Microgrid cluster optimal control method and system based on generalized benders decomposition method

Abstract

The present application relates to the technical field of micro-grid group optimization control, and discloses a micro-grid group optimization control method and system based on generalized Benders decomposition method, which builds a micro-grid group coordinated optimization control model by taking the maximum economic benefits of the micro-grid and the power quality of the upper distribution network as the target condition, decomposes the micro-grid group coordinated optimization control model into a distribution network operation main problem and a micro-grid autonomous optimization sub-problem by using the generalized Benders decomposition method, reduces the solution difficulty of the optimization problem, and performs distributed iterative solution on the distribution network operation main problem and the micro-grid autonomous optimization sub-problem to obtain an optimal solution as the micro-grid group coordinated optimization control scheme, so that the micro-grid group is flexibly controlled, the influence of communication failure is small, and the accuracy and reliability of the coordinated optimization control of the micro-grid group are improved.

Description

Technical Field

[0001] This invention relates to the field of microgrid group optimization control technology, and in particular to a microgrid group optimization control method and system based on the generalized Benders decomposition method. Background Technology

[0002] Furthermore, fossil fuels have caused environmental problems such as global warming and air pollution, and the traditional fossil fuel-based energy structure is facing unprecedented challenges. Therefore, environmentally friendly and resource-rich renewable energy sources and distributed generation have been widely adopted worldwide. Microgrids are small-scale power generation and distribution systems comprised of distributed power sources, energy storage systems, energy conversion devices, load and monitoring and protection devices, etc. They can be connected to the upper-level grid or operated in islanded mode. As an effective management method for distributed power sources, microgrids, through advanced operation control and energy management technologies, reduce the adverse impact of distributed power sources on the grid, promote the consumption and utilization of renewable energy, improve energy efficiency, and enhance power quality and local power supply reliability.

[0003] However, due to limitations in current control technology, individual microgrids may suffer from poor disturbance immunity, limited operating capacity, and lack of backup. Therefore, geographically proximate microgrids can be interconnected with the upper-level power grid to form a microgrid cluster based on the distribution network. Through autonomous optimization of each microgrid and coordinated scheduling with the upper-level power grid and adjacent microgrids, the coordination and complementarity between microgrids and the power supply quality of the upper-level power grid can be guaranteed.

[0004] Currently, traditional microgrid group coordination and optimization control strategies can utilize real-time information for control. However, when the communication system fails, the microgrid group system will be out of control. Furthermore, as the communication network expands on a large scale with the increase of microgrids, when the communication link fails, the system will lose key information and issue inaccurate command information, resulting in low precision and poor reliability of microgrid group coordination and optimization control. Summary of the Invention

[0005] This invention provides a microgrid group optimization control method and system based on the generalized Benders decomposition method, which solves the technical problems of low accuracy and poor reliability of current microgrid group coordination optimization control strategies.

[0006] In view of this, the first aspect of the present invention provides a microgrid group optimization control method based on the generalized Benders decomposition method, comprising:

[0007] With the objective of maximizing the power quality of the upper-level distribution network and the economic benefits of the microgrid, and setting the output power of the equipment in the microgrid, the exchange power between the distribution network and the microgrid, and the operation of the reactive power equipment in the distribution network as control variables, a microgrid group coordinated optimization control model is built. The microgrid group coordinated optimization control model includes the overall objective function and constraints.

[0008] Based on the generalized Benders decomposition method, the microgrid group coordinated optimization control model is decomposed into a distribution network operation master problem and a microgrid autonomous optimization subproblem. The distribution network operation master problem and the microgrid autonomous optimization subproblem are solved iteratively in a distributed manner. The solution results corresponding to the distribution network operation master problem and the microgrid autonomous optimization subproblem are respectively taken as the optimal solution of the microgrid group coordinated optimization control model. The microgrid group coordinated optimization control scheme is obtained based on the optimal solution.

[0009] Preferably, with the objective condition of maximizing the power quality of the upper-level distribution network and the economic benefits of the microgrid, and setting the output power of equipment within the microgrid, the exchange power between the distribution network and the microgrid, and the operation of reactive power equipment in the distribution network as control variables, a microgrid group coordinated optimization control model is established. The specific steps of the microgrid group coordinated optimization control model, including the overall objective function and constraints, include:

[0010] With the goal of maximizing the power quality of the upper-level distribution network and the economic benefits of the microgrid, the output power of the equipment in the microgrid, the exchange power between the distribution network and the microgrid, and the operation of the reactive power equipment in the distribution network are set as control variables. The control objective functions corresponding to the economic benefits of the microgrid, the power balance of the upper-level distribution network area, and the power quality of the distribution network are determined respectively.

[0011] The control objective functions corresponding to the microgrid economic benefits, the regional power balance of the upper-level distribution network, and the power quality of the distribution network are weighted and calculated to obtain the overall objective function of the microgrid group coordinated optimization control model, and the constraints of the microgrid group coordinated optimization control model are determined.

[0012] Preferably, the objective function for controlling the economic benefits of the microgrid is:

[0013]

[0014] In the formula, Let N be the power generation of microgrid n, T be the dispatch cycle of the microgrid, n be the serial number of the microgrid, and N be the generation capacity of microgrid n. PV N wind N g N ES These refer to the number of photovoltaic units, wind turbines, conventional generating units, and energy storage batteries within the microgrid. These represent the power generation of photovoltaic, wind turbine, and conventional units, the charging power of energy storage batteries, and the discharging power of energy storage batteries, respectively. sale,t Let a be the electricity price at time t. gi ,b gi ,c gi C represents the power generation cost coefficient of conventional unit i. i,su C i,sd These are the start-up cost and shutdown cost of conventional unit i, respectively. Let be the start / stop state of conventional unit i at time t, where It is in the startup state. The machine is in a stopped state.

[0015] The control objective function for the power balance of the upper-level distribution network area is:

[0016]

[0017] In the formula, N MG N d These refer to the number of microgrids and the number of regional microgrid clusters on the upper-level distribution network. Let λ be the exchange power between the microgrid n and the upstream distribution network. ex Let S(i) be the microgrid switching cost, S(i) be the set of microgrids contained in regional microgrid cluster i, and P be the microgrid exchange cost. t ex This refers to the exchange power between the microgrid group and the upper-level distribution network;

[0018] The objective function for controlling the power quality of the upstream distribution network is:

[0019]

[0020] In the formula, ε n , These are the distribution network line set and the node set, R. ij Let be the resistance of line ij. These represent the active and reactive power transmitted by line ij, respectively. U rate Let be the voltage and rated voltage of node i at time t, respectively.

[0021] Preferably, the steps of weightedly calculating the control objective functions corresponding to the microgrid economic benefits, the power balance of the upstream distribution network area, and the power quality of the distribution network to obtain the overall objective function of the microgrid group coordinated optimization control model, and determining the constraints of the microgrid group coordinated optimization control model specifically include:

[0022] By weighting the control objective functions corresponding to the microgrid economic benefits, the power balance of the upstream distribution network area, and the power quality of the distribution network, the overall objective function of the microgrid group coordinated optimization control model is obtained as follows:

[0023]

[0024] In the formula, λ1, λ2, λ3 are the weight coefficients of the objective function, and N is the number of microgrids in the microgrid group under the AC distribution network.

[0025] The constraints of the microgrid group coordinated optimization control model include distribution network operation constraints and microgrid operation constraints. The distribution network operation constraints include distribution network area microgrid cluster power balance constraints, distribution network power flow constraints, distribution network safe operation constraints, and distribution network equipment operation constraints. The microgrid operation constraints include microgrid power balance constraints, conventional generator set constraints, energy storage battery constraints, photovoltaic constraints, and wind turbine constraints.

[0026] Preferably, the power balance constraint of the microgrid cluster in the distribution network area is:

[0027]

[0028] In the formula, ΔP i ,ΔQ i These represent the maximum unbalanced power of microgrid cluster i in the distribution network area;

[0029] The power flow constraints of the distribution network are:

[0030]

[0031] In the formula, These represent the active power flow and reactive power flow of line ij, respectively. Let be the active power and reactive power of node j, respectively. Let i and j be the voltages at points i and j, respectively. R is the current in line ij. ij ,X ij Let εij be the resistance and reactance of line j, respectively, and let ε(j) be the node connected to node j.

[0032] The constraints for the safe operation of the distribution network are:

[0033]

[0034] In the formula, U max U min These represent the maximum and minimum values ​​of the bus voltage, I. ij,max The maximum current in line ij;

[0035] The operating constraints for the reactive power equipment in the distribution network are as follows:

[0036]

[0037] In the formula, Q cap This refers to the capacitance of a single capacitor in a capacitor bank. n max,j These represent the adjustment level and maximum setting of the capacitor bank at a specific moment. The reactive power output of capacitor bank j at time t is... Let Q be the reactive power output of SVG j at time t. SVG,max,j Q SVG,min,j These are the upper and lower limits of SVG capacity, respectively. k ij,0 These represent the OLTC conversion rate at a certain moment and the initial conversion rate, respectively. Let Z(j), χ(j), and C(ij) be the control level and maximum level at a certain moment, respectively, and let Z(j), χ(j), and C(ij) be the set of capacitor bank installation bus, SVG installation bus, and OLTC line, respectively.

[0038] The microgrid power balance constraint is:

[0039]

[0040]

[0041] In the formula, Let be the active and reactive power of the load within the microgrid at time t, respectively. Let be the reactive power of photovoltaic i at time t. Let be the reactive power of fan i at time t. Let i be the reactive power of the stored energy at time t. Let be the reactive power of conventional unit i at time t. Let t be the reactive power exchanged between the microgrid and the upper-level distribution network;

[0042] The constraints for the conventional generating units are:

[0043]

[0044]

[0045]

[0046] In the formula, These represent the maximum and minimum active power of conventional generating units, respectively. This is the maximum power factor for conventional generating units. T represents the rate of increase and decrease in output of conventional units, respectively. gon,min ,T goff,min These are the maximum and minimum start-up times for conventional generating units;

[0047] The constraints of the energy storage battery are:

[0048]

[0049]

[0050] In the formula, S i,t Let S be the state of charge of energy storage battery i at time t. i,max ,S i,min E represents the maximum and minimum states of charge of the energy storage battery. i,s For the energy storage battery capacity, η i,ch ,η i,dis The efficiency of the charging and discharging power of the energy storage battery, where Δt is the single scheduling time period. These are the maximum and minimum values ​​of the charging and discharging power of the energy storage battery. This is the maximum power factor of the energy storage battery;

[0051] The photovoltaic constraints and wind turbine constraints are as follows:

[0052]

[0053]

[0054] In the formula, For photovoltaic capacity, a t η is the light intensity. i,PV For photovoltaic photoelectric conversion efficiency, For wind turbine wind energy capture efficiency, v i,t The wind speed at the fan location. This represents the maximum power factor for both wind turbines and photovoltaic systems.

[0055] Preferably, the microgrid group coordinated optimization control model is decomposed into a distribution network operation master problem and a microgrid autonomous optimization subproblem based on the generalized Benders decomposition method. The distribution network operation master problem and the microgrid autonomous optimization subproblem are solved iteratively in a distributed manner. The solution results corresponding to the distribution network operation master problem and the microgrid autonomous optimization subproblem are taken as the optimal solution of the microgrid group coordinated optimization control model. The specific steps for obtaining the microgrid group coordinated optimization control scheme based on the optimal solution include:

[0056] Based on the generalized Benders decomposition method, the microgrid group coordinated optimization control model is decomposed into a distribution network operation master problem and a microgrid autonomous optimization subproblem. The objective function of the distribution network operation master problem is to minimize the distribution network voltage deviation and network loss, and is constructed by setting the exchange power between the distribution network and the microgrid, as well as the operation of the reactive power equipment in the distribution network, as control variables. The constraints of the distribution network operation master problem are distribution network operation constraints. The objective function of the microgrid autonomous optimization subproblem is to optimize the interaction cost with the upper-level grid and the power balance of the microgrid cluster, and is constructed by setting the output power of the equipment within the microgrid as control variables. The constraints of the microgrid autonomous optimization subproblem are microgrid operation constraints, exchange power constraints, and power flow sensitivity constraints.

[0057] Initialize the operation of reactive power equipment in the distribution network and the power transmission between the distribution network and the microgrid;

[0058] Under the constraints of the microgrid operation, the objective function of the microgrid autonomous optimization subproblem is solved, and the feasible cutting plane and the optimal cutting plane are obtained based on the solution results.

[0059] Substituting the optimal solution of the microgrid autonomous optimization subproblem into the overall objective function of the microgrid group coordinated optimization control model, we obtain the upper bound of the overall objective function;

[0060] The feasible cutting plane and the optimal cutting plane are added to the constraints of the main problem of distribution network operation, and the objective function of the main problem of distribution network operation is solved to obtain the optimal solution.

[0061] Substituting the optimal solution of the main problem of distribution network operation into the overall objective function of the microgrid group coordinated optimization control model, the lower bound of the overall objective function is obtained;

[0062] If the difference between the upper and lower bounds of the overall objective function is less than a preset difference threshold, then the optimal solution of the microgrid autonomous optimization subproblem and the optimal solution of the distribution network operation main problem are output as the optimal solution of the microgrid group coordinated optimization control model. The microgrid group coordinated optimization control scheme is obtained based on the optimal solution. If the difference between the upper and lower bounds of the overall objective function is not less than the preset difference threshold, then the process jumps to solving the objective function of the microgrid autonomous optimization subproblem under the microgrid operation constraints, and obtains the feasible cutting plane and the optimal cutting plane based on the solution results.

[0063] Preferably, the switching power constraint and the power flow sensitivity constraint are respectively:

[0064]

[0065]

[0066] In the formula, These represent the active power and reactive power interactions between the microgrid and the distribution network with respect to node i, obtained from solving the microgrid autonomous optimization subproblem. These represent the active power and reactive power interactions between the microgrid and the distribution network at node j, obtained from solving the microgrid autonomous optimization subproblem. These represent the changes in active power interaction and reactive power interaction between the microgrid and the distribution network, respectively, obtained from solving the microgrid autonomous optimization subproblem. ΔU i t Let ΔP be the voltage change at node i. loss This represents the change in system network loss. The active and reactive power interactions between the microgrid and the distribution network are obtained from solving the main problem of distribution network operation. Both are bus voltage sensitivity. Both are network loss sensitivity, and N(i) is the set of connection node numbers of all microgrids and the upper-level distribution network within the microgrid cluster on the distribution network where node i is located.

[0067] Preferably, the steps of solving the objective function of the microgrid autonomous optimization sub-problem under the microgrid operation constraints, and obtaining the feasible cutting plane and the optimal cutting plane based on the solution results, specifically include:

[0068] Under the constraints of the microgrid operation, the objective function of the microgrid autonomous optimization subproblem is solved to obtain the solution result;

[0069] Determine whether the solution result has an optimal solution. If the solution result has an optimal solution, then obtain the optimal cutting surface L based on the solution result. m,n for:

[0070]

[0071] In the formula, x m For the optimal solution, μ m,P ,μ m,Q All are Lagrange multipliers. The objective function for the autonomous optimization operation subproblem of microgrids is... This refers to the power exchange between the distribution network and the microgrid, as well as the operation of the reactive power equipment in the distribution network. It is a constant;

[0072] If it is determined that the solution does not have an optimal solution, then the following optimization problem is solved:

[0073]

[0074] In the formula, α i Let α be the Lagrange multiplier, x be the output power scheduling action of the devices in the microgrid, and α be the control variable. i And the output power scheduling action x of the equipment in the microgrid, setting the Lagrange multiplier λ of the feasible cutting plane as λ=[λ m,p ,λ m,q ] = [α1-α3, α2-α4], thus obtaining the feasible cutting plane. for:

[0075]

[0076] In the formula, λ m,p ,λ m,q All are Lagrange multipliers.

[0077] Secondly, the present invention also provides a microgrid group optimization control system based on the generalized Benders decomposition method, comprising:

[0078] An optimized control module is used to build a microgrid group coordinated optimization control model with the objective conditions of maximizing the power quality of the upper-level distribution network and the economic benefits of the microgrid. The output power of the equipment in the microgrid, the exchange power between the distribution network and the microgrid, and the operation of the reactive power equipment of the distribution network are set as control variables. The microgrid group coordinated optimization control model includes a total objective function and constraints.

[0079] The optimization solution module is used to decompose the microgrid group coordinated optimization control model into a distribution network operation master problem and a microgrid autonomous optimization subproblem based on the generalized Benders decomposition method, and to perform distributed iterative solution on the distribution network operation master problem and the microgrid autonomous optimization subproblem. The solution results corresponding to the distribution network operation master problem and the microgrid autonomous optimization subproblem are respectively used as the optimal solution of the microgrid group coordinated optimization control model, and the microgrid group coordinated optimization control scheme is obtained based on the optimal solution.

[0080] As can be seen from the above technical solutions, the present invention has the following advantages:

[0081] This invention establishes a microgrid group coordinated optimization control model with the objective of maximizing the power quality of the upper-level distribution network and the economic benefits of the microgrid. It employs the generalized Benders decomposition method to decompose the model into a main distribution network operation problem and a microgrid autonomous optimization sub-problem, reducing the difficulty of solving the optimization problem. Furthermore, it uses distributed iterative solutions to both the main distribution network operation problem and the microgrid autonomous optimization sub-problem to obtain the optimal solution as the microgrid group coordinated optimization control scheme. This allows for flexible control of the microgrid group, with minimal impact from communication failures, thus improving the accuracy and reliability of the coordinated optimization control of the microgrid group. Attached Figure Description

[0082] Figure 1 A schematic diagram of a microgrid group topology based on an AC distribution network provided in an embodiment of the present invention;

[0083] Figure 2 A flowchart of a microgrid group optimization control method based on the generalized Benders decomposition method provided in this embodiment of the invention;

[0084] Figure 3 This is a schematic diagram of a microgrid group optimization control system based on the generalized Benders decomposition method, provided as an embodiment of the present invention. Detailed Implementation

[0085] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0086] Microgrid topology based on AC distribution network, such as Figure 1 As shown, this invention provides a distributed coordinated optimization control method for AC power distribution microgrid groups, such as... Figure 2 As shown, Figure 2 The flowchart illustrates a microgrid group optimization control method based on the generalized Benders decomposition method, which includes:

[0087] 101. Taking the maximization of power quality of the upper-level distribution network and the economic benefits of the microgrid as the objective condition, the output power of the equipment in the microgrid, the exchange power between the distribution network and the microgrid, and the operation of the reactive power equipment of the distribution network are set as control variables to build a microgrid group coordinated optimization control model. The microgrid group coordinated optimization control model includes the overall objective function and constraints.

[0088] 102. Based on the generalized Benders decomposition method, the microgrid group coordinated optimization control model is decomposed into the main problem of distribution network operation and the microgrid autonomous optimization sub-problem. The main problem of distribution network operation and the microgrid autonomous optimization sub-problem are solved in a distributed iterative manner. The solution results corresponding to the main problem of distribution network operation and the microgrid autonomous optimization sub-problem are respectively used as the optimal solution of the microgrid group coordinated optimization control model. The microgrid group coordinated optimization control scheme is obtained based on the optimal solution.

[0089] It should be noted that the microgrid group optimization control method provided by this invention, based on the generalized Benders decomposition method, establishes a microgrid group coordinated optimization control model with the objective conditions of maximizing the power quality of the upper-level distribution network and the economic benefits of the microgrid. The generalized Benders decomposition method is used to decompose the microgrid group coordinated optimization control model into a main problem of distribution network operation and a sub-problem of autonomous optimization of the microgrid, which reduces the difficulty of solving the optimization problem. The main problem of distribution network operation and the sub-problem of autonomous optimization of the microgrid are solved in a distributed iterative manner to obtain the optimal solution as the microgrid group coordinated optimization control scheme. This allows for flexible control of the microgrid group, with less impact from communication failures, and improves the accuracy and reliability of the coordinated optimization control of the microgrid group.

[0090] In one specific embodiment, step 101 specifically includes:

[0091] 1011. Taking the maximization of the power quality of the upper-level distribution network and the economic benefits of the microgrid as the objective conditions, the output power of the equipment in the microgrid, the exchange power between the distribution network and the microgrid, and the operation of the reactive power equipment in the distribution network are set as control variables. The control objective functions corresponding to the economic benefits of the microgrid, the power balance of the upper-level distribution network area, and the power quality of the distribution network are determined respectively.

[0092] The objective function for controlling the economic benefits of microgrids is:

[0093]

[0094] In the formula, Let N be the power generation of microgrid n, T be the dispatch cycle of the microgrid, n be the serial number of the microgrid, and N be the generation capacity of microgrid n. PV N wind N g N ES These refer to the number of photovoltaic units, wind turbines, conventional generating units, and energy storage batteries within the microgrid. These represent the power generation of photovoltaic, wind turbine, and conventional units, the charging power of energy storage batteries, and the discharging power of energy storage batteries, respectively. sale,t Let a be the electricity price at time t. gi ,b gi ,c gi C represents the power generation cost coefficient of conventional unit i. i,su C i,sd These are the start-up cost and shutdown cost of conventional unit i, respectively. Let be the start / stop state of conventional unit i at time t, where It is in the startup state. The machine is in a stopped state.

[0095] The control objective function for power balance in the upper-level distribution network area is:

[0096]

[0097] In the formula, N MG N d These refer to the number of microgrids and the number of regional microgrid clusters on the upper-level distribution network. Let λ be the exchange power between the microgrid n and the upstream distribution network. ex Let S(i) be the microgrid switching cost, S(i) be the set of microgrids contained in regional microgrid cluster i, and P be the microgrid exchange cost. t ex This refers to the exchange power between the microgrid group and the upper-level distribution network;

[0098] The objective function for controlling the power quality of the upstream distribution network is:

[0099]

[0100] In the formula, ε n , These are the distribution network line set and the node set, R. ij Let be the resistance of line ij. These represent the active and reactive power transmitted by line ij, respectively. U rate Let be the voltage and rated voltage of node i at time t, respectively.

[0101] The overall objective function of the microgrid group coordinated optimization control model is obtained by weighting the control objective functions corresponding to the microgrid economic benefits, the power balance of the upstream distribution network area, and the power quality of the distribution network, and the constraints of the microgrid group coordinated optimization control model are determined.

[0102] Specifically, by weighting the control objective functions corresponding to the microgrid economic benefits, the power balance of the upstream distribution network area, and the power quality of the distribution network, the overall objective function of the microgrid group coordinated optimization control model is obtained as follows:

[0103]

[0104] In the formula, λ1, λ2, and λ3 are the weighting coefficients of the objective function, and N is the number of microgrids in the microgrid group under the AC distribution network.

[0105] The constraints for determining the microgrid group coordinated optimization control model include distribution network operation constraints and microgrid operation constraints. Distribution network operation constraints include power balance constraints of the distribution network area microgrid cluster, power flow constraints of the distribution network, distribution network safety operation constraints, and distribution network equipment operation constraints. Microgrid operation constraints include microgrid power balance constraints, conventional generator set constraints, energy storage battery constraints, photovoltaic constraints, and wind turbine constraints.

[0106] The power balance constraint of the microgrid cluster in the distribution network area is as follows:

[0107]

[0108] In the formula, ΔP i ,ΔQ i These represent the maximum unbalanced power of microgrid cluster i in the distribution network area;

[0109] The power flow constraints of the distribution network are:

[0110]

[0111] In the formula, These represent the active power flow and reactive power flow of line ij, respectively. Let be the active power and reactive power of node j, respectively. Let i and j be the voltages at points i and j, respectively. R is the current in line ij. ij ,X ij Let εij be the resistance and reactance of line j, respectively, and let ε(j) be the node connected to node j.

[0112] The constraints for safe operation of the distribution network are:

[0113]

[0114] In the formula, U max U min These represent the maximum and minimum values ​​of the bus voltage, I. ij,max The maximum current in line ij;

[0115] The operating constraints for reactive power equipment in the distribution network are as follows:

[0116]

[0117] In the formula, Q cap This refers to the capacitance of a single capacitor in a capacitor bank. n max,j These represent the adjustment level and maximum setting of the capacitor bank at a specific moment. The reactive power output of capacitor bank j at time t is... Let Q be the reactive power output of SVG j at time t. SVG,max,j Q SVG,min,j These are the upper and lower limits of SVG capacity, respectively. k ij,0 These represent the OLTC conversion rate at a certain moment and the initial conversion rate, respectively. Let Z(j), χ(j), and C(ij) be the control level and maximum level at a certain moment, respectively, and let Z(j), χ(j), and C(ij) be the set of capacitor bank installation bus, SVG installation bus, and OLTC line, respectively.

[0118] The power balance constraint of the microgrid is:

[0119]

[0120]

[0121] In the formula, Let be the active and reactive power of the load within the microgrid at time t, respectively. Let be the reactive power of photovoltaic i at time t. Let be the reactive power of fan i at time t. Let i be the reactive power of the stored energy at time t. Let be the reactive power of conventional unit i at time t. Let t be the reactive power exchanged between the microgrid and the upper-level distribution network;

[0122] The constraints for conventional generating units are:

[0123]

[0124]

[0125]

[0126] In the formula, These represent the maximum and minimum active power of conventional generating units, respectively. This is the maximum power factor for conventional generating units. T represents the rate of increase and decrease in output of conventional units, respectively. gon,min ,T goff,min These are the maximum and minimum start-up times for conventional generating units;

[0127] The constraints of energy storage batteries are:

[0128]

[0129]

[0130] In the formula, S i,t Let S be the state of charge of energy storage battery i at time t. i,max ,S i,min E represents the maximum and minimum states of charge of the energy storage battery. i,s For the energy storage battery capacity, η i,ch ,η i,dis The efficiency of the charging and discharging power of the energy storage battery, where Δt is the single scheduling time period. These are the maximum and minimum values ​​of the charging and discharging power of the energy storage battery. This is the maximum power factor of the energy storage battery;

[0131] The constraints for photovoltaics and wind turbines are as follows:

[0132]

[0133]

[0134] In the formula, For photovoltaic capacity, a t η is the light intensity. i,PV For photovoltaic photoelectric conversion efficiency, For wind turbine wind energy capture efficiency, v i,t The wind speed at the fan location. This represents the maximum power factor for both wind turbines and photovoltaic systems.

[0135] In one specific embodiment, step 102 specifically includes:

[0136] 1021. Based on the generalized Benders decomposition method, the microgrid group coordinated optimization control model is decomposed into a main distribution network operation problem and a microgrid autonomous optimization subproblem. The objective function of the main distribution network operation problem is to minimize the distribution network voltage deviation and network loss, and the exchange power between the distribution network and the microgrid and the operation of the reactive power equipment in the distribution network are set as control variables. The constraints of the main distribution network operation problem are distribution network operation constraints. The objective function of the microgrid autonomous optimization subproblem is to optimize the interaction cost with the upper-level grid and the power balance of the microgrid cluster, and the output power of the equipment in the microgrid is set as control variables. The constraints of the microgrid autonomous optimization subproblem are microgrid operation constraints, exchange power constraints, and power flow sensitivity constraints.

[0137] The objective function of the main problem of distribution network operation is:

[0138]

[0139] In the formula, f master Let f1() be the objective function value of the main problem of distribution network operation, f2() be the control objective function corresponding to the economic benefits of the microgrid, f3() be the control objective function corresponding to the regional power balance of the upper-level distribution network, and f4() be the control objective function corresponding to the power quality of the distribution network. Let y be the output power of the equipment in the microgrid, which is a constant. The equipment in the microgrid includes conventional units, photovoltaics, wind turbines and energy storage batteries. Let y be the power exchanged between the distribution network and the microgrid and the operation of the reactive power equipment in the distribution network. Let y be the control variable.

[0140] The objective function of the microgrid autonomous optimization subproblem is:

[0141]

[0142] In the formula, f subLet x be the objective function value of the microgrid autonomous optimization subproblem, x be the output power of the devices within the microgrid, and x be the control variable. y represents the power exchange between the distribution network and the microgrid, as well as the operation of the reactive power equipment in the distribution network. y is a constant.

[0143] Among them, the switching power constraint and the power flow sensitivity constraint are as follows:

[0144]

[0145]

[0146] In the formula, These represent the active power and reactive power interactions between the microgrid and the distribution network with respect to node i, obtained from solving the microgrid autonomous optimization subproblem. These represent the active power and reactive power interactions between the microgrid and the distribution network at node j, obtained from solving the microgrid autonomous optimization subproblem. These represent the changes in active power interaction and reactive power interaction between the microgrid and the distribution network, respectively, obtained from solving the microgrid autonomous optimization subproblem. Let ΔP be the voltage change at node i. loss This represents the change in system network loss. The active and reactive power interactions between the microgrid and the distribution network are obtained from solving the main problem of distribution network operation. Both are bus voltage sensitivity. Both are network loss sensitivity, and N(i) is the set of connection node numbers of all microgrids and the upper-level distribution network within the microgrid cluster on the distribution network where node i is located.

[0147] 1022. Initialize the operation of reactive power equipment in the distribution network and the power transmission between the distribution network and the microgrid;

[0148] 1023. Under the constraints of microgrid operation, solve the objective function of the microgrid autonomous optimization subproblem, and obtain the feasible cutting plane and the optimal cutting plane based on the solution results;

[0149] 1024. Substitute the optimal solution of the microgrid autonomous optimization subproblem into the overall objective function of the microgrid group coordinated optimization control model to obtain the upper bound of the overall objective function;

[0150] 1025. Add the feasible cutting plane and the optimal cutting plane to the constraints of the main problem of distribution network operation, solve the objective function of the main problem of distribution network operation, and obtain the optimal solution;

[0151] 1026. Substitute the optimal solution of the main problem of distribution network operation into the overall objective function of the microgrid group coordinated optimization control model to obtain the lower bound of the overall objective function;

[0152] 1027. Determine whether the difference between the upper and lower bounds of the overall objective function is less than a preset difference threshold. If the difference between the upper and lower bounds of the overall objective function is less than the preset difference threshold, output the optimal solution of the microgrid autonomous optimization subproblem and the optimal solution of the distribution network operation main problem as the optimal solution of the microgrid group coordinated optimization control model. Obtain the microgrid group coordinated optimization control scheme based on the optimal solution. If the difference between the upper and lower bounds of the overall objective function is not less than the preset difference threshold, jump to step 1023.

[0153] In one specific embodiment, step 1023 specifically includes:

[0154] 10231. Under the constraints of microgrid operation, the objective function of the microgrid autonomous optimization subproblem is solved, and the solution results are obtained;

[0155] It should be noted that, when solving the objective function of the microgrid autonomous optimization subproblem, the simplified expression of the objective function of the microgrid autonomous optimization subproblem is as follows:

[0156]

[0157] In the formula, G1(x) represents the operational constraints at the microgrid level. This represents the global constraints for microgrids and distribution networks.

[0158] 10232. Determine if an optimal solution exists. If an optimal solution exists, obtain the optimal cutting surface L based on the solution. m,n for:

[0159]

[0160] In the formula, x m For the optimal solution, μ m,P ,μ m,Q All are Lagrange multipliers. The objective function for the autonomous optimization operation subproblem of microgrids is... This refers to the power exchange between the distribution network and the microgrid, as well as the operation of the reactive power equipment in the distribution network. It is a constant;

[0161] During initialization, the number of iterations k = 1 and the number of optimal cutting surfaces for the subproblem is p. m =0, the number of feasible cutting planes is q m =0. The upper bound of the total objective function is UB = +∞, and the lower bound is LB = -∞. If the solution result is determined to have an optimal solution, the number of optimal cutting planes is incremented by 1. If the solution result is determined not to have an optimal solution, the number of feasible cutting planes is incremented by 1.

[0162] 10233. If it is determined that there is no optimal solution, then solve the following optimization problem as follows:

[0163]

[0164] In the formula, α i Let α be the Lagrange multiplier, x be the output power scheduling action of the devices in the microgrid, and α be the control variable. i And the output power scheduling action x of the equipment in the microgrid, setting the Lagrange multiplier λ of the feasible cutting plane as λ=[λ m,p ,λ m,q ] = [α1-α3, α2-α4], thus obtaining the feasible cutting plane. for:

[0165]

[0166] In the formula, λ m,p ,λ m,q All are Lagrange multipliers.

[0167] By incorporating the feasible cutting plane and the optimal cutting plane into the constraints of the main problem of distribution network operation, we obtain:

[0168]

[0169]

[0170]

[0171] In the formula, G2(y) represents the distribution network operation constraints.

[0172] The above is a detailed description of an embodiment of a microgrid group optimization control method based on the generalized Benders decomposition method provided by the present invention. The following is a detailed description of an embodiment of a microgrid group optimization control system based on the generalized Benders decomposition method provided by the present invention.

[0173] For easier understanding, please refer to Figure 3 The present invention provides a microgrid group optimization control system based on the generalized Benders decomposition method, comprising:

[0174] The optimization control module 100 is used to build a microgrid group coordinated optimization control model with the objective condition of maximizing the power quality of the upper-level distribution network and the economic benefits of the microgrid. The output power of the equipment in the microgrid, the exchange power between the distribution network and the microgrid, and the operation of the reactive equipment of the distribution network are set as control variables. The microgrid group coordinated optimization control model includes the overall objective function and constraints.

[0175] The optimization solution module 200 is used to decompose the microgrid group coordinated optimization control model into the main problem of distribution network operation and the autonomous optimization sub-problem of microgrid based on the generalized Benders decomposition method, and to perform distributed iterative solution on the main problem of distribution network operation and the autonomous optimization sub-problem of microgrid. The solution results corresponding to the main problem of distribution network operation and the autonomous optimization sub-problem of microgrid are respectively used as the optimal solution of the microgrid group coordinated optimization control model, and the coordinated optimization control scheme of microgrid group is obtained based on the optimal solution.

[0176] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the system described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0177] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A microgrid group optimization control method based on the generalized Benders decomposition method, characterized in that, include: With the objective of maximizing the power quality of the upper-level distribution network and the economic benefits of the microgrid, and setting the output power of equipment within the microgrid, the exchange power between the distribution network and the microgrid, and the operation of reactive power equipment in the distribution network as control variables, a microgrid group coordinated optimization control model is established. This model includes a general objective function and constraints, including: With the goal of maximizing the power quality of the upper-level distribution network and the economic benefits of the microgrid, the output power of the equipment in the microgrid, the exchange power between the distribution network and the microgrid, and the operation of the reactive power equipment in the distribution network are set as control variables. The control objective functions corresponding to the economic benefits of the microgrid, the power balance of the upper-level distribution network area, and the power quality of the distribution network are determined respectively. The control objective functions corresponding to the microgrid economic benefits, the power balance of the upper-level distribution network area, and the power quality of the distribution network are weighted and calculated to obtain the overall objective function of the microgrid group coordinated optimization control model, and the constraints of the microgrid group coordinated optimization control model are determined. The objective function for controlling the economic benefits of the microgrid is: In the formula, Let T be the power generation capacity of microgrid n, T be the dispatch cycle of the microgrid, and n be the serial number of the microgrid. These refer to the number of photovoltaic units, wind turbines, conventional generating units, and energy storage batteries within the microgrid. These represent the power generation capacity of photovoltaic, wind turbine, and conventional units, the charging power of energy storage batteries, and the discharging power of energy storage batteries, respectively. Let be the electricity price at time t. These are the power generation cost coefficients for conventional unit i, respectively. These are the start-up cost and shutdown cost of conventional unit i, respectively. Let be the start / stop state of conventional unit i at time t, where =1 indicates the startup state. =0 indicates a shutdown state; The control objective function for the power balance of the upper-level distribution network area is: In the formula, N MG N d These refer to the number of microgrids and the number of regional microgrid clusters on the upper-level distribution network. This represents the exchange power between the microgrid n and the upstream distribution network. For microgrid switching costs, Let i be the set of micronets contained in the regional micronet cluster. This refers to the exchange power between the microgrid group and the upper-level distribution network; The objective function for controlling the power quality of the upstream distribution network is: In the formula, These are the distribution network line set and the node set, respectively. Let be the resistance of line ij. These represent the active and reactive power transmitted by line ij, respectively. Let be the voltage and rated voltage of node i at time t, respectively; Based on the generalized Benders decomposition method, the microgrid group coordinated optimization control model is decomposed into a distribution network operation master problem and a microgrid autonomous optimization subproblem. The distribution network operation master problem and the microgrid autonomous optimization subproblem are solved step by step iteratively. The solution results corresponding to the distribution network operation master problem and the microgrid autonomous optimization subproblem are respectively taken as the optimal solution of the microgrid group coordinated optimization control model. The microgrid group coordinated optimization control scheme is obtained based on the optimal solution.

2. The microgrid group optimization control method based on the generalized Benders decomposition method according to claim 1, characterized in that, The steps of weighted calculation of the control objective functions corresponding to the microgrid economic benefits, the power balance of the upstream distribution network area, and the power quality of the distribution network to obtain the overall objective function of the microgrid group coordinated optimization control model, and determining the constraints of the microgrid group coordinated optimization control model specifically include: By weighting the control objective functions corresponding to the microgrid economic benefits, the power balance of the upstream distribution network area, and the power quality of the distribution network, the overall objective function of the microgrid group coordinated optimization control model is obtained as follows: In the formula, , where are the weighting coefficients of the objective function, and N is the number of microgrids within a microgrid group under an AC distribution network; The constraints of the microgrid group coordinated optimization control model include distribution network operation constraints and microgrid operation constraints. The distribution network operation constraints include distribution network area microgrid cluster power balance constraints, distribution network power flow constraints, distribution network safe operation constraints, and distribution network equipment operation constraints. The microgrid operation constraints include microgrid power balance constraints, conventional generator set constraints, energy storage battery constraints, photovoltaic constraints, and wind turbine constraints.

3. The microgrid group optimization control method based on the generalized Benders decomposition method according to claim 2, characterized in that, The power balance constraint of the microgrid cluster in the distribution network area is: In the formula, These represent the maximum unbalanced power of microgrid cluster i in the distribution network area; The power flow constraints of the distribution network are: In the formula, These represent the active power flow and reactive power flow of line ij, respectively. Let be the active power and reactive power of node j, respectively. Let i and j be the voltages at points i and j, respectively. Let be the current in line ij. These represent the resistance and reactance of line ij, respectively. The node connected to node j; The constraints for the safe operation of the distribution network are: In the formula, These are the maximum and minimum values ​​of the bus voltage, respectively. The maximum current in line ij; The operational constraints of the distribution network equipment are as follows: In the formula, This refers to the capacitance of a single capacitor in a capacitor bank. These represent the adjustment level and maximum level of the capacitor bank at a specific moment. The reactive power output of capacitor bank j at time t is... Let SVG j be the reactive power output at time t. These are the upper and lower limits of SVG capacity, respectively. These represent the OLTC conversion rate at a certain moment and the initial conversion rate, respectively. These refer to the gear setting at a specific moment and the maximum gear setting, respectively. These are the capacitor bank installation busbar set, the SVG installation busbar set, and the OLTC line set, respectively. The microgrid power balance constraint is: In the formula, Let be the active and reactive power of the load within the microgrid at time t, respectively. Let be the reactive power of photovoltaic i at time t. Let be the reactive power of fan i at time t. Let i be the reactive power of the stored energy at time t. Let be the reactive power of conventional unit i at time t. Let t be the reactive power exchanged between the microgrid and the upper-level distribution network; The constraints of the conventional generator set are: In the formula, These represent the maximum and minimum active power of conventional generating units, respectively. This is the maximum power factor for conventional generating units. These represent the rate of climb for both the decrease and increase in output of conventional generating units. These are the maximum and minimum start-up times for conventional generating units; The constraints of the energy storage battery are: In the formula, Let be the state of charge of energy storage battery i at time t. This refers to the maximum and minimum states of charge of the energy storage battery. For energy storage battery capacity, The efficiency of charging and discharging power for energy storage batteries. For a single scheduling time period, These are the maximum and minimum values ​​of the charging and discharging power of the energy storage battery. This is the maximum power factor of the energy storage battery; The photovoltaic constraints and wind turbine constraints are as follows: In the formula, For photovoltaic capacity, Light intensity, For photovoltaic photoelectric conversion efficiency, For wind turbine wind energy capture efficiency, The wind speed at the fan location. This represents the maximum power factor for both wind turbines and photovoltaic systems.

4. The microgrid group optimization control method based on the generalized Benders decomposition method according to claim 3, characterized in that, Based on the generalized Benders decomposition method, the microgrid group coordinated optimization control model is decomposed into a main distribution network operation problem and a microgrid autonomous optimization sub-problem. The main distribution network operation problem and the microgrid autonomous optimization sub-problem are solved iteratively step by step. The solution results for each sub-problem are taken as the optimal solution of the microgrid group coordinated optimization control model. The specific steps for obtaining the microgrid group coordinated optimization control scheme based on the optimal solution include: Based on the generalized Benders decomposition method, the microgrid group coordinated optimization control model is decomposed into a distribution network operation master problem and a microgrid autonomous optimization subproblem. The objective function of the distribution network operation master problem is to minimize the distribution network voltage deviation and network loss, and is constructed by setting the exchange power between the distribution network and the microgrid, as well as the operation of the reactive power equipment in the distribution network, as control variables. The constraints of the distribution network operation master problem are distribution network operation constraints. The objective function of the microgrid autonomous optimization subproblem is to optimize the interaction cost with the upper-level grid and the power balance of the microgrid cluster, and is constructed by setting the output power of the equipment within the microgrid as control variables. The constraints of the microgrid autonomous optimization subproblem are microgrid operation constraints, exchange power constraints, and power flow sensitivity constraints. Initialize the operation of reactive power equipment in the distribution network and the power transmission between the distribution network and the microgrid; Under the constraints of the microgrid operation, the objective function of the microgrid autonomous optimization subproblem is solved, and the feasible cutting plane and the optimal cutting plane are obtained based on the solution results. Substituting the optimal solution of the microgrid autonomous optimization subproblem into the overall objective function of the microgrid group coordinated optimization control model, we obtain the upper bound of the overall objective function; The feasible cutting plane and the optimal cutting plane are added to the constraints of the main problem of distribution network operation, and the objective function of the main problem of distribution network operation is solved to obtain the optimal solution; Substituting the optimal solution of the main problem of distribution network operation into the overall objective function of the microgrid group coordinated optimization control model, the lower bound of the overall objective function is obtained; If the difference between the upper and lower bounds of the overall objective function is less than a preset difference threshold, then the optimal solution of the microgrid autonomous optimization subproblem and the optimal solution of the distribution network operation main problem are output as the optimal solution of the microgrid group coordinated optimization control model. The microgrid group coordinated optimization control scheme is obtained based on the optimal solution. If the difference between the upper and lower bounds of the overall objective function is not less than the preset difference threshold, then the process jumps to solving the objective function of the microgrid autonomous optimization subproblem under the microgrid operation constraints, and obtains the feasible cutting plane and the optimal cutting plane based on the solution results.

5. The microgrid group optimization control method based on the generalized Benders decomposition method according to claim 4, characterized in that, The switching power constraint and the power flow sensitivity constraint are respectively: In the formula, These represent the active power and reactive power interactions between the microgrid and the distribution network with respect to node i, obtained from solving the microgrid autonomous optimization subproblem. These represent the active power and reactive power interactions between the microgrid and the distribution network at node j, obtained from solving the microgrid autonomous optimization subproblem. These represent the changes in active power interaction and reactive power interaction between the microgrid and the distribution network, respectively, obtained from solving the microgrid autonomous optimization subproblem. Let be the voltage change at node i. This represents the change in system network loss. The active and reactive power interactions between the microgrid and the distribution network are obtained from solving the main problem of distribution network operation. All are bus voltage sensitivity. All are network loss sensitivity. Let i be the set of connection node numbers of all microgrids and their superior distribution networks within the microgrid cluster on the distribution network where node i is located.

6. The microgrid group optimization control method based on the generalized Benders decomposition method according to claim 5, characterized in that, The steps of solving the objective function of the microgrid autonomous optimization subproblem under the microgrid operation constraints and obtaining the feasible cutting plane and the optimal cutting plane based on the solution results specifically include: solving the objective function of the microgrid autonomous optimization subproblem under the microgrid operation constraints and obtaining the solution results; Determine whether the solution result has an optimal solution. If the solution result has an optimal solution, then obtain the optimal cutting surface based on the solution result. for: In the formula, This is the optimal solution. All are Lagrange multipliers. The objective function for the autonomous optimization operation subproblem of microgrids is... This refers to the power exchange between the distribution network and the microgrid, as well as the operation of the reactive power equipment in the distribution network. It is a constant; If it is determined that the solution does not have an optimal solution, then the following optimization problem is solved: In the formula, Let x be the Lagrange multiplier, and let x be the output power scheduling action of the devices in the microgrid. The control variable is the Lagrange multiplier. And the output power scheduling action x of the equipment in the microgrid, setting the Lagrange multipliers of the feasible cutting plane. for A feasible cutting plane is obtained. for: In the formula, All are Lagrange multipliers.

7. A microgrid group optimization control system based on the generalized Benders decomposition method, used to execute the microgrid group optimization control method based on the generalized Benders decomposition method as described in any one of claims 1 to 6, characterized in that, include: An optimized control module is used to build a microgrid group coordinated optimization control model with the objective conditions of maximizing the power quality of the upper-level distribution network and the economic benefits of the microgrid. The output power of the equipment in the microgrid, the exchange power between the distribution network and the microgrid, and the operation of the reactive power equipment of the distribution network are set as control variables. The microgrid group coordinated optimization control model includes a total objective function and constraints. The optimization solution module is used to decompose the microgrid group coordinated optimization control model into a distribution network operation master problem and a microgrid autonomous optimization subproblem based on the generalized Benders decomposition method, and to solve the distribution network operation master problem and the microgrid autonomous optimization subproblem step by step iteratively. The solution results corresponding to the distribution network operation master problem and the microgrid autonomous optimization subproblem are respectively used as the optimal solution of the microgrid group coordinated optimization control model, and the microgrid group coordinated optimization control scheme is obtained based on the optimal solution.

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