Fixed frequency island microgrid power optimization distribution control method

Abstract

The application relates to a frequency-fixed island micro-grid power optimization distribution control method, belonging to the field of micro-grid system control. S1 calculates the flow parameters of each node in the production micro-grid; S2 the micro-grid coordination controller uniformly collects the node information of the micro-type synchronous phasor measurement device in the whole micro-grid, forms a scheduling instruction and distributes the scheduling instruction to each adjustable distributed power node; and S3 accurately distributes the power among multiple adjustable DGs. The frequency-fixed island micro-grid power optimization distribution control method aims at the problems that the peer-to-peer control method of the current island micro-grid generally has large system frequency deviation, low power distribution accuracy, difficulty in plug-and-play of the power supply and difficulty in interconnection of the multi-region micro-grid, realizes the plug-and-play of the power supply and the flexible interconnection control of the multi-region micro-grid, avoids the control error introduced by the line voltage drop, realizes the accurate distribution of the power among the power supplies and reduces the bus voltage control deviation.

Description

Technical Field

[0001] The power optimization and allocation control method for fixed-frequency islanded microgrids belongs to the field of microgrid system control. Background Technology

[0002] Currently, isolated microgrids mainly employ three control modes: peer-to-peer control, master-slave control, and hierarchical control. Among these, peer-to-peer control allows each distributed generator (DG) within the microgrid to adopt a droop control strategy that simulates the external characteristics of a synchronous machine. Each DG has equal control status, making it easy to implement plug-and-play and a current research hotspot. However, under this method, the power distribution balance among DGs within the microgrid is relatively low, there is a steady-state frequency difference in the microgrid, and interconnection control between different microgrids is difficult.

[0003] To address these issues, scholars have proposed several novel droop control strategies that use satellite timing signals as synchronization signals to fix the frequency of isolated microgrids. Power-phase droop control follows the traditional grid control approach and is relatively mature, but it suffers from low power allocation accuracy and poor stability, still failing to achieve plug-and-play power supply and interconnection control of multiple microgrids. Voltage-current (UI) droop control controls the output voltage based on the dq-axis component of the output current, simplifying power allocation between DGs to current allocation, improving system stability, and solving the plug-and-play problem. However, it suffers from the problem of cross-influence between active and reactive power, leading to low power allocation accuracy and a tendency to generate circulating current. Using synchronization signals to control the output current of DGs with consistent phase angles suppresses reactive circulating current between DGs, but it does not address the impact of line impedance on power allocation and cannot decouple active and reactive power control. Currently, a peer-to-peer control method that simultaneously optimizes global power allocation, enables plug-and-play power supply, and allows for flexible interconnection of multiple microgrids is still lacking.

[0004] In recent years, in response to the development needs of active distribution networks, a micro synchronous phasor measurement unit (μPMU) with high measurement accuracy, small size, and low cost has been rapidly developed. In active distribution networks, μPMUs are deployed at key nodes such as DG outlets and microgrid connection points, providing accurate timing and voltage and current measurement information for the measurement, control, and protection of the distribution network. Xu Yin, Wang Sijia, Wu Xiangyu, et al. A method for multi-source coordinated control of distribution network islands based on synchronous phasor measurement [J]. Power System Technology, 2019, 43(03): 872-880, proposed a coordinated control method for distribution network islands based on μPMUs. It realizes the accurate allocation of active power between DGs based on the voltage information of the point of common coupling (PCC), but does not consider the global optimization of key parameters such as network loss and voltage, and has the problem of difficulty in high-precision real-time voltage phase angle measurement during power allocation. Summary of the Invention

[0005] The technical problem to be solved by this invention is to overcome the shortcomings of the prior art and provide a fixed-frequency islanded microgrid power optimization allocation control method for global optimization of various key indicators of islanded microgrid operation, plug-and-play and flexible absorption of distributed power sources, and global optimization control of microgrid power.

[0006] The technical solution adopted by this invention to solve its technical problem is: a fixed-frequency islanded microgrid power optimization and allocation control method, characterized by including the following steps:

[0007] S1 transforms the non-convex nonlinear model of the fixed-frequency microgrid into a convex optimization model to calculate the power flow parameters of each node in the production microgrid.

[0008] The S2 microgrid coordinator collects information from nodes equipped with micro-synchronous phasor measurement devices throughout the microgrid, constructs a fixed-frequency microgrid optimization target based on the data uploaded by the micro-synchronous phasor measurement devices, generates scheduling instructions, and distributes them to each schedulable distributed power generation node.

[0009] S3 utilizes timing signals and common coupling point voltage provided by a micro synchronous phasor measurement device to precisely allocate power among multiple dispatchable DGs while retaining the advantages of UI droop control power supply's plug-and-play capability and flexible interconnection of multi-regional microgrids.

[0010] Preferably, the method further includes the following constraints in the fixed-frequency microgrid optimization model:

[0011]

[0012] Among them, P is Qis These represent the active and reactive power outputs of the i-th node, respectively; V i V j These are the operating voltages at the i-th and j-th nodes, respectively; G ij B ij These represent the conductance and susceptance between nodes i and j, respectively; θ ij P represents the voltage phase difference between nodes i and j; BSS Q BSS These represent the energy storage capacity, with upper and lower power limits P respectively. BSSmin P BSSmax Q BSSmin Q BSSmax ;P PV Photovoltaic power output has upper and lower power limits of 0 and P, respectively. PVmax ;P WP For wind power output, the upper and lower power limits are respectively P WPmin P WPmax ;P EV W EV The electric vehicle's power and energy are respectively, and their corresponding upper and lower limit constraints are P. EVmin P EVmax With W EVmin W EVmax ;P DG Q DG The output power of the distributed power source is given by P, and its upper and lower power limits are respectively P. DGmin P DGmax With Q DGmin Q DGmax ;P OE Q OE The output power of other forms of energy are respectively, and their upper and lower power limits are P. OEmin P OEmax With Q OEmin Q OEma x; For any node i, under the constraint of safe operation of the power grid, the upper and lower limits of the voltage of each node are V. imin V imax Line loss P between nodes ij The upper and lower limits of line loss are 0, P ijmax .

[0013] Preferably, the method further includes establishing multiple optimization objectives that take into account minimizing power generation cost, minimizing grid loss, and minimizing voltage deviation. The multi-objective joint optimization model for active power and reactive power is as follows:

[0014]

[0015] Where C is the objective function; C1, C2, and C3 represent the total generation cost, line loss, and voltage deviation, respectively; a i b i V represents the active power cost factor and the reactive power cost factor, respectively; Ni P is the rated voltage of node i; G Q represents the active power at the generation end; G N represents the reactive power at the generation end. G N k V represents the number of branches; j V represents the voltage at the j-th node; Ni This represents the rated voltage of the i-th node.

[0016] Preferably, the method further includes a method for converting inequality constraints into equality constraints:

[0017]

[0018] Here, u and l are relaxation variables, which can be extended to each node as a matrix u = [u1...U...]. N ], l = [l1...l N ]; h BSS Let f(u) be the barrier function constructed from the constraints; f(u) and g(l) are the corresponding mappings of different slack variables, respectively.

[0019] Preferably, the method further includes, when using conventional peer-to-peer control, the circulating current between power supplies is:

[0020]

[0021] in, It is a medium circulation; Z represents the virtual internal potential formed within the DG under different droop control strategies. k1 Z k2 Z1 and Z2 are the equivalent output impedances of each DG droop coefficient; Z1 and Z2 are the line impedances.

[0022] Preferably, the method further includes the following: the current distribution relationship between the parallel DGs is as follows:

[0023]

[0024] in, These represent the currents in branch 1 and branch 2, respectively.

[0025] Preferably, the method further includes providing U based on a micro synchronous phasor measurement device. pcc The designed IU droop curve is as follows:

[0026]

[0027] Among them, u dpcc u qpcc In the dq rotating coordinate system dq components; r d r q This represents the droop factor for the active and reactive power output of each DG.

[0028] Preferably, the method further includes the DG's output power being:

[0029]

[0030] Where P and Q represent the output active power and reactive power, respectively.

[0031] Preferably, the output power of the line DG in actual operation of the microgrid is:

[0032]

[0033] Preferably, the method further includes the following: the distribution relationship between the active and reactive power of each DG is as follows:

[0034] P1r d =P2r d =…=P n r d

[0035] Q1r q =Q2r q =…=Q n r q ;

[0036] Among them, P n Q n These represent the active power and reactive power output of line n, respectively.

[0037] Compared with the prior art, the beneficial effects of this invention are:

[0038] This fixed-frequency islanded microgrid power optimization and distribution control method addresses the common problems of large system frequency deviation, low power distribution accuracy, difficulty in plug-and-play power supply, and difficulty in interconnecting multiple microgrids in current peer-to-peer control methods for islanded microgrids. It achieves plug-and-play power supply and flexible interconnection control of multiple microgrids. Based on the bus voltage provided by the micro synchronous phasor measurement device, it participates in IU droop control, avoids control errors introduced by line voltage drop, achieves accurate power distribution between power supplies, and reduces bus voltage control deviation. Attached Figure Description

[0039] Figure 1 A flowchart for multi-objective optimization scheduling.

[0040] Figure 2 This is a control flowchart for a dispatchable distributed power inverter.

[0041] Figure 3 This is a flowchart of the voltage outer loop droop control. Detailed Implementation

[0042] Figures 1-3 This is the preferred embodiment of the present invention, which is described below in conjunction with the accompanying drawings. Figures 1-3 The present invention will be further described below.

[0043] For microgrid systems with fixed frequencies, considering constraints such as generation, grid power flow, and safe operation, this invention utilizes a miniature synchronous phasor measurement device to collect voltage and current phasors at each measurement point and uploads them to the microgrid coordination and optimization control unit. Within this control unit, multiple optimization objectives are set, including minimizing generation cost, grid loss, and voltage deviation, to calculate and generate a dispatchable power command. The optimization employs a second-order cone relaxation method to transform the non-convex nonlinear model of the fixed-frequency microgrid into a convex optimization model. Computer-aided solution methods such as Newton's method or convex relaxation method are used, resulting in high success rate and fast computation speed.

[0044] To address the common problems in current peer-to-peer control methods for isolated microgrids, such as large system frequency deviation, low power distribution accuracy, difficulty in plug-and-play power supply, and challenges in interconnecting multiple microgrid areas, this new method consists of an IU droop control and a cascaded current loop. It utilizes the timing signal from a micro-synchronous phasor measurement device to generate internal potentials with consistent amplitude and phase within each distributed power source, controlling the microgrid frequency to always operate at 50Hz. This enables plug-and-play power supply and flexible interconnection control across multiple microgrid areas. Furthermore, the method incorporates the bus voltage provided by the micro-synchronous phasor measurement device into the IU droop control, avoiding control errors introduced by line voltage drops, achieving precise power distribution between power sources, and reducing bus voltage control deviation.

[0045] The power optimization and allocation control method for fixed-frequency islanded microgrids includes the following steps:

[0046] S1 transforms the non-convex nonlinear model of the fixed-frequency microgrid into a convex optimization model to calculate the power flow parameters of each node in the production microgrid.

[0047] The S2 microgrid coordinator collects information from nodes equipped with micro-synchronous phasor measurement devices throughout the microgrid, constructs a fixed-frequency microgrid optimization target based on the data uploaded by the micro-synchronous phasor measurement devices, generates scheduling instructions, and distributes them to each schedulable distributed power generation node.

[0048] S3 utilizes timing signals and common coupling point voltage provided by a micro synchronous phasor measurement device to precisely allocate power among multiple dispatchable DGs while retaining the advantages of UI droop control power supply's plug-and-play capability and flexible interconnection of multi-regional microgrids.

[0049] The specific implementation method is as follows:

[0050] For fixed-frequency microgrid systems, considering constraints such as generation end, power flow, and safe operation, this invention adopts a second-order cone relaxation method to transform the non-convex nonlinear model of the fixed-frequency microgrid into a convex optimization model, and calculates the power flow parameters such as voltage, current, and power of each node in the production microgrid.

[0051] Photovoltaics, wind power, and other forms of unidirectional energy supply power to the fixed-frequency microgrid. Bidirectional energy sources such as energy storage and electric vehicles actively adjust the direction and magnitude of energy supply according to the needs of the microgrid and users. The microgrid system has a unified dispatching mechanism. The microgrid coordinator collects information from nodes equipped with micro-synchronous phasor measurement devices throughout the microgrid, constructs optimization targets for the fixed-frequency microgrid based on the data uploaded by these devices, generates dispatch instructions, and distributes them to each dispatchable distributed power generation node.

[0052] Considering a microgrid system composed of distributed power sources such as photovoltaic power generation, wind power generation, energy storage power supply, and electric vehicles, the constraints in the fixed-frequency microgrid optimization model are as follows:

[0053]

[0054] Among them, P is Q is These represent the active and reactive power outputs of the i-th node, respectively; V i V j These are the operating voltages at the i-th and j-th nodes, respectively; G ij B ij These represent the conductance and susceptance between nodes i and j, respectively; θ ij P represents the voltage phase difference between nodes i and j; BSS Q BSS These represent the energy storage capacity, with upper and lower power limits P respectively. BSSmin P BSSmax Q BSSmin Q BSSmax ;P PV Photovoltaic power output has upper and lower power limits of 0 and P, respectively. PVmax ;P WP For wind power output, the upper and lower power limits are respectively P WPmin P WPmax ;P EV WEV The electric vehicle's power and energy are respectively, and their corresponding upper and lower limit constraints are P. EVmin P EVmax With W EVmin W EVmax ;P DG Q DG The output power of the distributed power source is given by P, and its upper and lower power limits are respectively P. DGmin P DGmax With Q DGmin QDG max ;P OE Q OE The output power of other forms of energy are respectively, and their upper and lower power limits are P. OEmin P OEmax With Q OEmin Q OEmax For any node i, under the constraint of safe operation of the power grid, the upper and lower limits of the voltage at each node are V. imin V imax Line loss P between nodes ij The upper and lower limits of line loss are 0, P ijmax .

[0055] A multi-objective joint optimization model for active and reactive power is established, taking into account multiple optimization objectives such as minimizing power generation cost, minimizing grid loss, and minimizing voltage deviation:

[0056]

[0057] Where C is the objective function; C1, C2, and C3 represent the total generation cost, line loss, and voltage deviation, respectively; a i b i V represents the active power cost factor and the reactive power cost factor, respectively; Ni P is the rated voltage of node i; G Q represents the active power at the generation end; G N represents the reactive power at the generation end. G N k V represents the number of branches; j V represents the voltage at the j-th node; Ni This represents the rated voltage of the i-th node.

[0058] Single-objective optimizations are performed on C1, C2, and C3 respectively, the values ​​of each optimization function are calculated, and the final optimization objective is determined. The final optimization objective function is transformed into a convex optimization function using a second-order cone relaxation method for the solver to solve. In this invention, the method for transforming inequality constraints into equality constraints is as follows, taking the power of the energy storage power source as an example:

[0059]

[0060] Here, u and l are relaxation variables, which can be extended to each node as a matrix u = [u1... ... N ], l = [l1...l N ]; h BSS Let f(u) be the barrier function constructed from the constraints; f(u) and g(l) are the corresponding mappings for different slack variables, respectively. Taking the logarithmic function as an example, logu and logl are the corresponding mappings for different slack variables, and μ is the weight of the barrier function.

[0061] like Figure 1 As shown: Taking Newton's method or convex relaxation method as an example, the multi-objective optimization process includes the following steps:

[0062] S1001, the microgrid coordination and optimization control unit reads the voltage, current and other information of each node of the micro synchronous phasor measurement device;

[0063] S1002 performs preliminary processing on the read voltage, current and other information to calculate the physical quantities related to the constraints.

[0064] S1003, target optimization for a single objective in multi-objective optimization;

[0065] S1004, Determine the overall optimization objective based on the single-objective optimization results;

[0066] S1005 uses a second-order cone relaxation method to transform the objective function into a convex optimization function;

[0067] S1006, the optimization function is solved using Newton's method or convex relaxation, etc.

[0068] S1007, Check if the result converges. If it converges, output the result and execute S1008; if it does not converge, execute step S1006.

[0069] S1008 distributes the final scheduling instructions to each schedulable distributed power source.

[0070] Once the optimal power flow scheduling command is obtained, it is distributed to each schedulable distributed power source. Each distributed power source updates its power according to the latest power command and adjusts its own power output to gradually reach the optimal power flow operating point.

[0071] To address the issues of low power distribution accuracy, difficulty in plug-and-play power supply, and complex interconnection control of multi-regional microgrids in fixed-frequency islanded microgrids, a current-voltage (IU) droop-based DG control strategy is designed using timing signals provided by a micro synchronous phasor measurement device and the voltage at the point of common coupling. While retaining the advantages of plug-and-play power supply and flexible interconnection of multi-regional microgrids, the strategy achieves precise power distribution among multiple dispatchable DGs and reduces voltage control deviation.

[0072] This section on multi-source coordinated control is a strategy at the microgrid device level, implemented locally in the inverters of various dispatchable distributed power sources (such as energy storage power sources and electric vehicles).

[0073] In an isolated microgrid, the distributed generation (DG) can be equivalent to a voltage source with series output impedance. Z represents the virtual internal potential formed within the DG under different droop control strategies. k1 Z k2 Z1 and Z2 are the equivalent output impedances of each DG droop coefficient; Z1 and Z2 are the line impedances. Traditional analog synchronous machine external characteristic peer control methods often require the introduction of active-frequency (Pf) and reactive-voltage (QE) droop control strategies to achieve a reasonable distribution of active and reactive power among parallel DGs. However, the frequency regulation characteristics of this strategy result in a slight phase angle deviation in the virtual internal potential of each DG, leading to circulating currents that affect the power distribution and safe, stable operation of the system. It can be seen that when using traditional peer control, the circulating current between power sources is:

[0074]

[0075] in, It is a medium circulation.

[0076] Because the line impedance in a microgrid is small, and Even minor deviations or fluctuations can generate significant circulating currents between power sources. Therefore, the commissioning of new power sources and the interconnection between regional microgrids require complex and precise pre-synchronization control to prevent inrush currents from affecting the safe and stable operation of the system.

[0077] Fixed-frequency control utilizes satellite timing signals to generate a virtual internal potential with consistent amplitude and phase within the DG. The wall was exempted from the above formula for circulation. The generation of this technology allows for plug-and-play power supply and interconnection control of multiple microgrids without the need for pre-synchronization control. However, the current distribution between distributed generation (DG) units is still affected by line parameters, and the current distribution relationship between parallel DG units is as follows:

[0078]

[0079] in, These represent the currents in branch 1 and branch 2, respectively.

[0080] To address the power distribution problem between distributed generation (DG) sources in current fixed-frequency control and simplify control loop design, multi-source coordinated control is primarily implemented locally within the microgrid devices, specifically in the inverters of dispatchable distributed power sources such as energy storage systems. This method's inverter control strategy consists of a cascaded IU droop control and a current loop, employing a control loop design with dq-axis decoupling in the dq0 coordinate system. Specifically, the IU droop control first generates a virtual internal potential with a phase angle of zero relative to the rising edge of the pulse signal, based on the timing signal provided by a micro-synchronous phasor measurement device. Then, based on the information provided by the micro synchronous phasor measurement device The control command for the output current is calculated from the droop curve; the current loop quickly adjusts the output current according to the current command, realizing rapid tracking of the output current command.

[0081] Under this method, the control strategy of each dispatchable distributed power inverter device consists of two cascaded parts: IU droop and current loop. It adopts a control loop design with dq axis decoupling in the dq0 coordinate system, including the following steps:

[0082] S2001 generates a virtual internal potential with a phase angle of zero relative to the rising edge of the pulse signal based on the timing signal provided by the micro synchronous phasor measurement device.

[0083] S2002, based on the micro synchronous phasor measurement device provided The control command for calculating the output current is derived from the droop curve.

[0084] S2003's current loop rapidly adjusts the output current based on current commands, enabling rapid tracking of output current commands.

[0085] Based on a micro synchronous phasor measurement device The designed IU droop curve is as follows:

[0086]

[0087] Among them, u dpcc u qpcc In the dq rotating coordinate system dq components; r d r q The active and reactive power outputs of each DG can be determined based on different droop coefficients on the dq axis.

[0088] Neglecting line losses, the output power of DG can be deduced as follows:

[0089]

[0090] Where P and Q represent the output active power and reactive power, respectively.

[0091] In actual operation of a microgrid, both the line voltage drop and the phase angle of the voltage at the PCC point are very small, i.e., E0-u dpcc ≈0, u qp Since cc ≈ 0, the above formula can be simplified to:

[0092]

[0093] The active and reactive power outputs of each DG can be determined based on the different droop coefficients r of the dq axis. d r q Independent adjustments are made. Therefore, the distribution relationship of active and reactive power in each DG is derived as follows:

[0094] The distribution relationship of active and reactive power in each DG is as follows:

[0095] P1r d =P2r d =…=P n r d

[0096] Q1r q =Q2r q =…=Q n r q ;

[0097] Among them, P n Q n These represent the active power and reactive power output of line n, respectively.

[0098] The IU droop control proposed in this invention enables the active and reactive power output of each DG to be distributed proportionally according to the configured droop coefficient. Therefore, this method effectively avoids the circulating current problem caused by line impedance mismatch and improves the power distribution accuracy between DGs.

[0099] like Figure 2 As shown: The inverter periodically reads the real-time values ​​of voltage and current at fixed interrupt cycles. The voltage and current values ​​at the point of common coupling are obtained through a miniature synchronous phasor measurement device, while the voltage and current values ​​at each inverter output port are obtained through the inverter's own sensors. The specific steps are as follows:

[0100] S3001, determine whether the node parameters of the common connection point are read periodically. If yes, execute S3002; otherwise, execute 3003.

[0101] S3002 reads the voltage, current and other parameters of the micro synchronous phasor measurement device at the common connection point node;

[0102] S3003 reads parameters such as voltage and current at the inverter output terminal;

[0103] S3004, voltage outer loop droop control for the dq axis;

[0104] S3005, dq axis current inner loop closed-loop control;

[0105] S3006 outputs a PWM modulated wave and executes S3001.

[0106] like Figure 3 As shown: The branch containing the inverter should have power regulation functionality. When the upper-level controller specifies that a certain inverter should change its output power, the program determines whether a power adjustment command is needed. If modification is required, the droop coefficient r should be recalculated. d and r q Then update the latest droop parameters; if the power output command does not need to be modified, keep the original parameters unchanged. The specific steps are as follows:

[0107] S4001, Determine whether the inverter power output command has been modified. If so, proceed to step S4002; otherwise, proceed to step S4005.

[0108] S4002, read the latest power flow command for this node;

[0109] S4003, Calculate the dq-axis droop coefficient r d and r q ;

[0110] S4004, Update the dq axis droop coefficient r d and r q And execute step S4006;

[0111] S4005, maintain the original droop coefficient r d and r q The process remains unchanged, and step S4006 is executed.

[0112] S4006, command to calculate dq axis output current.

[0113] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A control method for power optimization distribution of a fixed frequency islanded microgrid, characterized in that: The method comprises the following steps: S1, converting a non-convex nonlinear model of a fixed-frequency microgrid into a convex optimization model, and calculating power flow parameters of each node in the production microgrid; S2, a microgrid coordinated controller collects node information of the microgrid mounted with a micro-synchronous phasor measurement device, constructs an optimization target of the fixed-frequency microgrid according to data information uploaded by the micro-synchronous phasor measurement device, forms a scheduling instruction, and distributes the scheduling instruction to each adjustable distributed power node; S3, using a timing signal and a common connection point voltage provided by the micro-synchronous phasor measurement device, accurately distributing power among multiple adjustable DGs while retaining the plug-and-play advantage of U-I droop control power and the flexible interconnection advantage of a multi-region microgrid; The method further comprises that, when a traditional peer-to-peer control is used, a circulating current among the power sources is: ； in, It is a medium circulation; , , The virtual internal potential formed in the DG under different droop control strategies; , The equivalent output impedance for each DG droop coefficient; , Line impedance; The method also includes providing, based on the micro-synchrophasor measurement device The designed I-U droop curve is as follows: ； wherein, , is the dq component of in the dq rotating coordinate system; , is the droop coefficient of active and reactive power output of each DG.

2. The fixed frequency islanded microgrid power optimization allocation control method of claim 1, wherein: The method further comprises that, a constraint condition in the fixed-frequency microgrid optimization model is: ； in, , The first Each node has active and reactive power outputs; , The first One and Operating voltage at each node; , They are nodes , The electrical conductance and susceptance between them; For nodes , Voltage phase difference between them; , These represent the energy storage capacity, and their upper and lower power limits are respectively... , , , ; Photovoltaic power output has upper and lower power limits of 0 and 0 respectively. ; For wind power output, the upper and lower power limits are respectively... , ; , The upper and lower limits of electric vehicle power and energy are respectively, and their corresponding upper and lower limits are respectively. , and , ; , These represent the output power of the distributed power source, and their upper and lower power limits are respectively... , and , ; , The output power of other forms of energy are respectively, and their upper and lower power limits are respectively. , and , For any For nodes, under the constraint of safe operation of the power grid, the upper and lower limits of the voltage constraints for each node are: , Line loss between nodes The upper and lower limits of the line loss are 0. .

3. The fixed frequency islanded microgrid power optimization allocation control method of claim 2, wherein: The method further comprises that, a multi-objective joint optimization model of active power and reactive power is established by taking into account multiple optimization targets of minimum generation cost, minimum network loss, and minimum voltage deviation: ； wherein, is the objective function; , , represent the total generation cost, line loss and voltage deviation, respectively; , are the active and reactive cost coefficients, respectively; is the rated voltage of node ; represents the active power at the generation end; represents the reactive power at the generation end; , represents the number of branches; represents the voltage of the th node; represents the rated voltage of the th node.

4. The fixed frequency islanded microgrid power optimization distribution control method of claim 2, wherein: The method further comprises that, a method for converting an inequality constraint condition into an equality constraint condition is: ； where and are the slack variables, which can be expressed as matrices , ; is the barrier function constructed from the constraints; and are the corresponding mappings of different slack variables.

5. The fixed frequency islanded microgrid power optimization allocation control method of claim 1, wherein: The method further comprises that, a current distribution relationship among the parallel DGs is: ； wherein , I1and I2represent the current of branch 1 and branch 2, respectively.

6. The fixed frequency islanded microgrid power optimization distribution control method of claim 1, wherein: The method further comprises that, an output power of the DG is: ； wherein, , are the output active and reactive power, respectively.

7. The fixed frequency islanded microgrid power optimization allocation control method of claim 6, wherein: An output power of a line DG in actual operation of the microgrid is: 。 8. The fixed frequency islanded microgrid power optimization distribution control method of claim 6, wherein: The method further comprises that, a distribution relationship of active power and reactive power of each DG is: ； wherein, , Pn and Qn are the output active and reactive power of line n, respectively.

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