Active-reactive cooperative regulation and control method and device for grid-connected transformer area micro-grid

Abstract

The invention relates to an active-reactive cooperative regulation and control method and device for a grid-connected district micro-grid, and the method comprises the steps: initializing the district micro-grid, and inputting the parameters and scenes of the district micro-grid; establishing a transformer area micro-grid optimization model, and taking minimization of the operation cost of the transformer area micro-grid as a target function; considering the power constraint of each device, the start-stop constraint of a schedulable unit, the operation constraint of a battery energy storage device, the operation constraint of distributed power generation and the power flow constraint; the input and reactive power of the active power generation model of the renewable energy equipment are used as decision variables of the model to realize reactive energy scheduling of a renewable energy inverter; and converting the established model into a mixed integer linear programming (MILP) solvable form, initializing the model, and solving the model by using a Gurobi solver. The active-reactive cooperative regulation and control of the grid-connected transformer area micro-grid is expressed as a mixed integer linear programming problem with accurate calculation and high efficiency, and has relatively high solving efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management and power system optimization operation of a grid-connected microgrid, and specifically to a method and device for coordinated active-reactive power control of a grid-connected microgrid. Background Art

[0002] In recent years, the power industry has been committed to energy transformation and actively promoting an increase in the proportion of renewable energy in the energy mix. Against this backdrop, distributed energy resources (DERs), such as photovoltaics (PV) and wind turbines (WT), have rapidly developed. However, these DERs present numerous challenges. PV and WT are connected to the grid through inverters, resulting in poor frequency and voltage support. Furthermore, their power generation is subject to instability due to natural conditions, often requiring integration with energy storage systems. Multiple DERs, loads, electric vehicle charging facilities, and dispatchable generators together form a substation microgrid (MG). MGs are often operated with economic or environmental objectives in mind, and their optimal scheduling relies on an energy management system (EMS). EMSs must process a wide range of complex data, including economic, technical, and environmental parameters, and must be integrated with forecasting tools. However, some existing EMS models have shortcomings. For example, single-bus models ignore the impact of reactive power, while multi-bus models fail to fully analyze the role of energy storage and dispatchable units. Many studies also fail to consider the impact of startup and shutdown cycles and the relationship between battery state of charge and power.

[0003] In summary, current research still has some shortcomings in the active-reactive coordinated control of grid-connected microgrids, which needs further improvement and perfection. Summary of the Invention

[0004] In order to solve the problem that existing research has insufficient consideration of active-reactive coordinated regulation in energy management of grid-connected microgrids, the present invention proposes an active-reactive coordinated regulation method and device for grid-connected microgrids.

[0005] A method for coordinated active and reactive power control of a grid-connected microgrid, characterized by comprising the following steps:

[0006] Initialize the microgrid in the substation area, input the microgrid line parameters, access capacity and nodes of each device, and load forecast benchmark values;

[0007] An optimization model for the substation microgrid is established, with minimizing the operating cost of the substation microgrid (MG) as the objective function, taking into account the power constraints of each device, the start and stop constraints of dispatchable units, the operation constraints of battery energy storage system (BESS), the operation constraints of distributed generation (DG), and the power flow constraints.

[0008] Active power generation of renewable energy equipment (RES) is used as input of the substation microgrid optimization model, and reactive power is used as a decision variable of the substation microgrid optimization model to achieve reactive energy scheduling of renewable energy inverters;

[0009] The substation microgrid optimization model is converted into a form that can be solved by mixed integer linear programming (MILP), and the substation microgrid optimization model is initialized and solved using a Gurobi solver.

[0010] Furthermore, the objective function is:

[0011]

[0012] Where, represents the total cost incurred by exchanging active and reactive power between the substation microgrid (MG) and the external distribution network at time t; represents the fuel cost of the i-th diesel generator (DG) at time t, N and N DG They represent the number of time steps within the optimization time span (N = T / Δt);

[0013] The power constraints of each device, the start and stop constraints of dispatchable units, the operation constraints of battery energy storage systems (BESS), the operation constraints of distributed generation (DG), and the power flow constraints are as follows:

[0014] ①Power constraints of each device

[0015]

[0016] Where, Indicates the maximum apparent power that a power electronic device (such as an inverter, etc.) can output; Refers to the active power of the equipment at time t; is the reactive power; P i X,max Indicates the maximum active power value that the device can output;

[0017] ② Constraints on start and stop of dispatchable units

[0018]

[0019]

[0020] Where, and is a binary variable, N X,on Indicates the minimum number of time steps that the unit must remain open after it is turned on; N X,off Indicates the minimum number of time steps that the unit must remain closed after it is closed; M is a sufficiently large positive number;

[0021] ③BESS operation constraints

[0022]

[0023] Where, Represents the charging and discharging power of the i-th battery energy storage system at time t; ∈ is a very small positive number; and binary variables representing the battery charging and discharging points, respectively; represents the energy storage capacity of the i-th battery energy storage system at time t+1; represents the energy storage capacity of the i-th battery energy storage system at time t; and represents the charging efficiency and discharging efficiency of the i-th battery energy storage system; represents the self-discharge rate of the i-th battery energy storage system; represents the rated capacity of the i-th battery energy storage system; represents the maximum state of charge allowed for the i-th battery energy storage system; is the minimum state of charge allowed for the i-th battery energy storage system; and It is the state of charge limit when the battery injection / absorption power begins to decrease;

[0024] ④Distributed Generation (DG) Operation Constraints

[0025]

[0026] Where, represents the output active power of the i-th diesel generator at time t; represents the output active power of the i-th diesel generator at time t-1; P i DG,RU The ramp rate of the i-th diesel generator represents the maximum active power that the diesel generator can increase per unit time; P i DG,RD represents the downslope rate of the i-th diesel generator; P i DG,min represents the minimum stable operating power of the i-th diesel generator; Indicates the number of time steps required to reach the minimum stable operating power when the diesel generator starts; Indicates the number of time steps the diesel generator needs to continue running after the power drops below the minimum stable operating power during the process of stopping from the running state to the complete stop;

[0027] ⑤ Flow constraints

[0028]

[0029]

[0030] V min ≤V i,t ≤V max

[0031] δ min ≤δ i,t ≤δ max

[0032] Finally, at time t, the cost of absorbing active power and delivering reactive power to or from the distribution network is given by:

[0033]

[0034] Where, represents the complex power of node i at time t; is the voltage phasor of node i at time t; is the complex conjugate of the current phasor injected into node i at time t; represents the mutual admittance between node i and node k; represents the voltage phasor of node k at time t; G ik represents the conductance of the branch between node i and node k; B ik represents the susceptance of the branch between node i and node k; δ k,t represents the voltage phase angle of node k at time t; V i,t represents the voltage amplitude of node i at time t; represents the total cost of exchanging active and reactive power between the substation microgrid (MG) and the external distribution network at time t; S B That is, the reference power; and are the unit costs of the microgrid in the substation purchasing and selling active power from and to the distribution network at time t; and They represent the unit penalty cost when the microgrid in the substation absorbs or injects reactive power from the distribution network at time t; P 0,t and Q 0,t represents the active power and reactive power at the connection point at time t; and is an auxiliary variable,

[0035] Furthermore, the active power generation of renewable energy equipment (RES) is used as the input of the substation microgrid optimization model, and the reactive power is used as the decision variable of the substation microgrid optimization model, specifically including: using the active power output curve of the renewable energy unit as the input data of the model, the renewable energy unit includes a photovoltaic power generation system or a wind turbine; using the renewable energy inverter as a reactive power dispatchable unit, and achieving it through the following capacity constraints:

[0036]

[0037] represents the maximum apparent power that can be output by the i-th power electronic device belonging to the asset of technology type X, represents the active power of the i-th asset of technology type X at time t, Refers to the reactive power of the i-th asset of technology type X at time t, n is an index variable used for discretization, N PW is a parameter used for linearization processing, representing the number of constraints used when linearizing nonlinear power constraints; where X technology type assets refer to photovoltaic power generation systems or wind turbines.

[0038] Furthermore, the substation microgrid optimization model is converted into a form that can be solved by mixed integer linear programming (MILP), specifically including: linearizing the nonlinear formulas of the objective function and constraints in the substation microgrid optimization model:

[0039] In constraint ①:

[0040]

[0041] By considering a set N of the form PW Linear constraints are used to achieve linearization:

[0042]

[0043] In constraint ③:

[0044]

[0045] Since continuous variables and Multiply to simulate the choice of charge and discharge efficiency. The above formula is a nonlinear function. These nonlinear terms are obtained by using two auxiliary continuous variables. and Instead of linearizing:

[0046]

[0047] In constraint ⑤:

[0048]

[0049] The above equation is nonlinear. One possible way to linearize it is to use the operating point Perform a first-order Taylor expansion at the per-unit value:

[0050]

[0051] In low-voltage networks, the parallel admittance is very small and can be ignored. The sum of the elements in each column and row of the admittance matrix is ​​zero. After simplification, we get:

[0052]

[0053] A device for coordinated active and reactive power control of a grid-connected microgrid, comprising:

[0054] The substation microgrid initialization module is used to initialize the substation microgrid and input the substation microgrid line parameters, the access capacity and nodes of each device, and the load forecast reference value;

[0055] The module for establishing the substation microgrid optimization model is used to minimize the operating cost of the substation microgrid (MG) as the objective function, taking into account the power constraints of each device, the start and stop constraints of dispatchable units, the operation constraints of battery energy storage system (BESS), the operation constraints of distributed generation (DG), and the power flow constraints;

[0056] A reactive energy scheduling module is used to use the active power generation of renewable energy equipment (RES) as the input of the substation microgrid optimization model and the reactive power as the decision variable of the substation microgrid optimization model to achieve reactive energy scheduling of renewable energy inverters;

[0057] The model solving module is used to convert the substation microgrid optimization model into a form that can be solved by mixed integer linear programming (MILP), initialize the substation microgrid optimization model and solve it using the Gurobi solver.

[0058] Furthermore, the objective function is:

[0059]

[0060] Where, represents the total cost incurred by exchanging active and reactive power between the substation microgrid (MG) and the external distribution network at time t; represents the fuel cost of the i-th diesel generator (DG) at time t, N and N DG They represent the number of time steps within the optimization time span (N = T / Δt);

[0061] The power constraints of each device, the start and stop constraints of dispatchable units, the operation constraints of battery energy storage systems (BESS), the operation constraints of distributed generation (DG), and the power flow constraints are as follows:

[0062] ①Power constraints of each device

[0063]

[0064] Where, Indicates the maximum apparent power that a power electronic device (such as an inverter, etc.) can output; Refers to the active power of the equipment at time t; is the reactive power; P i X,max Indicates the maximum active power value that the device can output;

[0065] ② Constraints on start and stop of dispatchable units

[0066]

[0067] Where, and is a binary variable, N X,on Indicates the minimum number of time steps that the unit must remain open after it is turned on; N X,off Indicates the minimum number of time steps that the unit must remain closed after it is closed; M is a sufficiently large positive number;

[0068] ③BESS operation constraints

[0069]

[0070]

[0071] Where, Represents the charging and discharging power of the i-th battery energy storage system at time t; ∈ is a very small positive number; and binary variables representing the battery charging and discharging points, respectively; represents the energy storage capacity of the i-th battery energy storage system at time t+1; represents the energy storage capacity of the i-th battery energy storage system at time t; and represents the charging efficiency and discharging efficiency of the i-th battery energy storage system; represents the self-discharge rate of the i-th battery energy storage system; represents the rated capacity of the i-th battery energy storage system; represents the maximum state of charge allowed for the i-th battery energy storage system; is the minimum state of charge allowed for the i-th battery energy storage system; and It is the state of charge limit when the battery injection / absorption power begins to decrease;

[0072] ④Distributed Generation (DG) Operation Constraints

[0073]

[0074] Where, represents the output active power of the i-th diesel generator at time t; represents the output active power of the i-th diesel generator at time t-1; P i DG,RU The ramp rate of the i-th diesel generator represents the maximum active power that the diesel generator can increase per unit time; P i DG,RD represents the downslope rate of the i-th diesel generator; P i DG,min represents the minimum stable operating power of the i-th diesel generator; Indicates the number of time steps required to reach the minimum stable operating power when the diesel generator starts; Indicates the number of time steps the diesel generator needs to continue running after the power drops below the minimum stable operating power during the process of stopping from the running state to the complete stop;

[0075] ⑤ Flow constraints

[0076]

[0077] V min ≤V i,t ≤V max

[0078] δ min ≤δ i,t ≤δ max

[0079] Finally, at time t, the cost of absorbing active power and delivering reactive power to or from the distribution network is given by:

[0080]

[0081] Where, represents the complex power of node i at time t; is the voltage phasor of node i at time t; is the complex conjugate of the current phasor injected into node i at time t; represents the mutual admittance between node i and node k; represents the voltage phasor of node k at time t; G ik represents the conductance of the branch between node i and node k; B ik represents the susceptance of the branch between node i and node k; δ k,t represents the voltage phase angle of node k at time t; V i,t represents the voltage amplitude of node i at time t; represents the total cost of exchanging active and reactive power between the substation microgrid (MG) and the external distribution network at time t; S B That is, the reference power; and are the unit costs of the microgrid in the substation purchasing and selling active power from and to the distribution network at time t; and They represent the unit penalty cost when the microgrid in the substation absorbs or injects reactive power from the distribution network at time t; P 0,t and Q 0,t represents the active power and reactive power at the connection point at time t; and is an auxiliary variable,

[0082] Furthermore, the reactive energy scheduling module uses the active power generation of renewable energy equipment (RES) as the input of the substation microgrid optimization model, and reactive power as the decision variable of the substation microgrid optimization model, specifically including: using the active power output curve of the renewable energy unit as the input data of the model, the renewable energy unit including the photovoltaic power generation system or the wind turbine; using the renewable energy inverter as the reactive power dispatchable unit, and realizing it through the following capacity constraints:

[0083]

[0084] represents the maximum apparent power that can be output by the i-th power electronic device belonging to the asset of technology type X, represents the active power of the i-th asset of technology type X at time t, Refers to the reactive power of the i-th asset of technology type X at time t, n is an index variable used for discretization, N PW is a parameter used for linearization processing, representing the number of constraints used when linearizing nonlinear power constraints; where X technology type assets refer to photovoltaic power generation systems or wind turbines.

[0085] Furthermore, the reactive energy scheduling module converts the substation microgrid optimization model into a form that can be solved by mixed integer linear programming (MILP), specifically including: linearizing the nonlinear formulas existing in the objective function and constraint conditions in the substation microgrid optimization model:

[0086] In constraint ①:

[0087]

[0088] By considering a set N of the form PW Linear constraints are used to achieve linearization:

[0089]

[0090] In constraint ③:

[0091]

[0092] Since continuous variables and Multiply to simulate the choice of charge and discharge efficiency. The above formula is a nonlinear function. These nonlinear terms are obtained by using two auxiliary continuous variables. and Instead of linearizing:

[0093]

[0094] In constraint ⑤:

[0095]

[0096] The above equation is nonlinear. One possible way to linearize it is to use the operating point Perform a first-order Taylor expansion at the per-unit value:

[0097]

[0098] In low-voltage networks, the parallel admittance is very small and can be ignored. The sum of the elements in each column and row of the admittance matrix is ​​zero. After simplification, we get:

[0099]

[0100] An active-reactive coordinated control system for a grid-connected microgrid, comprising: a computer-readable storage medium and a processor;

[0101] The computer-readable storage medium is used to store executable instructions;

[0102] The processor is used to read the executable instructions stored in the computer-readable storage medium and execute the active-reactive coordinated control method of the grid-connected substation microgrid.

[0103] A non-transitory computer-readable storage medium stores a computer program, which, when executed by a processor, implements the active-reactive coordinated control method of the grid-connected microgrid.

[0104] In general, compared with the existing grid-connected microgrid active-reactive coordinated control, the present invention can achieve the following beneficial effects:

[0105] (1) In terms of voltage and reactive power management, the RES can effectively compensate for reactive power and maintain the inverter operation within the limit range, verifying the effectiveness of coordinated regulation.

[0106] (2) The active-reactive coordinated control of the grid-connected microgrid in the present invention is expressed as a precise and efficient mixed integer linear programming problem with high solution efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0107] Figure 1 It is a flow chart of the active-reactive coordinated control method of the grid-connected microgrid of the present invention. DETAILED DESCRIPTION

[0108] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0109] The present invention provides a method for coordinated control of active and reactive power in a grid-connected microgrid. Figure 1 As shown, the active-reactive coordinated control method of the grid-connected microgrid of the present invention includes the following steps:

[0110] Step 1: Initialization: Input the grid-connected microgrid line parameters, the access capacity and nodes of each device, and the load forecast benchmark value;

[0111] Step 2: Establish a substation microgrid optimization model with minimizing the operating cost of the substation microgrid (MG) as the objective function; then consider the power constraints of each device, the start and stop constraints of dispatchable units, the operation constraints of battery energy storage system (BESS), the operation constraints of distributed generation (DG), and the power flow constraints;

[0112] (1) Objective function:

[0113] Taking minimizing the operating cost of the substation microgrid (MG) as the objective function, the grid interaction cost and equipment operation constraints are used to reflect the ability of active-reactive coordinated regulation, which is given by the following formula:

[0114]

[0115] Where, represents the total cost incurred by exchanging active and reactive power between the substation microgrid (MG) and the external distribution network at time t; represents the fuel cost of the i-th diesel generator (DG) at time t.

[0116] (2) The power constraints of each device, the start and stop constraints of dispatchable units, the operation constraints of battery energy storage system (BESS), the operation constraints of distributed generation (DG), and the power flow constraints are described in order as follows:

[0117] ①Power constraints of each device

[0118]

[0119] Where, Indicates the maximum apparent power that a power electronic device (such as an inverter, etc.) can output; Refers to the active power of the equipment at time t; is the reactive power; P i X,max Indicates the maximum active power that the device can output.

[0120] ② Constraints on start and stop of dispatchable units

[0121]

[0122] Where, and is a binary variable, N X,on Indicates the minimum number of time steps that the unit must remain open after it is turned on; N X,off Indicates the minimum number of time steps that the unit must remain closed after it is closed; M is a sufficiently large positive number.

[0123] ③BESS operation constraints

[0124]

[0125]

[0126] Where, Represents the charging and discharging power of the i-th battery energy storage system at time t; ∈ is a very small positive number; and binary variables representing the battery charging and discharging points, respectively; represents the energy storage capacity of the i-th battery energy storage system at time t+1; represents the energy storage capacity of the i-th battery energy storage system at time t; and represents the charging efficiency and discharging efficiency of the i-th battery energy storage system; represents the self-discharge rate of the i-th battery energy storage system; represents the rated capacity of the i-th battery energy storage system; represents the maximum state of charge allowed for the i-th battery energy storage system; is the minimum state of charge allowed for the i-th battery energy storage system; and It is the state of charge limit at which the battery's injection / absorption power begins to decrease.

[0127] ④Distributed Generation (DG) Operation Constraints

[0128]

[0129] Where, represents the output active power of the i-th diesel generator at time t; represents the output active power of the i-th diesel generator at time t-1; P i DG,RU The ramp rate of the i-th diesel generator represents the maximum active power that the diesel generator can increase per unit time; P i DG,RD represents the downslope rate of the i-th diesel generator; P i DG,min represents the minimum stable operating power of the i-th diesel generator; Indicates the number of time steps required to reach the minimum stable operating power when the diesel generator starts; It indicates the number of time steps that the diesel generator needs to continue running after the power drops below the minimum stable operating power during the process of stopping from the running state to the complete stop.

[0130] ⑤ Flow constraints

[0131]

[0132] V min ≤V i,t ≤V max

[0133] δ min ≤δ i,t ≤δ max

[0134] Finally, at time t, the cost of absorbing active power and delivering reactive power to or from the distribution network is given by:

[0135]

[0136] Where, represents the complex power of node i at time t; is the voltage phasor of node i at time t; is the complex conjugate of the current phasor injected into node i at time t; represents the mutual admittance between node i and node k; represents the voltage phasor of node k at time t; G ik represents the conductance of the branch between node i and node k; B ik represents the susceptance of the branch between node i and node k; δ k,t represents the voltage phase angle of node k at time t; V i,t represents the voltage amplitude of node i at time t; represents the total cost of exchanging active and reactive power between the substation microgrid (MG) and the external distribution network at time t; S B That is, the reference power; and are the unit costs of the microgrid in the substation purchasing and selling active power from and to the distribution network at time t; and They represent the unit penalty cost when the microgrid in the substation absorbs or injects reactive power from the distribution network at time t; P 0,t and Q 0,t represents the active power and reactive power at the connection point at time t; and is an auxiliary variable,

[0137] Step 3: Then use the active power generation of renewable energy equipment (RES) as the input of the substation microgrid optimization model, and the reactive power as the decision variable of the substation microgrid optimization model to achieve reactive energy scheduling of renewable energy inverters;

[0138] The active power output curve of renewable energy units (such as photovoltaic power generation systems or wind turbines) can be used as the input data of the model. In terms of reactive power, renewable energy units can flexibly adjust the reactive power injection / absorption of the grid through power inverters. Therefore, the reactive power of the RES unit at node i and time t is is defined as a decision variable; at the same time, this regulation must satisfy the inverter rated capacity constraint. Therefore, in the model, the renewable energy inverter can be used as a reactive power dispatchable unit, and this function is achieved through relevant capacity constraints;

[0139]

[0140] Step 4: Convert the model established above into a form that can be solved by mixed integer linear programming (MILP), initialize the model and solve it using the Gurobi solver;

[0141] In the mathematical model constructed above, there are nonlinear formulas in the constraints and objective functions. To facilitate the solution, it is converted into a mixed integer linear programming (MILP) problem, and multiple formulas are linearized.

[0142]

[0143] Constraint ① can be solved by considering a set of N PW Linear constraints are used to achieve linearization:

[0144]

[0145] In constraint ③, due to the continuous variable and Multiplying to simulate the choice of charge and discharge efficiency, the above formula is a nonlinear function. These nonlinear terms can be obtained by using two auxiliary continuous variables and Instead of linearization.

[0146]

[0147] In the constraint condition ⑤, the above equation is nonlinear. One feasible method to linearize it is to use the operating point Perform a first-order Taylor expansion at the per-unit value.

[0148]

[0149] In low-voltage networks, the parallel admittance is very small and can be ignored. Therefore, the sum of the elements in each column (and row) of the admittance matrix is ​​zero, and after simplification, we get:

[0150]

[0151] Finally, the commercial solver Gurobi is used to solve the mixed integer linear programming problem established and transformed in the first three steps. The result of the solution is the operating cost of the grid-connected substation microgrid. The Gurobi solver is used to solve the energy management system (EMS) model based on the mixed integer linear programming (MILP) constructed in the previous steps. The results are centered around the operating cost of the grid-connected substation microgrid (active-reactive power management). In the constructed model, the EMS optimizes the power generation of the DG (diesel generator), the charging and discharging strategy of the BESS, and the power exchange with the distribution network to ensure that the load power demand is met while achieving a reasonable distribution of active and reactive power, thereby reducing system operating costs.

[0152] The embodiment of the present invention further provides an active-reactive coordinated control device for a grid-connected microgrid, comprising:

[0153] The substation microgrid initialization module is used to initialize the substation microgrid and input the substation microgrid line parameters, the access capacity and nodes of each device, and the load forecast reference value;

[0154] The module for establishing the substation microgrid optimization model is used to minimize the operating cost of the substation microgrid (MG) as the objective function, taking into account the power constraints of each device, the start and stop constraints of dispatchable units, the operation constraints of battery energy storage system (BESS), the operation constraints of distributed generation (DG), and the power flow constraints;

[0155] A reactive energy scheduling module is used to use the active power generation of renewable energy equipment (RES) as the input of the substation microgrid optimization model and the reactive power as the decision variable of the substation microgrid optimization model to achieve reactive energy scheduling of renewable energy inverters;

[0156] The model solving module is used to convert the substation microgrid optimization model into a form that can be solved by mixed integer linear programming (MILP), initialize the substation microgrid optimization model and solve it using the Gurobi solver.

[0157] Another embodiment of the present invention provides an active-reactive coordinated control system for a grid-connected microgrid, comprising: a computer-readable storage medium and a processor;

[0158] The computer-readable storage medium is used to store executable instructions;

[0159] The processor is used to read the executable instructions stored in the computer-readable storage medium and execute the active-reactive coordinated control method of the grid-connected substation microgrid.

[0160] Another embodiment of the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the active-reactive coordinated control method of the grid-connected substation microgrid is implemented.

[0161] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0162] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0163] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0164] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0165] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

[0166] This invention focuses on coordinated active and reactive power control in grid-connected microgrids. Technically, it constructs a MILP-based optimization model for grid-connected microgrids. This model employs a multi-busbar model and linearized power flow equations, with detailed modeling and constraints for each component. RES active power is used as the input, reactive power is the decision variable, and the RES inverter is treated as a dispatchable reactive energy device. These innovations have yielded significant results, including optimizing power distribution and reducing operating costs by approximately 21.84%. They also stabilize voltage and optimize reactive power distribution, reducing reactive power input from the grid. Furthermore, they extend the life of the BESS. Furthermore, they improve model performance, reduce computational complexity, and ensure accuracy, resulting in minimal voltage errors.

[0167] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for coordinated active and reactive power control of a grid-connected microgrid, characterized in that: The steps include: Initialize the microgrid in the substation area, input the microgrid line parameters, access capacity and nodes of each device, and load forecast benchmark values; An optimization model for the substation microgrid is established, with minimizing the operating cost of the substation microgrid (MG) as the objective function, taking into account the power constraints of each device, the start and stop constraints of dispatchable units, the operation constraints of battery energy storage system (BESS), the operation constraints of distributed generation (DG), and the power flow constraints. Active power generation of renewable energy equipment (RES) is used as input of the substation microgrid optimization model, and reactive power is used as a decision variable of the substation microgrid optimization model to achieve reactive energy scheduling of renewable energy inverters; The substation microgrid optimization model is converted into a form that can be solved by mixed integer linear programming (MILP), and the substation microgrid optimization model is initialized and solved using a Gurobi solver.

2. The active-reactive coordinated control method for a grid-connected microgrid according to claim 1, characterized in that: The objective function is: Where, represents the total cost incurred by exchanging active and reactive power between the substation microgrid (MG) and the external distribution network at time t; represents the fuel cost of the i-th diesel generator (DG) at time t, N and N DG They represent the number of time steps within the optimization time span (N = T / Δt); The power constraints of each device, the start and stop constraints of dispatchable units, the operation constraints of battery energy storage systems (BESS), the operation constraints of distributed generation (DG), and the power flow constraints are as follows: ①Power constraints of each device Where, Indicates the maximum apparent power that a power electronic device (such as an inverter, etc.) can output; Refers to the active power of the equipment at time t; is the reactive power; P i X,max Indicates the maximum active power value that the device can output; ② Constraints on start and stop of dispatchable units Where, and is a binary variable, N X,on Indicates the minimum number of time steps that the unit must remain open after it is turned on; N X,off Indicates the minimum number of time steps that the unit must remain closed after it is closed; M is a sufficiently large positive number; ③BESS operation constraints Where, Represents the charging and discharging power of the i-th battery energy storage system at time t; ∈ is a very small positive number; and binary variables representing the battery charging and discharging points, respectively; represents the energy storage capacity of the i-th battery energy storage system at time t+1; represents the energy storage capacity of the i-th battery energy storage system at time t; and represents the charging efficiency and discharging efficiency of the i-th battery energy storage system; represents the self-discharge rate of the i-th battery energy storage system; W i BESS,cap represents the rated capacity of the i-th battery energy storage system; represents the maximum state of charge allowed for the i-th battery energy storage system; is the minimum state of charge allowed for the i-th battery energy storage system; and It is the state of charge limit when the battery injection / absorption power begins to decrease; ④Distributed Generation (DG) Operation Constraints Where, represents the output active power of the i-th diesel generator at time t; represents the output active power of the i-th diesel generator at time t-1; P i DG,RU The ramp rate of the i-th diesel generator represents the maximum active power that the diesel generator can increase per unit time; P i DG,RD represents the downslope rate of the i-th diesel generator; P i DG,min represents the minimum stable operating power of the i-th diesel generator; Indicates the number of time steps required to reach the minimum stable operating power when the diesel generator starts; Indicates the number of time steps the diesel generator needs to continue running after the power drops below the minimum stable operating power during the process of stopping from the running state to the complete stop; ⑤ Flow constraints In min ≤V i,t ≤V max d min ≤δ i,t ≤δ max Finally, at time t, the cost of absorbing active power and delivering reactive power to or from the distribution network is given by: Where, represents the complex power of node i at time t; is the voltage phasor of node i at time t; is the complex conjugate of the current phasor injected into node i at time t; represents the mutual admittance between node i and node k; represents the voltage phasor of node k at time t; G ik represents the conductance of the branch between node i and node k; B ik represents the susceptance of the branch between node i and node k; δ k,t represents the voltage phase angle of node k at time t; V i,t represents the voltage amplitude of node i at time t; represents the total cost of exchanging active and reactive power between the substation microgrid (MG) and the external distribution network at time t; S B That is, the reference power; and are the unit costs of the microgrid in the substation purchasing and selling active power from and to the distribution network at time t; and They represent the unit penalty cost when the microgrid in the substation absorbs or injects reactive power from the distribution network at time t; P 0,t and Q 0,t represents the active power and reactive power at the connection point at time t; and is an auxiliary variable, 3. The method for coordinated active and reactive power control of a grid-connected microgrid according to claim 2, wherein: The active power generation of renewable energy equipment (RES) is used as the input of the substation microgrid optimization model, and reactive power is used as the decision variable of the substation microgrid optimization model. Specifically, the active power output curve of the renewable energy unit is used as the input data of the model, and the renewable energy unit includes a photovoltaic power generation system or a wind turbine; the renewable energy inverter is used as the reactive power dispatchable unit, and is achieved through the following capacity constraints: represents the maximum apparent power that can be output by the i-th power electronic device belonging to the asset of technology type X, represents the active power of the i-th asset of technology type X at time t, Refers to the reactive power of the i-th asset of technology type X at time t, n is an index variable used for discretization, N PW is a parameter used for linearization processing, representing the number of constraints used when linearizing nonlinear power constraints; where X technology type assets refer to photovoltaic power generation systems or wind turbines.

4. The method for coordinated active and reactive power control of a grid-connected microgrid according to claim 2, wherein: The substation microgrid optimization model is converted into a form that can be solved by mixed integer linear programming (MILP), specifically including: linearizing the nonlinear formulas of the objective function and constraint conditions in the substation microgrid optimization model: In constraint ①: By considering a set N of the form PW Linear constraints are used to achieve linearization: In constraint ③: Since continuous variables and Multiply to simulate the choice of charge and discharge efficiency. The above formula is a nonlinear function. These nonlinear terms are obtained by using two auxiliary continuous variables. and Instead of linearizing: In constraint ⑤: The above equation is nonlinear. One possible way to linearize it is to use the operating point Perform a first-order Taylor expansion at the per-unit value: In low-voltage networks, the parallel admittance is very small and can be ignored. The sum of the elements in each column and row of the admittance matrix is ​​zero. After simplification, we get:

5. A device for coordinated active and reactive power control of a grid-connected microgrid, characterized in that: include: The substation microgrid initialization module is used to initialize the substation microgrid and input the substation microgrid line parameters, the access capacity and nodes of each device, and the load forecast reference value; The module for establishing the substation microgrid optimization model is used to minimize the operating cost of the substation microgrid (MG) as the objective function, taking into account the power constraints of each device, the start and stop constraints of dispatchable units, the operation constraints of battery energy storage system (BESS), the operation constraints of distributed generation (DG), and the power flow constraints; A reactive energy scheduling module is used to use the active power generation of renewable energy equipment (RES) as the input of the substation microgrid optimization model and the reactive power as the decision variable of the substation microgrid optimization model to achieve reactive energy scheduling of renewable energy inverters; The model solving module is used to convert the substation microgrid optimization model into a form that can be solved by mixed integer linear programming (MILP), initialize the substation microgrid optimization model and solve it using the Gurobi solver.

6. The active-reactive coordinated control device for a grid-connected microgrid according to claim 5, characterized in that: The objective function is: Where, represents the total cost incurred by exchanging active and reactive power between the substation microgrid (MG) and the external distribution network at time t; represents the fuel cost of the i-th diesel generator (DG) at time t, N and N DG They represent the number of time steps within the optimization time span (N = T / Δt); The power constraints of each device, the start and stop constraints of dispatchable units, the operation constraints of battery energy storage systems (BESS), the operation constraints of distributed generation (DG), and the power flow constraints are as follows: ①Power constraints of each device Where, Indicates the maximum apparent power that a power electronic device (such as an inverter, etc.) can output; Refers to the active power of the equipment at time t; is the reactive power; P i X,max Indicates the maximum active power value that the device can output; ② Constraints on start and stop of dispatchable units Where, and is a binary variable, N X,on Indicates the minimum number of time steps that the unit must remain open after it is turned on; N X,off Indicates the minimum number of time steps that the unit must remain closed after it is closed; M is a sufficiently large positive number; ③BESS operation constraints Where, Represents the charging and discharging power of the i-th battery energy storage system at time t; ∈ is a very small positive number; and binary variables representing the battery charging and discharging points, respectively; represents the energy storage capacity of the i-th battery energy storage system at time t+1; represents the energy storage capacity of the i-th battery energy storage system at time t; and represents the charging efficiency and discharging efficiency of the i-th battery energy storage system; represents the self-discharge rate of the i-th battery energy storage system; W i BESS,cap represents the rated capacity of the i-th battery energy storage system; represents the maximum state of charge allowed for the i-th battery energy storage system; is the minimum state of charge allowed for the i-th battery energy storage system; and It is the state of charge limit when the battery injection / absorption power begins to decrease; ④Distributed Generation (DG) Operation Constraints Where, represents the output active power of the i-th diesel generator at time t; represents the output active power of the i-th diesel generator at time t-1; P i DG,RU The ramp rate of the i-th diesel generator represents the maximum active power that the diesel generator can increase in unit time; P i DG,RD represents the downslope rate of the i-th diesel generator; P i DG,min represents the minimum stable operating power of the i-th diesel generator; Indicates the number of time steps required to reach the minimum stable operating power when the diesel generator starts; Indicates the number of time steps the diesel generator needs to continue running after the power drops below the minimum stable operating power during the process of stopping from the running state to the complete stop; ⑤ Flow constraints In min ≤V i,t ≤V max d min ≤δ i,t ≤δ max Finally, at time t, the cost of absorbing active power and delivering reactive power to or from the distribution network is given by: Where, represents the complex power of node i at time t; is the voltage phasor of node i at time t; is the complex conjugate of the current phasor injected into node i at time t; represents the mutual admittance between node i and node k; represents the voltage phasor of node k at time t; G ik represents the conductance of the branch between node i and node k; B ik represents the susceptance of the branch between node i and node k; δ k,t represents the voltage phase angle of node k at time t; V i,t represents the voltage amplitude of node i at time t; represents the total cost of exchanging active and reactive power between the substation microgrid (MG) and the external distribution network at time t; S B That is, the reference power; and are the unit costs of the microgrid in the substation purchasing and selling active power from and to the distribution network at time t; and They represent the unit penalty cost when the microgrid in the substation absorbs or injects reactive power from the distribution network at time t; P 0,t and Q 0,t represents the active power and reactive power at the connection point at time t; and is an auxiliary variable, 7. The active-reactive coordinated control device for a grid-connected microgrid according to claim 6, characterized in that: The reactive energy scheduling module uses the active power generation of renewable energy equipment (RES) as the input of the substation microgrid optimization model and reactive power as the decision variable of the substation microgrid optimization model. Specifically, the module uses the active power output curve of the renewable energy unit as the input data of the model, wherein the renewable energy unit includes a photovoltaic power generation system or a wind turbine; uses the renewable energy inverter as the reactive power dispatchable unit, and implements the following capacity constraints: represents the maximum apparent power that can be output by the i-th power electronic device belonging to the asset of technology type X, represents the active power of the i-th asset of technology type X at time t, Refers to the reactive power of the i-th asset of technology type X at time t, n is an index variable used for discretization, N PW is a parameter used for linearization processing, representing the number of constraints used when linearizing nonlinear power constraints; where X technology type assets refer to photovoltaic power generation systems or wind turbines.

8. The active-reactive coordinated control device for a grid-connected microgrid according to claim 6, characterized in that: The reactive energy scheduling module converts the substation microgrid optimization model into a form that can be solved by mixed integer linear programming (MILP), specifically including: linearizing the nonlinear formulas of the objective function and constraints in the substation microgrid optimization model: In constraint ①: By considering a set N of the form PW Linear constraints are used to achieve linearization: In constraint ③: Since continuous variables and Multiply to simulate the choice of charge and discharge efficiency. The above formula is a nonlinear function. These nonlinear terms are obtained by using two auxiliary continuous variables. and Instead of linearizing: In constraint ⑤: The above equation is nonlinear. One possible way to linearize it is to use the operating point Perform a first-order Taylor expansion at the per-unit value: In low-voltage networks, the parallel admittance is very small and can be ignored. The sum of the elements in each column and row of the admittance matrix is ​​zero. After simplification, we get:

9. A coordinated active-reactive power control system for a grid-connected microgrid, comprising: Computer-readable storage media and processor; The computer-readable storage medium is used to store executable instructions; The processor is used to read the executable instructions stored in the computer-readable storage medium and execute the active-reactive coordinated control method of the grid-connected substation microgrid according to any one of claims 1 to 4.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for coordinated active and reactive power control of a grid-connected microgrid in a substation area according to any one of claims 1 to 4 is implemented.

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