Intelligent grid-connected method and system for multi-source microgrid

Abstract

The present invention discloses an intelligent grid-connected method and system for a multi-source microgrid, and belongs to the technical field of power systems. The method specifically comprises: collecting parameters of various energy sources in the microgrid, using an intelligent evaluation strategy to evaluate and predict the grid-connected conditions of each energy source in real time, analyzing potential grid-connected risks and optimization space, adjusting the grid-connected strategy according to the results of the grid-connected risk analysis, using an intelligent energy flow control strategy to dynamically adjust the grid-connected output power of each energy source according to real-time load demand and energy supply conditions, optimizing the energy flow path, and in the grid-connected process, automatically adjusting the grid-connected parameters according to real-time monitoring data and load changes, adaptively optimizing the grid connection, and realizing efficient coordinated grid connection of multi-source energy through an intelligent control system, reducing energy loss, and improving the grid connection efficiency and the overall performance of the microgrid.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, and in particular to an intelligent grid-connected method and system for multi-source microgrids. Background Art

[0002] With the continuous growth of global energy demand and the increasing attention to environmental protection, the development and utilization of clean energy has become particularly important. The rise of microgrid technology is an important direction for the modernization of energy systems. Microgrid refers to a small power system composed of distributed energy (such as solar energy, wind energy, etc.) and energy storage systems (such as battery energy storage systems), which can provide stable power supply in independent operation or grid-connected operation mode.

[0003] The grid-connected operation of these multi-source energy sources places higher requirements on the stability, energy efficiency and economy of microgrids. Existing grid-connected technologies are usually designed for single energy or simple grid-connected environments, and are difficult to cope with the complexity of multi-source energy grid connection, especially when load changes and energy fluctuations are drastic. Therefore, an intelligent grid-connected method and system for multi-source microgrids is proposed, aiming to achieve efficient and stable grid connection of multi-source energy through intelligent technology and improve the overall performance of microgrids.

[0004] For example, a Chinese patent with the authorization announcement number CN117713211B discloses a photovoltaic grid-connected intelligent scheduling method and system based on environmental analysis, which includes: obtaining the grid-connected demand rated power; performing steady-state prediction of power generation based on light source prediction information and photovoltaic power generation feature set, and establishing steady-state power generation nodes; performing node matching of steady-state power generation nodes with the demand rated power, establishing grid-connected connection, and configuring compensation power; performing power supply and configuring an updated prediction window; performing environmental information collection based on the updated prediction window, generating compensation control information; and scheduling and managing grid-connected connection and compensation power based on the compensation control information. The method can solve the technical problem that the existing photovoltaic grid-connected scheduling method has unreasonable settings of grid-connected time nodes and scheduling power, resulting in poor grid operation stability during photovoltaic grid connection, and can improve the accuracy of grid-connected time nodes and scheduling power settings during photovoltaic grid connection, thereby ensuring safe and stable operation of the power grid.

[0005] For example, the Chinese patent with publication number CN117543700A discloses a distributed wind power grid connection control method based on smart grid, including: obtaining the active power loss and reactive power loss of each node when the distributed wind power is not connected; obtaining the active power loss and reactive power loss of each node when the distributed wind power is connected; calculating the total loss rate; solving the total loss rate under different working conditions, and comparing the active power loss weight coefficient corresponding to the working condition with the minimum total loss rate as the optimal active power loss weight coefficient; solving the maximum access capacity corresponding to the optimal active power loss weight coefficient, and controlling the access of each node according to the maximum access capacity. This invention is used to control the grid connection of distributed wind power.

[0006] The defects of the above patents are: 1) It is difficult to coordinate the grid-connected operation of multiple energy sources, resulting in inefficient system scheduling and may even cause unstable grid fluctuations; 2) During the grid-connected process, inconsistent voltage, current and phase characteristics often lead to energy loss and overall efficiency reduction. Summary of the invention

[0007] In view of the shortcomings of the existing technology, the present invention proposes a method, which aims to achieve efficient and stable grid connection of multi-source energy through intelligent technology and improve the overall performance of the microgrid.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] Smart grid-connected methods for multi-source microgrids, including:

[0010] Collect parameters of each energy source of the microgrid;

[0011] Use intelligent assessment strategies to conduct real-time assessment and prediction of grid connection conditions for various energy sources, analyze potential grid connection risks and optimization space, and adjust grid connection strategies based on grid connection risk analysis results;

[0012] Using intelligent energy flow control strategies, the grid-connected output power of each energy source is dynamically adjusted according to real-time load demand and energy supply conditions to optimize the energy flow path;

[0013] During the grid connection process, the grid connection parameters are automatically adjusted according to real-time monitoring data and load changes, and the grid connection is adaptively optimized.

[0014] Specifically, the intelligent assessment strategy is used to conduct real-time assessment and prediction of the grid connection conditions of various energy sources, and analyze potential grid connection risks and optimization space, including:

[0015] Based on the physical characteristics, volatility and dynamic change laws of different energy sources, a multi-energy mathematical model is established. The specific formula is:

[0016] ,

[0017] Among them, P i (t) represents the output power of the i-th energy source at time t, P i,max represents the maximum output power of the i-th energy source, represents the fluctuation coefficient of the i-th energy at time t, represents the random fluctuation term;

[0018] Set the constraints of the multi-energy power demand of the microgrid. The specific formula is:

[0019] ,

[0020] Among them, P load (t) represents the constraints of the multi-energy power demand of the microgrid, n represents the number of energy types, P grid (t) represents the power supplemented from the grid at time t;

[0021] Combining historical data and real-time data to predict grid connection risk, the specific formula is:

[0022] ,

[0023] Among them, R(t) represents the predicted grid connection risk value, represents the weight of the i-th energy source, P i,pred (t) represents the predicted output power of the i-th energy source at time t.

[0024] Specifically, adjusting the grid connection strategy according to the grid connection risk analysis results includes:

[0025] Adjust the grid connection strategy and set the objective function. The specific formula is:

[0026] ,

[0027] Among them, C total represents the grid-connected strategy objective function, min represents the minimum value function, represents the power generation cost of the j-th energy source, R freq represents the risk of grid frequency fluctuation, R volt Indicates the risk of grid voltage fluctuations, Represents the weight coefficient of grid frequency fluctuation risk, Represents the weight coefficient of grid-connected voltage fluctuation risk.

[0028] Specifically, the dynamically adjusting the grid-connected output power of each energy source and optimizing the energy flow path includes:

[0029] According to the real-time load demand and energy supply, the total grid-connected output power at time t is calculated. The specific formula is:

[0030] ,

[0031] Among them, P out (t) represents the total grid-connected output power at time t, represents the weight coefficient of the i-th energy at time t, represents the load weight coefficient of the qth load unit at time t, represents the power demand of the qth load unit at time t, M represents the number of load units, represents the power balance adjustment coefficient, Indicates the amount of power imbalance;

[0032] Establish an energy optimization model to optimize the energy flow path. The specific formula of the energy optimization model is:

[0033] ,

[0034] in, represents the energy optimization model, i.e., optimal energy transmission, f1(P) represents the transmission loss minimization function, , P i represents the power generation of the i-th energy unit, P Lj represents the power demand of the jth load unit, R ij represents the resistance or impedance parameter between the energy transmission paths, f2(P) represents the power generation cost minimization function, , C i (P i ) represents the power generation cost function of energy unit i, f3(P) represents the energy balance optimization function, , represents the power imbalance penalty coefficient, represents the total power generation from all energy sources, Represents the total power demand of all load units.

[0035] Specifically, the automatic adjustment of grid connection parameters and adaptive optimization of grid connection according to real-time monitoring data and load changes include:

[0036] According to real-time monitoring data and load changes, the grid-connected parameters are automatically adjusted, including power factor and reactive power compensation. The power factor adjustment formula is:

[0037] ,

[0038] in, Indicates the adjustment value of power factor, P on represents active power, Q represents reactive power;

[0039] The reactive power during the grid connection process is regulated and compensated through reactive power compensation equipment.

[0040] Specifically, the parameters of each energy source of the microgrid include: voltage, current, frequency, and phase data.

[0041] An intelligent grid-connected system for a multi-source microgrid, used to implement the intelligent grid-connected method for a multi-source microgrid, characterized by comprising: a data acquisition module, an evaluation and adjustment module, a grid-connected optimization module and an adaptive adjustment module;

[0042] The data acquisition module is used to collect parameters of various energy sources of the microgrid;

[0043] The evaluation and adjustment module is used to use intelligent evaluation strategies to conduct real-time evaluation and prediction of the grid connection conditions of various energy sources, analyze potential grid connection risks and optimization space, and adjust the grid connection strategy according to the grid connection risk analysis results;

[0044] The grid-connected optimization module is used to dynamically adjust the grid-connected output power of each energy source and optimize the energy flow path according to the real-time load demand and energy supply situation by using an intelligent energy flow control strategy;

[0045] The self-adaptive adjustment module is used to automatically adjust the grid connection parameters and perform self-adaptive optimization on the grid connection according to the real-time monitoring data and load changes during the grid connection process.

[0046] Specifically, the evaluation and adjustment module includes: an intelligent evaluation unit and a grid-connected strategy adjustment unit;

[0047] The intelligent evaluation unit is used to establish a mathematical model of multiple energy sources, set constraints on the power demand of multiple energy sources in the microgrid, combine historical data, and perform grid connection risk assessment and prediction based on real-time data;

[0048] The grid connection strategy adjustment unit is used to adjust the grid connection strategy and set the objective function according to the grid connection risk analysis result.

[0049] Specifically, the grid-connected optimization module includes: a grid-connected power adjustment unit and a path optimization unit;

[0050] The grid-connected power adjustment unit is used to calculate the total grid-connected output power according to the real-time load demand and energy supply situation;

[0051] The path optimization unit is used to establish an energy optimization model and optimize the energy flow path.

[0052] Specifically, the adaptive adjustment module includes: a power factor adjustment unit and a reactive power adjustment and compensation unit;

[0053] The power factor adjustment unit is used to automatically adjust the power factor according to real-time monitoring data and load changes;

[0054] The reactive power regulation and compensation unit is used to automatically adjust reactive power according to real-time monitoring data and load changes.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] 1. The present invention proposes an intelligent grid-connected method for a multi-source microgrid, which realizes efficient coordinated grid-connected connection of multi-source energy through an intelligent control system to ensure the stability and consistency of the system.

[0057] 2. The present invention proposes an intelligent grid-connected method for a multi-source microgrid, which reduces energy loss, improves grid-connected efficiency, adopts an optimized grid-connected strategy, reduces energy loss caused by multi-source grid-connected, and improves overall energy efficiency.

[0058] 3. The present invention proposes an intelligent grid-connected method for multi-source microgrids, which enhances the dynamic response capability. The system can quickly respond to load changes and energy fluctuations, ensuring a smooth and efficient grid-connected process. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A flow chart of a smart grid-connected method for a multi-source microgrid provided by the present invention;

[0060] Figure 2 This is a diagram of the architecture of the intelligent grid-connected system for a multi-source microgrid provided by the present invention. DETAILED DESCRIPTION

[0061] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, in the description of the present invention, it should be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside" and the like indicate the orientation or position relationship based on the orientation or position relationship shown in the accompanying drawings, which is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "No. 1", "No. 2" and "No. 3" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. The present invention is further explained below in conjunction with specific embodiments.

[0062] Example 1

[0063] See also Figure 1 The present invention provides an embodiment: a smart grid-connected method for a multi-source microgrid, comprising the following specific steps:

[0064] Step S1: deploying intelligent sensing networks at key nodes of the microgrid, including real-time monitoring sensors for parameters such as voltage, current, frequency, and phase, and using the intelligent sensing network to collect parameters of various energy sources in the microgrid, including data such as voltage, current, frequency, and phase;

[0065] Step S2: Using intelligent evaluation strategies, the grid connection conditions of various energy sources are evaluated and predicted in real time, potential grid connection risks and optimization space are analyzed, and the grid connection strategy is adjusted according to the grid connection risk analysis results to ensure the best grid connection conditions;

[0066] The specific steps of step S2 are:

[0067] Step S201: Based on the physical characteristics, volatility and dynamic change rules of different energy sources, a mathematical model of multiple energy sources is established. The specific formula is:

[0068] ,

[0069] Among them, P i (t) represents the output power of the i-th energy source at time t, P i,max represents the maximum output power of the i-th energy source, It represents the fluctuation coefficient of the i-th energy at time t, which is affected by weather, environment and other factors and can be predicted through historical data and meteorological forecast models. represents the random fluctuation term, which is used to simulate the random disturbance in the system;

[0070] In this embodiment, the formula is used to simulate the output power fluctuation characteristics of various energy sources (such as wind energy, solar energy, etc.). Different types of renewable energy will be affected by environmental conditions, such as wind speed, light intensity, temperature, etc., which will cause their output power to fluctuate.

[0071] Step S202: Set the constraints of the multi-energy power demand of the microgrid. The specific formula is:

[0072] ,

[0073] Among them, P load (t) represents the constraint condition of the multi-energy power demand of the microgrid, that is, the power demand of the current microgrid load, n represents the number of energy types, P grid (t) represents the power added from the grid at time t, which is used to balance the power shortage or surplus caused by fluctuating renewable energy;

[0074] In this embodiment, the formula expresses the power balance condition in the power grid. In order to ensure the stability of the power grid, the power generation (including the power generation from different energy sources and the power input from the power grid) must always match the load demand, otherwise it will cause voltage fluctuations or frequency deviations, affecting the safety of the power grid.

[0075] Step S203: Combine historical data and real-time data to predict grid connection risk. The specific formula is:

[0076] ,

[0077] Among them, R(t) represents the predicted grid connection risk value, represents the weight of the i-th energy source, reflecting the impact of this energy source on the overall stability of the system. For energy sources with greater volatility (such as wind energy), its weight may be larger, while for energy sources with less volatility (such as nuclear energy), the weight may be smaller. i,pred (t) represents the predicted output power of the i-th energy source at time t;

[0078] In this embodiment, the core of the formula is to calculate the relative deviation between the actual power and the predicted power. By weighted summing the deviations, the total risk coefficient R(t) of the system can be obtained. When R(t) exceeds a preset threshold, the system determines that there is a greater risk in the grid connection conditions, and measures may need to be taken (such as reducing the power output of certain energy sources or increasing backup power generation) to reduce the risk.

[0079] Predict a type of energy P i,pred (t) Output power at time t requires comprehensive consideration of multiple factors, such as environmental conditions (such as wind speed, light, temperature, etc.), historical data and energy characteristics. Common methods include: 1) Linear regression model predicts output power. Linear regression assumes that output power is a linear combination of multiple input factors. The regression coefficient is estimated from historical data by minimizing the error (such as least squares method). This model is simple and intuitive and is suitable for situations where the data relationship is relatively linear; 2) Time series model (ARIMA / LSTM) predicts output power. ARIMA predicts future output by analyzing the autocorrelation of power output and combining past output power values ​​and errors. This model is suitable for energy output situations with obvious historical trends and periodicity. LSTM maintains long-term information through specially designed "memory units" and "gating mechanisms", which can effectively capture the time dependence and complex nonlinear relationships of output power. When dealing with energy output forecasts, it can automatically learn important laws and patterns from historical data; 3) Physical models predict output power. For renewable energy such as wind and solar energy, physical models usually rely on the physical characteristics of energy and environmental factors; 4) Comprehensive prediction formulas (hybrid models). Hybrid models can capture the basic physical laws of energy output, while data-driven models can discover nonlinear relationships from historical data. By combining the two, the accuracy and robustness of predictions can be improved, especially in complex nonlinear situations.

[0080] Step S204: According to the grid connection risk analysis results, the grid connection strategy is adjusted and the objective function is set. The specific formula is:

[0081] ,

[0082] Among them, C totalrepresents the objective function of the grid connection strategy, that is, the total cost, including the power generation cost and the risk cost of frequency and voltage fluctuations. min represents the minimum value function. represents the power generation cost of the j-th energy source, R freq represents the risk of grid frequency fluctuation, R volt Indicates the risk of grid voltage fluctuations, Represents the weight coefficient of grid frequency fluctuation risk, Represents the weight coefficient of grid-connected voltage fluctuation risk.

[0083] The purpose of this formula is to minimize the total cost C total The total cost includes not only the power generation cost but also the risk cost of grid operation. This is a multi-objective optimization model that not only optimizes the power generation cost but also considers the impact of frequency fluctuations and voltage fluctuations on grid safety and stability. The grid connection process is multi-objective and requires finding a balance between economic benefits and grid stability. A multi-objective optimization algorithm is used to find the optimal solution.

[0084] Step S3: Using intelligent energy flow control strategy, dynamically adjust the grid-connected output power of each energy source according to real-time load demand and energy supply, optimize the energy flow path, reduce transmission loss, and ensure efficient operation of the power grid;

[0085] The specific steps of step S3 are:

[0086] Step S301: Calculate the total grid-connected output power at time t according to the real-time load demand and energy supply situation. The specific formula is:

[0087] ,

[0088] Among them, P out (t) represents the total grid-connected output power at time t, that is, the sum of the output powers of all grid-connected power generation units at that time, which is allocated to the load after scheduling. represents the weight coefficient of the i-th energy at time t, represents the load weight coefficient of the qth load unit at time t, It represents the power demand of the qth load unit at time t. The value is obtained based on real-time load data and prediction models, such as linear regression model, time series model (ARIMA / LSTM) prediction model and physical prediction model. M represents the number of load units. It represents the power balance adjustment coefficient, that is, the dynamic adjustment strength of the system to the power imbalance at time t. It is determined by the system feedback mechanism and is continuously adjusted according to the overall operation of the power grid to ensure the balance of power between different energy sources and loads. represents the power imbalance, that is, the difference between supply and demand at time t. When it is positive, it indicates excess power generation. When it is negative, it indicates insufficient power supply;

[0089] In this embodiment, the basic idea of ​​the formula is to ensure that at any time t, the total output power of the system can meet the needs of all loads to avoid over-generation or power shortage. By dynamically adjusting the power generation power and load power of each energy unit, the system can flexibly adapt to changes in the power grid status.

[0090] Step S302: Establish an energy optimization model to optimize the energy flow path and reduce transmission loss. The specific formula of the energy optimization model is:

[0091] ,

[0092] in, represents the energy optimization model, i.e., optimal energy transmission, f1(P) represents the transmission loss minimization function, f2(P) represents the power generation cost minimization function, and f3(P) represents the energy balance optimization function.

[0093] The formula for the transmission loss minimization function is: , P i represents the power generation of the i-th energy unit, which is a variable that needs to be dynamically adjusted during the optimization process. Lj represents the power demand of the jth load unit, R ij It represents the resistance or impedance parameter between the energy transmission paths, and the transmission loss coefficient from energy unit i to load unit j. Its size affects the loss in energy transmission. The greater the resistance, the greater the loss.

[0094] The formula for the power generation cost minimization function is: , C i (P i ) represents the power generation cost function of energy unit i. Different types of energy units (such as thermal power generation, wind power, photovoltaic power, etc.) have different power generation costs. The commonly used cost model is in the form of a quadratic function: , , and denote the cost coefficient of energy unit i respectively;

[0095] The formula of energy balance optimization function is: ,in, It represents the power imbalance penalty coefficient, which is used to measure the cost when power generation and load demand do not match, impose penalties on deviations from the supply-demand balance, and ensure that the total power generation is consistent with the load demand. represents the total power generation from all energy sources, Represents the total power demand of all load units.

[0096] In this embodiment, the principle of the formula is: the main formula takes into account three main goals at the same time: minimizing transmission losses, minimizing power generation costs, and achieving a dynamic balance between energy supply and demand. This method not only focuses on a single economy or efficiency, but also integrates multiple needs of the entire system.

[0097] Transmission loss minimization formula: When electrical energy is transmitted from the power generation unit to the load unit, loss is inevitable. By optimizing the energy flow and selecting the optimal path, the loss caused by transmission resistance or impedance can be reduced. R in the formula ij The term represents the resistance on the transmission path, This reflects that the larger the power gap, the greater the loss, so optimization is needed to reduce this gap;

[0098] Formula for minimizing power generation costs: Different types of power generation have different power generation costs, such as the fuel cost of thermal power plants and the operation and maintenance cost of renewable energy. By optimizing the output of each power generation unit, the system can minimize the total power generation cost while meeting the load demand.

[0099] Power imbalance penalty formula: Mismatch between supply and demand can cause unstable system operation, so a penalty term is added to the formula to ensure that power generation and load demand are matched as closely as possible. If power generation is excessive or insufficient, the system will increase this penalty term, prompting the optimization algorithm to seek a balance between supply and demand.

[0100] Since energy supply and load demand change over time, the parameters in the optimization formula can be adjusted in real time according to actual conditions. By continuously updating transmission losses, power generation costs and load demand, the system can dynamically adjust the output of each energy unit based on current energy conditions, weather conditions, market prices and other factors.

[0101] Step S4: During the grid connection process, the grid connection parameters are automatically adjusted according to the real-time monitoring data and load changes to achieve adaptive optimization of the grid connection and ensure the stability of the power quality.

[0102] The specific steps of step S4 are:

[0103] Step S401: Automatically adjust the grid-connected parameters according to the real-time monitoring data and load changes, including power factor and reactive power compensation. The power factor adjustment formula is:

[0104] ,

[0105] in, Indicates the adjustment value of power factor, P onrepresents active power, which represents the electric energy actually used, and Q represents reactive power, which represents the electric energy that is not effectively used;

[0106] In this embodiment, when the load or power supply fluctuates, the system can on and Q value, by adjusting the reactive power compensation equipment (such as capacitors or reactors) in the power grid to maintain the optimized value of the power factor, thereby improving the transmission efficiency of electric energy;

[0107] For example, when the reactive power Q increases, the power factor Will decrease, resulting in a decrease in the effective power transmission capacity of the power grid. At this time, the system can increase reactive power compensation and reduce the Q value, thereby improving the power factor and achieving the purpose of grid optimization;

[0108] Step S402: Reactive power is adjusted and compensated during the grid connection process by using reactive power compensation equipment (such as SVC, static VAR compensator).

[0109] The compensation process is based on the system's measurement of the current power system load. The system can automatically select the appropriate reactive power compensation strategy to ensure the quality of grid-connected power.

[0110] During the grid-connected optimization process, the system determines the reactive power compensation demand and makes dynamic adjustments based on the real-time measured load data. For example, during certain load peaks, reactive power may increase, affecting the stability of the grid voltage. The system will automatically enable reactive power compensation equipment to ensure the stability of the grid voltage.

[0111] The present invention significantly improves the grid connection efficiency and stability of multi-source microgrids by introducing an intelligent multi-source grid connection control method. The real-time monitoring and perception network ensures that the system can obtain the accurate grid connection conditions of each energy source, and performs state evaluation and optimization through an intelligent evaluation algorithm. The combination of dynamic voltage and frequency synchronization control, intelligent energy flow control, and adaptive grid connection optimization mechanism enables the system to achieve efficient and stable energy grid connection in a complex and changeable environment. At the same time, the intelligent grid connection fault diagnosis and protection mechanism provides a strong security guarantee for the system, ensuring that faults in the grid connection process can be detected and processed in a timely manner.

[0112] Example 2

[0113] See also Figure 2 , another embodiment provided by the present invention: an intelligent grid-connected system for a multi-source microgrid, comprising: a data acquisition module, an evaluation and adjustment module, a grid-connected optimization module and an adaptive adjustment module;

[0114] The data acquisition module is used to collect parameters of various energy sources of the microgrid;

[0115] The evaluation and adjustment module is used to use intelligent evaluation strategies to conduct real-time evaluation and prediction of the grid connection conditions of various energy sources, analyze potential grid connection risks and optimization space, and adjust the grid connection strategy according to the grid connection risk analysis results;

[0116] The grid-connected optimization module is used to dynamically adjust the grid-connected output power of each energy source and optimize the energy flow path according to the real-time load demand and energy supply situation by using an intelligent energy flow control strategy;

[0117] The self-adaptive adjustment module is used to automatically adjust the grid connection parameters and perform self-adaptive optimization on the grid connection according to the real-time monitoring data and load changes during the grid connection process.

[0118] Evaluation and adjustment module, including: intelligent evaluation unit and grid-connection strategy adjustment unit;

[0119] The intelligent evaluation unit is used to establish a mathematical model of multiple energy sources, set constraints on the power demand of multiple energy sources in the microgrid, combine historical data, and perform grid connection risk assessment and prediction based on real-time data;

[0120] The grid connection strategy adjustment unit is used to adjust the grid connection strategy and set the objective function according to the grid connection risk analysis result.

[0121] A grid-connected optimization module, including: a grid-connected power adjustment unit and a path optimization unit;

[0122] The grid-connected power adjustment unit is used to calculate the total grid-connected output power according to the real-time load demand and energy supply situation;

[0123] The path optimization unit is used to establish an energy optimization model and optimize the energy flow path.

[0124] The adaptive adjustment module includes: a power factor adjustment unit and a reactive power adjustment and compensation unit;

[0125] The power factor adjustment unit is used to automatically adjust the power factor according to real-time monitoring data and load changes;

[0126] The reactive power regulation and compensation unit is used to automatically adjust reactive power according to real-time monitoring data and load changes.

[0127] In addition, the parts of the above-mentioned technical solutions provided in the embodiments of the present application that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive redundancy.

[0128] The specific implementation modes as described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation mode of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A smart grid-connected method for a multi-source microgrid, characterized in that: include: Collect parameters of each energy source of the microgrid; Using intelligent assessment strategies, we can conduct real-time assessment and prediction of the grid connection conditions of various energy sources, analyze potential grid connection risks and optimization space, and adjust the grid connection strategy based on the grid connection risk analysis results; Using intelligent energy flow control strategies, the grid-connected output power of each energy source is dynamically adjusted according to real-time load demand and energy supply conditions to optimize the energy flow path; During the grid connection process, the grid connection parameters are automatically adjusted according to real-time monitoring data and load changes, and the grid connection is adaptively optimized; The intelligent assessment strategy is used to conduct real-time assessment and prediction of the grid connection conditions of various energy sources, and analyze potential grid connection risks and optimization space, including: Based on the physical characteristics, volatility and dynamic change laws of different energy sources, a multi-energy mathematical model is established. The specific formula is: ， Among them, P i (t) represents the output power of the i-th energy source at time t, P i,max represents the maximum output power of the i-th energy source, represents the fluctuation coefficient of the i-th energy at time t, represents the random fluctuation term; Set the constraints of the multi-energy power demand of the microgrid. The specific formula is: ， Among them, P load (t) represents the constraints of the multi-energy power demand of the microgrid, n represents the number of energy types, P grid (t) represents the power added from the grid at time t; Combining historical data and real-time data to predict grid connection risk, the specific formula is: ， Among them, R(t) represents the predicted grid connection risk value, represents the weight of the i-th energy source, P i,pred (t) represents the predicted output power of the i-th energy source at time t; The method of dynamically adjusting the grid-connected output power of each energy source and optimizing the energy flow path includes: According to the real-time load demand and energy supply, the total grid-connected output power at time t is calculated. The specific formula is: ， Among them, P out (t) represents the total grid-connected output power at time t, represents the weight coefficient of the i-th energy at time t, represents the load weight coefficient of the qth load unit at time t, represents the power demand of the qth load unit at time t, M represents the number of load units, represents the power balance adjustment coefficient, Indicates the amount of power imbalance; Establish an energy optimization model to optimize the energy flow path. The specific formula of the energy optimization model is: ， in, represents the energy optimization model, i.e., optimal energy transmission, f1(P) represents the transmission loss minimization function, , P i represents the power generation of the i-th energy unit, P Lj represents the power demand of the jth load unit, R ij represents the resistance or impedance parameter between the energy transmission paths, f2(P) represents the power generation cost minimization function, , C i (P i ) represents the power generation cost function of energy unit i, f3(P) represents the energy balance optimization function, , represents the power imbalance penalty coefficient, represents the total power generation from all energy sources, Represents the total power demand of all load units.

2. The intelligent grid-connected method for a multi-source microgrid according to claim 1, characterized in that: The grid connection strategy is adjusted according to the grid connection risk analysis results, including: Adjust the grid connection strategy and set the objective function. The specific formula is: ， Among them, C total represents the grid-connected strategy objective function, min represents the minimum value function, represents the power generation cost of the j-th energy source, R freq represents the risk of grid frequency fluctuation, R volt Indicates the risk of grid voltage fluctuations, Represents the weight coefficient of grid frequency fluctuation risk, Represents the weight coefficient of grid voltage fluctuation risk.

3. The intelligent grid-connected method for a multi-source microgrid according to claim 2, characterized in that: The automatic adjustment of grid connection parameters and adaptive optimization of grid connection based on real-time monitoring data and load changes include: According to real-time monitoring data and load changes, the grid-connected parameters are automatically adjusted, including power factor and reactive power compensation. The power factor adjustment formula is: ， in, Indicates the adjustment value of power factor, P on represents active power, Q represents reactive power; The reactive power during the grid connection process is regulated and compensated through reactive power compensation equipment.

4. The intelligent grid-connected method for a multi-source microgrid according to claim 3, characterized in that: The parameters of each energy source of the microgrid include: voltage, current, frequency, and phase data.

5. An intelligent grid-connected system for a multi-source microgrid, used to implement the intelligent grid-connected method for a multi-source microgrid as claimed in any one of claims 1 to 4, characterized in that: include: Data acquisition module, evaluation and adjustment module, grid connection optimization module and adaptive adjustment module; The data acquisition module is used to collect parameters of various energy sources of the microgrid; The evaluation and adjustment module is used to use intelligent evaluation strategies to conduct real-time evaluation and prediction of the grid connection conditions of various energy sources, analyze potential grid connection risks and optimization space, and adjust the grid connection strategy according to the grid connection risk analysis results; The grid-connected optimization module is used to dynamically adjust the grid-connected output power of each energy source and optimize the energy flow path according to the real-time load demand and energy supply situation by using an intelligent energy flow control strategy; The self-adaptive adjustment module is used to automatically adjust the grid connection parameters and perform self-adaptive optimization on the grid connection according to the real-time monitoring data and load changes during the grid connection process.

6. The intelligent grid-connected system for multi-source microgrid according to claim 5, characterized in that: The evaluation and adjustment module includes: an intelligent evaluation unit and a grid-connected strategy adjustment unit; The intelligent evaluation unit is used to establish a mathematical model of multiple energy sources, set constraints on the power demand of multiple energy sources in the microgrid, combine historical data, and perform grid connection risk assessment and prediction based on real-time data; The grid connection strategy adjustment unit is used to adjust the grid connection strategy and set the objective function according to the grid connection risk analysis result.

7. The intelligent grid-connected system for multi-source microgrid according to claim 6, characterized in that: The grid-connected optimization module includes: a grid-connected power adjustment unit and a path optimization unit; The grid-connected power adjustment unit is used to calculate the total grid-connected output power according to the real-time load demand and energy supply situation; The path optimization unit is used to establish an energy optimization model and optimize the energy flow path.

8. The intelligent grid-connected system for multi-source microgrid according to claim 7, characterized in that: The adaptive adjustment module includes: a power factor adjustment unit and a reactive power adjustment and compensation unit; The power factor adjustment unit is used to automatically adjust the power factor according to real-time monitoring data and load changes; The reactive power regulation and compensation unit is used to automatically adjust reactive power according to real-time monitoring data and load changes.

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