load's power consumption characteristic parameters, the power quality analysis algorithm is used to conduct real-time evaluation of the microgrid's power quality indicators;

[0069] According to the power quality assessment results, combined with load priority and electricity price policy factors, a load dispatch strategy optimization model is constructed to optimize the power allocation of controllable loads and interruptible loads while ensuring continuous power supply to key loads;

[0070] Based on the optimization results of the load dispatching strategy, power dispatching instructions for various loads are generated to regulate the load power in real time. During the regulation process, the power output changes are continuously monitored. When the power output fluctuates, the load dispatching strategy rapid response mechanism is activated.

[0071] The load dispatch strategy quick response mechanism is based on preset response rules. It calculates the load power adjustment amount according to the power output fluctuation amplitude and load priority to ensure the power balance and stable operation of the microgrid.

[0072] In this embodiment, based on the real-time operation status data of the microgrid, a data fusion algorithm is used to comprehensively analyze the multi-source heterogeneous data of load power and power output, and the comprehensive operation status evaluation results of the microgrid are obtained, including:

[0073] Obtain real-time operating status data of each component of the microgrid;

[0074] Preprocess the acquired multi-source heterogeneous data and convert the data into a unified format;

[0075] The Kalman filter algorithm is used to fuse the preprocessed data to obtain an estimated value of the overall operating status of the microgrid;

[0076] According to the fused estimated values ​​of the microgrid operation status, key indicators reflecting the operation characteristics of the microgrid are extracted;

[0077] Compare the extracted key indicators with the preset thresholds to evaluate the operating status of the microgrid;

[0078] If the key indicators exceed the normal range, the early warning mechanism will be triggered, the early warning level will be determined according to the degree of deviation, and an operation status assessment report will be generated;

[0079] In the operation status assessment report, the specific values, normal ranges and deviations of each key indicator of the microgrid are described in detail to provide a basis for decision-making for dispatchers.

[0080] In this embodiment, based on the comprehensive operation status evaluation result, various types of loads in the microgrid are identified and classified through the load characteristic identification algorithm, and the power consumption characteristic parameters of different types of loads are obtained, including:

[0081] Obtain real-time operation data of the microgrid by comprehensively evaluating the operation status of the microgrid;

[0082] According to the acquired microgrid operation status data, a load characteristic recognition algorithm based on machine learning is used to identify and classify various loads in the microgrid, and obtain labels for different types of loads;

[0083] For the different types of loads identified, the power consumption characteristic parameters of each type of load are obtained by analyzing the historical power consumption data.

[0084] In this embodiment, in combination with the power consumption characteristic parameters of the load, a power quality analysis algorithm is used to perform real-time evaluation of the power quality indicators of the microgrid, including:

[0085] Obtain real-time operation data of the microgrid and power demand and characteristic parameters of different types of loads;

[0086] Input the acquired data into a pre-built power quality assessment model, which adopts a support vector machine algorithm and is obtained by training historical data; wherein the power quality assessment model is constructed using a support vector machine algorithm;

[0087] The model outputs real-time evaluation values ​​of various power quality indicators;

[0088] The evaluation value of the power quality index is compared with the preset quality threshold to determine whether the current power quality meets the power supply requirements of different loads.

[0089] In this embodiment, based on the power quality assessment results, combined with load priority and electricity price policy factors, a load dispatch strategy optimization model is constructed. Under the premise of ensuring continuous power supply to key loads, the power allocation of controllable loads and interruptible loads is optimized, including:

[0090] Obtain power quality assessment results, load priority data and electricity price policy information, and determine critical loads, controllable loads and interruptible loads based on power quality assessment results;

[0091] According to the load priority data, a clustering algorithm is used to classify the loads and obtain a set of loads with different priorities;

[0092] According to the electricity price policy information, a load dispatch optimization model considering electricity price factors is constructed, and load priority and electricity price factors are used as model constraints;

[0093] Taking maximizing energy efficiency and minimizing operating costs as optimization goals, a multi-objective optimization model is established, and power balance and voltage limit constraints are set;

[0094] The particle swarm optimization algorithm is used to solve the multi-objective optimization model, and the Pareto optimal solution set is obtained through iterative search;

[0095] Select the optimal allocation scheme that balances energy utilization efficiency and operating cost from the Pareto optimal solution set and determine the power allocation of each load;

[0096] Generate load dispatch control instructions, perform power control on controllable loads and interruptible loads according to the optimization results, and monitor load responses to ensure continuous power supply to critical loads.

[0097] Specifically, the optimal allocation scheme that balances energy utilization efficiency and operating cost is selected from the Pareto optimal solution set to determine the power allocation of each load, including:

[0098] Get all the optimal solutions in the Pareto optimal solution set, such as solution 1: energy utilization efficiency 90%, operating cost 100 yuan; solution 2: energy utilization efficiency 85%, operating cost 80 yuan; solution 3: energy utilization efficiency 95%, operating cost 120 yuan. For each optimal solution, calculate its energy utilization efficiency and operating cost. According to the energy utilization efficiency and operating cost, construct an efficiency-cost two-dimensional evaluation matrix. For example, with energy utilization efficiency as the horizontal axis and operating cost as the vertical axis, the above three solutions are plotted in a two-dimensional coordinate system to form a matrix. Use the weighted summation method to assign weights to efficiency and cost respectively, and convert the two-dimensional matrix into a one-dimensional scoring vector. For example, set the energy utilization efficiency weight to 0.6 and the operating cost weight to 0.4. Then the score of solution 1 is 0.6*90%+0.4*(1-100 / 220)=0.718 (cost normalization, 100 / (100+120+80)); the score of solution 2 is 0.6*85%+0.4*(1-80 / 220)=0.772; the score of solution 3 is 0.6*95%+0.4*(1-120 / 220)=0.75. The reason why the weighted summation method is used is that the two goals of energy efficiency and operating cost are usually conflicting, and they need to be weighed according to the actual situation. The weighted summation method can transform multiple goals into a single goal, which is convenient for comparison and selection. According to the score vector, the Pareto optimal solution set is sorted to obtain the optimal solution that balances energy efficiency and operating cost. For example, according to the above score, solution 2> solution 3> solution 1, then solution 2 is the optimal solution. The purpose of sorting is to select the solution that best meets the actual needs from many alternative solutions. Extract the power allocation scheme of each load from the optimal solution. For example, the power allocation scheme corresponding to solution 2 is: 100kW for critical load, 50kW for controllable load, and 30kW for interruptible load. Extracting the power allocation scheme is for subsequent control and scheduling. Optimize the power allocation by solving the linear programming model. For example, with the goal of minimizing the operating cost, and with power balance, voltage limit and load priority as constraints, a linear programming model is constructed to further optimize the power allocation scheme of solution 2. This can further improve the economy and reliability of the scheme. Monte Carlo simulation is used to verify the optimal power allocation scheme in multiple scenarios to ensure its robustness. For example, different scenarios such as load fluctuations and electricity price fluctuations are simulated to verify the effectiveness and stability of the optimal power allocation scheme under various circumstances. Monte Carlo simulation can evaluate the performance of the scheme under uncertain factors and improve the reliability of the scheme. The final optimal power allocation scheme is sent to each load control unit to achieve optimal energy scheduling. For example, the optimized power allocation scheme of solution 2 is sent to the control unit of each load, and the control unit adjusts the power of the load according to the instruction, and finally achieves optimal energy scheduling. Doing so can apply the optimization results to the actual system and achieve the goal of energy saving and consumption reduction.

[0099] In this embodiment, based on the optimization result of the load dispatching strategy, power dispatching instructions for various loads are generated, and the load power is regulated in real time. During the regulation process, the power output change is continuously monitored. When the power output fluctuates, the load dispatching strategy rapid response mechanism is started, including:

[0100] After receiving the power dispatch instruction, each load control unit uses the PID control algorithm to adjust the actual operating power of each load according to the power value in the instruction, thus realizing real-time control of the load power;

[0101] During the load power regulation process, the power output data is continuously collected, and the data analysis algorithm is used to determine whether the power output fluctuates;

[0102] If the power output fluctuation exceeds the preset threshold, the load allocation strategy rapid response mechanism is triggered;

[0103] After the rapid response mechanism is activated, the heuristic optimization algorithm is used to quickly calculate the load power adjustment plan and generate new load power dispatch instructions based on the power output fluctuation and load priority;

[0104] The newly generated load power dispatching instructions are sent to the corresponding load control unit through the microgrid energy management system. The load control unit quickly adjusts the operating power of each load according to the new instructions to achieve dynamic balance of load power;

[0105] During the dynamic balancing process of load power, the power output and load power data are continuously monitored, and an adaptive control algorithm is used to dynamically optimize the load power control parameters;

[0106] According to the evaluation results of load regulation effect, the reinforcement learning algorithm is used to perform self-learning optimization on the load dispatching strategy to realize adaptive optimization control of microgrid energy management.

[0107] In this embodiment, the load dispatching strategy quick response mechanism is based on a preset response rule, and calculates the load power adjustment amount according to the power output fluctuation amplitude and load priority to ensure the power balance and stable operation of the microgrid, including:

[0108] Obtain real-time data on power output, and calculate and judge the fluctuation range of power output;

[0109] Determine the priority adjustment order of each load according to the preset load priority rules;

[0110] Through the rapid response mechanism, the power adjustment amount of each load is calculated according to the power output fluctuation amplitude and load priority;

[0111] The calculated load power adjustment amount is sent to each load control unit in real time through the energy management system;

[0112] After receiving the adjustment instruction, each load control unit quickly performs power adjustment to achieve dynamic allocation of load power.

[0113] It should be noted that the method of the embodiment of the present disclosure can be performed by a single device, such as a computer or a server. The method of the present embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present disclosure, and the multiple devices will interact with each other to complete the described method.

[0114] It should be noted that some embodiments of the present disclosure are described above. Other embodiments are within the scope of the attached claims. In some cases, it should be understood that the size of the serial number of each step in the above embodiment does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention. The actions or steps recorded in the claims can be performed in an order different from that in the above embodiment and still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0115] Embodiment 2

[0116] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present invention further provides a flexible load regulation system applied to a microgrid, the system being used to implement any of the above-mentioned methods, comprising: a comprehensive operation status evaluation module, a load characteristic identification module, a power quality analysis module, a load dispatch strategy optimization module, a load power scheduling module, and a rapid response mechanism module;

[0117] The comprehensive operation status evaluation module is used to obtain the comprehensive operation status evaluation result of the microgrid by using the data fusion algorithm to comprehensively analyze the multi-source heterogeneous data of load power and power output according to the real-time operation status data of the microgrid;

[0118] The load characteristic identification module is used to identify and classify various types of loads in the microgrid based on the comprehensive operation status evaluation results and through the load characteristic identification algorithm to obtain the power consumption characteristic parameters of different types of loads;

[0119] The power quality analysis module is used to combine the power consumption characteristic parameters of the load and adopt the power quality analysis algorithm to conduct real-time evaluation of the power quality indicators of the microgrid;

[0120] The load dispatch strategy optimization module is used to build a load dispatch strategy optimization model based on the power quality assessment results, combined with load priority and electricity price policy factors, to optimize the power distribution of controllable loads and interruptible loads while ensuring continuous power supply to key loads;

[0121] The load power dispatch module is used to generate power dispatch instructions for various loads based on the optimization results of the load dispatch strategy, and to adjust the load power in real time. During the regulation process, it continuously monitors the changes in power output. When the power output fluctuates, it starts the load dispatch strategy rapid response mechanism.

[0122] The quick response mechanism module is used for load dispatching strategy. The quick response mechanism is based on preset response rules. According to the power output fluctuation amplitude and load priority, it calculates the load power adjustment amount to ensure the power balance and stable operation of the microgrid.

[0123] The system of the above embodiment is used to implement a corresponding flexible load regulation method applied to a microgrid in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0124] It should be noted that the above-mentioned flexible load regulation system applied to a microgrid is embodied in the form of a functional unit. The term "module" here can be implemented in the form of software and / or hardware, and is not specifically limited to this.

[0125] For example, a "module" may be a software program, a hardware circuit, or a combination of the two that implements the above functions. The hardware circuit may include an application specific integrated circuit (ASIC), an electronic circuit, a processor (such as a shared processor, a dedicated processor, or a group processor, etc.) and a memory for executing one or more software or firmware programs, a combined logic circuit, and / or other suitable components that support the described functions.

[0126] The embodiments of the present disclosure are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A flexible load regulation method applied to a microgrid, characterized in that: The method comprises: According to the real-time operation status data of the microgrid, a data fusion algorithm is used to comprehensively analyze the multi-source heterogeneous data of load power and power output to obtain the comprehensive operation status evaluation results of the microgrid; Based on the comprehensive operation status evaluation results, the various types of loads in the microgrid are identified and classified through the load characteristics identification algorithm to obtain the power consumption characteristic parameters of different types of loads; Combined with the load's power consumption characteristic parameters, the power quality analysis algorithm is used to conduct real-time evaluation of the microgrid's power quality indicators; According to the power quality assessment results, combined with load priority and electricity price policy factors, a load dispatch strategy optimization model is constructed to optimize the power allocation of controllable loads and interruptible loads while ensuring continuous power supply to key loads; Based on the optimization results of the load dispatching strategy, power dispatching instructions for various loads are generated to regulate the load power in real time. During the regulation process, the power output changes are continuously monitored. When the power output fluctuates, the load dispatching strategy rapid response mechanism is activated. The load dispatch strategy quick response mechanism is based on preset response rules. It calculates the load power adjustment amount according to the power output fluctuation amplitude and load priority to ensure the power balance and stable operation of the microgrid.

2. The method according to claim 1, characterized in that According to the real-time operation status data of the microgrid, the data fusion algorithm is used to comprehensively analyze the multi-source heterogeneous data of load power and power output, and the comprehensive operation status evaluation results of the microgrid are obtained, including: Obtain real-time operating status data of each component of the microgrid; Preprocess the acquired multi-source heterogeneous data and convert the data into a unified format; The Kalman filter algorithm is used to fuse the preprocessed data to obtain an estimated value of the overall operating status of the microgrid; According to the fused estimated values ​​of the microgrid operation status, key indicators reflecting the operation characteristics of the microgrid are extracted; Compare the extracted key indicators with the preset thresholds to evaluate the operating status of the microgrid; If the key indicators exceed the normal range, the early warning mechanism will be triggered, the early warning level will be determined according to the degree of deviation, and an operation status assessment report will be generated; In the operation status assessment report, the specific values, normal ranges and deviations of each key indicator of the microgrid are described in detail to provide a basis for decision-making for dispatchers.

3. The method according to claim 1, characterized in that: Based on the comprehensive operation status evaluation results, various types of loads in the microgrid are identified and classified through the load characteristics identification algorithm, and the power consumption characteristic parameters of different types of loads are obtained, including: Obtain real-time operation data of the microgrid by comprehensively evaluating the operation status of the microgrid; According to the acquired microgrid operation status data, a load characteristic recognition algorithm based on machine learning is used to identify and classify various loads in the microgrid, and obtain labels for different types of loads; For the different types of loads identified, the power consumption characteristic parameters of each type of load are obtained by analyzing the historical power consumption data.

4. The method according to claim 1, characterized in that: Combined with the load power consumption characteristic parameters, the power quality analysis algorithm is used to conduct real-time evaluation of the power quality indicators of the microgrid, including: Obtain real-time operation data of the microgrid and power demand and characteristic parameters of different types of loads; The acquired data is input into a pre-built power quality assessment model, which uses a support vector machine algorithm and is obtained by training historical data; The model outputs real-time evaluation values ​​of various power quality indicators; The evaluation value of the power quality index is compared with the preset quality threshold to determine whether the current power quality meets the power supply requirements of different loads.

5. The method according to claim 1, characterized in that According to the power quality assessment results, combined with load priority and electricity price policy factors, a load dispatch strategy optimization model is constructed. Under the premise of ensuring continuous power supply to key loads, the power distribution of controllable loads and interruptible loads is optimized, including: Obtain power quality assessment results, load priority data and electricity price policy information, and determine critical loads, controllable loads and interruptible loads based on power quality assessment results; According to the load priority data, a clustering algorithm is used to classify the loads and obtain a set of loads with different priorities; According to the electricity price policy information, a load dispatch optimization model considering electricity price factors is constructed, and load priority and electricity price factors are used as model constraints; Taking maximizing energy efficiency and minimizing operating costs as optimization goals, a multi-objective optimization model is established, and power balance and voltage limit constraints are set; The particle swarm optimization algorithm is used to solve the multi-objective optimization model, and the Pareto optimal solution set is obtained through iterative search; Select the optimal allocation scheme that balances energy utilization efficiency and operating cost from the Pareto optimal solution set and determine the power allocation of each load; Generate load dispatch control instructions, perform power control on controllable loads and interruptible loads according to the optimization results, and monitor load responses to ensure continuous power supply to critical loads.

6. The method according to claim 1, characterized in that Based on the optimization results of the load dispatching strategy, power dispatching instructions for various loads are generated to regulate the load power in real time. During the regulation process, the power output changes are continuously monitored. When the power output fluctuates, the load dispatching strategy rapid response mechanism is activated, including: After receiving the power dispatch instruction, each load control unit uses the PID control algorithm to adjust the actual operating power of each load according to the power value in the instruction, thus realizing real-time control of the load power; During the load power regulation process, the power output data is continuously collected, and the data analysis algorithm is used to determine whether the power output fluctuates; If the power output fluctuation exceeds the preset threshold, the load allocation strategy rapid response mechanism is triggered; After the rapid response mechanism is activated, the heuristic optimization algorithm is used to quickly calculate the load power adjustment plan and generate new load power dispatch instructions based on the power output fluctuation and load priority; The newly generated load power dispatching instructions are sent to the corresponding load control unit through the microgrid energy management system. The load control unit quickly adjusts the operating power of each load according to the new instructions to achieve dynamic balance of load power; During the dynamic balancing process of load power, the power output and load power data are continuously monitored, and an adaptive control algorithm is used to dynamically optimize the load power control parameters; According to the evaluation results of load regulation effect, the reinforcement learning algorithm is used to perform self-learning optimization on the load dispatching strategy to realize adaptive optimization control of microgrid energy management.

7. The method according to claim 1, characterized in that The load dispatch strategy quick response mechanism is based on the preset response rules. According to the power output fluctuation amplitude and load priority, the load power adjustment is calculated to ensure the power balance and stable operation of the microgrid, including: Obtain real-time data on power output, and calculate and judge the fluctuation range of power output; Determine the priority adjustment order of each load according to the preset load priority rules; Through the rapid response mechanism, the power adjustment amount of each load is calculated according to the power output fluctuation amplitude and load priority; The calculated load power adjustment amount is sent to each load control unit in real time through the energy management system; After receiving the adjustment instruction, each load control unit quickly performs power adjustment to achieve dynamic allocation of load power.

8. A flexible load regulation system applied to a microgrid, characterized in that: The system is used to implement the method described in any one of claims 1 to 7, and includes: a comprehensive operation status evaluation module, a load characteristic identification module, a power quality analysis module, a load dispatch strategy optimization module, a load power dispatch module, and a rapid response mechanism module; The comprehensive operation status evaluation module is used to comprehensively analyze the multi-source heterogeneous data of load power and power output according to the real-time operation status data of the microgrid by using a data fusion algorithm to obtain a comprehensive operation status evaluation result of the microgrid; The load characteristic identification module is used to identify and classify various types of loads in the microgrid based on the comprehensive operation status evaluation results and through the load characteristic identification algorithm to obtain power consumption characteristic parameters of different types of loads; The power quality analysis module is used to combine the power consumption characteristic parameters of the load and adopt the power quality analysis algorithm to perform real-time evaluation on the power quality indicators of the microgrid; The load dispatch strategy optimization module is used to construct a load dispatch strategy optimization model based on the power quality evaluation results, combined with load priority and electricity price policy factors, to optimize the power distribution of controllable loads and interruptible loads while ensuring continuous power supply to key loads; The load power dispatch module is used to generate power dispatch instructions for various loads according to the optimization results of the load dispatch strategy, and to perform real-time control on the load power. During the control process, the power output changes are continuously monitored. When the power output fluctuates, the load dispatch strategy rapid response mechanism is activated; The quick response mechanism module is used for the load dispatching strategy quick response mechanism. Based on the preset response rules, it calculates the load power adjustment amount according to the power output fluctuation amplitude and load priority to ensure the power balance and stable operation of the microgrid.

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