A multi-objective optimization scheduling management system based on micro-grid

Abstract

The present application relates to the technical fields of micro-grid operation control and energy management, in particular to a multi-objective optimization scheduling management system based on micro-grid to solve the technical problem that the prior art cannot consider the slow dynamic device operation continuity and the new and old scheme smooth transition when coping with sudden change of working condition; the technical scheme comprises: a state monitoring and triggering module, a sudden intervention signal is generated through a composite criterion; a new scheme generation module, a new scheduling scheme is generated based on the time-varying locking constraint of the slow dynamic device; a transition trajectory optimization module, the transition step number is set as a variable, a fusion coefficient sequence is generated to construct a virtual target instruction trajectory, a total target function is constructed with tracking deviation, control change rate, etc. to solve the optimal transition step number and target instruction sequence; an instruction distribution and execution module, the transition is executed step by step according to the optimal step number. The present application eliminates the impact of scheme mutation switching on the device, and realizes the safe, smooth and economic optimal operation transition of the micro-grid under sudden disturbance.

Description

Technical Field

[0001] This invention relates to the field of microgrid operation control and energy management technology, and more specifically, to a multi-objective optimization scheduling and management system based on microgrids. Background Technology

[0002] Microgrids can effectively improve the absorption rate of renewable energy and the reliability of power supply by integrating distributed power sources, energy storage systems and local loads. In the operation and management of microgrids, the optimized dispatch system is responsible for generating power commands for each controllable unit to achieve multiple objectives such as minimizing operating costs and maximizing the absorption of renewable energy.

[0003] In existing technologies, typical microgrid dispatching systems often adopt a two-stage framework of day-ahead planning and intraday rolling correction. Day-ahead planning generates the basic dispatching scheme for the next day based on source and load forecast data, while intraday planning fine-tunes the basic scheme based on real-time operating conditions. However, this type of method has two prominent drawbacks.

[0004] Firstly, when a microgrid faces sudden and significant changes in operating conditions, such as a sharp drop in photovoltaic output or sudden load switching, the response speed of intraday rolling correction is limited. It often requires re-invoking the complete optimization calculation process to generate a completely new scheduling scheme from the current moment and immediately switch to execution. This method of sudden reconfiguration and instantaneous switching completely ignores the continuous operation constraints of a large number of slow-dynamic devices in the microgrid, such as diesel generators and agreed interruptible loads. Direct switching will cause these devices to suffer severe shocks or violate their physical constraints such as minimum start-up and shutdown times, which may lead to system instability in severe cases.

[0005] Secondly, even if a new scheme is generated, the existing system does not consider the technical issues of how to smoothly transition from the old scheme to the new scheme. Directly changing the scheduling command from the command value of the old scheme to the command value of the new scheme will introduce an excessive power change rate, which will exceed the ramping capacity of equipment such as energy storage converters, causing actual execution deviations, and even triggering the protection device to malfunction, thus disrupting the stable operation of the system.

[0006] Therefore, a multi-objective optimization scheduling and management system based on microgrids is proposed. Summary of the Invention

[0007] The purpose of this invention is to provide a multi-objective optimization scheduling and management system based on microgrids to solve the technical problem that existing technologies cannot simultaneously ensure the continuity of slow-dynamic equipment operation and the smooth transition between old and new solutions when dealing with sudden changes in operating conditions.

[0008] To address the aforementioned technical problems, the present invention aims to provide a multi-objective optimization scheduling and management system based on a microgrid, comprising: The status monitoring and triggering module is used to collect real-time actual operating status data of the microgrid and the original scheduling scheme prediction data at the corresponding time, calculate the prediction deviation value, and generate a sudden intervention signal when the prediction deviation value meets the preset composite triggering criterion. The new scheme generation module is used to receive the sudden intervention signal, use the real-time actual operating status data as the initial boundary feature, call the latest prediction data, and generate a new scheduling scheme after applying time-varying locking constraints to the slow dynamic devices in the microgrid to keep the state consistent with the original scheduling scheme at the corresponding time. The transition trajectory optimization module is used to receive the new scheduling scheme and the original scheduling scheme, set the transition step number as the variable to be optimized, generate a monotonically increasing fusion coefficient sequence, generate a virtual target command trajectory based on the fusion coefficient sequence, the original scheduling scheme and the new scheduling scheme, and construct a total objective function based on the tracking deviation, control change rate and transition time of the virtual target command trajectory of the actual scheduling command sequence. Under the condition of satisfying the microgrid power balance constraint and equipment dynamic constraint, the total objective function is solved, and the optimal transition step number and the target actual scheduling command sequence are output. The instruction distribution and execution module is used to receive the target actual scheduling instruction sequence, distribute the target actual scheduling instruction sequence to the local controller of the microgrid for execution, and gradually transition the operation control logic of the microgrid from the original scheduling scheme to the new scheduling scheme according to the optimal transition steps, until the fusion coefficient value in the fusion coefficient sequence reaches the maximum boundary value.

[0009] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This microgrid-based multi-objective optimization scheduling and management system adopts a composite triggering criterion that combines the accumulated net load deviation with the duration of the deviation. This not only avoids false triggering caused by instantaneous source-load fluctuations, but also prevents scheduling deviations caused by the long-term accumulation of small deviations. It reduces the consumption of computing resources while ensuring the timeliness of sudden change response and improves the rationality of the scheduling triggering mechanism.

[0010] 2. In this microgrid-based multi-objective optimization scheduling and management system, time-varying locking constraints are applied to slow-dynamic equipment. Within the locking time window, the start-stop state of the diesel generator and the switching state of the interruptible load remain unchanged, avoiding frequent start-stop or switching of slow-dynamic equipment during scheme switching, reducing equipment losses and switching impact. At the same time, fast-dynamic equipment is used to quickly smooth out deviations, taking into account both scheduling feasibility and response speed.

[0011] 3. In this microgrid-based multi-objective optimization scheduling and management system, the number of transition steps is used as the variable to be optimized. A virtual target trajectory is constructed by fusing coefficient sequences. The tracking deviation, control change rate and transition time are used to construct a multi-objective total function for optimization. Under the premise of meeting the dynamic constraints of the equipment, it can automatically balance the tracking accuracy, control stability and transition speed of the transition process, realize the smooth switching between the two types of scheduling schemes, and ensure the power stability of the microgrid.

[0012] 4. In this microgrid-based multi-objective optimization scheduling and management system, a gradual transition execution mechanism is adopted, which switches the control logic step by step according to the optimal transition steps, avoiding the impact of command jumps on the power grid and equipment, and improving the security of scheduling execution. Attached Figure Description

[0013] Figure 1 This is an overall system block diagram of the present invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example

[0015] This embodiment provides a logical architecture for a multi-objective optimization scheduling and management system based on microgrids. Considering the high uncertainty of source loads in microgrids, existing technologies often adopt a strategy of directly replacing the old scheme with the new one when faced with sudden changes in predicted data. However, this strategy does not consider the physical adjustment rate of the underlying equipment of the microgrid, such as the maximum ramp rate of energy storage and the minimum start-stop time of diesel generators, which can easily cause power imbalance or equipment damage. Therefore, the core design motivation of this invention is to provide a physically executable control system that smoothly transitions from the original scheduling scheme to the new scheduling scheme under abnormal operating conditions.

[0016] like Figure 1 As shown, this embodiment provides a multi-objective optimization scheduling and management system based on a microgrid, including: The status monitoring and triggering module is used to collect real-time actual operating status data of the microgrid and the original scheduling scheme prediction data at the corresponding time, calculate the prediction deviation value, and generate a sudden intervention signal when the prediction deviation value meets the preset composite triggering criterion. The new scheme generation module is used to receive the sudden intervention signal, use the real-time actual operating status data as the initial boundary feature, call the latest prediction data, and generate a new scheduling scheme after applying time-varying locking constraints to the slow dynamic devices in the microgrid to keep the state consistent with the original scheduling scheme at the corresponding time. The transition trajectory optimization module is used to receive the new scheduling scheme and the original scheduling scheme, set the transition step number as the variable to be optimized, generate a monotonically increasing fusion coefficient sequence, generate a virtual target command trajectory based on the fusion coefficient sequence, the original scheduling scheme and the new scheduling scheme, and construct a total objective function based on the tracking deviation, control change rate and transition time of the virtual target command trajectory of the actual scheduling command sequence. Under the condition of satisfying the microgrid power balance constraint and equipment dynamic constraint, the total objective function is solved, and the optimal transition step number and the target actual scheduling command sequence are output. The instruction distribution and execution module is used to receive the target actual scheduling instruction sequence, distribute the target actual scheduling instruction sequence to the local controller of the microgrid for execution, and gradually transition the operation control logic of the microgrid from the original scheduling scheme to the new scheduling scheme according to the optimal transition steps, until the fusion coefficient value in the fusion coefficient sequence reaches the maximum boundary value.

[0017] The overall workflow of the system is as follows: the status monitoring and triggering module collects running data in real time and determines whether to trigger rescheduling; after triggering, the new scheme generation module generates a new scheduling scheme that adapts to the current state; the transition trajectory optimization module generates the optimal transition trajectory from the original scheme to the new scheme; the instruction distribution and execution module issues instructions and completes the smooth transition. The specific implementation methods of each module are as follows.

[0018] Since existing fixed-cycle scheduling cannot adaptively adjust scheduling timing according to the degree of deviation, and single-value triggering is easily affected by instantaneous fluctuations, a state monitoring and triggering module is proposed. This module employs a composite triggering criterion combining the accumulated net load deviation and the duration of the deviation, balancing triggering accuracy and timeliness. The state monitoring and triggering module further includes: a net load deviation calculation submodule, used to acquire the total load data of the microgrid, subtract the output data of unschedulable renewable energy from the total load data to obtain the net load data, and calculate the absolute value of the difference between the net load data and the corresponding net load in the original scheduling scheme's predicted data; a time integration accumulation submodule, used to integrate the absolute value of the difference along the time axis, output the accumulated net load deviation, and start a timer to record the duration of continuous deviation; and a trigger determination submodule, used to compare the accumulated net load deviation with a preset energy threshold, and compare the duration with a preset minimum duration threshold. When the accumulated net load deviation is greater than or equal to the energy threshold, and the duration is greater than or equal to the minimum duration threshold, the composite triggering criterion is confirmed to be satisfied. The specific implementation is as follows.

[0019] First, net load deviation calculation: The module collects real-time total load data of the microgrid through electrical sensing devices, and simultaneously collects real-time output data of photovoltaic and wind power; it subtracts the output data of unschedulable renewable energy from the total load data to obtain real-time net load data; net load represents the power that needs to be borne by controllable resources and is the core object of dispatch optimization; the difference between the real-time net load data and the predicted net load data at the corresponding time in the original dispatch plan is taken as the net load deviation value at the current time; the original dispatch plan is the baseline dispatch plan generated a day before, which includes the predicted net load value at each time and the dispatch command value of each device; the reason for using net load instead of total load to calculate deviation is that the fluctuation of renewable energy output is the main source of microgrid deviation, and net load can directly reflect the supply and demand gap of controllable resources, eliminate the double calculation of the fluctuation of renewable energy itself, and improve the pertinence of deviation judgment.

[0020] Second, time-integrated accumulation: The net load deviation value is integrated along the time axis to obtain the accumulated net load deviation power. The integration operation uses the trapezoidal integration method, with a step size of minutes, and the unit of accumulated power is kWh. Simultaneously, a timer is started to record the duration of continuous deviation, i.e., the length of time the net load deviation value is continuously greater than zero. If the net load deviation value returns to zero at a certain moment, the accumulated power and duration are simultaneously reset to zero, and counting restarts. The reason for performing time integration and duration statistics is that instantaneous high-power deviations may be short-lived fluctuations that do not require rescheduling; however, small, continuous deviations can accumulate over time to form a large power gap, which can also lead to scheduling deviations. Combining accumulated power and duration allows for a comprehensive assessment of the severity of the deviation, avoiding false triggering and missed triggering.

[0021] Third, trigger determination: two determination parameters are preset power threshold and minimum duration threshold. The power threshold is determined based on the adjustable capacity margin of the microgrid. In this embodiment, it is taken as 5% of the rated capacity of the energy storage system, i.e., 5kWh. This value corresponds to the rapid adjustment margin of the energy storage system. When the accumulated power deviation exceeds this value, the short-term adjustment of the energy storage alone cannot completely suppress the deviation, and a new scheduling scheme needs to be generated. The minimum duration threshold is determined based on the scheduling calculation time and equipment response characteristics. In this embodiment, it is taken as 5 minutes. This duration is longer than the calculation time of a single round of scheduling optimization, which can eliminate the interference of instantaneous fluctuations at the second and minute levels. When the accumulated power deviation of the net load is greater than or equal to the power threshold and the duration is greater than or equal to the minimum duration threshold, it is confirmed that the composite trigger criterion is met, and a sudden intervention signal is generated. The effect of using the dual threshold composite determination is that it will not frequently trigger rescheduling due to instantaneous peak deviation, wasting computing resources, nor will it cause long-term non-response due to long deviation duration but small amplitude, leading to scheduling loss of control. It realizes adaptive optimization of trigger timing.

[0022] Considering that if a sudden change occurs and the entire time period is re-optimized, slow-dynamic devices such as diesel generators may start and stop or significantly adjust their output in a short period of time. Since the actual response speed of slow devices is slow, scheduling commands may become unexecutable, and frequent start-stops can damage the devices. Therefore, a new scheme generation module is proposed. By introducing time-varying locking constraints for slow-dynamic devices, the new scheme is ensured to be compatible with the current device state. The new scheme generation module further includes: a device classification submodule, used to parse the device attributes within the microgrid, marking diesel generators and interruptible loads as slow-dynamic devices, and energy storage systems and grid-connected interfaces as fast-dynamic devices; and a state forced alignment submodule, used to extract the discrete states of diesel generator start-stop and interruptible load switching from the original scheduling scheme within a preset locking time window from the generation time to the new scheduling scheme. These discrete states are then input as unchangeable fixed constants into the solution matrix of the new scheduling scheme to form the time-varying locking constraints. The specific implementation is as follows.

[0023] First, equipment classification: Analyze the attributes of all controllable equipment within the microgrid and classify them based on their dynamic response time. Diesel generators require 10 to 15 minutes to complete the start-stop process and stabilize output after receiving a start-stop command, while interruptible loads require 3 to 8 minutes to complete load disconnection or connection after receiving a switching command. Both types of equipment have response times exceeding minutes and are labeled as slow-dynamic equipment. The bidirectional converter power regulation response time of the energy storage system is in the millisecond range, and the power regulation response time of the grid-connected interface is in the second range. Both types of equipment can quickly follow dispatch commands and are labeled as fast-dynamic equipment. The reason for classifying by dynamic characteristics is that different equipment have significantly different regulation capabilities and response speeds. Using uniform dispatch constraints would lead to wasted capacity of fast equipment and infeasibility of commands for slow equipment. Classification can balance the flexibility and feasibility of dispatch.

[0024] Second, forced state alignment: A preset locking time window is used, the window length of which is equal to the minimum start / stop / switching interval of the slow-dynamic equipment. In this embodiment, it is 15 minutes, consistent with the minimum start / stop interval of the diesel generator. When generating a new scheduling scheme, the discrete start / stop states of the diesel generator and the discrete switching states of the interruptible loads are extracted from the original scheduling scheme from the time of generation to the end of the locking time window. These state values ​​are input as unchangeable fixed constants into the optimization solution matrix of the new scheduling scheme to form time-varying locking constraints. During the period after the locking time window ends, the state of the slow-dynamic equipment can participate in optimization adjustment; the state of the fast-dynamic equipment can participate in optimization adjustment. All time periods are involved in optimization and are not subject to locking constraints. The optimization objectives of the new scheduling scheme include operating costs and carbon emission costs, and the constraints include power balance, upper and lower limits of equipment output, and upper and lower limits of energy storage SOC. A mixed-integer linear programming solver is used for solving the problem. The effect of applying time-varying locking constraints is that the slow equipment state in the initial stage of the new scheme is completely consistent with the original scheme and aligned with the current actual operating state, avoiding the problem of the new scheme's instructions not matching the actual equipment state. At the same time, the adjustment pressure is transferred to fast dynamic equipment, and the rapid response capability of fast equipment is used to smooth out sudden deviations, maximizing the response speed while ensuring the feasibility of the scheme.

[0025] Considering that a direct switch would cause power fluctuations and equipment impact due to command jumps, and that a fixed-step transition would fail to automatically balance transition speed and stability based on the magnitude of sudden changes and equipment constraints, a transition trajectory optimization module is proposed. This module uses the number of transition steps as the variable to be optimized and obtains the optimal transition trajectory through multi-objective optimization. The transition trajectory optimization module further includes: a virtual trajectory construction submodule, used to obtain the fusion coefficient value of the current transition step, using the difference between the current transition step's fusion coefficient value and the current transition step's fusion coefficient value as a first weight, using the current transition step's fusion coefficient value as a second weight, multiplying the command value of the corresponding transition step in the original scheduling scheme by the first weight (first product), and adding this product to the command value of the corresponding transition step in the new scheduling scheme multiplied by the second weight (second product), and using the summed sequence as the virtual target command trajectory; and an objective function construction submodule, used to extract the distance between the actual scheduling command sequence and the virtual target command trajectory as the tracking deviation, and extract the difference between adjacent transition steps in the actual scheduling command sequence as the control change. The following modules are used to construct the overall objective function: a tracking deviation rate, a control change rate, a transition time penalty term, and operating cost and carbon emission cost terms, linearly weighted; a constraint solving submodule, which calls a mixed-integer nonlinear programming solver to perform optimization iteration on the overall objective function under the constraints of the microgrid power balance, and outputs the optimal number of transition steps and the target actual scheduling instruction sequence; and a multi-objective priority dynamic adjustment module, which is connected to the transition trajectory optimization module, for calculating the reliability penalty weight based on a preset benchmark penalty weight value and the fusion coefficient value of the current transition step, and loading the reliability penalty weight into the optimization process of the overall objective function. The calculation rules include: calculating the exponent of the difference between the fusion coefficient value of the current transition step and 0.5, which is five times the base of the natural constant; multiplying the reciprocal of the exponent plus one by a first preset constant and adding it to a second preset constant to obtain a dynamic adjustment factor; and multiplying the benchmark penalty weight value by the dynamic adjustment factor to obtain the reliability penalty weight of the current transition step. The specific implementation is as follows.

[0026] First, virtual trajectory construction: When generating the monotonically increasing fusion coefficient sequence, the transition trajectory optimization module uses a dynamic step-size truncation and accumulation mechanism to generate the monotonically increasing fusion coefficient sequence: the fusion coefficient value ranges from 0 to 1, where 0 corresponds to fully executing the original scheduling scheme and 1 corresponds to fully executing the new scheduling scheme; the fusion coefficient sequence is generated using a truncation and accumulation mechanism: the fusion coefficient value of the first transition step is set to 0; starting from the second transition step, the fusion coefficient value of the previous transition step is added to the set rate of change step size to obtain the initial superposition value; the dynamic rate of change step size is not a fixed constant, but is obtained by dividing the value of 1 by the number of transition steps in the current optimization iteration. This mechanism, inversely defining the step size by the optimization variable, ensures that the number of transition steps can be used as a unique variable. The variables participate in the optimization of the overall objective function; the initial superposition value is compared with the value 1. If the initial superposition value is less than 1, the initial superposition value is used as the fusion coefficient value of the current transition step; if the initial superposition value is greater than or equal to 1, the truncation control is triggered, the fusion coefficient value of the current transition step is locked to 1, and the current transition step is marked as the last transition step. This mechanism ensures that the increment of the fusion coefficient value between two adjacent transition steps is less than or equal to the rate of change step size, and the fusion coefficient will not exceed the maximum boundary value of 1, avoiding reverse adjustment or overshoot in the transition process. The reason for adopting the truncation accumulation mechanism is that its logic is simple, the amount of computation is small, it is suitable for the limited computing power of edge computing nodes, and it can guarantee the unidirectionality and monotonicity of the transition process, avoiding power oscillation caused by repeated adjustments.

[0027] After obtaining the fusion coefficient for each step, a virtual target instruction value for the corresponding transition step is constructed. The difference between 1 and the current fusion coefficient value is used as the first weight, and the current fusion coefficient value is used as the second weight. The instruction value for the corresponding transition step in the original scheduling scheme is multiplied by the first weight to obtain the first product. The instruction value for the corresponding transition step in the new scheduling scheme is multiplied by the second weight to obtain the second product. The two products are added together to obtain the virtual target instruction value for the current transition step. The virtual target instruction values ​​of all transition steps constitute the virtual target instruction trajectory. The reason for using linear weighting to construct the virtual trajectory is that the physical meaning of linear transition is clear, the equipment control implementation is simple, and it can ensure that the instruction value changes smoothly between the original scheme and the new scheme, without extreme values ​​exceeding the range of the two schemes.

[0028] Second, a multi-objective priority dynamic adjustment module is set up to dynamically adjust the penalty weights according to the transition progress: the overall objective function is composed of tracking deviation, control change rate, transition duration penalty term, and operating cost and carbon emission cost terms linearly weighted; the tracking deviation is the distance between the actual scheduling command sequence and the virtual target command trajectory, calculated by the sum of the absolute value deviations of the actual command value and the virtual command value of each transition step, used to measure the degree of fit between the actual transition path and the ideal path. The calculation method is as follows: extract the absolute value deviation between the actual command value and the virtual command value in each transition step, multiply the absolute value deviation by the corresponding transition step's dynamic adjustment factor one by one, and then sum them to obtain the tracking deviation; the smaller the tracking deviation, the more the transition process conforms to expectations; the control change rate is the distance between the actual scheduling command sequence and the virtual target command trajectory of each transition step. The sum of the absolute values ​​of the differences between transition steps is used to measure the smoothness of the command; the smaller the control change rate, the smoother the equipment adjustment and the smaller the impact on the equipment; the transition time penalty term is positively correlated with the number of transition steps, the more transition steps, the larger the penalty value, which is used to promote the optimization process to shorten the transition time and switch to the new scheme as soon as possible; the operating cost and carbon emission cost terms are the sum of the operating costs and carbon emission costs of each piece of equipment during the transition process, which is used to balance the economy and environmental protection of the transition process; in order to eliminate the dimensional differences between different physical objectives, the system first divides the tracking deviation, the control change rate, the number of transition steps, and the cost term by their respective corresponding benchmark nominal values ​​for dimensionless processing; the expression of the overall objective function after processing is to minimize J, where J equals W1 multiplied by the normalized tracking deviation E. track Add W2 and multiply by the normalized rate of change of control E rate Add W3 and multiply by the normalized transition step number N step Add W4 and multiply by the normalized operating cost C op With carbon emission cost C carbon The sum, as expressed by the formula: minJ = W1 × E track +W2×E rate +W3×N step +W4×(C op +C carbon ), where W1 to W4 are the baseline penalty weights for the corresponding items; the reason for constructing a multi-objective total function is that the transition process needs to meet multiple requirements such as tracking accuracy, equipment stability, transition speed, economy and environmental protection at the same time. A single objective cannot take into account the overall performance. The linear weighting method can adjust the priority of each objective through weights to adapt to different operating scenarios.

[0029] For the aforementioned objective function, this system sets up a multi-objective priority dynamic adjustment module, which is connected to the transition trajectory optimization module. This module calculates the reliability penalty weight based on a preset baseline penalty weight value and the fusion coefficient value of the current transition step, and loads the reliability penalty weight into the optimization process of the overall objective function. The calculation rules include: using the natural constant as the base, calculating the exponent of the difference between the fusion coefficient value of the current transition step and 0.5, which is five times the difference; multiplying the reciprocal of the exponent plus one by a first preset constant and adding it to a second preset constant to obtain a dynamic adjustment factor; and multiplying the baseline penalty weight value by the dynamic adjustment factor to obtain the reliability penalty weight of the current transition step. The specific implementation method is as follows.

[0030] In the initial stage of the transition, the system state adjustment range is large, requiring priority to ensure stable control and reliable tracking. In the later stage of the transition, as the system approaches the target state, reliability requirements can be appropriately reduced to improve economy and transition speed. Fixed weights cannot adapt to this phased requirement. Therefore, a dynamic adjustment factor based on the fusion coefficient is used to correct the weights. The calculation rule for the dynamic adjustment factor is as follows: with the natural constant e as the base, calculate the exponent of the difference between the current transition step fusion coefficient value and 0.5, which is five times the current transition step fusion coefficient value. Multiply the reciprocal of this exponent plus 1 by the first preset constant a, and then add it to the second preset constant b to obtain the dynamic adjustment factor k(λ). The formula is: k(λ) = a / (1 + exp(5 × (λ - 0.5))) + b. This function is an S-shaped function centered at the fusion coefficient λ = 0.5. When the fusion coefficient is small in the initial stage of the transition, the adjustment factor is large; when the fusion coefficient is large in the later stage of the transition, the adjustment factor is small. In this embodiment, the baseline reliability weight corresponding to w is taken. The combined weights of 1 and w2 are set at twice the baseline value in the early transition phase and 0.5 times the baseline value in the later transition phase. Substituting the boundary conditions of fusion coefficient λ=0 and fusion coefficient λ=0, the first preset constant is approximately 1.77 and the second preset constant is approximately 0.37. The baseline penalty weight value is multiplied by the dynamic adjustment factor to obtain the real-time reliability penalty weight of the current transition step, and as mentioned above, it is nested into the cumulative summation step of tracking deviation, thus being dimensionlessly loaded into the optimization process of the overall objective function. Through dynamic weight adjustment, the priority of each objective is automatically adjusted in the transition phase, emphasizing stability and reliability in the early stage and economy and speed in the later stage, which improves the overall performance of the transition process without the need for manual adjustment of weights for different scenarios.

[0031] Third, solving the overall objective function requires satisfying two types of constraints: microgrid power balance constraints and equipment dynamic constraints. Power balance constraints require that the sum of the outputs of all controllable devices at each transition step equals the net load at the corresponding moment, ensuring a balance between supply and demand in the microgrid. Equipment dynamic constraints mainly target the energy storage system. The maximum active power ramp rate is extracted from the energy storage converter's hardware parameter table. In this embodiment, the maximum ramp rate of the energy storage converter is 5kW / minute, meaning the maximum change in active power per minute is 5kW. The upper limit of the difference between control change rates between any two adjacent transition steps is locked within the range of this maximum ramp rate to ensure that dispatch commands conform to the equipment hardware capabilities and do not trigger equipment overcurrent protection. Furthermore, it is set that... The dynamic constraints also include conventional constraints such as the upper and lower limits of diesel generator output, the upper and lower limits of energy storage SOC, and the upper and lower limits of grid-connected power. The mixed-integer nonlinear programming solver is called to perform optimization iteration on the overall objective function. The solution variables include the actual scheduling command values ​​of each transition step and the total number of transition steps. After the iteration converges, the optimal number of transition steps and the corresponding target actual scheduling command sequence are output. By using the number of transition steps as the optimization variable, the system can automatically select the most suitable transition duration according to the magnitude of the change and the equipment constraints: when the magnitude of the change is small, the number of transition steps is small and the switching speed is fast; when the magnitude of the change is large, the number of transition steps is large to ensure smooth control. Compared with the transition method with a fixed number of steps, the adaptive capability is stronger and the overall performance is better.

[0032] To improve the real-time and synchronous nature of instruction issuance, a data parsing and concurrent scheduling module is proposed. This module is connected to the instruction distribution and execution module and is used to discretize the continuous sequence of actual scheduling instructions for the target into standard communication messages. Each standard communication message is timestamped, and the standard communication messages are synchronously issued to the communication gateway nodes of the photovoltaic inverter, wind power converter, and energy storage bidirectional converter in the microgrid through a multi-threaded concurrent communication channel. The specific implementation is as follows.

[0033] After receiving the actual scheduling instruction sequence from the target, the instruction distribution and execution module discretizes the continuous instruction sequence into messages conforming to industrial communication standards by time step. This embodiment uses standard industrial communication messages with high real-time performance, such as IEC 61850. MMS messages or extended Modbus-TCP messages containing custom timestamp registers are used to precisely timestamp each message to ensure time synchronization of instruction execution across devices. Messages are synchronously sent to the hardware communication gateway nodes of photovoltaic inverters, wind power converters, and energy storage bidirectional converters within the microgrid via a multi-threaded concurrent communication channel, where the local controllers of each device execute the corresponding instructions. Multi-threaded concurrent communication is used because serial sending would result in execution time differences between devices, causing short-term power imbalances. Concurrent sending reduces these time differences and improves power stability. During execution, the operating control logic is gradually adjusted based on the optimal number of transition steps. After each transition step instruction is executed, the current fusion coefficient value is updated, and the control weights of each device are adjusted accordingly until the fusion coefficient value in the fusion coefficient sequence reaches the maximum boundary value of 1. At this point, the system completely switches to the new scheduling scheme, and the transition process ends.

[0034] Considering that secondary or even multiple source-load mutations may occur during the transition process, continuing to execute the original transition trajectory would lead to a continuous increase in the deviation between the actual operating state and the target trajectory, and may even cause safety issues. Therefore, this system is equipped with a nested mutation handling module. When the instruction distribution and execution module is executing the actual scheduling instruction sequence according to the target, and it detects that the fusion coefficient value in the fusion coefficient sequence is less than the maximum boundary value and a new mutation intervention signal is received again, it sends an interrupt instruction to the instruction distribution and execution module to stop the issuance of the execution instructions for the original scheduling scheme and the new scheduling scheme. The real-time actual equipment operating state of the microgrid at the current physical breakpoint is extracted as a new initial boundary feature, triggering the new scheme generation module and the transition trajectory optimization module to re-execute the calculation process of generating the scheduling scheme and optimizing the trajectory with the new initial boundary feature as the zero time starting point. The specific implementation method is as follows.

[0035] The nested mutation handling module monitors the transition execution status and mutation intervention signals in real time. When the instruction distribution and execution module is executing according to the target actual scheduling instruction sequence, if it detects that the fusion coefficient value is less than the maximum boundary value of 1 and receives a new mutation intervention signal again, it indicates that a new mutation has occurred during the transition. At this time, an interrupt instruction is sent to the instruction distribution and execution module to stop the issuance of the execution instructions of the original scheduling scheme and the new scheduling scheme, and lock the current equipment operating status. Then, the real-time actual equipment operating status of the microgrid at the current physical breakpoint is extracted, including the output value of each device, the energy storage SOC value, the start / stop / switching status of slow devices, etc., and used as the new initial boundary features to trigger the new scheme generation module and the transition trajectory optimization module to re-execute the calculation. The new round of calculation takes the breakpoint state as the zero time starting point, regenerates the new scheduling scheme and the corresponding transition trajectory, and continues to issue and execute after generation. Through the nested mutation handling of the above process, the closed-loop processing of continuous mutations during the transition is realized. No matter when a mutation occurs, the optimal path can be regenerated based on the current actual state, avoiding the loss of control during the transition and improving the robustness of the system to continuous disturbances.

[0036] To avoid delays caused by long-distance network transmission and to meet the stringent latency requirements for rapid response to sudden events, the status monitoring and triggering module, the new scheme generation module, the transition trajectory optimization module, and the instruction distribution and execution module are all deployed in the edge computing server nodes of the distribution network area. The edge computing server is directly connected to the electrical sensing devices and local controllers of each device in the microgrid via industrial Ethernet. Data does not need to be uploaded to the cloud for processing, which can effectively reduce communication latency, improve dispatch response speed, and reduce cloud bandwidth consumption, making it suitable for the localized autonomous operation of the distribution area-level microgrid.

[0037] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-objective optimization scheduling and management system based on microgrids, characterized in that, include: The status monitoring and triggering module is used to collect real-time actual operating status data of the microgrid and the original scheduling scheme prediction data at the corresponding time, calculate the prediction deviation value, and generate a sudden intervention signal when the prediction deviation value meets the preset composite triggering criterion. The new scheme generation module is used to receive the sudden intervention signal, use the real-time actual operating status data as the initial boundary feature, call the latest prediction data, and generate a new scheduling scheme after applying time-varying locking constraints to the slow dynamic devices in the microgrid to keep the state consistent with the original scheduling scheme at the corresponding time. The transition trajectory optimization module receives the new scheduling scheme and the original scheduling scheme, sets the transition step number as the variable to be optimized, generates a monotonically increasing fusion coefficient sequence, and uses a dynamic step-size truncation accumulation mechanism to determine the fusion coefficient value in the fusion coefficient sequence: extracts the transition step value in the current optimization iteration process, divides the value one by the transition step value to obtain the dynamic change rate step size; sets the fusion coefficient value to zero in the first transition step; for subsequent transition steps, adds the fusion coefficient value of the previous transition step to the set change rate step size to obtain an initial superposition value, and compares the initial superposition value with the value one; when the initial superposition value is less than one, the initial superposition value is used as the fusion coefficient value of the current transition step; when the initial superposition value is greater than or equal to one, truncation control is triggered, the fusion coefficient value of the current transition step is locked to one, and the current transition step is marked as the end. Tail transition step, wherein the increment of the fusion coefficient value between two adjacent transition steps is less than or equal to the change rate step size; obtain the fusion coefficient value of the current transition step, take the difference between the current transition step's fusion coefficient value and the current transition step's fusion coefficient value as the first weight, take the current transition step's fusion coefficient value as the second weight, multiply the instruction value of the corresponding transition step in the original scheduling scheme by the first weight's first product, add it to the instruction value of the corresponding transition step in the new scheduling scheme by the second weight's second product, and take the summed result sequence as the virtual target instruction trajectory; construct the overall objective function based on the tracking deviation, control change rate and transition time of the virtual target instruction trajectory using the actual scheduling instruction sequence, solve the overall objective function under the conditions of satisfying the microgrid power balance constraint and equipment dynamic constraint, and output the optimal number of transition steps and the target actual scheduling instruction sequence; The instruction distribution and execution module is used to receive the target actual scheduling instruction sequence, distribute the target actual scheduling instruction sequence to the local controller of the microgrid for execution, and gradually transition the operation control logic of the microgrid from the original scheduling scheme to the new scheduling scheme according to the optimal transition steps, until the fusion coefficient value in the fusion coefficient sequence reaches the maximum boundary value.

2. The multi-objective optimization scheduling and management system based on microgrids according to claim 1, characterized in that, The transition trajectory optimization module further includes: The objective function construction submodule is used to extract the distance between the actual scheduling instruction sequence and the virtual target instruction trajectory as the tracking deviation, extract the difference between adjacent transition steps of the actual scheduling instruction sequence as the control change rate, and linearly weight the tracking deviation, the control change rate, the transition time penalty term, and the operating cost and carbon emission cost term to construct the total objective function. The constraint solving submodule is used to call the mixed integer nonlinear programming solver to perform optimization iteration on the total objective function under the constraints of the microgrid power balance, and output the optimal transition step number and the target actual scheduling instruction sequence.

3. The multi-objective optimization scheduling and management system based on microgrids according to claim 1, characterized in that, The status monitoring and triggering module further includes: The net load deviation calculation submodule is used to obtain the total load data of the microgrid, subtract the output data of non-dispatchable renewable energy from the total load data to obtain the net load data, and calculate the absolute value of the difference between the net load data and the corresponding net load in the original dispatch scheme prediction data. The time integration accumulation submodule is used to integrate the absolute value of the difference along the time axis, output the net load deviation accumulated power, and start a timer to record the duration of continuous deviation. The trigger determination submodule is used to compare the accumulated net load deviation power with a preset power threshold, and to compare the duration with a preset minimum duration threshold. When the accumulated net load deviation power is greater than or equal to the power threshold, and the duration is greater than or equal to the minimum duration threshold, the composite trigger criterion is confirmed to be satisfied.

4. The multi-objective optimization scheduling and management system based on microgrids according to claim 1, characterized in that, The new scheme generation module further includes: The equipment classification submodule is used to parse the equipment attributes within the microgrid, marking diesel generators and interruptible loads as slow-dynamic equipment, and energy storage systems and grid-connected interfaces as fast-dynamic equipment. The state forced alignment submodule is used to extract the discrete states of diesel generator start-stop and interruptible load switching from the original scheduling scheme within a preset locking time window from the time of generation to the new scheduling scheme during the process of generating the new scheduling scheme. The discrete states of diesel generator start-stop and interruptible load switching are input as unchangeable fixed constants into the solution matrix of the new scheduling scheme to form the time-varying locking constraint.

5. The multi-objective optimization scheduling and management system based on microgrids according to claim 1, characterized in that, It also includes a nested mutation handling module; The nested mutation processing module is used to send an interrupt command to the instruction distribution and execution module when, during the execution of the instruction distribution and execution module according to the target actual scheduling instruction sequence, it detects that the fusion coefficient value in the fusion coefficient sequence is less than the maximum boundary value and receives a new mutation intervention signal again, thereby stopping the issuance of the execution instructions for the original scheduling scheme and the new scheduling scheme; extracting the real-time actual equipment operating status of the microgrid at the current physical breakpoint as a new initial boundary feature, and triggering the new scheme generation module and the transition trajectory optimization module to re-execute the calculation process of generating the scheduling scheme and trajectory optimization with the new initial boundary feature as the zero time starting point.

6. The multi-objective optimization scheduling and management system based on microgrids according to claim 1, characterized in that, It also includes a multi-objective priority dynamic adjustment module; The multi-objective priority dynamic adjustment module is connected to the transition trajectory optimization module. It is used to calculate a reliability penalty weight based on a preset baseline penalty weight value and the fusion coefficient value of the current transition step, and to load the reliability penalty weight into the optimization process of the overall objective function. The calculation rules include: Using the natural constant as the base, calculate the exponent of the difference between the fusion coefficient value of the current transition step, which is five times positive, and zero. The reciprocal of the exponent plus one is multiplied by a first preset constant and added to a second preset constant to obtain a dynamic adjustment factor; The reliability penalty weight of the current transition step is obtained by multiplying the baseline penalty weight value by the dynamic adjustment factor.

7. The multi-objective optimization scheduling and management system based on microgrids according to claim 1, characterized in that, It also includes a data parsing and concurrent scheduling module; The data parsing and concurrent scheduling module is connected to the instruction distribution and execution module. It is used to discretize the continuous sequence of actual scheduling instructions for the target into communication standard messages, assign a timestamp to each communication standard message, and synchronously send the communication standard messages to the communication gateway nodes of the photovoltaic inverter, wind power converter and energy storage bidirectional converter in the microgrid through a multi-threaded concurrent communication channel.

8. The multi-objective optimization scheduling and management system based on microgrids according to claim 1, characterized in that: When the transition trajectory optimization module solves the overall objective function under the condition of satisfying the dynamic constraints of the equipment, it extracts the maximum active power ramp rate from the parameter table of the energy storage converter in the microgrid and locks the upper limit of the difference between any adjacent transition steps of the control change rate within the numerical range of the maximum active power ramp rate.

9. The multi-objective optimization scheduling and management system based on microgrids according to claim 1, characterized in that: The status monitoring and triggering module, the new scheme generation module, the transition trajectory optimization module, and the instruction distribution and execution module are all deployed in the edge computing server node of the distribution network area, and the edge computing server node is connected to the electrical sensing device in the microgrid.

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