Micro-grid intelligent management and control terminal

Abstract

The invention relates to the technical field of micro-grid operation control and energy management, and discloses a micro-grid intelligent management and control terminal. An HMI module; an encryption module; the communication module is used for reading topological information of the micro-grid and collecting real-time output and load power requirements of the distributed power supply; and the calculation module is used for constructing a micro-grid electrical model by using the topological information, generating an optimization model taking maximization of local consumption of new energy as a core target based on the micro-grid electrical model, refreshing the optimization model by using real-time output and load power requirements of the distributed power supply, and outputting a control instruction of the micro-grid. According to the invention, the micro-grid modeling and optimization algorithm is integrated to the local terminal, through optimal power flow calculation and multiple safety verification, the optimization regulation and control instruction is generated and issued, the global optimization and closed-loop control of the micro-grid are realized, and the economy, safety and new energy consumption capability of system operation are effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of micro-grid operation control and energy management, in particular to a micro-grid intelligent management and control terminal. BACKGROUND

[0002] With the wide application of renewable energy technologies such as wind power and photovoltaic, the penetration rate of distributed power in distribution networks continues to increase. Micro-grid, as a self-consistent system that can integrate distributed power generation, energy storage, load and control and protection devices, can operate in grid-connected mode or island mode, and is an effective solution to improve power supply reliability and promote local consumption of new energy.

[0003] However, the operation control of micro-grid faces many challenges. First, the output of distributed power sources such as wind power and photovoltaic has significant intermittency and randomness, combined with load fluctuations, making it difficult to maintain system power balance. Second, micro-grid operation needs to consider multiple objectives such as economy, safety and power quality, and traditional control methods based on artificial experience or offline planning cannot adapt to real-time changes in operating conditions.

[0004] Currently, common micro-grid control systems mostly use a centralized monitoring and remote communication architecture, but this architecture relies heavily on the computing power of the central controller and the reliability of the communication link. At the same time, most existing field terminals have single functions and mainly play the role of data acquisition and transmission, lacking the ability to perform complex optimization calculations locally, resulting in a lag in the generation of control instructions and an inability to respond to rapid changes in system state in a timely manner. In addition, many systems fail to fully consider real-time changes in grid topology and physical constraints of device operation when building optimization models, resulting in optimization results that often deviate from reality and easily causing problems such as new energy abandonment, node voltage out-of-limit, and overcharging and overdischarging of energy storage batteries.

[0005] To address the problems in the related art, no effective solutions have been proposed so far. SUMMARY

[0006] To address the problems in the related art, the present application proposes a micro-grid intelligent management and control terminal to overcome the above technical problems existing in the prior art.

[0007] To this end, the specific technical solutions adopted by the present application are as follows:

[0008] A micro-grid intelligent management and control terminal, comprising:

[0009] a power module;

[0010] an HMI module for setting operating parameters through an HMI interface;

[0011] The encryption module is used to encrypt and securely protect operating parameters and control commands, and to perform operating parameter verification and recovery processing according to comparison rules when it is necessary to read operating parameters.

[0012] The communication module is used to read the topology information of the microgrid and collect the real-time output and load power demand of distributed power sources.

[0013] The calculation module is used to construct a microgrid electrical model using topology information, generate an optimization model based on the microgrid electrical model and operating parameters with the core objective of maximizing the local consumption of new energy, refresh the optimization model using the real-time output of distributed power sources and load power demand, and output microgrid control commands.

[0014] Preferably, the communication module includes:

[0015] The information reading module is used to read the microgrid topology data from the database. The topology data includes the resistance and reactance parameters of the lines, the turns ratio and impedance of the transformers, the installation location and rated capacity of the distributed power sources, the configuration of energy storage devices, and the distribution of load nodes.

[0016] The control command issuing module is used to connect with microgrid equipment and send control commands to the microgrid.

[0017] Preferably, the encryption module includes:

[0018] The parameter partition storage module is used to write operating parameters with specifications lower than the target standard to non-volatile memory, and to write operating parameters with specifications higher than the target standard to an independent partition with hardware write protection lock, and to store control instructions.

[0019] The parameter storage module is used to calculate the hash value of the running parameters when reading them, compare it with the integrity label of the hash value before storage, and output the parameter recovery decision result based on the comparison result.

[0020] Preferably, the calculation module includes:

[0021] The electrical model building module is used to calculate branch admittance using the topology information of the microgrid, and generate the microgrid electrical model based on the state-space equations describing the electrical characteristics of the microgrid using the branch admittance.

[0022] The optimization model building module is used to output the energy storage state of charge based on the microgrid electrical model, and to establish an optimization model with the core objective of maximizing the local consumption of new energy by combining the energy storage state of charge as a constraint with the operating parameters.

[0023] The control sequence output module is used to obtain the key parameters of the dynamic refresh optimization model based on the real-time output of the distributed power source and the load power demand, and to combine the refreshed optimization model with the original dual interior point method to output the optimal control sequence.

[0024] The control command output module is used to verify the optimal control sequence according to multiple security verification mechanisms, adjust the optimal control sequence according to the verification results, and output the control commands of the microgrid.

[0025] Preferably, the branch admittance is calculated using the microgrid topology information, and the state-space equations describing the electrical characteristics of the microgrid based on the branch admittance are used to generate the microgrid electrical model, including:

[0026] Based on the topology information of the microgrid, the physical structure of the microgrid is abstracted into an electrical network diagram composed of nodes and branches, and the relationship between nodes and branches is obtained. Nodes represent the access points and common connection points of distributed power sources, loads and energy storage, and branches represent the lines connecting the nodes.

[0027] Based on the relationship between nodes and branches, resistance and resistance parameters are assigned to the branches to form a branch impedance matrix. Ground admittance parameters are assigned to nodes with grounding connections to generate a ground admittance matrix. Based on the branch impedance matrix and the ground admittance matrix, Kirchhoff's current law is used to construct the node admittance matrix.

[0028] Based on the nodal admittance matrix and Kirchhoff's current law, a spatial state equation describing the steady-state electrical characteristics of a microgrid is established. The microgrid is then converted into a digital network model using the spatial state equation, thus obtaining the microgrid electrical model.

[0029] Preferably, the optimization model that outputs the energy storage state of charge based on the microgrid electrical model, and uses the energy storage state of charge as a constraint in combination with operating parameters to establish an optimization model with the core objective of maximizing local consumption of new energy includes:

[0030] The exchange power between new energy sources and the microgrid is obtained based on the microgrid electrical model. The power of new energy sources that are not consumed locally at any time is calculated in combination with the operating status of energy storage devices. The objective function is obtained by minimizing the total amount of new energy sent back to the microgrid during the entire scheduling cycle based on the power of new energy sources and operating parameters.

[0031] Define the power balance constraints that the energy storage device must meet at any time period based on the common connection point to which the energy storage device is connected, and generate dynamic constraints on the state of charge of the energy storage device based on the charging and discharging power and rated capacity of the energy storage device.

[0032] Based on power balance constraints and dynamic state of charge constraints of energy storage devices, the operating constraints of the microgrid during the management and control process are output, and the operating constraints are combined with the objective function to obtain an optimization model with the core objective of maximizing the local consumption of new energy.

[0033] Preferably, the optimal control sequence is output by combining the refreshed optimized model with the original dual interior point method, including:

[0034] By introducing nonnegative relaxation variables, the refreshed optimization model is transformed into a standard nonlinear programming form. Based on the transformation result, a Lagrangian function is constructed to output a set of nonlinear equations. The nonlinear equations are then subjected to a first-order Taylor expansion to obtain the Newton search direction.

[0035] The safety boundary is determined based on the Newton search direction, and a backtracking linear search is performed on the basis of the safety boundary to solve the optimization problem containing the running constraints and objective function. The optimal control sequence is output based on the solution results.

[0036] Preferably, nonnegative relaxation variables are introduced to transform the refreshed optimization model into a standard nonlinear programming form. Based on the transformation result, a Lagrangian function is constructed to output a system of nonlinear equations. A first-order Taylor expansion is then performed on the system of nonlinear equations to obtain the Newton search direction, including:

[0037] Non-negative relaxation variables are introduced for all inequality constraints in the refreshed optimization model to transform the inequality constraints into equality constraints, thus completing the quasi-nonlinear programming form transformation of the optimization model.

[0038] An augmented objective function is constructed by incorporating a barrier function term into the transformed optimization model to prepare for solving the optimization model. A Lagrangian function is then constructed based on the augmented objective function and equality constraints.

[0039] Based on the first-order optimality condition, a system of nonlinear equations containing core variables is derived from the Lagrangian function, and a first-order Taylor expansion is performed on the system of nonlinear equations. The core variables include Lagrange multipliers and nonnegative relaxation variables.

[0040] Based on the expansion results, the nonlinear equation system is linearized into a linear equation that includes the gradient of the objective function, dual feasibility, and complementary relaxation conditions. The Newton search direction at the current iteration point is obtained by solving the linear equation.

[0041] Preferably, a safety boundary is determined based on the Newton search direction, and a backtracking linear search is performed on the basis of the safety boundary to solve the optimization problem containing the running constraints and objective function. The optimal control sequence is output based on the solution results, including:

[0042] Obtain the maximum theoretical step size that meets the requirements of microgrid variables, and select and define a safety boundary for the maximum theoretical step size. After the safety boundary is clear, establish a comprehensive benefit function to consider the degree of improvement of the objective function and the degree of satisfaction of the operating constraints.

[0043] The step size is gradually reduced by a preset decay factor, starting with the maximum theoretical step size. The step size is verified by the line search condition during the reduction process until the maximum feasible step size that satisfies the line search condition is found.

[0044] After determining the maximum feasible step size, the variables are updated along the Newton search direction using the maximum feasible step size to complete a single iteration process. At the same time, the variable update is repeatedly executed to solve the optimization problem containing the objective function and various constraints.

[0045] The degree of improvement of the objective function and the degree of satisfaction of the operating constraints are verified based on the solution results. When the analysis results approach the optimal solution, the optimal control sequence for managing the microgrid is output.

[0046] Preferably, the optimal control sequence is verified according to a multi-security verification mechanism, and the optimal control sequence is adjusted based on the verification results. The output microgrid control commands include:

[0047] Verify whether the charging and discharging power of the energy storage device in the optimal control sequence meets the physical limits of the device and whether the state of charge of the energy storage device meets the preset safety range. If the verification results are both met, directly generate a control command containing the optimal setting value of the microgrid.

[0048] If the physical limit of the equipment fails to pass the verification between itself and the preset safety range, the continuous failure technical mechanism will be activated. When the number of consecutive failures reaches the preset threshold, the braking mechanism will be executed to force the output of a suboptimal solution that satisfies all safety constraints as the control command for the microgrid.

[0049] The beneficial effects of this invention are as follows:

[0050] This invention integrates microgrid modeling and optimization algorithms into a local terminal. Through optimal power flow calculation and multiple security checks, it generates and issues optimization control commands, realizing global optimization and closed-loop control of the microgrid. This effectively improves the system's economy, security, and renewable energy absorption capacity. By integrating real-time modeling and high-speed computing capabilities into the local terminal, it achieves coordinated optimization and closed-loop control of distributed power sources, energy storage systems, and loads in the microgrid. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a schematic diagram of a microgrid intelligent management and control terminal according to an embodiment of the present invention;

[0053] Figure 2 This is a schematic diagram of the operation of a microgrid intelligent management and control terminal according to an embodiment of the present invention;

[0054] Figure 3This is a schematic diagram of the hardware operating environment involved in the embodiments of the present invention;

[0055] Figure 4 This is a flowchart of the optimization model construction in a microgrid intelligent management and control terminal according to an embodiment of the present invention;

[0056] Figure 5 This is a flowchart of the optimal control sequence output in a microgrid intelligent management and control terminal according to an embodiment of the present invention.

[0057] In the picture:

[0058] 1. Power supply module; 2. HMI module; 3. Encryption module; 4. Communication module; 5. Computing module. Detailed Implementation

[0059] To further illustrate the various embodiments, the present invention provides accompanying drawings, which are part of the disclosure of the present invention. These drawings are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these drawings, those skilled in the art should be able to understand other possible implementation methods and the advantages of the present invention.

[0060] According to an embodiment of the present invention, a smart control terminal for microgrids is provided.

[0061] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments, such as... Figure 1 and Figures 4 to 5 As shown, the microgrid intelligent management and control terminal according to an embodiment of the present invention includes:

[0062] Power module 1.

[0063] HMI module 2 is used to set operating parameters through the HMI interface.

[0064] Encryption module 3 is used to encrypt and protect operating parameters and control commands, and to perform operating parameter verification and recovery processing according to comparison rules when operating parameters need to be read.

[0065] In one embodiment, encryption module 3 includes:

[0066] The parameter partition storage module is used to write operating parameters with specifications lower than the target standard to non-volatile memory, and to write operating parameters with specifications higher than the target standard to an independent partition with hardware write protection lock, and to store control instructions.

[0067] The parameter storage module is used to calculate the hash value of the running parameters when reading them, compare it with the integrity label of the hash value before storage, and output the parameter recovery decision result based on the comparison result.

[0068] Communication module 4 is used to read the topology information of the microgrid and collect the real-time output and load power demand of distributed power sources.

[0069] In one embodiment, the communication module 4 includes:

[0070] The information reading module is used to read the microgrid topology data from the database. The topology data includes the resistance and reactance parameters of the lines, the turns ratio and impedance of the transformers, the installation location and rated capacity of the distributed power sources, the configuration of energy storage devices, and the distribution of load nodes.

[0071] The control command issuing module is used to connect with microgrid equipment and send control commands to the microgrid.

[0072] The calculation module 5 is used to construct a microgrid electrical model using topology information, generate an optimization model based on the microgrid electrical model and operating parameters with the core objective of maximizing the local consumption of new energy, refresh the optimization model using the real-time output of distributed power sources and load power demand, and output microgrid control commands.

[0073] In one embodiment, the computing module 5 includes:

[0074] The electrical model building module is used to calculate branch admittance using the topology information of the microgrid, and generate the microgrid electrical model based on the state-space equations describing the electrical characteristics of the microgrid using the branch admittance.

[0075] The optimization model building module is used to output the energy storage state of charge based on the microgrid electrical model, and to establish an optimization model with the core objective of maximizing the local consumption of new energy by combining the energy storage state of charge as a constraint with the operating parameters.

[0076] The control sequence output module is used to obtain the key parameters of the dynamic refresh optimization model based on the real-time output of the distributed power source and the load power demand, and to combine the refreshed optimization model with the original dual interior point method to output the optimal control sequence.

[0077] The control command output module is used to verify the optimal control sequence according to multiple security verification mechanisms, adjust the optimal control sequence according to the verification results, and output the control commands of the microgrid.

[0078] In one embodiment, calculating branch admittance using the microgrid's topology information and generating a microgrid electrical model based on the state-space equations describing the microgrid's electrical characteristics using the branch admittances includes: abstracting the physical structure of the microgrid into an electrical network diagram composed of nodes and branches based on the microgrid's topology information, and obtaining the node-branch association relationships. Nodes represent the access points and common connection points of distributed power sources, loads, and energy storage, while branches represent the lines connecting the nodes; assigning resistance and impedance parameters to branches according to the node-branch association relationships to form a branch impedance matrix, and assigning ground admittance parameters to nodes with grounding connections to generate a ground admittance matrix; constructing a node admittance matrix using Kirchhoff's current law based on the branch impedance matrix and the ground admittance matrix; establishing a spatial state equation describing the steady-state electrical characteristics of the microgrid based on the node admittance matrix and Kirchhoff's current law, and converting the microgrid into a digital network model using the spatial state equations to obtain the microgrid electrical model.

[0079] In one embodiment, the optimization model, which outputs the energy storage state of charge (SOC) based on the microgrid electrical model and uses the SOC as a constraint in conjunction with operating parameters to maximize the local consumption of renewable energy, includes: obtaining the exchange power between renewable energy and the microgrid based on the microgrid electrical model, calculating the renewable energy power not locally consumed at any given time based on the operating status of the energy storage device, minimizing the total amount of renewable energy fed back to the microgrid during the entire scheduling cycle based on the renewable energy power and operating parameters to obtain the objective function; defining the power balance constraint that the energy storage device must satisfy at any time period based on the point of common coupling (PCC) to which it is connected, and generating dynamic constraints on the SOC of the energy storage device based on the charging and discharging power and rated capacity of the energy storage device; outputting the operating constraints of the microgrid during the management and control process based on the power balance constraints and the dynamic constraints on the SOC of the energy storage device, and combining the operating constraints with the objective function to obtain the optimization model with the core objective of maximizing the local consumption of renewable energy.

[0080] In one embodiment, combining the refreshed optimization model with the original dual interior-point method to output the optimal control sequence includes: introducing non-negative relaxation variables to transform the refreshed optimization model into a standard nonlinear programming form; constructing a Lagrangian function to output a nonlinear equation system based on the transformation result; performing a first-order Taylor expansion on the nonlinear equation system to obtain the Newton search direction; determining the safety boundary based on the Newton search direction; and performing a backtracking linear search on the basis of the safety boundary to solve the optimization problem containing the running constraints and objective function; and outputting the optimal control sequence based on the solution result.

[0081] In one embodiment, introducing nonnegative relaxation variables transforms the refreshed optimization model into a standard nonlinear programming form. Based on the transformation result, a Lagrangian function is constructed to output a nonlinear equation system. A first-order Taylor expansion is then performed on the nonlinear equation system to obtain the Newton search direction. This process includes: introducing nonnegative relaxation variables for all inequality constraints in the refreshed optimization model to transform them into equality constraints, thus completing the quasi-nonlinear programming transformation of the optimization model; incorporating obstacle function terms into the transformed optimization model to construct an augmented objective function, preparing the optimization model for solution; constructing a Lagrangian function based on the augmented objective function and equality constraints; deriving a nonlinear equation system containing core variables from the Lagrangian function according to the first-order optimality condition; performing a first-order Taylor expansion on the nonlinear equation system, where the core variables include Lagrange multipliers and nonnegative relaxation variables; linearizing the nonlinear equation system into a linear equation system containing the objective function gradient, dual feasibility, and complementary relaxation conditions based on the expansion result; and obtaining the Newton search direction for the current iteration point by solving the linear equation system.

[0082] In one embodiment, a safety boundary is determined based on the Newton search direction, and a backtracking linear search is performed on the basis of the safety boundary to solve the optimization problem containing operational constraints and objective functions. The optimal control sequence is output based on the solution results, including: obtaining the maximum theoretical step size that meets the requirements of microgrid variables, and selecting and defining a safety boundary for the maximum theoretical step size; after the safety boundary is clear, establishing a comprehensive benefit function to consider the degree of improvement of the objective function and the degree of satisfaction of operational constraints; gradually reducing the step size with the maximum theoretical step size as the initial value according to a preset attenuation factor, and verifying it through the linear search conditions during the reduction process until the maximum feasible step size that meets the linear search conditions is found; after determining the maximum feasible step size, updating the variables along the Newton search direction using the maximum feasible step size to complete a single iteration process, while repeatedly performing variable updates to solve the optimization problem containing objective functions and various constraints; verifying the degree of improvement of the objective function and the satisfaction of operational constraints based on the solution results, and outputting the optimal control sequence for managing the microgrid when the analysis results approach the optimal solution.

[0083] In one embodiment, the optimal control sequence is verified according to a multi-security verification mechanism, and the optimal control sequence is adjusted according to the verification results. The output microgrid management and control instructions include: verifying whether the charging and discharging power of the energy storage device in the optimal control sequence meets the physical limits of the device and whether the state of charge of the energy storage device meets the preset safety range. If both verification results are met, a management and control instruction containing the optimal setting value of the microgrid is directly generated. If either the physical limit of the device or the preset safety range fails the verification, a continuous failure technical mechanism is activated, and when the number of continuous failures reaches a preset threshold, a braking mechanism is executed to force the output of a suboptimal solution that meets all safety constraints as the microgrid management and control instruction.

[0084] It should be noted that the implementation process of the microgrid intelligent management and control terminal and its optimized operation method encompasses two core parts: hardware architecture design and software control flow. Through the organic combination of modular terminals and intelligent algorithms, real-time modeling, target optimization, and closed-loop control of the microgrid system are achieved. The implementation process of the microgrid intelligent management and control terminal will be described in detail below, based on the hardware architecture and control flow of this implementation:

[0085] At the hardware level, the intelligent control terminal adopts an industrial-grade modular design, including a power module 1, an HMI module 2, a communication module 4, an encryption module 3, and a computing module 5. The power module 1, through AC / DC dual-input and redundancy backup mechanisms, ensures seamless switching of input sources and maintains continuous and stable power supply when facing microgrid grid-connected or off-grid switching, voltage dips, or short-term faults, providing a fundamental guarantee for the long-term stable operation of the terminal. The HMI module 2 is equipped with a touch screen, providing a graphical human-machine interface that supports initial setting of operating parameters, real-time system status monitoring, and abnormal alarm display, facilitating local configuration and monitoring by operators. The communication module 4 integrates multiple communication methods such as Ethernet, 4G wireless communication, and power line carrier, enabling communication with… Data interaction between the upper-level database and microgrid field devices; Encryption module 3 integrates a hardware encryption chip, adopts national cryptographic standard algorithms, and provides data encryption and security authentication services for the terminal to ensure the confidentiality and integrity of operating parameters and control commands during storage and transmission; Computation module 5, as the intelligent core of the terminal, is equipped with a high-performance embedded processor. Its core value lies in the built-in microgrid modeling tool library and optimization algorithm library. Based on the static topology and real-time data obtained by the communication module, this module autonomously completes the generation of microgrid node admittance matrix, construction of optimization model, efficient solution of optimal power flow, and generation of control command sequence locally, realizing the marginalization of control decision-making and fundamentally overcoming the dependence of traditional architecture on central server and remote communication.

[0086] After the terminal powers on and completes its hardware self-test, it enters the following state: Figure 2 The intelligent control process shown is a complete automated process that integrates initialization, static modeling, periodic closed-loop control, and termination evaluation. The specific implementation process is as follows:

[0087] Step 1: Operators set key operating parameters through the HMI interface, including: the allowable deviation range of node voltage, the renewable energy absorption target, the optimization algorithm parameters, and the total runtime of the control program. These parameters provide the core basis and constraints for subsequent model construction, optimization calculation, and safety control. Specifically, the allowable deviation range of node voltage will be directly converted into voltage safety constraints in the optimization model; the renewable energy absorption target will be specified as the objective function of the optimization model; the optimization algorithm parameters will guide the iterative process of the optimal power flow solver; and the total runtime of the control program determines the total number of closed-loop control cycles. All parameters are written to the terminal's non-volatile memory and secured by an encryption module to ensure reliable recovery after an unexpected power outage and to prevent leakage or tampering during storage and operation. Encryption module 3 provides a multi-layered parameter security protection mechanism: all operating parameters are stored after symmetric encryption based on national cryptographic algorithms to prevent parameter leakage caused by physical disassembly of the terminal; digital fingerprints based on hash algorithms are generated for the encrypted parameter data, and integrity checks are performed after each parameter read. If a fingerprint discrepancy is found, the parameter is determined to have been tampered with, a security alarm is immediately triggered, and the execution of the control program is refused; special protection is provided for critical parameters (such as node voltage safety boundaries and SOC operating ranges) by writing them to a specific memory area with write protection lock functionality, which can only be modified after high-level authentication by the encryption module, effectively preventing malicious or accidental modification during operation.

[0088] The secure storage process executed by encryption module 3 is as follows:

[0089] (1) Parameter encryption: Call the built-in encryption module 3, use national cryptographic algorithms such as SM4 to encrypt the set of running parameters to be stored as a whole, and generate ciphertext data blocks.

[0090] (2) Integrity tag generation: The encryption module 3 calculates the SM3 hash value of the encrypted ciphertext data block, uses the hash value as the data integrity verification tag, and stores it separately from the ciphertext data block.

[0091] (3) Hierarchical storage: The ordinary operating parameters (i.e., specifications lower than the target standard) processed by the encryption module 3 are written into the general Flash storage area. Meanwhile, for core parameters that are related to the safe and stable operation of the system (specifications higher than the target standard), such as the upper and lower limits of the allowable deviation of the node voltage and the hard safety range of the SOC of the energy storage system, they are written into the independent partition of the EEPROM with hardware write protection lock. This partition is in a write-locked state during the normal operation cycle of the system. It can only be temporarily unlocked for modification after the encryption module verifies the high-level password of the maintenance personnel through the HMI module.

[0092] (4) Runtime verification and recovery: Each time the terminal starts up or periodically reads the running parameters from the memory, the encryption module 3 first recalculates the hash value of the ciphertext data block and compares it with the integrity tag of the storage; if the comparison is consistent, the parameters are decrypted using the key and loaded into memory; if they are inconsistent, it is immediately determined that the parameters have been corrupted or tampered with, the terminal automatically switches to the security isolation mode, stops issuing control commands, and sends high-level security alarm information to the operation and maintenance center through the communication module 4, thereby constructing a full-cycle parameter security protection system from storage to loading and running.

[0093] Step 2: After parameter configuration is completed, the terminal reads the static topology data of the microgrid from the database through communication module 4, including the resistance and reactance parameters of the lines, the turns ratio and impedance of the transformers, the installation location and rated capacity of distributed power sources, the configuration of the energy storage system, and the distribution of load nodes. Then, the calculation module 5 performs the following steps to construct the electrical model of the microgrid:

[0094] (1) Network topology analysis and node numbering: The system analyzes the obtained topology data, identifies all electrical nodes and assigns them independent numbers, and determines the balance node, PV node and PQ node.

[0095] (2) Branch admittance calculation: Based on the resistance (R) and reactance (X) parameters of the branch, calculate the series admittance Y=1 / (R+jX) of each branch; at the same time, based on the line's capacitance to ground or the excitation branch parameters of the transformer, calculate its ground admittance.

[0096] (3) Assembly of node admittance matrix: Based on the network connection relationship, the node admittance matrix Y is automatically assembled. Specifically, the diagonal elements of the matrix Y are... ii Equal to the sum of the admittances of all branches connected to node i; the off-diagonal elements Y of the matrix ij (i≠j) equals the negative of the admittance of the branch directly connected to node i and node j; if there is no direct connection, then Y ij =0.

[0097] (4) Establishment of state-space equations: Based on the node admittance matrix Y, the state-space equation Y·U=I describing the steady-state electrical characteristics of the system is established, where I is the node injected current vector and U is the node voltage vector. This equation, as the matrix expression of Kirchhoff's current law, constitutes the electrical network basis for power balance constraints in subsequent optimal power flow calculations, ensuring that the current injection and outflow of all nodes reach a balance. Through the above steps, the microgrid system is transformed into an accurate and computable mathematical network model, which builds a real digital twin environment for subsequent optimal power flow calculations.

[0098] Step 3: After obtaining the accurate electrical model, an optimization model is established with the core objective of maximizing the local consumption of renewable energy. The objective function is to minimize the amount of renewable energy fed back to the grid (this objective is derived from the renewable energy consumption target set in Step 1). The optimization model also incorporates multiple operational constraints, including power balance constraints, dynamic and upper / lower limit constraints of the state of charge of the energy storage system, charging and discharging power and state constraints, node voltage safety constraints (the allowable deviation range is determined by the allowable deviation range of node voltage set in Step 1), and power exchange constraints with the main grid. These constraints together ensure that the optimization results are physically feasible while meeting system safety requirements.

[0099] An optimization model is established based on a precise electrical model, with the core objective of maximizing local consumption of renewable energy. The objective function of this model is specifically expressed as: minimizing the total amount of renewable energy fed back to the main grid during the entire dispatch cycle, and its mathematical expression is as follows:

[0100] ;

[0101] In the formula, This represents the amount of electricity fed back to the grid from new energy sources during time period t, expressed in kW.

[0102] Electricity fed back to the grid from new energy sources Accurate calculation is key to achieving this goal. By monitoring the power exchange with the main grid at the point of common coupling (PCC) and combining this with the operating status of the energy storage system, the amount of renewable energy power not consumed locally in each time period can be accurately calculated. The calculation formula is as follows:

[0103] ;

[0104] In the formula, The power supplied to the grid is measured by the meter at the time t, and the unit is kW. The average discharge power of the energy storage system during time period t is expressed in kW. t represents the discharge state of the energy storage system during time period t, with values ​​ranging from 1 to 0, indicating whether the energy storage system is in a discharge state. When the value is 0, the power measured by the gate meter is entirely provided by new energy sources, and the unconsumed power from new energy sources is... ; At that time, the power of new energy connected to the grid equals the power measured by the meter at the gateway minus the output power of the energy storage system. The unconsumed new energy power is... .

[0105] To achieve the above objectives, the solution to the model must adhere to the following operational constraints:

[0106] 1. Power balance constraints;

[0107] For the common connection point to which the energy storage system is connected, the following must be satisfied at any time period t:

[0108] ;

[0109] In the formula, The average output power of photovoltaic power during time period t, The average output power of wind power during time period t, represents the average power of the load during time period t, in kW; The average charging power of the energy storage system during time period t is expressed in kW. The charging status of the energy storage system during time period t, with values ​​ranging from 1 to 0, respectively indicating whether the energy storage system is in a charging state; The power supplied to the grid is measured by the meter at the time t, and the unit is kW. This formula is used to ensure the instantaneous balance of power supply and demand at any time in the microgrid.

[0110] 2. Dynamic constraints on the state of charge (SOC) of energy storage systems;

[0111] ;

[0112] In the formula, The State of Charge (SOC) at time t takes a value between 0 and 1. and These refer to the charging and discharging efficiencies of the energy storage system, respectively. The rated capacity of the energy storage system is expressed in kWh and is used to ensure that the energy state changes of the energy storage system conform to its charging and discharging physical laws.

[0113] 3. Constraints on the charge and discharge states of the energy storage system;

[0114] ;

[0115] ;

[0116] This is used to ensure that the energy storage system is in one of three states at any given time: charging, discharging, or standby.

[0117] 4. Node voltage safety constraints;

[0118] ;

[0119] In the formula, Let be the voltage amplitude at node i during time period t; and These are the lower and upper limits of the allowable node voltage, respectively. It is the set of all nodes in the system, used to ensure the power quality and system safety of the microgrid, that is, the voltage of all nodes must be maintained within the allowable deviation range.

[0120] 5. Power constraints when exchanging data with the main grid;

[0121] ;

[0122] In the formula, The rated capacity of the distribution transformer, in kVA, is used to ensure that the power exchanged between the microgrid and the main grid does not exceed the transformer capacity limit at the connection point. These constraints together constitute the feasible solution space of the optimization problem, ensuring that the solution is both optimal and feasible.

[0123] Step 4: Perform closed-loop control periodically at fixed time intervals. Each control cycle begins with real-time data acquisition: the terminal obtains key information such as the real-time output of the distributed power source and the load power demand through communication module 4. After the acquisition is completed, it immediately enters the model update stage, using the latest acquired data to dynamically refresh the key parameters in the optimization model, ensuring that the optimization model can accurately reflect the current state and future trend of the system, thereby ensuring that the optimization decision is always based on the real-time state of the system.

[0124] Step 5: Activate the optimal power flow calculation function, using the original-dual interior point method as the core solution algorithm (its maximum number of iterations and convergence accuracy are controlled by the optimization algorithm parameters set in Step 1), and combining it with an adaptive step size adjustment strategy to meet the dual requirements of real-time control for computational efficiency and convergence accuracy. The complete implementation process of this algorithm includes the following four key steps:

[0125] (1) Problem standardization and introduction of obstacle function: The microgrid optimization operation model is transformed into a standard nonlinear programming form. For all inequality constraints, non-negative relaxation variables are introduced to transform them into equality constraints. At the same time, the obstacle function term is introduced into the objective function to form an augmented objective function. In the initialization stage, the obstacle parameter μ is set to a large positive value. This parameter will gradually decay to close to zero during the iteration process to ensure that the solution gradually approaches the boundary optimal solution from the inside of the feasible region.

[0126] (2) Construction of Lagrangian function and formation of KKT system: Based on the augmented objective function and equality constraints, a complete Lagrangian function is constructed. According to the first-order optimality condition, the corresponding KKT (Karush-Kuhn-Tucker) system is derived. The system constitutes a nonlinear equation system containing three types of core variables: original variables (power, voltage, etc.), dual variables (Lagrange multipliers) and relaxation variables.

[0127] (3) Solving for the Newton direction: In each iteration, the KKT nonlinear equation system is expanded by first-order Taylor expansion and linearized into a linear system of the form JΔz=-F. This equation integrates the gradient of the objective function, the original feasibility, the dual feasibility and the complementary relaxation condition. The Newton search direction Δz at the current iteration point is obtained by solving this linear system.

[0128] (4) Adaptive step size calculation and variable update: By calculating the maximum theoretical step size that satisfies the non-negativity constraints of all variables, the safety boundary for step size selection is determined, and a benefit function that comprehensively considers the improvement of the objective function and the satisfaction of constraints is established to provide a quantitative index for step size evaluation. On this basis, a backtracking linear search is performed. Starting from the maximum theoretical step size, the step size is gradually reduced according to the preset decay factor until the maximum feasible step size that satisfies the Armijo (line search) condition is found. This adaptive mechanism enables the algorithm to dynamically adjust the search strategy according to the local characteristics of the optimization problem: a larger step size is used to accelerate convergence in relatively flat regions, and the step size is automatically reduced in complex regions to ensure stability. Finally, the determined adaptive step size is used to update all variables along the Newton direction to complete this iteration.

[0129] By iteratively executing the above steps, an optimization problem involving the objective function and various constraints is solved, ultimately obtaining the optimal control sequence, including the charging and discharging power of the energy storage system and the output setpoint of the distributed power source.

[0130] Step Six: After the optimization calculation is completed, a strict multi-layered security verification mechanism is executed to verify the following in sequence:

[0131] Verification 1: Whether the optimal power flow calculation converges to a feasible solution;

[0132] Verification 2: Does the charging and discharging power of the energy storage system exceed the physical limits of the equipment, i.e., does it meet the following requirements?

[0133] ;

[0134] In the formula, This indicates the rated power of the energy storage system, measured in kW.

[0135] Verification 3: Whether the state of charge of the energy storage unit is within the preset safe range, i.e., whether it meets the following requirements:

[0136] ;

[0137] In the formula, These two values ​​represent the SOC limits corresponding to the termination of discharge and charging of the energy storage system, respectively.

[0138] If all checks pass, instruction A is generated, which is the optimized scheduling instruction. This instruction contains the best setting value obtained by the optimized calculation of each device. If any check fails, the continuous failure counting mechanism is activated. When the number of consecutive failures reaches the preset threshold, the braking mechanism will be activated, and the suboptimal solution that satisfies all safety constraints will be output as the final control instruction B to avoid the risk of falling into an infinite loop and prompting for troubleshooting.

[0139] Step 7: The generated control commands are sent to each execution device, including distributed power inverters and energy storage converters, through the communication module. At the end of each control cycle, it is determined whether the cumulative running time has reached the preset threshold of the total running time of the control program set in Step 1. If the condition is met, the control program is terminated; if not, the process automatically returns to Step 4 to continue executing a new round of closed-loop control.

[0140] The terminal simultaneously activates the execution status monitoring mechanism to collect real-time action feedback and operation data of the equipment, verify the control effect, and thus form a complete decision-making, execution, and feedback control closed loop. This feedback information also provides an important initial state for the optimization calculation of the next cycle.

[0141] At the end of each control cycle, the system determines whether the cumulative runtime has reached the total duration threshold set during the initialization phase. If the condition is met, the entire control program is automatically terminated. If the termination condition is not met, the process automatically executes a closed-loop control cycle and continues to execute the closed-loop control of the next cycle. This process repeats until the task is completed.

[0142] Furthermore, through the deep integration of hardware modular design and software intelligent algorithms, the microgrid has achieved fully automated management of the entire process from initial modeling and real-time optimization calculation to safe closed-loop control, significantly improving its autonomy and power supply reliability.

[0143] Furthermore, the present invention also provides an electronic device. For example... Figure 3 The diagram illustrates the hardware operating environment of an electronic device, which may include: a processor (e.g., CPU), memory, a user interface, a network interface, and a communication bus. The communication bus is used to enable communication between components. The user interface may include a display screen and an input unit such as a keyboard; optionally, the user interface may also include a standard wired interface or a wireless interface. The network interface may optionally include a standard wired interface or a wireless interface. The memory may be high-speed RAM or stable non-volatile memory, such as disk storage. Alternatively, the memory may be a storage device independent of the aforementioned processor.

[0144] Those skilled in the art will understand that Figure 3The electronic devices shown do not constitute a limitation on electronic devices and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0145] like Figure 3 As shown, a memory, as a type of computer storage medium, may include an operating system, a network communication module, a user interface module, and device management programs. The operating system is a program that manages and controls the hardware and software resources of electronic devices, supporting the operation of electronic devices and other software or programs. Figure 3 In the electronic device shown, the user interface is mainly used to connect to the terminal and communicate with the terminal, such as receiving user signaling data sent by the terminal; the network interface is mainly used to communicate with the backend server; the processor can be used to call the program stored in the memory and execute the steps of the method or system described above.

[0146] Furthermore, the present invention also proposes a computer-readable storage medium storing a device management program, which, when executed by a processor, implements the steps of the method or system described above.

[0147] The specific embodiments of the computer-readable storage medium of the present invention are basically the same as those of the above-described methods or systems, and will not be repeated here. Furthermore, to achieve the above objectives, the present invention also provides a computer program product, comprising: a computer program, which, when executed by a processor, implements the steps of the methods or systems described above.

[0148] Those skilled in the art will recognize that the units and algorithm steps described in conjunction with the embodiments herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0149] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart control terminal for microgrids, characterized in that, include: Power module; The HMI module is used to set operating parameters through the HMI interface; The encryption module is used to encrypt and securely protect operating parameters and control commands, and to perform operating parameter verification and recovery processing according to comparison rules when it is necessary to read operating parameters. The communication module is used to read the topology information of the microgrid and collect the real-time output and load power demand of distributed power sources. The calculation module is used to construct a microgrid electrical model using topology information, generate an optimization model based on the microgrid electrical model and operating parameters with the core objective of maximizing the local consumption of new energy, refresh the optimization model using the real-time output of distributed power sources and load power demand, and output microgrid control commands.

2. The intelligent control terminal for microgrids according to claim 1, characterized in that, The communication module includes: The information reading module is used to read the topology data of the microgrid from the database. The topology data includes the resistance and reactance parameters of the lines, the turns ratio and impedance of the transformers, the installation location and rated capacity of the distributed power sources, the configuration of energy storage devices, and the distribution of load nodes. The control command issuing module is used to connect with microgrid equipment and send control commands to the microgrid.

3. The intelligent control terminal for microgrids according to claim 1, characterized in that, The encryption module includes: The parameter partition storage module is used to write operating parameters with specifications lower than the target standard to non-volatile memory, and to write operating parameters with specifications higher than the target standard to an independent partition with hardware write protection lock, and to store control instructions. The parameter storage module is used to calculate the hash value of the running parameters when reading them, compare it with the integrity label of the hash value before storage, and output the parameter recovery decision result based on the comparison result.

4. A microgrid intelligent control terminal according to claim 1, characterized in that, The computing module includes: The electrical model building module is used to calculate branch admittance using the topology information of the microgrid, and generate the microgrid electrical model based on the state-space equations describing the electrical characteristics of the microgrid using the branch admittance. The optimization model building module is used to output the energy storage state of charge based on the microgrid electrical model, and to establish an optimization model with the core objective of maximizing the local consumption of new energy by combining the energy storage state of charge as a constraint with the operating parameters. The control sequence output module is used to obtain the key parameters of the dynamic refresh optimization model based on the real-time output of the distributed power source and the load power demand, and to combine the refreshed optimization model with the original dual interior point method to output the optimal control sequence. The control command output module is used to verify the optimal control sequence according to multiple security verification mechanisms, adjust the optimal control sequence according to the verification results, and output the control commands of the microgrid.

5. A microgrid intelligent control terminal according to claim 4, characterized in that, The process of calculating branch admittance using the microgrid's topology information and generating a microgrid electrical model based on the state-space equations describing the microgrid's electrical characteristics using the branch admittances includes: Based on the topology information of the microgrid, the physical structure of the microgrid is abstracted into an electrical network diagram composed of nodes and branches, and the relationship between nodes and branches is obtained. The nodes represent the access points and common connection points of distributed power sources, loads and energy storage, and the branches represent the lines connecting the nodes. Based on the relationship between nodes and branches, resistance and resistance parameters are assigned to the branches to form a branch impedance matrix. Ground admittance parameters are assigned to nodes with grounding connections to generate a ground admittance matrix. Based on the branch impedance matrix and the ground admittance matrix, Kirchhoff's current law is used to construct the node admittance matrix. Based on the nodal admittance matrix and Kirchhoff's current law, a spatial state equation describing the steady-state electrical characteristics of a microgrid is established. The microgrid is then converted into a digital network model using the spatial state equation, thus obtaining the microgrid electrical model.

6. A microgrid intelligent management and control terminal according to claim 5, characterized in that, The optimization model, which outputs the energy storage state of charge based on the microgrid electrical model and uses the energy storage state of charge as a constraint in conjunction with operating parameters to establish an optimization model with the core objective of maximizing local consumption of new energy, includes: The exchange power between new energy sources and the microgrid is obtained based on the microgrid electrical model. The power of new energy sources that are not consumed locally at any time is calculated in combination with the operating status of energy storage devices. The objective function is obtained by minimizing the total amount of new energy sent back to the microgrid during the entire scheduling cycle based on the power of new energy sources and operating parameters. Define the power balance constraints that the energy storage device must meet at any time period based on the common connection point to which the energy storage device is connected, and generate dynamic constraints on the state of charge of the energy storage device based on the charging and discharging power and rated capacity of the energy storage device. Based on power balance constraints and dynamic state of charge constraints of energy storage devices, the operating constraints of the microgrid during the management and control process are output, and the operating constraints are combined with the objective function to obtain an optimization model with the core objective of maximizing the local consumption of new energy.

7. A microgrid intelligent control terminal according to claim 6, characterized in that, The step of combining the refreshed optimized model with the original dual interior point method to output the optimal control sequence includes: By introducing nonnegative relaxation variables, the refreshed optimization model is transformed into a standard nonlinear programming form. Based on the transformation result, a Lagrangian function is constructed to output a set of nonlinear equations. The nonlinear equations are then subjected to a first-order Taylor expansion to obtain the Newton search direction. The safety boundary is determined based on the Newton search direction, and a backtracking linear search is performed on the basis of the safety boundary to solve the optimization problem containing the running constraints and objective function. The optimal control sequence is output based on the solution results.

8. A microgrid intelligent control terminal according to claim 7, characterized in that, The introduction of nonnegative relaxation variables transforms the refreshed optimization model into a standard nonlinear programming form. Based on the transformation result, a Lagrangian function is constructed to output a system of nonlinear equations. A first-order Taylor expansion is then performed on the system of nonlinear equations to obtain the Newton search direction, including: Non-negative relaxation variables are introduced for all inequality constraints in the refreshed optimization model to transform the inequality constraints into equality constraints, thus completing the quasi-nonlinear programming form transformation of the optimization model. An augmented objective function is constructed by incorporating a barrier function term into the transformed optimization model to prepare for solving the optimization model. A Lagrangian function is then constructed based on the augmented objective function and equality constraints. Based on the first-order optimality condition, a system of nonlinear equations containing core variables is derived from the Lagrangian function, and a first-order Taylor expansion is performed on the system of nonlinear equations. The core variables include Lagrange multipliers and nonnegative relaxation variables. Based on the expansion results, the nonlinear equation system is linearized into a linear equation that includes the gradient of the objective function, dual feasibility, and complementary relaxation conditions. The Newton search direction at the current iteration point is obtained by solving the linear equation.

9. A microgrid intelligent control terminal according to claim 8, characterized in that, The process of determining the safety boundary based on the Newton search direction, and then performing a backtracking linear search based on the safety boundary to solve the optimization problem containing the running constraints and objective function, and outputting the optimal control sequence based on the solution results, includes: Obtain the maximum theoretical step size that meets the requirements of microgrid variables, and select and define a safety boundary for the maximum theoretical step size. After the safety boundary is clear, establish a comprehensive benefit function to consider the degree of improvement of the objective function and the degree of satisfaction of the operating constraints. The step size is gradually reduced by a preset decay factor, starting with the maximum theoretical step size. The step size is verified by the line search condition during the reduction process until the maximum feasible step size that satisfies the line search condition is found. After determining the maximum feasible step size, the variables are updated along the Newton search direction using the maximum feasible step size to complete a single iteration process. At the same time, the variable update is repeatedly executed to solve the optimization problem containing the objective function and various constraints. The degree of improvement of the objective function and the degree of satisfaction of the operating constraints are verified based on the solution results. When the analysis results approach the optimal solution, the optimal control sequence for managing the microgrid is output.

10. A microgrid intelligent control terminal according to claim 9, characterized in that, The process of verifying the optimal control sequence according to a multi-security verification mechanism, adjusting the optimal control sequence based on the verification results, and outputting microgrid control commands includes: Verify whether the charging and discharging power of the energy storage device in the optimal control sequence meets the physical limits of the device and whether the state of charge of the energy storage device meets the preset safety range. If the verification results are both met, directly generate a control command containing the optimal setting value of the microgrid. If the physical limit of the equipment fails to pass the verification between itself and the preset safety range, the continuous failure technical mechanism will be activated. When the number of consecutive failures reaches the preset threshold, the braking mechanism will be executed to force the output of a suboptimal solution that satisfies all safety constraints as the control command for the microgrid.

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