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

Abstract

The invention discloses a multi-source synchronous regulation and control method and system for an intelligent micro-grid, and belongs to the technical field of new energy power generation control. Output parameters of multi-source power generation equipment are collected in real time, reference parameters of the micro-grid are set, and the multi-source power generation equipment is synchronously controlled through a constructed PID control model; pID parameters are dynamically adjusted based on real-time feedback of the multi-source power generation equipment, the PID control model is optimized, synchronous control over the multi-source power generation equipment and the micro-grid is achieved, the response speed and stability of the micro-grid are improved, and energy loss is reduced; 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 scheduling optimization scheme is formulated, the overall energy utilization efficiency is improved, and the overall energy cost is reduced.

Description

Technical Field

[0001] The present invention relates to a multi-source synchronous control method and system for a smart microgrid, belonging to the technical field of new energy power generation control. Background Art

[0002] With the continuous growth of global energy demand and the enhancement of environmental protection awareness, the transformation of energy structure has become a global consensus. The traditional energy system faces severe challenges such as resource depletion and environmental pollution, while renewable energy has attracted widespread attention due to its clean and sustainable characteristics. As an important way to achieve efficient utilization of renewable energy and distributed energy management, the multi-source synchronous control system of smart microgrid is of great significance to promoting the transformation of energy structure.

[0003] Smart microgrids integrate multiple distributed energy sources (such as solar energy, wind energy, fuel cells, etc.), energy storage devices, energy conversion devices, loads, and monitoring and protection devices to form a complex and diverse system. These distributed energy sources have different power generation characteristics, cost structures, and environmental benefits, and the loads also present diverse demand characteristics.

[0004] The existing Chinese patent application with publication number CN115021312A discloses an intelligent control system and method for multi-mode integrated control of distributed power sources. The method includes: collecting voltage and current information at the grid-connected location of each distributed power generation unit, as well as 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 the 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 control is adopted according to its operating frequency and voltage state.

[0005] Although the prior art delegates complex functions that originally require local control to the upper-level controller and realizes comprehensive coordination among the three control modes, it does not consider the synchronous regulation among multi-source power generation units; therefore, the present invention provides a multi-source synchronous regulation method and system for a smart microgrid, which adjusts the frequency and phase of each power generation unit in real time through adaptive control to achieve high-precision synchronous control, coordinate the relationship between various energy sources and loads, dynamically adjust the output of each power generation unit, improve the coordination ability of multiple energy sources in the microgrid, and ensure the stability and energy efficiency of the power grid. Summary of the invention

[0006] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a multi-source synchronous control method and system for a smart microgrid, which utilizes an adaptive control algorithm and a multivariable coordinated optimization algorithm to achieve high-precision synchronous control of multi-source power generation in the smart microgrid, comprehensively consider the characteristics of each energy source, dynamically adjust the output of each source, and maintain the stability of the power grid.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] The multi-source synchronous control method for smart microgrid includes:

[0009] Step S1: using sensors inside the multi-source power generation equipment to collect key parameters of the multi-source power generation equipment in real time, and pre-processing the collected data;

[0010] Step S2: setting the benchmark parameters of the microgrid, constructing a PID control model to synchronously control the multi-source power generation equipment, configuring an adaptive control strategy in the PID control model, dynamically adjusting the PID parameters according to the real-time feedback of the multi-source power generation equipment, and optimizing the PID control model;

[0011] Step S3: Based on the intermittency of renewable energy and the cost of the multi-source power generation equipment, combined with the load demand of the microgrid, a multi-source coordination model is constructed;

[0012] Step S4: using an ant colony optimization strategy to solve the multi-source coordination model and formulate an energy scheduling optimization plan.

[0013] Specifically, the specific steps of step S2 include:

[0014] S2.1: Set the base frequency of the microgrid and reference phase , and calculate the deviation between the actual parameters and the reference parameters of the multi-source power generation equipment ;

[0015] S2.2: Construct a PID control model, including a frequency control model and a phase control model, and initialize PID parameters;

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

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

[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 , to complete multi-source synchronous control; if , and execute adaptive control strategies.

[0019] Specifically, the specific steps of the adaptive control strategy in S2.5 include:

[0020] S2.51: Calculate the rate of change of deviation ;

[0021] S2.52: Defining Bias and the rate of change of deviation is the input variable, the parameter adjustment amount of the PID control model , , is the output variable;

[0022] S2.53: Fuzzify the input and output variables, define the domain for the input and output variables, divide the domain of the variables into several fuzzy subsets, and define the membership function for each fuzzy subset;

[0023] S2.54: Design a fuzzy rule base to describe the adjustment rules of PID parameters under different deviations and deviation change rates;

[0024] S2.55: using the fuzzy rule base and the fuzzy value of the input variable, calculating the fuzzy value of the output variable through a fuzzy reasoning mechanism;

[0025] S2.56: Use the centroid method to convert the fuzzy value of the output variable into the actual parameter adjustment , , , and update the PID parameters;

[0026] S2.57: Update the PID control model and calculate the updated control signal , and returns to S2.4.

[0027] Specifically, the specific steps of step S3 include:

[0028] S3.1: Due to the non-dispatchability of renewable energy generation, renewable energy generation equipment is set as the basic generation equipment and the generation cost is calculated ;

[0029] S3.2: In the daily operation of the microgrid, there is an overload or underload situation. Introducing energy storage equipment to participate in energy scheduling solves the intermittent problem of renewable energy, and calculates the charging and discharging cost of the energy storage equipment ;

[0030] S3.3: When the energy storage is insufficient, the diesel generator set will start up and supply power quickly to ensure the power supply of the microgrid; set the dispatch priority of the diesel generator set lower than that of the energy storage device, and calculate the power generation cost ;

[0031] S3.4: In order to avoid the threat to the safe operation of the power grid during peak hours, load coordination and dispatching are used to achieve the purpose of load peak reduction and valley filling, and the load dispatching cost is calculated. ;

[0032] S3.5: Taking the basic power generation cost, charging and discharging cost, diesel power generation cost and load dispatching cost as sub-goals, a multi-source coordination model is constructed. The objective function expression of the model is:

[0033]

[0034] In the formula, , , , is the weight coefficient of each sub-goal in the objective function, and .

[0035] Specifically, the constraints of the multi-source coordination model in S3.5 include:

[0036] Balancing constraints on microgrid energy supply and power equipment energy demand;

[0037] Constrain the charging and discharging power of the energy storage device so that the charging and discharging of the energy storage device work within the rated power range;

[0038] The power generation capacity of the diesel generator set is constrained so that the diesel generator set operates within the rated active power output range.

[0039] Specifically, the constraints of the multi-source coordination model in S3.5 also include:

[0040] Constrain load transfer so that the load transfer in the microgrid works within the rated power range, and the load transfer within the microgrid should maintain a balance between transfer in and transfer out;

[0041] Limit load shedding to prevent power equipment from collapsing due to overload;

[0042] Load shifting is constrained so that the power load can be distributed in different time periods.

[0043] Specifically, the specific steps of the ant colony optimization strategy in step S4 include:

[0044] S4.1: Initialize parameters and set the number of iterations ;

[0045] S4.2: Based on the constraint conditions, using an ant colony algorithm, calculating the objective function value in the multi-source coordination model in the current iteration;

[0046] S4.3: record the optimal solution of the current iteration and save it to the constructed global database;

[0047] S4.4: Update the pheromone of the current iterative optimal solution to avoid the algorithm falling into the local optimal solution;

[0048] S4.5: Determine whether the maximum number of iterations has been reached; if not, set , return to S4.2 and start a new round of iterative calculation; if the maximum number of iterations is reached, select the global optimal solution according to the global database.

[0049] A multi-source synchronous control system for a smart microgrid, comprising: a data acquisition module, a communication module, a multi-source synchronization module and a multi-source coordination module;

[0050] The data acquisition module is used to obtain the network topology of the microgrid and monitor the operating status of the microgrid in real time, and use the sensors inside the multi-source power generation equipment to collect the parameters of each power generation equipment in real time and pre-process the parameters;

[0051] The communication module is used to transmit the data collected by the data acquisition module using wireless communication technology;

[0052] The multi-source synchronization module is used to realize the synchronous control of the multi-source power generation equipment and the microgrid, and adjust the output frequency and output phase of the power generation equipment in real time by constructing a frequency control model and a phase control model;

[0053] The multi-source coordination module is used to realize coordinated scheduling among multi-source power generation equipment in a microgrid, comprehensively consider load demand, energy supply, energy storage status and economic cost, construct a multi-source coordination model, use an optimization algorithm to solve the multi-source coordination model, and formulate an energy scheduling plan.

[0054] Specifically, an adaptive control strategy is configured in the multi-source synchronization module; the adaptive control strategy uses fuzzy logic to define input and output variables, and fuzzifies the variables, calculates the fuzzy value of the output variable using the constructed fuzzy rule base and fuzzy reasoning 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.

[0055] 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; the ant's travel path 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.

[0056] Beneficial effects of the present invention:

[0057] 1. By constructing a PID control model, the synchronous control of multi-source power generation equipment and microgrid is realized. Through precise synchronous control, the fluctuation of the output power of the power generation equipment is reduced, thereby alleviating the impact on the stability of the microgrid and reducing energy loss. The PID parameters are adaptively adjusted using fuzzy logic, so that the PID control model can flexibly respond to the nonlinear changes and uncertainties of the power generation equipment parameters, improve the robustness and adaptability of the microgrid system, improve the response speed and stability of the microgrid, and reduce energy loss.

[0058] 2. Build a multi-source coordination model based on load demand, energy supply capacity, energy storage equipment status and economic cost, solve the model through ant colony optimization strategy, formulate an energy scheduling plan, ensure the rational allocation of various energy sources in time and space, improve the overall energy utilization efficiency, and ensure that the economic benefits of the microgrid are improved while meeting the load demand. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 Schematic diagram of a multi-source synchronous control method for a smart microgrid;

[0060] Figure 2 A multi-source synchronization flow chart of a multi-source synchronization control method for a smart microgrid;

[0061] Figure 3 A multi-source coordination flow chart of a multi-source synchronous control method for a smart microgrid;

[0062] Figure 4 This is a structural diagram of the multi-source synchronous control system for smart microgrids. DETAILED DESCRIPTION

[0063] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. The embodiments of the present invention and the technical features in the embodiments may be combined with each other unless there is a conflict.

[0064] Example 1

[0065] refer to Figures 1 to 3As shown, this embodiment introduces a multi-source synchronous control method for a smart microgrid, comprising the following steps:

[0066] Step S1: obtaining the network topology of the microgrid, including the types, quantities, and distribution of multi-source power generation equipment in the microgrid; using sensors inside the multi-source power generation equipment to collect key parameters of the multi-source power generation equipment in real time, and preprocessing the collected data; wherein the multi-source power generation equipment adopts distributed control, including but not limited to wind power generation equipment, photovoltaic power generation equipment, diesel power generation equipment, and energy storage batteries;

[0067] Step S2: Set the benchmark parameters of the microgrid, build a PID control model, adjust the output frequency and output phase of the power generation equipment in real time, perform synchronous control, and achieve synchronization between the power generation equipment and the microgrid; since the parameters of the power generation equipment change over time and there are power generation equipment with multiple energy sources, the PID control model cannot adapt to the changes in equipment parameters. In the synchronous control process, formulate an adaptive control strategy, dynamically adjust the PID parameters according to the real-time feedback of the multi-source power generation equipment, optimize the PID control model, improve the response speed and stability of the microgrid, and reduce energy loss;

[0068] Step S3: Considering the intermittency of renewable energy and the cost of multi-source power generation equipment, combined with the load demand of the microgrid, a multi-source coordination model is constructed to achieve efficient and economical use of energy;

[0069] Step S4: Using the ant colony optimization strategy to solve the multi-source coordination model and formulate an energy scheduling optimization plan, which can maximize the use of available resources while ensuring the economy and stability of the system.

[0070] Specifically, the specific steps of step S2 include:

[0071] S2.1: Set the benchmark parameters of the microgrid, including the benchmark frequency and reference phase ; In a microgrid, multi-source power generation is to operate multiple power generation equipment in parallel. If the phases between multiple power generation equipment are not synchronized, there will be differences in the current and voltage waveforms of the power generation equipment, resulting in huge impact currents. At the same time, frequency deviation will have an adverse effect on the normal operation of the power equipment, causing equipment damage, where the power equipment is the power consumption equipment at the user end; therefore, achieving phase synchronization of multi-source power generation equipment ensures seamless connection of current and voltage waveforms between different power generation equipment, thereby achieving reliable transmission of electric energy. Frequency synchronization can avoid grid instability and power quality degradation caused by frequency deviation, achieve coordinated operation of multi-source power generation equipment in a microgrid, and reduce the loss and maintenance cost of power equipment; calculate the deviation between the actual parameters of each power generation equipment and the benchmark parameters , including frequency difference and phase difference. According to the size and direction of the deviation, the output is adjusted and the deviation is reduced in combination with PID control to achieve multi-source synchronous control. The expression is as follows:

[0072]

[0073]

[0074] In the formula, is the frequency difference of the power generation equipment, is the actual frequency of the power generation equipment, is the operating time of the parameters in the power generation equipment; is the phase difference of the power generation equipment, and , is the actual phase of the power generation equipment, is the floor rounding symbol;

[0075] 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; initialize the PID parameters of the PID control model;

[0076] S2.3: Calculate the control signal of the PID control model , the control signal is calculated and generated according to the deviation between the current actual parameters and the reference parameters, and is used to adjust the output of the power generation equipment to reach the reference parameters, thereby realizing multi-source synchronous control; the expression is as follows:

[0077]

[0078] In the formula, , and are proportional, integral and derivative gains respectively;

[0079] S2.4: Apply the control signal output by the PID control model to the power generation equipment to obtain the output frequency of the power generation equipment in the current state in real time and output phase ;

[0080] 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 , the actual parameters of the power generation equipment are synchronized with the reference parameters to complete multi-source synchronous control; if , there is still a deviation between the actual parameters of the power generation equipment and the benchmark parameters, and the adaptive control strategy is executed.

[0081] Specifically, the specific steps of the adaptive control strategy in S2.5 include:

[0082] S2.51: Calculate the rate of change of deviation , the expression of the deviation change rate is as follows:

[0083]

[0084] S2.52: Use fuzzy logic to adaptively adjust PID parameters and define deviations and the rate of change of deviation is the input variable, the parameter adjustment amount of the PID control model , , is the output variable;

[0085] S2.53: Fuzzify the input and output variables, define the domain for the input and output variables, divide the domain of the input and output variables into several fuzzy subsets, and define the membership function for each fuzzy subset; for example, define the domain of the deviation as , the fuzzy set is "negative", "zero", and "positive". The triangular membership function is used to define the fuzzy set. The domain of the deviation change rate is , the fuzzy sets are "low", "medium" and "high", and the triangular membership function is used to define the fuzzy set;

[0086] S2.54: Design a fuzzy rule base to describe the adjustment rules of PID parameters under different deviations and deviation change rates; the specific contents of the fuzzy rule base include: When the deviation is positive, take Positive value to increase , speed up the response, take Negative value to reduce , to prevent integral saturation and overshoot, take Negative value to reduce , stabilize the system. If the deviation is "negative", adopt the opposite adjustment strategy; if the deviation change rate is When the deviation is large, if the deviation change rate has the same sign as the deviation, take Negative value to reduce , reduce overshoot, take Positive value to increase , speed up the response to the deviation change. If the deviation change rate and the deviation have different signs, take Negative value to reduce ,Pick Positive value to increase ,Pick Positive value to increase ; When the deviation is small but persistent, take Positive value to increase , eliminate steady-state error; when the deviation and the rate of change of deviation are close to zero, maintain the PID parameters to avoid unnecessary adjustments;

[0087] 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 (such as Mamdani reasoning);

[0088] S2.56: Since the output variable is a set of multiple fuzzy elements and does not have a single certainty, the fuzzy value of the output variable is converted into the actual parameter adjustment value by calculating the weighted average of the fuzzy elements. , , , the expression is as follows:

[0089]

[0090]

[0091]

[0092] In the formula, , , The output variables are , , Elements in the domain, , , is the corresponding membership value;

[0093] S2.57: Update PID parameters and PID control model, calculate the updated control signal , and returns S2.4; the expression of PID parameters is as follows:

[0094]

[0095]

[0096]

[0097] Specifically, the specific steps of step S3 include:

[0098] S3.1: In a microgrid, the power generation of renewable energy is affected by natural factors and is volatile, which causes the voltage level in the microgrid to fluctuate accordingly; when the power generation of renewable energy suddenly increases or decreases, the voltage in the power equipment will change accordingly, resulting in voltage instability, thereby affecting the normal operation of the power equipment; at the same time, due to the intermittent nature of renewable energy, its power generation changes drastically in a short period of time, making it difficult to balance supply and demand. Therefore, a flexible power generation and energy storage scheduling plan is formulated to maintain the stable operation of the microgrid; the present invention uses renewable energy power generation equipment as the basic power generation equipment, including wind power generation equipment and photovoltaic power generation equipment; and, by considering the operating cost of the power generation equipment and scheduling based on the cost, the overall operating cost of the microgrid is reduced, the economic benefits of the microgrid are improved, and it maintains its advantage in market competition; at this time, the expression of the basic power generation cost is as follows:

[0099]

[0100] In the formula, is the power generation cost coefficient of unit active output of wind power generation equipment, For wind power generation equipment exist Active power output at all times; Photovoltaic power generation equipment exist Active power output at all times; is the power generation cost coefficient of unit active output of photovoltaic power generation equipment; , are the number of wind and photovoltaic power generation equipment, respectively; is the duration of each period, For a complete scheduling time, take , ;

[0101] S3.2: In the daily operation of microgrids, due to the instability of renewable energy power generation, there will inevitably be overload or underload. Energy storage equipment solves the intermittent problem of renewable energy by reasonable charging and discharging. For example, when renewable energy power generation is sufficient, energy storage equipment can store excess electricity, and when renewable energy power generation is insufficient, energy storage equipment releases electricity. At this time, the expression of the charging and discharging cost of energy storage equipment is as follows:

[0102]

[0103] In the formula, For energy storage devices exist The storage power at the moment, For energy storage devices exist Active power output at all times; , are the cost coefficients for charging and discharging the energy storage device, respectively; is the number of energy storage devices;

[0104] S3.3: When renewable energy generation, insufficient discharge of energy storage equipment or power outage caused by grid failure causes a power outage, the diesel generator equipment starts and supplies power to ensure the power supply of the microgrid; for example, when renewable energy generation or insufficient discharge of energy storage equipment occurs, the diesel generator equipment starts and supplies power to ensure the stable operation of the microgrid; the dispatch priority of the diesel generator equipment is set to be lower than that of the energy storage equipment, and the expression of the diesel power generation cost is as follows:

[0105]

[0106] In the formula, is the power generation cost coefficient of unit active output of diesel generator equipment; For diesel generators exist Active power output at all times; is the number of diesel generating equipment;

[0107] S3.4: In order to avoid the threat to the safe operation of the power grid during peak power consumption, load coordination and dispatching are used to achieve the purpose of load peak shaving and valley filling. The load is mainly transferable and the use time is flexible. The load during peak power consumption is transferred to the period of low power consumption. This can not only alleviate the power congestion of the microgrid, but also reduce the cost of the microgrid. The expression of load dispatching cost is as follows:

[0108]

[0109] In the formula, For real-time electricity purchase price, is the load power, is the load quantity; is the compensation factor for transferable loads, for The amount of load transferred at any time, for The electricity price at the time; is the compensation factor for load reduction, for The state variable of whether load reduction is performed at the moment, the value is 0 or 1, for The load power that can be reduced at any time; is the compensation factor for the translatable load, for The state variable of whether load translation is performed at the moment, the value is 0 or 1, for The load power that can be shifted at any time.

[0110] S3.5: Taking power generation cost, charging and discharging cost and load dispatching cost as sub-goals, constructing a multi-source coordination model can help reduce energy costs and improve economic benefits. At the same time, through real-time updating and adjustment of the model, it can cope with various emergencies in power equipment and improve the robustness and reliability of power generation. The objective function expression of the model is:

[0111]

[0112] In the formula, , , , is the weight coefficient of each sub-goal in the objective function, and .

[0113] Specifically, the constraints of the multi-source coordination model in S3.5 include:

[0114] Introducing energy storage equipment and diesel generators into the microgrid, the energy supply of the microgrid and the energy demand of the power equipment are balanced through multi-energy complementation, which improves the stability and reliability of the power equipment. Power imbalance can cause unstable phenomena such as frequency deviation or voltage fluctuation in the power equipment. At this time, the power balance expression of the microgrid is as follows:

[0115]

[0116] In the formula, For power equipment The power demand at the time;

[0117] By constraining the charging and discharging power of energy storage equipment, it is ensured that the energy storage equipment works within a safe power range, thereby extending the service life of the battery, maintaining the stability and reliability of the energy storage equipment, and optimizing energy supply needs; the energy storage charging and discharging power constraint should satisfy the following formula:

[0118]

[0119]

[0120] In the formula, and are the lower and upper limits of the storage power of the energy storage device, respectively. and They are the lower and upper limits of the active output of the energy storage device respectively;

[0121] By constraining the power generation of diesel generators, the actual power generation cannot be higher than the upper limit of active output to prevent equipment damage or safety accidents caused by overload. 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 active output. The power generation constraint should satisfy the following formula:

[0122]

[0123] In the formula, and They are the lower and upper limits of the active output of diesel generator equipment respectively;

[0124] By constraining load transfer, we can avoid equipment damage or grid collapse due to excessive load, and also avoid the inability of power equipment to operate in the optimal state due to low load. In addition, the load transfer within the microgrid should maintain a balance between transfer in and transfer out. The load transfer constraint should satisfy the following formula:

[0125]

[0126]

[0127] In the formula, is the power of the load being transferred, and Respectively represent the lower and upper limits of the transferred load power, is the total amount of load transferred out, is the total amount of load transferred in;

[0128] By restricting load reduction, power equipment can be prevented from crashing due to overload; load reduction constraints should satisfy the following formula:

[0129]

[0130]

[0131]

[0132]

[0133] In the formula, is the total duration of load shedding, is the maximum duration of load scheduling reduction, for The reduction factor of time, is the maximum reduction factor, To reduce the load The power reduction value of the time period, for The state variable at the moment indicates whether the load is reduced. , are the load power values ​​before and after load reduction respectively;

[0134] By constraining load shifting, the power load can be distributed in different time periods to improve the flexibility and adaptability of the microgrid; the load shift constraint should satisfy the following formula:

[0135]

[0136]

[0137]

[0138] In the formula, After translation The load power value at the moment, For the translatable load The input power value at time yes The power value removed at any moment, is the span duration of load translation, is the maximum span duration.

[0139] Specifically, the specific steps of the ant colony optimization strategy in step S4 include:

[0140] S4.1: Initialize parameters and set the number of iterations ;

[0141] S4.2: Based on the constraints of the multi-source coordination model, the ant colony algorithm is used to calculate the objective function of the multi-source coordination model under the conditions of the current iteration;

[0142] S4.3: Use the insertion sort method to sort the objective function values ​​of the current iteration in positive order, take the last value as the optimal solution of the current iteration, and save the optimal solution to the constructed global database;

[0143] S4.4: Update the pheromone of the current iterative optimal solution to avoid the algorithm falling into the local optimal solution;

[0144] S4.5: Determine whether the maximum number of iterations has been reached; if not, set , return to step S4.2 and start a new round of iterative calculation; if the maximum number of iterations is reached, select the global optimal solution according to the global database.

[0145] Example 2

[0146] See also Figure 4, another embodiment provided by the present invention: a multi-source synchronous control system for a smart microgrid, comprising: a data acquisition module, a communication module, a multi-source synchronization module and a multi-source coordination module;

[0147] The data acquisition module is used to obtain the network topology of the microgrid and monitor the operating status of the microgrid in real time. The sensors inside the multi-source power generation equipment are used to collect the parameters of each power generation equipment in real time, such as phase, frequency, voltage, current, and power. The collected data is pre-processed, such as filtering, denoising, data compression, and normalization, to ensure the accuracy and reliability of the data.

[0148] 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 makes subsequent maintenance simple and convenient;

[0149] The multi-source synchronization module is used to achieve synchronous control of multi-source power generation equipment and microgrids; set the benchmark parameters of the microgrid , , and use the PID control principle to build a frequency control model and a phase control model to generate a control signal The control signal is applied to the power generation equipment to adjust the output frequency and output phase of the power generation equipment in real time, so as to synchronize the output parameters of the multi-source power generation equipment with the reference parameters;

[0150] The multi-source coordination module is used to realize the coordinated dispatch among multi-source power generation equipment in the microgrid; based on the basic power generation cost of renewable energy , Charging and discharging costs of energy storage equipment , diesel power generation cost and load dispatch costs Construct a multi-source coordination model for the sub-goals, solve the multi-source coordination model using optimization algorithms, formulate energy dispatch plans, optimize energy storage plans, and ensure efficient use of energy and economic optimization while meeting load demand;

[0151] Specifically, the multi-source synchronization module is configured with an adaptive control strategy, which uses fuzzy logic and adaptively adjusts the PID parameters of the control model according to the real-time feedback of the multi-source power generation equipment; defines the deviation and the rate of change of deviation is the input variable, the adjustment amount of PID parameters , and The fuzzy value of the output variable is calculated by the designed fuzzy rule base and the fuzzy reasoning mechanism is used to convert the fuzzy value into the actual value, update the PID parameters and generate a new control signal, so as to dynamically adjust the output of the multi-source power generation equipment to synchronize it with the microgrid.

[0152] Specifically, the multi-source coordination module is configured with an ant colony optimization strategy, which is used to solve the multi-source coordination model. The next path of each ant is selected by the calculated probability and the ant is moved to a new position. The first iteration calculation is performed, and the optimal solution of the current iteration is recorded. The pheromone update of the optimal solution of the current iteration is performed to prevent the algorithm from falling into a local optimal solution. Until the iteration is completed, the global optimal solution is output.

[0153] In summary, the present invention constructs a PID control model to dynamically adjust the output parameters of the multi-source power generation equipment, realizes the synchronous control of the multi-source power generation equipment and the microgrid, and constructs a multi-source coordination model based on load demand, energy supply and economic cost. The multi-source coordination model is solved by using the ant colony optimization strategy, and an energy scheduling plan is formulated to achieve efficient energy utilization and economic optimization.

[0154] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A multi-source synchronous control method for a smart microgrid, characterized in that: include: Step S1: using sensors inside the multi-source power generation equipment to collect key parameters of the multi-source power generation equipment in real time, and preprocessing the collected data; Step S2: setting the benchmark parameters of the microgrid, constructing a PID control model to synchronously control the multi-source power generation equipment, configuring an adaptive control strategy in the PID control model, dynamically adjusting the PID parameters according to the real-time feedback of the multi-source power generation equipment, and optimizing the PID control model; Step S3: Based on the intermittency of renewable energy and the cost of the multi-source power generation equipment, combined with the load demand of the microgrid, a multi-source coordination model is constructed; Step S4: using an ant colony optimization strategy to solve the multi-source coordination model and formulate an energy scheduling optimization plan.

2. The multi-source synchronous control method for a smart microgrid according to claim 1, characterized in that: The specific steps of step S2 include: S2.1: Set the base frequency of the microgrid and reference phase , and calculate the deviation between the actual parameters and the reference parameters of the multi-source power generation equipment ; S2.2: Construct a PID control model, including a frequency control model and a phase control model, and initialize PID parameters; S2.3: Calculate the control signal of the PID control model ; S2.4: applying the control signal of the PID control model to the power generation equipment, and obtaining the actual output parameters of the power generation equipment in the current state in real time; 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 , and execute adaptive control strategies.

3. The multi-source synchronous control method for a smart microgrid according to claim 2, characterized in that: The specific steps of the adaptive control strategy in S2.5 include: S2.51: Calculate the rate of change of deviation ; S2.52: Defining Bias and the rate of change of deviation is the input variable, and the parameter adjustment of the PID control model is the output variable; S2.53: Fuzzify the input and output variables, define the domain for the input and output variables, divide the domain of the variables into several fuzzy subsets, and define the membership function for each fuzzy subset; S2.54: Design a fuzzy rule base to describe the adjustment rules of PID parameters under different deviations and deviation change rates; S2.55: using the fuzzy rule base and the fuzzy value of the input variable, calculating the fuzzy value of the output variable through a fuzzy reasoning mechanism; S2.56: Convert the fuzzy value of the output variable into the actual parameter adjustment by calculating the weighted average of the fuzzy elements; S2.57: Update PID parameters and PID control model, calculate the updated control signal , and returns to S2.

4.

4. The multi-source synchronous control method for a smart microgrid according to claim 3 is characterized in that: The specific steps of step S3 include: S3.1: Set renewable energy power generation equipment as the basic power generation equipment and calculate the basic power generation cost ; S3.2: Introduce energy storage equipment into the microgrid and calculate the charging and discharging costs of the energy storage equipment ; S3.3: Introduce diesel generators into the microgrid, set the dispatch priority of diesel generators lower than that of energy storage devices, and calculate the cost of diesel generators ; S3.4: Calculate load dispatch costs ; S3.5: Taking the basic power generation cost, charging and discharging cost, diesel power generation cost and load dispatching cost as sub-goals, a multi-source coordination model is constructed. The objective function expression of the model is: ； In the formula, , , , is the weight coefficient of each sub-goal in the objective function, and .

5. The multi-source synchronous control method for a smart microgrid according to claim 4, characterized in that: The constraints of the multi-source coordination model in S3.5 include: Balancing constraints on microgrid energy supply and power equipment energy demand; Constrain the charging and discharging power of the energy storage device so that the charging and discharging of the energy storage device work within the rated power range; The power generation capacity of the diesel generator set is constrained so that the diesel generator set operates within the rated active power output range.

6. The multi-source synchronous control method for a smart microgrid according to claim 5, characterized in that: The constraints of the multi-source coordination model in S3.5 also include: Constrain load transfer so that the load transfer in the microgrid works within the rated power range, and the load transfer within the microgrid should maintain a balance between transfer in and transfer out; Limit load shedding to prevent power equipment from collapsing due to overload; Load shifting is constrained so that the power load can be distributed in different time periods.

7. The multi-source synchronous control method for a smart microgrid according to claim 6, characterized in that: The specific steps of the ant colony optimization strategy in 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, calculating 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: Update the pheromone of the current iterative optimal solution; S4.5: Determine whether the maximum number of iterations has been reached; if not, set , return to S4.2 and start a new round of iterative calculation; if the maximum number of iterations is reached, select the global optimal solution according to the global database.

8. A multi-source synchronous control system for a smart microgrid, which is used to implement the multi-source synchronous control method for a smart microgrid as claimed in any one of claims 1 to 7, characterized in that: include: 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 operating status of the microgrid in real time, and use the sensors inside the multi-source power generation equipment to collect the parameters of each power generation equipment in real time and pre-process the parameters; The communication module is used to transmit the data collected by the data acquisition module using wireless communication technology; The multi-source synchronization module is used to realize the synchronous control of the multi-source power generation equipment and the microgrid, and adjust the output frequency and output phase of the power generation equipment in real time by constructing a frequency control model and a phase control model; The multi-source coordination module is used to realize coordinated scheduling among multi-source power generation equipment in a microgrid, comprehensively consider load demand, energy supply, energy storage status and economic cost, construct a multi-source coordination model, use an optimization algorithm to solve the multi-source coordination model, and formulate an energy scheduling plan.

9. The multi-source synchronous control system for a smart microgrid according to claim 8, characterized in that: The multi-source synchronization module is configured with an adaptive control strategy; the adaptive control strategy uses fuzzy logic to define input and output variables, and fuzzifies the variables, calculates the fuzzy value of the output variable using the constructed fuzzy rule base and fuzzy reasoning 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.

10. The multi-source synchronous control system for a smart microgrid according to claim 9, characterized in that: 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; the ant's travel path 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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