Multi-source synchronous regulation method and system for intelligent micro-grid

Abstract

The application discloses a multi-source synchronous regulation method and system for an intelligent micro-grid, belongs to the technical field of new energy power generation control, and collects output parameters of multi-source power generation equipment in real time, sets reference parameters of the micro-grid, performs synchronous control on the multi-source power generation equipment through a constructed PID control model, dynamically adjusts PID parameters based on real-time feedback of the multi-source power generation equipment, optimizes the PID control model, realizes synchronous control of the multi-source power generation equipment and the micro-grid, improves response speed and stability of the micro-grid, and reduces energy loss; after synchronous control, a multi-source coordination model is constructed based on load demand, energy supply, energy storage state and economic cost, the multi-source coordination model is solved by using an ant colony optimization strategy, an energy dispatching optimization scheme is formulated, overall energy utilization efficiency is improved, and overall energy cost is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to a multi-source synchronous regulation method and system for intelligent microgrids, belonging to the field of new energy generation control. BACKGROUND

[0002] With the increasing global energy demand and the growing awareness of environmental protection, energy structure transformation has become a global consensus. Traditional energy systems face severe challenges such as resource depletion and environmental pollution, while renewable energy is widely concerned due to its clean and sustainable characteristics. Intelligent microgrids, as an important way to achieve efficient utilization of renewable energy and distributed energy management, have a significant impact on promoting energy structure transformation.

[0003] Intelligent microgrids integrate various distributed energy sources (such as solar, wind, fuel cells, etc.), energy storage devices, energy conversion devices, loads, and monitoring and protection devices, forming a complex and diverse system. These distributed energy sources have different generation characteristics, cost structures, and environmental benefits, while loads also exhibit diverse demand characteristics.

[0004] The existing Chinese patent application with publication number CN115021312A discloses an intelligent regulation system and method for multi-mode comprehensive control of distributed power sources. The method includes collecting voltage and current information at the grid-connected point of each distributed generation unit and the synchronization signal of the bus-side power grid; if the microgrid is in a grid-connected operation state, its operating frequency and voltage are supported by the bus-side power grid; at this time, the PQ reference value of the energy storage device in each DG is calculated by droop control, and photovoltaic or wind power generation equipment operates at the maximum power point; if the microgrid operates in an island state, a control method combining master-slave control and peer-to-peer control is adopted according to its operating frequency and voltage state.

[0005] Although the existing technology transfers the complex functions that need to be controlled locally to the upper controller and achieves comprehensive coordination among the three control modes, it does not consider the synchronous regulation between multi-source generation units; therefore, the present application provides a multi-source synchronous regulation method and system for intelligent microgrids, which adjusts the frequency and phase of each generation unit in real time through adaptive control, achieves high-precision synchronous control, coordinates the relationship between various energy sources and loads, dynamically adjusts the output of each generation unit, improves the coordination ability of various energy sources in the microgrid, and ensures the stability and energy efficiency of the power grid. SUMMARY

[0006] In view of the deficiencies of the prior art, the purpose of the present application is to provide a multi-source synchronous regulation method and system for an intelligent micro-grid, which utilizes an adaptive control algorithm and a multivariable coordinated optimization algorithm to achieve high-precision synchronous regulation of multi-source power generation in the intelligent micro-grid, dynamically adjusts the output of each source while comprehensively considering the characteristics of each energy source, and maintains the stability of the power grid.

[0007] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0008] The multi-source synchronous regulation method for an intelligent micro-grid comprises the following steps:

[0009] Step S1: Obtain the network topology of the micro-grid, including the types, quantities and distribution of the multi-source power generation equipment in the micro-grid; utilize the sensors inside the multi-source power generation equipment to collect the key parameters of the multi-source power generation equipment in real time, and pre-process the collected data;

[0010] Step S2: Set the reference parameters of the micro-grid, construct a PID control model to perform synchronous control on the multi-source power generation equipment, and adjust the output frequency and output phase of the power generation equipment in real time; configure an adaptive regulation strategy in the PID control model, dynamically adjust the PID parameters according to the real-time feedback of the multi-source power generation equipment, and optimize the PID control model;

[0011] Step S3: Based on the intermittency of renewable energy sources and the cost of the multi-source power generation equipment, and in combination with the load demand of the micro-grid, construct a multi-source coordination model;

[0012] Step S4: Utilize an ant colony optimization strategy to solve the multi-source coordination model, and formulate an energy dispatching optimization scheme;

[0013] The specific steps of step S2 comprise:

[0014] S2.1: Set the reference frequency and the reference phase of the micro-grid, and calculate the deviation between the actual parameters and the reference parameters of the multi-source power generation equipment, including the frequency difference and the phase difference , and adjust the output and reduce the deviation according to the size and direction of the deviation to achieve multi-source synchronous control;

[0015] S2.2: Construct a PID control model, including a frequency control model and a phase control model, the frequency control model is used to adjust the output frequency of the power generation equipment, and the phase control model is used to adjust the output phase of the power generation equipment, and initialize the PID parameters;

[0016] S2.3: Calculate the control signal ;

[0017] S2.4: Apply the control signal of the PID control model to the power generation equipment, and obtain the actual output parameters of the power generation equipment in the current state in real time, including the output frequency and the output phase ;

[0018] S2.5: Calculate the deviation of the power generation equipment in the current state , evaluate the PID control effect, and determine whether the deviation value is zero; if , complete the multi-source synchronization control; if , execute the adaptive control strategy;

[0019] The adaptive control strategy includes fuzzy processing, defining the domain of input and output variables, dividing the domain into several fuzzy subsets, defining the membership function of each fuzzy subset, and defining the domain of deviation , the fuzzy set is negative, zero, and positive, and the domain of the rate of change of deviation is , the fuzzy set is low, medium, and high, and a triangular membership function is used to define the fuzzy set;

[0020] It also includes designing a fuzzy rule base, when the deviation is large, if the deviation is positive, take as a positive value, take , as a negative value, if the deviation is negative, use the opposite adjustment strategy; if the rate of change of deviation is large, if the rate of change of deviation and the deviation are of the same sign, take as a negative value, take as a positive value, if the rate of change of deviation and the deviation are of different signs, take as a negative value, take , as a positive value; when the deviation is very small but continues to exist, take as a positive value; when the deviation and the rate of change of deviation are both close to zero, keep the PID parameters;

[0021] The constraint conditions of the multi-source coordination model include:

[0022] Balancing the energy supply of the microgrid and the energy demand of the power equipment;

[0023] Constraining the charging and discharging power of the energy storage equipment, so that the charging and discharging of the energy storage equipment work within the rated power range, and optimize the energy supply demand;

[0024] Constraining the power generation of the diesel generator, so that the diesel generator works within the rated active output range, and the actual power generation is not higher than the upper limit of the active output and not lower than the lower limit of the active output;

[0025] The load transfer is constrained so that the load transfer in the micro-grid works within the rated power range, and the load transfer inside the micro-grid should keep the balance of the load transfer in and out;

[0026] The load reduction is constrained to avoid the collapse of power equipment due to overload;

[0027] The load shift is constrained so that the power load is distributed in different time periods.

[0028] Specifically, the specific steps of the adaptive regulation strategy in S2.5 include:

[0029] S2.51: Calculate the rate of change of deviation ;

[0030] S2.52: Define the deviation and the rate of change of deviation as input variables, and the parameter adjustment amount of the PID control model 、 and as output variables;

[0031] S2.53: Fuzzification processing is performed on the input and output variables, the domain of the input and output variables is defined, and the domain of the variables is divided into several fuzzy subsets, and the membership function of each fuzzy subset is defined;

[0032] S2.54: Design a fuzzy rule base to describe the adjustment rules of the PID parameters under different deviations and rates of change of deviation;

[0033] S2.55: Using the fuzzy rule base and the fuzzy values of the input variables, the fuzzy values of the output variables are calculated through a fuzzy reasoning mechanism;

[0034] S2.56: The fuzzy values of the output variables are converted into actual parameter adjustment amounts 、 、 by using the centroid method, and the PID parameters are updated;

[0035] S2.57: Update the PID control model, calculate the updated control signal , and return to S2.4.

[0036] Specifically, the specific steps of the step S3 include:

[0037] S3.1: Due to the non-schedulability of renewable energy generation, set the renewable energy generation device as the basic generation device, and calculate the generation cost ;

[0038] S3.2: In the daily operation of the micro-grid, there are situations of load surplus or deficiency, the introduction of energy storage devices participating in energy scheduling solves the intermittent problem of renewable energy, and the charging and discharging cost of the energy storage device is calculated ;

[0039] S3.3: The diesel generator set is started quickly and supplies power when the energy storage scheduling is insufficient, ensuring the power supply of the micro-grid; the scheduling priority of the diesel generator set is set to be lower than that of the energy storage device, and the power generation cost is calculated ;

[0040] S3.4: In order to avoid the threat to the safe operation of the power grid during the peak electricity consumption period, the purpose of load peak clipping and valley filling is realized by load coordinated scheduling, and the load scheduling cost is calculated ;

[0041] S3.5: The multi-source coordination model is constructed with the basic power generation cost, the charging and discharging cost, the diesel power generation cost and the load scheduling cost as the sub-targets, and the objective function expression of the model is:

[0042]

[0043] In the formula, 、 、 、 are the weight coefficients of each sub-target in the objective function, and .

[0044] Specifically, the specific steps of the ant colony optimization strategy in the step S4 include:

[0045] S4.1: initialize parameters and set the number of iterations ;

[0046] S4.2: based on the constraint condition, the ant colony algorithm is used to calculate the objective function value in the multi-source coordination model in the current iteration;

[0047] S4.3: record the optimal solution of the current iteration and save it to the global database constructed;

[0048] S4.4: update the pheromone of the optimal solution of the current iteration to avoid the algorithm falling into a local optimal solution;

[0049] S4.5: judge whether the maximum number of iterations is reached; if the maximum number of iterations is not reached, let , return to the S4.2 and start a new round of iteration operation; if the maximum number of iterations is reached, the global optimal solution is selected according to the global database.

[0050] A multi-source synchronous regulation system for intelligent micro-grid, comprising a data acquisition module, a communication module, a multi-source synchronization module and a multi-source coordination module;

[0051] The data acquisition module is used for acquiring the network topology of the micro-grid and monitoring the operation state of the micro-grid in real time, collecting the parameters of each power generation device in real time by using the sensors inside the multi-source power generation device, and preprocessing the parameters;

[0052] The communication module is used for transmitting the data collected by the data acquisition module by using wireless communication technology;

[0053] The multi-source synchronization module is used for realizing the synchronous control of the multi-source power generation device and the micro-grid, and adjusting the output frequency and output phase of the power generation device in real time by constructing a frequency control model and a phase control model.

[0054] The multi-source coordination module is used for realizing the coordinated scheduling among the multi-source power generation devices in the micro-grid, considering the load demand, energy supply, energy storage state and economic cost, constructing a multi-source coordination model, solving the multi-source coordination model by using an optimization algorithm, and formulating an energy scheduling scheme.

[0055] Specifically, the multi-source synchronization module is configured with an adaptive regulation strategy; the adaptive regulation strategy defines input and output variables by using fuzzy logic, and performs fuzzy processing on the variables, calculates the fuzzy value of the output variable by using the constructed fuzzy rule base and fuzzy inference mechanism, converts the fuzzy value into an actual value and updates the PID parameter, and dynamically adjusts the output of the multi-source power generation device.

[0056] Specifically, the multi-source coordination module is configured with an ant colony optimization strategy; the ant colony optimization strategy is used for solving the multi-source coordination model; the travel path of the ant is selected by probability and the ant is moved to a new position, the optimal solution of the current iteration is recorded, and the pheromone of the optimal solution is updated until the iteration is completed, and the global optimal solution is output.

[0057] The beneficial effects of the present application are:

[0058] 1. By constructing a PID control model, the synchronous control of the multi-source power generation device and the micro-grid is realized, the fluctuation of the output power of the power generation device is reduced by accurate synchronous control, thereby reducing the impact on the stability of the micro-grid and reducing energy loss; and the PID parameter is adaptively adjusted by using fuzzy logic, so that the PID control model can flexibly cope with the non-linear change and uncertainty of the power generation device parameters, improve the robustness and adaptability of the micro-grid system, improve the response speed and stability of the micro-grid, and reduce energy loss.

[0059] 2. A multi-source coordination model is constructed by load demand, energy supply capacity, energy storage device state and economic cost, and an ant colony optimization strategy is used to solve the model to develop an energy dispatching scheme, ensure reasonable allocation of various types of energy in time and space, improve overall energy utilization efficiency, and ensure that load demand is met while improving the economic efficiency of the microgrid. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 A schematic diagram of a multi-source synchronous regulation method for an intelligent microgrid is shown.

[0061] Figure 2 A multi-source synchronous flowchart of a multi-source synchronous regulation method for an intelligent microgrid is shown.

[0062] Figure 3 A multi-source coordination flowchart of a multi-source synchronous regulation method for an intelligent microgrid is shown.

[0063] Figure 4 A system structure diagram of a multi-source synchronous regulation method for an intelligent microgrid is shown. DETAILED DESCRIPTION

[0064] The technical solutions of the present application will be described in detail below with reference to the drawings and specific embodiments. It should be understood that the specific features of the embodiments and the specific features in the embodiments are detailed descriptions of the technical solutions of the present application, and are not limitations of the technical solutions of the present application. In the case of no conflict, the technical features of the embodiments and the specific features in the embodiments can be combined with each other.

[0065] Embodiment 1

[0066] Reference Figures 1 to 3 As shown in the figure, the present embodiment introduces a multi-source synchronous regulation method for an intelligent microgrid, including the following steps:

[0067] Step S1: Obtain the network topology structure of the microgrid, including the types, quantities and distribution of the multi-source power generation devices in the microgrid; use the sensors inside the multi-source power generation devices to collect the key parameters of the multi-source power generation devices in real time, and preprocess the collected data; wherein the multi-source power generation devices adopt distributed control, including but not limited to wind power generation devices, photovoltaic power generation devices, diesel power generation devices and energy storage batteries;

[0068] Step S2: set the reference parameters of the micro-grid, construct the PID control model, real-time adjust the output frequency and output phase of the power generation equipment, perform synchronous control, realize the synchronization of the power generation equipment and the micro-grid; due to the parameters of the power generation equipment changing over time and the existence of multiple energy power generation equipment, the PID control model cannot adapt to the changes of the equipment parameters, in the process of synchronous control, an adaptive regulation strategy is formulated, the PID parameters are dynamically adjusted according to the real-time feedback of the multi-source power generation equipment, the PID control model is optimized, the response speed and stability of the micro-grid are improved, and the energy loss is reduced;

[0069] Step S3: considering the intermittency of renewable energy and the cost of multi-source power generation equipment, combining with the load demand of the micro-grid, a multi-source coordination model is constructed to realize efficient utilization and economy of energy;

[0070] Step S4: use ant colony optimization strategy to solve the multi-source coordination model, formulate energy scheduling optimization scheme, and greatly utilize available resources while ensuring the economy and stability of the system.

[0071] Specifically, the specific steps of step S2 include:

[0072] S2.1: set the reference parameters of the micro-grid, including the reference frequency and the reference phase ; in the micro-grid, multi-source power generation is to parallelly operate multiple power generation equipment, if the phases of multiple power generation equipment are not synchronized, the current and voltage waveforms of the power generation equipment are different, thus a huge impact current is generated, and frequency deviation will have adverse effects on the normal operation of the power equipment, leading to equipment damage, wherein the power equipment is the user's power equipment; therefore, the phase synchronization of the multi-source power generation equipment is realized, the current and voltage waveforms between different power generation equipment are seamlessly connected, thus realizing reliable transmission of electric energy, frequency synchronization can avoid power grid instability and electric energy quality decline caused by frequency deviation, realizing coordinated operation of multi-source power generation equipment in the micro-grid, reducing the loss and maintenance cost of the power equipment; calculate the deviation between the actual parameters and the reference parameters of each power generation equipment, including frequency difference and phase difference, according to the size and direction of the deviation, adjust the output combined with PID control and reduce the deviation to realize multi-source synchronous control, the expression is as follows:

[0073]

[0074]

[0075] In the formula, is the frequency difference of the power generation equipment, is the actual frequency of the power generation equipment, is the running time of the parameter in the power generation equipment; the phase difference of the power generation device, and , the actual phase of the power generation device, is a floor symbol;

[0076] S2.2: Constructing a PID control model, including a frequency control model and a phase control model, the frequency control model being used to adjust the output frequency of the power generation device, and the phase control model being used to adjust the output phase of the power generation device; initializing the PID parameters of the PID control model;

[0077] S2.3: Calculating the control signal of the PID control model , the control signal being generated according to the deviation between the current actual parameter and the reference parameter, and being used to adjust the output of the power generation device so as to reach the reference parameter, thereby realizing the multi-source synchronization control; the expression is as follows:

[0078]

[0079] In the formula, 、 and are proportional, integral and differential gains, respectively;

[0080] S2.4: Applying the control signal output by the PID control model to the power generation device, and real-time acquiring the output frequency and the output phase of the power generation device under the current state;

[0081] S2.5: Calculating the deviation of the power generation device under the current state, evaluating the PID control effect, and judging whether the deviation value is zero; if , the actual parameter of the power generation device is synchronized with the reference parameter, and the multi-source synchronization control is completed; if , the actual parameter of the power generation device still has a deviation from the reference parameter, and an adaptive regulation strategy is executed.

[0082] Specifically, the specific steps of the adaptive regulation strategy in S2.5 include:

[0083] S2.51: Calculating the deviation change rate , the expression of the deviation change rate being as follows:

[0084]

[0085] S2.52: Adjusting the PID parameters adaptively by using fuzzy logic, defining the deviation and the deviation change rate as input variables, and the parameter adjustment amount 、 、 Output variable;

[0086] S2.53: fuzzification of input and output variables, definition of domain of input and output variables, division of domain of input and output variables into several fuzzy subsets, definition of membership function for each fuzzy subset; for example, the domain of deviation is , the fuzzy set is "negative", "zero", "positive", and the triangular membership function is selected to define the fuzzy set, the domain of deviation rate of change is , the fuzzy set is "low", "medium", "high", and the triangular membership function is selected to define the fuzzy set;

[0087] S2.54: design of fuzzy rule base, description of adjustment rules of PID parameters under different deviations and deviation rates of change; the specific content in the fuzzy rule base includes: when the deviation is large, if the deviation is "positive", take as a positive value to increase and speed up the response, take as a negative value to reduce , prevent integral saturation and overshoot, take as a negative value to reduce , stabilize the system, and if the deviation is "negative", adopt the opposite adjustment strategy; if the deviation rate of change is large, if the deviation rate of change and the deviation are of the same sign, take as a negative value to reduce , reduce overshoot, take as a positive value to increase , speed up the response to the change of deviation, if the deviation rate of change and the deviation are of opposite signs, take as a negative value to reduce , take as a positive value to increase , take as a positive value to increase ; when the deviation is very small but persistent, take as a positive value to increase , eliminate steady-state error; when the deviation and the deviation rate of change are both close to zero, keep the PID parameters, avoid unnecessary adjustment;

[0088] S2.55: using the fuzzy values of the input variables and the fuzzy rule base, calculate the fuzzy values of the output variables through the fuzzy reasoning mechanism (such as Mamdani reasoning);

[0089] S2.56: since the output variable is a set composed of multiple fuzzy elements, it does not have a single certainty, convert the fuzzy values of the output variables into actual parameter adjustment values , , , the expression is as follows:

[0090]

[0091]

[0092]

[0093] In the formula, , , respectively, the output variable , , elements in the domain, , , corresponding membership values;

[0094] S2.57: update PID parameters and PID control model, calculate the updated control signal , and return to S2.4; the expression of PID parameter is as follows:

[0095]

[0096]

[0097]

[0098] Specifically, the specific steps of step S3 include:

[0099] S3.1: In the micro-grid, the power generation of renewable energy is affected by natural factors, and has volatility, which leads to the fluctuation of voltage level in the micro-grid; when the renewable energy generation increases or decreases suddenly, the voltage in the power equipment will change accordingly, leading to voltage instability, thereby affecting the normal operation of the power equipment; at the same time, due to the intermittency of renewable energy, its power generation has large changes in a short time, which is difficult to balance supply and demand, therefore, by formulating flexible generation and energy storage scheduling plan to maintain the stable operation of micro-grid; the renewable energy power generation equipment is used as the basic power generation equipment, including wind power generation equipment and photovoltaic power generation equipment; and by considering the operation cost of power generation equipment, and scheduling based on cost, the overall operation cost of micro-grid is reduced, the economic benefit of micro-grid is improved, and it maintains the advantage in market competition; at this time, the expression of basic power generation cost is as follows:

[0100]

[0101] In the formula, is the power generation cost coefficient of unit active output of wind power generation equipment, for wind power generation equipment at active power output; for photovoltaic power generation equipment at active power output; generation cost coefficient of unit active power output of photovoltaic power generation equipment; , respectively for the number of wind and photovoltaic power generation equipment; for the duration of each time period, for a complete scheduling time, take , ;

[0102] S3.2: In the daily operation of the micro-grid, due to the instability of renewable energy power generation, it is inevitable that there will be excess or insufficient load. Energy storage devices solve the intermittency problem of renewable energy through reasonable charging and discharging. For example, when renewable energy generation is sufficient, the energy storage device can store excess power, and when renewable energy generation is insufficient, the energy storage device releases power. At this time, the expression of the energy storage device charging and discharging cost is as follows:

[0103]

[0104] In the formula, is the storage power of the energy storage device at time, is the active power output of the energy storage device at time; , respectively for the cost coefficients when the energy storage device charges and discharges; is the number of energy storage devices;

[0105] S3.3: The diesel generator starts quickly and supplies power when renewable energy generation, energy storage device discharge is insufficient or power grid failure causes power failure, ensuring the power supply of the micro-grid. For example, when renewable energy generation or energy storage device discharge is insufficient, the diesel generator starts and supplies power to ensure the stable operation of the micro-grid. The scheduling priority of the diesel generator is set to be lower than that of the energy storage device, and the expression of the diesel generator cost is as follows:

[0106]

[0107] In the formula, is the generation cost coefficient of unit active power output of the diesel generator; is the active power output of the diesel generator at time. The number of diesel power generation equipment;

[0108] S3.4: In order to avoid the threat to the safe operation of the power grid during the peak electricity consumption period, the purpose of load peak shaving and valley filling is realized by load coordinated scheduling, mainly using transferable load, using time flexibly, and transferring the load during the peak electricity consumption period to the period with lower electricity consumption. This can not only alleviate the power congestion of the microgrid, but also reduce the cost of the microgrid. The expression of the load scheduling cost is as follows:

[0109]

[0110] In the formula, is the real-time electricity purchase price, is the load power, is the load quantity; is the compensation coefficient of transferable load, is the load quantity transferred at the moment, is the electricity price at the moment; is the compensation coefficient of reducible load, is the state variable of whether load reduction is performed at the moment, taking values 0 or 1, is the reducible load power at the moment; is the compensation coefficient of shiftable load, is the state variable of whether load shifting is performed at the moment, taking values 0 or 1, is the shiftable load power at the moment.

[0111] S3.5: A multi-source coordination model is constructed with generation cost, charging and discharging cost, and load scheduling cost as sub-targets, which helps to reduce energy cost and improve economic efficiency. At the same time, through real-time updating and adjustment of the model, various sudden situations in power equipment can be coped with, and the robustness and reliability of power generation are improved. The objective function expression of the model is:

[0112]

[0113] In the formula, , , , are the weight coefficients of each sub-target in the objective function, and .

[0114] Specifically, the constraint conditions of the multi-source coordination model in S3.5 include:

[0115] The energy storage device and diesel generator are introduced into the micro-grid, and the balance between the energy supply of the micro-grid and the energy demand of the power equipment is realized through multi-energy complementation, the stability and reliability of the power equipment are improved, and power imbalance will lead to frequency deviation or voltage fluctuation of the power equipment. At this time, the power balance expression of the micro-grid is as follows:

[0116]

[0117] In the formula, is the power demand of the power equipment at moment;

[0118] By constraining the charging and discharging power of the energy storage device, the energy storage device is ensured to work in a safe power range, thereby prolonging the service life of the battery, maintaining the stability and reliability of the energy storage device, and optimizing the energy supply demand. The energy storage charging and discharging power constraint should satisfy the following formula:

[0119]

[0120]

[0121] In the formula, and are the lower limit and the upper limit of the storage power of the energy storage device, and are the lower limit and the upper limit of the active output of the energy storage device;

[0122] By constraining the power generation of the diesel generator, the actual power generation cannot be higher than the upper limit of the active output, so as to prevent damage or safety accidents caused by overload, and at the same time, in order to ensure the stability of the power generation equipment during operation, the actual power generation cannot be lower than the lower limit of the active output. The power generation constraint should satisfy the following formula:

[0123]

[0124] In the formula, and are the lower limit and the upper limit of the active output of the diesel generator;

[0125] By constraining the load transfer, it is avoided that the equipment is damaged or the power grid is collapsed due to excessive load, and it is also avoided that the power equipment cannot operate in the optimal state due to low load, and the load transfer in the micro-grid should keep the balance between the transferred-in and the transferred-out. The load transfer constraint should satisfy the following formula:

[0126]

[0127]

[0128] In the formula, The power of the transferred load. and These represent the lower and upper limits of the transferred load power, respectively. The total amount of load transferred out, This represents the total load transferred in;

[0129] By imposing load shedding constraints, power equipment can be prevented from collapsing due to overload; the load shedding constraints should satisfy the following formula:

[0130]

[0131]

[0132]

[0133]

[0134] In the formula, The total duration of load reduction, The maximum duration of load scheduling reduction, for The reduction factor of time, The maximum reduction coefficient, To reduce load in Power reduction value during the period for The state variable at any given time indicates whether the load is being reduced. , These are the load power values ​​before and after the load reduction, respectively;

[0135] By constraining load shifting, the power load is distributed across different time periods, improving the flexibility and adaptability of the microgrid. The load shifting constraint should satisfy the following equation:

[0136]

[0137]

[0138]

[0139] In the formula, After translation Load power value at time 10:00 For the transferable load in The shift-in power value at time , yes The power value removed at any given time. The span and duration of the load translation. For the maximum span length.

[0140] Specifically, the specific steps of the ant colony optimization strategy in step S4 include:

[0141] S4.1: initialize parameters and set the iteration number value ;

[0142] S4.2: based on the constraint conditions of the multi-source coordination model, use the ant colony algorithm to calculate the objective function of the multi-source coordination model under the conditions of the current iteration;

[0143] S4.3: use the insertion sort method to sort the target function value of the current iteration in ascending order, take the last value as the optimal solution of the current iteration, and save the optimal solution to the global database constructed;

[0144] S4.4: update the pheromone of the current iteration optimal solution to avoid the algorithm falling into a local optimal solution;

[0145] S4.5: determine whether the maximum iteration number is reached; if the maximum iteration number is not reached, let , return to step S4.2 and start a new round of iteration operation; if the maximum iteration number is reached, select the global optimal solution according to the global database.

[0146] Embodiment 2

[0147] Please refer to Figure 4 , the application provides another embodiment: a multi-source synchronous regulation system for intelligent micro-grid, comprising: a data acquisition module, a communication module, a multi-source synchronization module and a multi-source coordination module;

[0148] The data acquisition module is used to obtain the network topology of the micro-grid and monitor the running state of the micro-grid in real time, and uses the sensors inside the multi-source power generation equipment to collect the parameters of each power generation equipment in real time, such as phase, frequency, voltage, current and power. The collected data is preprocessed, such as filtering, denoising, data compression and normalization, to ensure the accuracy and reliability of the data;

[0149] The communication module is used to send the data collected by the data acquisition module to the multi-source synchronization module and the multi-source coordination module through wireless communication technology, which reduces the complexity of wiring, improves the efficiency and reliability of data transmission, and is simple and convenient for later maintenance;

[0150] The multi-source synchronization module is used to realize the synchronous control of the multi-source power generation equipment and the micro-grid; the reference parameters of the micro-grid are set 、 , and the frequency control model and the phase control model are constructed by using the PID control principle to generate control signals And the control signal is applied to the power generation device, the output frequency and the output phase of the power generation device are adjusted in real time, and the output parameters of the multi-source power generation device are synchronized with the reference parameters;

[0151] The multi-source coordination module is used for realizing coordinated scheduling between the multi-source power generation devices in the micro-grid, and taking the basic power generation cost of the renewable energy as a sub-target , the charging and discharging cost of the energy storage device , the diesel power generation cost and the load scheduling cost as sub-targets, a multi-source coordination model is constructed, an optimization algorithm is used to solve the multi-source coordination model, an energy scheduling scheme is formulated, an energy storage plan is optimized, and efficient utilization and economic optimization of energy are realized while meeting the load demand.

[0152] Specifically, the multi-source synchronization module is configured with an adaptive control strategy, the adaptive control strategy utilizes fuzzy logic, and PID parameters of a control model are adaptively adjusted according to real-time feedback of the multi-source power generation device; a deviation and a deviation change rate are defined as input variables, and an adjustment amount , and of the PID parameters are defined as output variables, the variables are fuzzified, a fuzzy value of the output variable is calculated by using a fuzzy inference mechanism through a designed fuzzy rule base, the fuzzy value is converted into an actual value, the PID parameters are updated, new control signals are generated, and the output of the multi-source power generation device is dynamically adjusted to make the multi-source power generation device synchronized with the micro-grid.

[0153] Specifically, the multi-source coordination module is configured with an ant colony optimization strategy, the ant colony optimization strategy is used to solve the multi-source coordination model, a next path of each ant is selected by calculation and the ant is moved to a new position, a first iteration is calculated, an optimal solution of the current iteration is recorded, pheromone of the optimal solution of the current iteration is updated, the algorithm is prevented from falling into a local optimal solution, and a global optimal solution is output until the iteration is completed.

[0154] In the above embodiments, the PID control model is constructed, the output parameters of the multi-source power generation device are dynamically adjusted, the multi-source power generation device is synchronized with the micro-grid, the multi-source coordination model is constructed based on the load demand, the energy supply and the economic cost, the ant colony optimization strategy is used to solve the multi-source coordination model, the energy scheduling scheme is formulated, and efficient utilization and economic optimization of energy are realized.

[0155] The above merely describes the preferred embodiments of the present application, and the protection scope of the present application is not limited to the above-described embodiments. Any technical solution falling within the concept of the present application shall fall within the protection scope of the present application. It should be noted that, for ordinary skilled persons in the art, some improvements and refinements without departing from the principles of the present application shall also be considered as falling within the protection scope of the present application.

Claims

1. A multi-source synchronous regulation method for intelligent microgrid, characterized in that, The application relates to a micro-grid energy scheduling method and device. Step S1: acquiring the network topology structure of a micro-grid, including the types, quantity and distribution of multi-source power generation equipment in the micro-grid; collecting key parameters of the multi-source power generation equipment in real time by using sensors in the multi-source power generation equipment, and pre-processing the collected data; Step S2: setting reference parameters of the micro-grid, constructing a PID control model to synchronously control the multi-source power generation equipment, adjusting the output frequency and output phase of the power generation equipment in real time, configuring an adaptive regulation strategy in the PID control model, dynamically adjusting PID parameters according to real-time feedback of the multi-source power generation equipment, and optimizing the PID control model; Step S3: constructing a multi-source coordination model based on intermittency of renewable energy and cost of the multi-source power generation equipment, and combining load demand of the micro-grid; Step S4: solving the multi-source coordination model by using an ant colony optimization strategy, and formulating an energy scheduling optimization scheme; The specific steps of the step S2 include: S2.1: set the reference frequency of the micro-grid and the reference phase , and calculate the deviation between the actual parameters of the multi-source power generation equipment and the reference parameters , including the frequency difference and the phase difference , according to the size and direction of the deviation, adjust the output and reduce the deviation to realize multi-source synchronization control in combination with PID control; S2.2: constructing a PID control model, including a frequency control model and a phase control model, the frequency control model is used for adjusting the output frequency of the power generation equipment, the phase control model is used for adjusting the output phase of the power generation equipment, and PID parameters are initialized; S2.3: calculating a control signal of the PID control model ; S2.4: apply the control signal of the PID control model to the power generation device, and acquire the actual output parameters of the power generation device in the current state in real time, including output frequency and output phase ; S2.5: calculate the deviation of the power generation equipment in the current state , evaluate the PID control effect, and determine whether the deviation value is zero; if , complete multi-source synchronous control; if , execute the adaptive control strategy; The adaptive control strategy includes fuzzy processing, defining the domain of input and output variables, dividing the domain into several fuzzy subsets, defining membership functions for each fuzzy subset, defining the domain of deviation as , the fuzzy set as negative, zero, positive, and the domain of deviation rate of change as , the fuzzy set as low, medium, high, all using triangular membership functions to define the fuzzy set; Also included is a design of fuzzy rule base, when the deviation is large, if the deviation is positive, take as a positive value, take , as a negative value, if the deviation is negative, take the opposite adjustment strategy; if the deviation rate of change is large, if the deviation rate of change and the deviation are of the same sign, take as a negative value, take as a positive value, if the deviation rate of change and the deviation are of different signs, take as a negative value, take , as a positive value; when the deviation is very small but persistent, take as a positive value; when the deviation and the deviation rate of change are both close to zero, keep the PID parameters. 2.The multi-source synchronous regulation and control method for intelligent micro-grid according to claim 1, wherein, The constraint conditions of the multi-source coordination model include: Balancing constraint of micro-grid energy supply and power equipment energy demand; Constraining the charging and discharging power of the energy storage equipment, so that the charging and discharging of the energy storage equipment work in the rated power range, and the energy supply demand is optimized; Constraining the power generation power of the diesel generator set, so that the diesel generator set works in the rated active output range, the actual power generation power is not higher than the upper limit of the active output, and is not lower than the lower limit of the active output; Constraining load transfer, so that the load transfer in the micro-grid works in the rated power range, and the load transfer in the micro-grid should keep the balance of transfer-in and transfer-out; Constraining load reduction to avoid the collapse of the power equipment due to overload; Constraining load translation, so that the power load is distributed in different time periods. 3.The multi-source synchronous regulation and control method for smart micro-grid according to claim 2, characterized in that, The specific steps of the adaptive regulation strategy in the S2.5 include: S2.51: Calculate the rate of change of bias ; S2.52: define the deviation and the deviation change rate is an input variable, and the parameter adjustment amount of the PID control model is an output variable; S2.53: fuzzy processing is conducted on input and output variables, a domain is defined for the input and output variables, the domain of the variable is divided into a plurality of fuzzy subsets, and a membership function is defined for each fuzzy subset; S2.54: a fuzzy rule base is designed to describe the adjustment rules of the PID parameters under different deviations and deviation change rates; S2.55: the fuzzy value of the output variable is calculated by using the fuzzy rule base and the fuzzy value of the input variable through a fuzzy inference mechanism; S2.56: the fuzzy value of the output variable is converted into an actual parameter adjustment amount by calculating the weighted average value of the fuzzy elements; S2.57: update PID parameters and PID control model, calculate updated control signal and return to said S2.

4. 4.The multi-source synchronous regulation and control method for smart micro-grid according to claim 3, characterized in that, The specific steps of the step S3 include: S3.1: Set the renewable energy power generation device as a base power generation device, and calculate the base power generation cost ; S3.2: Introducing an energy storage device in the microgrid and calculating the charge and discharge cost of the energy storage device ; S3.3: Introducing diesel generator in the microgrid, setting the dispatch priority of the diesel generator lower than the energy storage device, and calculating the diesel generation cost ; S3.4: Calculate the load scheduling cost ; S3.5: a multi-source coordination model is constructed by taking the basic power generation cost, the charging and discharging cost, the diesel power generation cost and the load scheduling cost as sub-targets, and the objective function expression of the model is: ； wherein , , , are the weight coefficients of each sub-object in the objective function, and . 5.The multi-source synchronous regulation and control method for smart micro-grid according to claim 4, characterized in that, The specific steps of the ant colony optimization strategy in the step S4 include: S4.1: initialize parameters and set the number of iterations ; S4.2: based on the constraint conditions, using an ant colony algorithm to calculate the objective function value in the multi-source coordination model in the current iteration; S4.3: record the optimal solution of the current iteration and save it to the constructed global database; S4.4: pheromone update is performed on the optimal solution of the current iteration; S4.5: determine whether the maximum iteration number is reached; if not, let , return to S4.2 and start a new iteration; if the maximum iteration number is reached, select the global optimal solution from the global database.

6. A multi-source synchronous regulation system for smart microgrid, which is used to implement the multi-source synchronous regulation method for smart microgrid as claimed in any one of claims 1-5, characterized in that, Comprise: Data acquisition module, communication module, multi-source synchronization module and multi-source coordination module; The data acquisition module is used to obtain the network topology of the microgrid and monitor the operation state of the microgrid in real time, and real-time acquisition of the parameters of each power generation equipment is realized by using the sensors inside the multi-source power generation equipment, and the parameters are pretreated; The communication module is used to transmit the data collected by the data acquisition module by using wireless communication technology; The multi-source synchronization module is used to realize the synchronization control of the multi-source power generation equipment and the microgrid, and the output frequency and output phase of the power generation equipment are adjusted in real time by constructing the frequency control model and the phase control model; The multi-source coordination module is used to realize the coordinated scheduling among the multi-source power generation equipment in the microgrid, and the load demand, energy supply, energy storage state and economic cost are comprehensively considered to construct a multi-source coordination model, and an optimization algorithm is used to solve the multi-source coordination model to formulate an energy scheduling scheme.

7. The multi-source synchronization regulation system for intelligent microgrid according to claim 6, characterized in that: An adaptive regulation strategy is configured in the multi-source synchronization module; the adaptive regulation strategy defines input and output variables and performs fuzzy processing on the variables by using fuzzy logic, calculates the fuzzy value of the output variables by using the constructed fuzzy rule base and fuzzy inference mechanism, converts the fuzzy value into an actual value and updates the PID parameters, and dynamically adjusts the output of the multi-source power generation equipment.

8. The multi-source synchronization regulation system for intelligent microgrid according to claim 7, characterized in that: An ant colony optimization strategy is configured in the multi-source coordination module; the ant colony optimization strategy is used to solve the multi-source coordination model; the travel path of the ant is selected by probability and the ant is moved to a new position, the optimal solution of the current iteration is recorded, and the pheromone of the optimal solution is updated until the iteration is completed, and the global optimal solution is output.

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