Flexible interconnection micro-grid networking planning method, device, equipment and medium

Abstract

The invention relates to a flexible interconnection micro-grid networking planning method, device and equipment and a medium, and the method comprises the steps: building a flexible interconnection micro-grid group networking planning double-layer model; solving the flexible interconnected micro-grid group networking planning double-layer model by adopting a particle swarm algorithm in combination with a solver to obtain a flexible interconnected micro-grid networking planning scheme; and performing networking and planning of a micro-grid group by adopting the flexible interconnection micro-grid networking planning scheme. According to the invention, the photovoltaic consumption rate can be improved, the system operation cost is reduced, and the overall economy and reliability of the flexible power distribution network are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid group networking planning in flexible distribution networks, and particularly to a flexible interconnected microgrid networking planning method, device, equipment, and medium. Background Art

[0002] With the rapid development of renewable energy, especially the continuous progress of photovoltaic power generation technology, more and more regions have started to achieve high-proportion photovoltaic power consumption. However, the access of high-proportion photovoltaic power poses new challenges to the stability and economy of the power grid. The volatility and intermittency of photovoltaic power generation make traditional power grids face problems such as voltage and frequency control and energy storage utilization when dealing with large-scale distributed energy access, especially in flexible distribution networks, these problems are particularly prominent.

[0003] As a power system capable of achieving local autonomy, the microgrid plays an important role in high-proportion photovoltaic power consumption. The main advantages of the microgrid lie in its flexible energy management ability and good load regulation ability. However, when a single microgrid consumes high-proportion photovoltaic power, it often faces problems such as insufficient capacity, difficulty in frequency and voltage control, and low utilization efficiency of energy storage devices, resulting in limited photovoltaic power consumption capacity.

[0004] In order to improve the photovoltaic power consumption rate, optimize the economic operation of the power grid, and enhance the reliability and flexibility of the system, the optimal networking and planning of microgrid clusters become crucial. How to reasonably plan the microgrid clusters, optimize their operation and dispatching, and effectively improve the photovoltaic power consumption capacity has become an urgent problem to be solved in flexible distribution networks. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a flexible interconnected microgrid networking planning method, device, equipment, and medium, which can improve the photovoltaic power consumption rate, reduce the system operation cost, and enhance the overall economy and reliability of the flexible distribution network.

[0006] The technical solution adopted by the present invention to solve its technical problems is: to provide a flexible interconnected microgrid networking planning method, including the following steps:

[0007] Establish a two-layer model for flexible interconnected microgrid group networking planning. The two-layer model for flexible interconnected microgrid group networking planning includes an outer layer model and an inner layer model. The outer layer model is established with the goal of minimizing the investment cost of the microgrid group within the planning scope while ensuring the optimal networking index of the microgrid group. The inner layer model is established with the goals of minimizing the power grid loss, the total power reverse transmission amount of the distribution network, the total operation cost of the microgrid group, and the number of configured flexible interconnected switches.

[0008] The particle swarm optimization algorithm is combined with a solver to solve the bi - level model of the flexible interconnected micro - grid group networking planning, and a flexible interconnected micro - grid networking planning scheme is obtained;

[0009] The obtained flexible interconnected micro - grid networking planning scheme is used for the networking and planning of the micro - grid group.

[0010] The networking indexes include the power self - balance degree of the micro - grid group, the voltage - reactive power sensitivity index, and the voltage - active power sensitivity index.

[0011] The objective function of the outer - layer model is: where \(F\) is the objective function of the outer - layer model, \(\min\) represents taking the minimum value, \(\beta_1\) and \(\beta_2\) are the distribution coefficients of the micro - grid group planning and networking division respectively, \(c\) ess is the unit capacity cost of energy storage, \(S\) ess is the total capacity of energy storage configuration, \(C\) ess,ope is the annual operating cost of the system, \(K(X\) MGC ) is the networking division index value under the networking scheme \(X\) MGC , which is expressed as: \(K(X\) MGC )=\(\alpha_1\eta+\alpha_2S\) VQ +\(\alpha_3S\) VP , \(\eta\) is the power self - balance degree of the micro - grid group, \(S\) VQ is the voltage - reactive power sensitivity index, \(S\) VP is the voltage - active power sensitivity index, and \(\alpha_1\), \(\alpha_2\) and \(\alpha_3\) are the weights of each networking index.

[0012] The power self - balance degree of the micro - grid group is calculated by , where \(P\) Gi (t) is the output of the \(i\) - th distributed power generation module in the micro - grid group at time \(t\); \(P\) Ln (t) is the power consumption of the \(n\) - th load in the micro - grid group at time \(t\); \(T\) is the calculation time interval, \(N\) G is the number of distributed power generation modules in the micro - grid group, \(N\) L is the number of loads in the micro - grid group; the voltage - reactive power sensitivity index is calculated by \(S\) VQ =\(\Delta V / \Delta Q\), and the voltage - active power sensitivity index is calculated by \(S\) VP =\(\Delta V / \Delta P\), where \(\Delta V\) is the change in the voltage amplitude of the micro - grid group, \(\Delta Q\) is the change in the reactive power of the micro - grid group, and \(\Delta P\) is the change in the active power of the micro - grid group.

[0013] The constraint conditions of the outer - layer model are: where \(ESS\) max represents the upper limit of the energy storage capacity configured at each node, \(X\) ess,i represents the access status of the energy storage configured at each node, \(S\) ess,iis the capacity of the energy storage connected to node i in the system, and n represents the number of nodes.

[0014] The objective function of the inner layer model is: f = λ1C ope + λ2P loss + λ3R lost + λ4X sop , where f is the objective function of the inner layer model, λ1 is the operation cost weight coefficient in the microgrid cluster; λ2 is the power loss weight coefficient of the power grid where the microgrid cluster is located; λ3 is the power reverse transmission weight coefficient of the distribution network where the microgrid cluster is located; λ4 is the weight coefficient of the number of flexible interconnection soft switches accessed in the microgrid cluster; C ope is the total operation cost of the microgrid cluster; P loss is the power loss of the distribution network where the microgrid cluster is located; R lost is the total power reverse transmission of the distribution network where the microgrid cluster is located; X sop is the number of flexible interconnection switches accessed in the microgrid cluster.

[0015] The total operation cost of the microgrid cluster is obtained through calculation, and the power loss of the distribution network where the microgrid cluster is located is obtained through calculation, where f grid,t is the electricity price at time t, is the interactive power of the common connection point of microgrid i at time t; a ess is the scheduling cost of the energy storage; is the charging and discharging power of energy storage j, T is the calculation time interval, N is the number of microgrids in the microgrid cluster, M is the number of energy storages in the microgrid cluster, N z is the set of lines between nodes; I ij,t is the current of branch ij at time t, R ij is the resistance value of branch ij.

[0016] The constraints of the inner layer model include:

[0017] Active power balance constraint of the flexible interconnection switch, expressed as:

[0018] Active power loss constraint of the flexible interconnection switch, expressed as:

[0019] Capacity limit constraint of the flexible interconnection switch, expressed as:

[0020] Power flow constraint of the distribution network AC line, expressed as:

[0021] AC node power balance constraint, expressed as:

[0022] Line node voltage and current constraints, expressed as:

[0023] Energy storage capacity constraints, expressed as:

[0024] Energy storage charge and discharge power constraints, expressed as:

[0025] Where is the active power transmitted through the port of the flexible interconnection switch connected to node i during time period t, is the active power transmitted through the port of the flexible interconnection switch connected to node j during time period t, is the converter power loss of the port of the flexible interconnection switch connected to node i during time period t, is the converter power loss of the port of the flexible interconnection switch connected to node j during time period t; is the converter loss coefficient of the flexible interconnection switch connected at node i, is the converter loss coefficient of the flexible interconnection switch connected at node j, is the reactive power transmitted through the port of the flexible interconnection switch connected to node i during time period t, is the reactive power transmitted through the port of the flexible interconnection switch connected to node j during time period t; is the converter capacity of the flexible interconnection switch connected at node i, is the converter capacity of the flexible interconnection switch connected at node j; U i,t and U j,t are the voltage values of node i and node j during time period t respectively; P ij,t and Q ij,t represent the active power and reactive power flowing through the head end of branch ij during time period t respectively; R ij and X ij are the resistance and reactance of branch ij respectively, I ij,t is the current flowing through branch ij during time period t; B κ,i and B χ,i are the sets of the head and end nodes of the outgoing / incoming lines corresponding to node i respectively, and are the injected active power and reactive power of node i respectively; and are the active powers of the microgrid and the load on node i at time t respectively; and are the reactive powers of the microgrid and the load on node i at time t respectively; is the power of the energy storage on node i at time t, and are the charging power and discharging power of the energy storage on node i at time t respectively; Uli and U ui are the lower and upper voltage limits of node i respectively, and I lmax is the maximum current on branch ij; ESS i,t and ESS i,t+Δt represent the remaining capacity values of the energy storage on node i at time t and t+Δt respectively, and η i,min and η i,max are the minimum and maximum values of the state of charge of the energy storage on node i respectively; S ess,i represents the capacity of the energy storage on node i, and k bi and k bo are the charging efficiency and discharging efficiency of the energy storage respectively; U i,ch,t and U i,dis,t are the charging state and discharging state of node i at time t respectively, and are the maximum charging power and maximum discharging power of the energy storage on node i respectively.

[0026] The technical solution adopted by the present invention to solve its technical problems is: to provide a flexible interconnected microgrid networking planning device, including:

[0027] A building module, used to build a two-layer model for the flexible interconnected microgrid group networking planning. The two-layer model for the flexible interconnected microgrid group networking planning includes an outer layer model and an inner layer model. The outer layer model is built with the goal of minimizing the investment cost of the microgrid group within the planning scope while ensuring the optimal networking index of the microgrid group. The inner layer model is built with the goals of minimizing the grid power loss, the total amount of reverse power flow in the distribution network, the total operating cost of the microgrid group, and the number of configured flexible interconnected switches;

[0028] A solving module, used to solve the two-layer model for the flexible interconnected microgrid group networking planning by using the particle swarm optimization algorithm combined with a solver to obtain a flexible interconnected microgrid networking planning scheme;

[0029] A networking planning module, used to perform the networking and planning of the microgrid group by using the flexible interconnected microgrid networking planning scheme.

[0030] The technical solution adopted by the present invention to solve its technical problems is: to provide an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned flexible interconnected microgrid networking planning method are implemented.

[0031] The technical solution adopted by the present invention to solve its technical problems is: to provide a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned flexible interconnected microgrid networking planning method are implemented.

[0032] Beneficial effects

[0033] Due to the adoption of the above technical solution, compared with the prior art, the present invention has the following advantages and positive effects: The present invention fully considers the source-load matching of each microgrid, the grid structure and voltage level of the distribution network, and optimizes the cluster division of multiple microgrids for key factors such as the photovoltaic accommodation rate, system network loss, and power supply reliability. The double-layer network planning model of the present invention significantly reduces the investment and operation costs of the microgrid group through effective network planning, optimizes the overall network planning indicators, improves the photovoltaic accommodation rate and system economy, and thus further enhances the photovoltaic accommodation capacity. Brief description of the drawings

[0034] Figure 1 is a flowchart of the flexible interconnected microgrid network planning method according to the first embodiment of the present invention;

[0035] Figure 2 is a flowchart for solving the double-layer model of the flexible interconnected microgrid group network planning in the first embodiment of the present invention;

[0036] Figure 3 is a schematic diagram of the flexible interconnected microgrid network division after adopting the first embodiment of the present invention. Specific embodiments

[0037] The following further elaborates the present invention in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of this application.

[0038] The first embodiment of the present invention relates to a flexible interconnected microgrid network planning method, which proposes flexible interconnected microgrid group network division indicators, establishes a flexible interconnected microgrid group network planning double-layer model including an outer layer model (microgrid group network planning) and an inner layer model (microgrid group optimal scheduling), and uses an alternating iteration method between the inner and outer layers for solution to obtain a microgrid network planning scheme.

[0039] The flexible interconnected microgrid group network division indicators in this embodiment include the microgrid group power self-balance degree, voltage-reactive power sensitivity, and voltage-active power sensitivity.

[0040] Among them, the microgrid group power self-balance degree η represents the proportion of the power supplied by the power generation units in the microgrid group to the load in the total load demand variable, and its calculation method is:

[0041]

[0042] Among them, PGi P(i)(t) is the output of the i-th distributed power supply module in the microgrid cluster at time t; P Ln Q(n)(t) is the power consumption of the n-th load in the microgrid cluster at time t; T is the calculation time interval, N G is the number of distributed power supply modules in the microgrid cluster, N L is the number of loads in the microgrid cluster.

[0043] Considering the role of the microgrid cluster in the voltage regulation of the distribution network, the division of the cluster should focus on improving its voltage regulation ability. The voltage-reactive power sensitivity is usually used for the division of the voltage control area of the power grid, measured by the relationship between the voltage amplitude change ΔV of the microgrid cluster and the reactive power change ΔQ of the microgrid cluster. The voltage-reactive power sensitivity S VQ can be calculated through the following formula:

[0044] S VQ = ΔV / ΔQ;

[0045] The voltage-reactive power sensitivity S VQ can be expressed in matrix form. Among them, the element S in the i-th row and j-th column VQ,ij represents the change value of the voltage at node i corresponding to a unit value change in the reactive power at node j.

[0046] In the distribution network, the influence of the active power on the voltage is also relatively obvious. Therefore, the voltage-active power sensitivity is also a factor that cannot be ignored. The voltage-active power sensitivity S VP can be measured by the relationship between the voltage amplitude change ΔV of the microgrid cluster and the active power change ΔP of the microgrid cluster. The voltage-active power sensitivity S VP is calculated as follows:

[0047] S VP = ΔV / ΔP;

[0048] The voltage-active power sensitivity S VP can also be expressed in matrix form. Among them, the element S in the i-th row and j-th column VP,ij represents the change value of the voltage at node i corresponding to a unit value change in the active power at node j.

[0049] As Figure 1 shown, the flexible interconnected microgrid networking planning method of this embodiment includes the following steps:

[0050] Step 1, establish a two-layer model for the flexible interconnected microgrid group networking planning.

[0051] The two - layer model of the flexible interconnected micro - grid group network planning established in this step includes an outer - layer model and an inner - layer model. Among them, the outer - layer model is established with the goal of minimizing the investment cost of the micro - grid group within the planning scope while ensuring the optimal network - forming index of the micro - grid group. The inner - layer model is established with the goals of minimizing the grid power loss, the total amount of reverse power flow in the distribution network, the total operating cost of the micro - grid group, and the number of flexible interconnected switches configured.

[0052] The decision variables of the outer - layer model are the network - forming scheme of the micro - grid group and the energy - storage configuration capacity. The optimization goal is to minimize the investment cost of the micro - grid group within the planning scope and ensure the optimal network - forming index of the micro - grid group. The constraint condition is the installation limit of the energy - storage device. The objective function of the outer - layer model established in this step is:

[0053]

[0054] Given that the location and installation capacity of distributed power sources in the distribution network have been determined, in the outer - layer model, only the energy - storage configuration in the micro - grid group and the network - forming scheme of the micro - grid group are optimized. Among them, F is the objective function of the outer - layer model, min represents taking the minimum value, c ess is the unit - capacity cost of the energy - storage, C ess,ope is the annual operating cost of the system, K(X MGC ) is the network - forming division index value under the network - forming scheme X MGC . The micro - grid network - forming scheme X MGC takes integer variables, and its value ranges from 0 to N cluster , N cluster represents the total number of network - forming schemes. S ess is the total capacity of the energy - storage configuration. β1 and β2 are the distribution coefficients of the micro - grid group planning and network - forming division respectively.

[0055] The calculation method of the network - forming division index is the sum of the products of each network - forming planning index and its weight, and its calculation method is as follows:

[0056] K(X MGC ) = α1η+α2S VQ +α3S VP ;

[0057] Among them, α1, α2 and α3 are the weights of each network - forming index.

[0058] In the outer - layer model, the main constraint conditions are the access capacity and access location of the energy - storage. The access capacity and access location of the energy - storage device are specifically constrained as follows:

[0059]

[0060] Among them, ESS maxRepresents the upper limit of the energy storage capacity configured for each node, which is a known quantity, X ess,i Represents the connection status of the energy storage configured for each node, which is a 0-1 variable. When it is 0, it means the node does not connect to the energy storage; when it is 1, it means the node connects to the energy storage, S ess,i Is the capacity of the energy storage connected to node i in the system, and n represents the number of nodes.

[0061] The inner-layer model established in this step is the optimal dispatch model of the microgrid group. Based on obtaining the information of the outer-layer microgrid capacity and networking scheme, the collaborative effect within each microgrid group is considered. The optimization objective of this inner-layer model is to minimize the grid power loss, the total reverse power flow of the distribution network (i.e., the PV accommodation capacity), the total operation cost of the microgrid group, and minimize the number of configured flexible interconnection switches (SOP). Therefore, the objective function of this inner-layer model is:

[0062] f = λ1C ope + λ2P loss + λ3R lost + λ4X sop ;

[0063] Among them, f is the objective function of the inner-layer model, λ1 is the operation cost weight coefficient within the microgrid group; λ2 is the power loss weight coefficient of the power grid where the microgrid group is located; λ3 is the reverse power flow weight coefficient of the distribution network where the microgrid group is located; λ4 is the weight coefficient of the number of flexible interconnection soft switches connected within the microgrid group; C ope Is the total operation cost of the microgrid group; P loss Is the power loss of the distribution network where the microgrid group is located; R lost Is the total reverse power flow of the distribution network where the microgrid group is located; X sop Is the number of flexible interconnection switches connected within the microgrid group.

[0064] In the above formula, the total operation cost C of the microgrid group ope And the power loss P of the distribution network where the microgrid group is located loss The calculation formulas are as follows:

[0065]

[0066] Among them, f grid,t Is the electricity price at time t, Is the interactive power at the common connection point of microgrid i at time t; a ess Is the scheduling cost of the energy storage; Is the charge and discharge power of energy storage j, T is the calculation time interval, N is the number of microgrids in the microgrid group, M is the number of energy storages in the microgrid group, N z Is the set of lines between nodes; I ij,t Is the current of branch ij at time t, R ij Is the resistance value of branch ij.

[0067] The constraints of the inner-layer model include: the active power balance constraint of the flexible interconnection switch, the active power loss constraint of the flexible interconnection switch, the capacity limit constraint of the flexible interconnection switch, the power flow constraint of the distribution network AC line, the AC node power balance constraint, the line node voltage and current constraint, the energy storage capacity constraint, and the energy storage charge and discharge power constraint.

[0068] Active power balance constraint of the flexible interconnection switch:

[0069] The active power output by the converter at one end of the flexible interconnection switch should be equal to the input active power of the converter at the other end it is connected to, plus the total power loss generated by the two converters. Therefore, the active power balance constraint of the flexible interconnection switch can be expressed as:

[0070]

[0071] Among them, is the active power transmitted by the port of the flexible interconnection switch connected to node i during the t time period, is the active power transmitted by the port of the flexible interconnection switch connected to node j during the t time period, is the converter power loss of the port of the flexible interconnection switch connected to node i during the t time period, is the converter power loss of the port of the flexible interconnection switch connected to node j during the t time period.

[0072] Active power loss constraint of the flexible interconnection switch:

[0073] Although the efficiency of the current voltage source converter is already high enough, losses are still inevitable when performing high-power transmission. The loss of each end converter of the flexible interconnection switch is proportional to the sum of the squares of the active and reactive powers it transmits. The active power loss constraint of the flexible interconnection switch is as follows:

[0074]

[0075] Among them, is the converter loss coefficient of the flexible interconnection switch connected at node i, is the converter loss coefficient of the flexible interconnection switch connected at node j, is the reactive power transmitted by the port of the flexible interconnection switch connected to node i during the t time period, is the reactive power transmitted by the port of the flexible interconnection switch connected to node j during the t time period.

[0076] Capacity limit constraint of the flexible interconnection switch:

[0077] At any time, the active and reactive powers transmitted by the converters at each port of the flexible interconnection switch should be less than its rated capacity. The capacity limit constraints of the flexible interconnection switch are as follows:

[0078]

[0079] where is the capacity of the converter of the flexible interconnection switch connected at node i, is the capacity of the converter of the flexible interconnection switch connected at node j.

[0080] Power flow constraints of the distribution network AC line:

[0081]

[0082] where U i,t and U j,t are the voltage values of node i and node j at time t, respectively; P ij,t and Q ij,t represent the active power and reactive power flowing through the head end of branch ij at time t, respectively; R ij and X ij are the resistance and reactance of branch ij, respectively, and I ij,t is the current flowing through branch ij at time t.

[0083] AC node power balance constraints:

[0084]

[0085] where B κ,i and B χ,i are the sets of the head and end nodes of the outgoing / incoming lines corresponding to node i, respectively, and are the injected active power and reactive power of node i, respectively; and are the active powers of the microgrid and the load on node i at time t, respectively; and are the reactive powers of the microgrid and the load on node i at time t, respectively; is the power of the energy storage on node i at time t, and are the charging power and discharging power of the energy storage on node i at time t, respectively.

[0086] Line node voltage and current constraints:

[0087]

[0088] where U li and U ui are the lower and upper voltage limits of node i, respectively, and Ilmax is the maximum current value on branch ij.

[0089] Energy storage capacity constraint:

[0090] Considering the impact of the state of charge (SOC) of the energy storage on the battery life, it is necessary to set the upper and lower limits of the real-time capacity of the energy storage, which can be expressed as:

[0091]

[0092] Among them, ESS i,t and ESS i,t+Δt represent the remaining capacity values of the energy storage on node i at time t and t+Δt, η i,min and η i,max are the minimum and maximum values of the state of charge of the energy storage on node i respectively; S ess,i represents the capacity of the energy storage on node i, k bi and k bo are the charging efficiency and discharging efficiency of the energy storage respectively.

[0093] Energy storage charging and discharging power constraint:

[0094] Since the energy storage battery is connected to the power grid through an inverter, the charging and discharging power of the energy storage system is not only affected by the charging and discharging rate of each battery, but also limited by the capacity of the grid-connected inverter. The constraints are as follows:

[0095]

[0096] Among them, U i,ch,t and U i,dis,t are the charging state and discharging state of node i at time t respectively. U i,ch,t =1 indicates that the energy storage is charging at time t, and U i,dis,t =1 indicates that the energy storage is discharging at time t. and are the maximum charging power and maximum discharging power of the energy storage on node i respectively.

[0097] Based on the above objective function and constraint conditions, through solution, the energy storage configuration, network division and flexible interconnection switch access positions within the flexible interconnected microgrid group can be obtained.

[0098] Step 2: Use the particle swarm optimization algorithm combined with a solver to solve the hierarchical model of the flexible interconnected microgrid network planning, and obtain the flexible interconnected microgrid network planning scheme.

[0099] To calculate the hierarchical model of the flexible interconnected microgrid network planning, this step proposes to use the particle swarm optimization algorithm (PSO) + solver for solution. The outer layer uses the particle swarm optimization algorithm, and the inner layer uses the solver for solution. As Figure 2 shown, the specific solution process is as follows:

[0100] Step (1): Input the parameters of the access location and capacity of distributed power sources, the networking division indicators and alternative solutions of the microgrid group, the distribution network and the particle swarm algorithm parameters, and randomly generate an initial population.

[0101] Step (2): Transfer the networking division plan, ESS location and capacity in the particle swarm to the inner-layer scheduling model, relax the inner-layer model by the SOCP method, and obtain the SOP access capacity and location, as well as the operating parameters of the distribution network, based on the cplex commercial solver.

[0102] Step (3): Return the parameters optimized by the inner-layer operation to the outer-layer model, use the objective function of the outer-layer model as the fitness value, and calculate the fitness value of the current population.

[0103] Step (4): Perform non-dominated sorting on the population to find the current optimal solution and the global optimal solution.

[0104] Step (5): Calculate the particle position difference value and the dynamic inertia weight, and update the particle position and velocity according to the constraint conditions.

[0105] Step (6): Perform crossover and mutation operations on the updated particles, and calculate the fitness value of the offspring population.

[0106] Step (7): Mix the parent generation and the offspring to form a new population, perform non-dominated sorting on the new population, and select the better particles to form the next generation population.

[0107] Step (8): Repeat Step (2) until the iteration is completed and output the optimal decision variables.

[0108] In Step 3, the networking and planning of the microgrid group are carried out by adopting the flexible interconnected microgrid networking planning scheme, and finally the division result as shown in Figure 3 is obtained.

[0109] It is not difficult to find that the present invention fully considers the source-load matching of each microgrid, the grid structure and voltage level of the distribution network, optimizes the cluster division of multiple microgrids for key factors such as the photovoltaic accommodation rate, system network loss and power supply reliability. The double-layer networking planning model of the present invention significantly reduces the investment and operation costs of the microgrid group through effective networking planning, optimizes the overall networking indicators, improves the photovoltaic accommodation rate and system economy, and thus further enhances the photovoltaic accommodation capacity.

[0110] The second embodiment of the present invention relates to a flexible interconnected microgrid networking planning device, including:

[0111] A building module, which is used to build a two - layer model for the networking planning of a flexible interconnected micro - grid group. The two - layer model for the networking planning of the flexible interconnected micro - grid group includes an outer - layer model and an inner - layer model. The outer - layer model is established with the goal of minimizing the investment cost of the micro - grid group within the planning scope while ensuring the optimal networking index of the micro - grid group. The inner - layer model is established with the goals of minimizing the grid power loss, the total amount of reverse power flow in the distribution network, the total operating cost of the micro - grid group, and the number of configured flexible interconnected switches.

[0112] A solving module, which is used to solve the two - layer model for the networking planning of the flexible interconnected micro - grid group by using a particle swarm algorithm combined with a solver to obtain a networking planning scheme for the flexible interconnected micro - grid.

[0113] A networking planning module, which is used to perform the networking and planning of the micro - grid group by using the networking planning scheme for the flexible interconnected micro - grid.

[0114] The networking indexes include the power self - balance degree of the micro - grid group, the voltage - reactive power sensitivity index, and the voltage - active power sensitivity index.

[0115] The objective function of the outer - layer model established by the building module is: where \(F\) is the objective function of the outer - layer model, \(min\) represents taking the minimum value, \(\beta_1\) and \(\beta_2\) are respectively the distribution coefficients for the planning and networking division of the micro - grid group, \(c\) ess is the cost per unit capacity of energy storage, \(S\) ess is the total capacity of the configured energy storage, \(C\) ess,ope is the annual operating cost of the system, \(K(X\) MGC ) is the value of the networking division index under the networking scheme \(X\) MGC , which is expressed as: \(K(X\) MGC )=\(\alpha_1\eta+\alpha_2S\) VQ +\(\alpha_3S\) VP , \(\eta\) is the power self - balance degree of the micro - grid group, \(S\) VQ is the voltage - reactive power sensitivity index, \(S\) VP is the voltage - active power sensitivity index, and \(\alpha_1\), \(\alpha_2\) and \(\alpha_3\) are the weights of each networking index.

[0116] The power self - balance degree of the micro - grid group is calculated by , where \(P\) Gi (t) is the output of the \(i\) - th distributed power module in the micro - grid group at time \(t\); \(P\) Ln (t) is the power consumption of the \(n\) - th load in the micro - grid group at time \(t\); \(T\) is the calculation time interval, \(N\) G is the number of distributed power modules in the micro - grid group, \(N\) L is the number of loads in the micro - grid group; the voltage - reactive power sensitivity index is calculated by \(S\) VQis calculated by ΔV / ΔQ, and the voltage active sensitivity index is obtained through S VP is calculated by ΔV / ΔP, where ΔV is the change in voltage amplitude of the microgrid cluster, ΔQ is the change in reactive power of the microgrid cluster, and ΔP is the change in active power of the microgrid cluster.

[0117] The constraint conditions of the outer layer model established by the establishment module are: Among them, ESS max represents the upper limit of the energy storage capacity configured at each node, X ess,i represents the access status of the energy storage configured at each node, S ess,i is the capacity of the energy storage connected to node i in the system, and n represents the number of nodes.

[0118] The objective function of the inner layer model established by the establishment module is: f = λ1C ope +λ2P loss +λ3R lost +λ4X sop , where f is the objective function of the inner layer model, λ1 is the operation cost weight coefficient in the microgrid cluster; λ2 is the power loss weight coefficient of the power grid where the microgrid cluster is located; λ3 is the power reverse transmission weight coefficient of the distribution network where the microgrid cluster is located; λ4 is the weight coefficient of the number of flexible interconnection soft switches accessed in the microgrid cluster; C ope is the total operation cost of the microgrid cluster; P loss is the power loss of the distribution network where the microgrid cluster is located; R lost is the total amount of power reverse transmission of the distribution network where the microgrid cluster is located; X sop is the number of flexible interconnection switches accessed in the microgrid cluster.

[0119] The total operation cost of the microgrid cluster is obtained through calculation, and the power loss of the distribution network where the microgrid cluster is located is obtained through calculation, where f grid,t is the electricity price at time t, is the interactive power at the common connection point of microgrid i at time t; a ess is the scheduling cost of the energy storage; is the charge and discharge power of energy storage j, T is the calculation time interval, N is the number of microgrids in the microgrid cluster, M is the number of energy storages in the microgrid cluster, N z is the set of lines between nodes; I ij,t is the current of branch ij at time t, R ij is the resistance value of branch ij.

[0120] The constraints of the inner layer model established by the establishment module include:

[0121] The active power balance constraint of the flexible interconnection switch is expressed as:

[0122] The active power loss constraint of the flexible interconnection switch is expressed as:

[0123] The capacity limit constraint of the flexible interconnection switch is expressed as:

[0124] The power flow constraint of the distribution network AC line is expressed as:

[0125] The AC node power balance constraint is expressed as:

[0126] The line node voltage and current constraint is expressed as:

[0127] The energy storage capacity constraint is expressed as:

[0128] The charge and discharge power constraint of the energy storage is expressed as:

[0129] Where, is the active power transmitted by the port where the flexible interconnection switch is connected to node i during the t time period, is the active power transmitted by the port where the flexible interconnection switch is connected to node j during the t time period, is the converter power loss of the port where the flexible interconnection switch is connected to node i during the t time period, is the converter power loss of the port where the flexible interconnection switch is connected to node j during the t time period; is the converter loss coefficient of the flexible interconnection switch connected to node i, is the converter loss coefficient of the flexible interconnection switch connected to node j, is the reactive power transmitted by the port where the flexible interconnection switch is connected to node i during the t time period, is the reactive power transmitted by the port where the flexible interconnection switch is connected to node j during the t time period; is the converter capacity of the flexible interconnection switch connected to node i, is the converter capacity of the flexible interconnection switch connected to node j; U i,t and U j,t are the voltage values of node i and node j during the t time period respectively; P ij,t and Q ij,t represent the active power and reactive power flowing through the head end of branch ij during the t time period respectively; R ij and X ij are the resistance and reactance of branch ij respectively, I ij,t is the current flowing through branch ij during the t time period; B κ,i and B χ,iThey are respectively the sets of the start and end nodes of the outflow / inflow lines corresponding to node i. and are respectively the active power injection and reactive power injection of node i. and are respectively the active power of the microgrid and the load on node i at time t. and are respectively the reactive power of the microgrid and the load on node i at time t. is the power of the energy storage on node i at time t. and are respectively the charging power and discharging power of the energy storage on node i at time t. U li and U ui are respectively the lower voltage limit and upper voltage limit of node i. I lmax is the maximum current on branch ij. ESS i,t and ESS i,t+Δt represent the remaining capacity values of the energy storage on node i at time t and at time t+Δt. η i,min and η i,max are respectively the minimum and maximum values of the state of charge of the energy storage on node i. S ess,i represents the capacity of the energy storage on node i. k bi and k bo are respectively the charging efficiency and discharging efficiency of the energy storage. U i,ch,t and U i,dis,t are respectively the charging state and discharging state of node i at time t. and are respectively the maximum charging power and maximum discharging power of the energy storage on node i.

[0130] The third embodiment of the present invention relates to an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of the flexible interconnected microgrid networking planning method of the first embodiment are implemented.

[0131] The fourth embodiment of the present invention relates to a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the flexible interconnected microgrid networking planning method of the first embodiment are implemented.

[0132] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories and optical memories, etc.) that contain computer-usable program code.

[0133] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for realizing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0134] These computer program instructions can 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, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction method that realizes the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0135] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for realizing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0136] As mentioned above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, and all should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A flexible interconnected microgrid networking planning method, characterized in that Including the following steps: Establish a two - layer model for the networking planning of a flexible interconnected micro - grid group. The two - layer model for the networking planning of the flexible interconnected micro - grid group includes an outer - layer model and an inner - layer model. The outer - layer model is established with the goal of minimizing the investment cost of the micro - grid group within the planning scope while ensuring the optimal networking indicators of the micro - grid group. The inner - layer model is established with the goals of minimizing the power grid loss, the total power reverse transmission amount of the distribution network, the total operating cost of the micro - grid group, and the number of configured flexible interconnected switches; Use the particle swarm optimization algorithm combined with a solver to solve the two - layer model for the networking planning of the flexible interconnected micro - grid group, and obtain a networking planning scheme for the flexible interconnected micro - grid; Use the obtained networking planning scheme for the flexible interconnected micro - grid to carry out the networking and planning of the micro - grid group.

2. The flexible interconnected microgrid networking planning method according to claim 1, characterized in that The networking indicators include the self - balancing degree of micro - grid group power, the voltage - reactive sensitivity index, and the voltage - active sensitivity index.

3. The flexible interconnected microgrid networking planning method according to claim 1, wherein The objective function of the outer layer model is: F = min[β1(c ess S ess +C ess,ope ) + β2K(X MGC )], where F is the objective function of the outer layer model, min represents taking the minimum value, β1 and β2 are the distribution coefficients for the microgrid cluster planning and network formation division respectively, c ess is the cost per unit capacity of energy storage, S ess is the total capacity of energy storage configuration, C ess,ope is the annual operating cost of the system, K(X MGC ) is the network formation division index value under the network formation scheme X MGC , which is expressed as: K(X MGC ) = α1η + α2S VQ +α3S VP , η is the self - balance degree of power in the microgrid cluster, S VQ is the voltage - reactive power sensitivity index, S VP is the voltage - active power sensitivity index, and α1, α2 and α3 are the weights of each network formation index.

4. The flexible interconnected microgrid networking planning method according to claim 3, wherein The power self - balance degree of the micro - grid group is obtained through calculation. Among them, P Gi (t) is the output power of the i - th distributed power supply module in the micro - grid group at time t; P Ln (t) is the power consumption of the n - th load in the micro - grid group at time t; T is the calculation time interval, N G is the number of distributed power supply modules in the micro - grid group, N L is the number of loads in the micro - grid group; the voltage - reactive power sensitivity index is obtained through S VQ =ΔV / ΔQ calculation, and the voltage - active power sensitivity index is obtained through S VP =ΔV / ΔP calculation, where ΔV is the change in the voltage amplitude of the micro - grid group, ΔQ is the change in reactive power of the micro - grid group, and ΔP is the change in active power of the micro - grid group.

5. The flexible interconnected microgrid networking planning method according to claim 1, characterized in that The constraint conditions of the outer layer model are as follows: Among them, ESS max represents the upper limit of the energy storage capacity configured for each node, and X ess,i represents the access status of the energy storage configured for each node. S ess,i is the capacity of the energy storage connected to node i in the system, and n represents the number of nodes.

6. The flexible interconnected microgrid networking planning method according to claim 1, wherein The objective function of the inner-layer model is: f = λ1C ope + λ2P loss + λ3R lost + λ4X sop , where f is the objective function of the inner-layer model, λ1 is the weight coefficient of the operating cost within the microgrid cluster; λ2 is the weight coefficient of the power loss of the power grid where the microgrid cluster is located; λ3 is the weight coefficient of the reverse power flow of the distribution network where the microgrid cluster is located; λ4 is the weight coefficient of the number of flexible interconnection soft switches accessed within the microgrid cluster; C ope is the total operating cost of the microgrid cluster; P loss is the power loss of the distribution network where the microgrid cluster is located; R lost is the total reverse power flow of the distribution network where the microgrid cluster is located; X sop is the number of flexible interconnection switches accessed within the microgrid cluster.

7. The flexible interconnected microgrid networking planning method according to claim 6, characterized in that The total operating cost of the microgrid cluster is obtained through calculation, and the power loss of the distribution network where the microgrid cluster is located is obtained through calculation. Among them, f grid,t is the electricity price at time t, is the interactive power at the point of common coupling of microgrid i at time t; a ess is the scheduling cost of the energy storage; is the charging and discharging power of energy storage j, T is the calculation time interval, N is the number of microgrids in the microgrid cluster, M is the number of energy storages in the microgrid cluster, N z is the set of lines between nodes; I ij,t is the current of branch ij at time t, R ij is the resistance value of branch ij.

8. The flexible interconnected microgrid networking planning method according to claim 1, characterized in that, The constraints of the inner - layer model include: The active power balance constraint of the flexible interconnected switch is expressed as: The active power loss constraint of the flexible interconnected switch, expressed as: The capacity limit constraint of the flexible interconnected switch, expressed as: The power flow constraint of the distribution network AC line, expressed as: The AC node power balance constraint, expressed as: The line node voltage and current constraint, expressed as: The energy storage capacity constraint, expressed as: Energy storage charge and discharge power constraint, expressed as: Wherein, is the active power transmitted by the flexible interconnection switch at the port connected to node i during time period t, is the active power transmitted by the flexible interconnection switch at the port connected to node j during time period t, is the converter power loss of the flexible interconnection switch at the port connected to node i during time period t, is the converter power loss of the flexible interconnection switch at the port connected to node j during time period t; is the converter loss coefficient of the flexible interconnection switch connected at node i, is the converter loss coefficient of the flexible interconnection switch connected at node j, is the reactive power transmitted by the flexible interconnection switch at the port connected to node i during time period t, is the reactive power transmitted by the flexible interconnection switch at the port connected to node j during time period t; is the converter capacity of the flexible interconnection switch connected at node i, is the converter capacity of the flexible interconnection switch connected at node j; U i,t and U j,t are the voltage values of node i and node j during time period t respectively; P ij,t and Q ij,t represent the active power and reactive power flowing through the head end of branch ij during time period t respectively; R ij and X ij are the resistance and reactance of branch ij respectively, I ij,t is the current flowing through branch ij during time period t; B κ,i and B χ,i are the sets of the head and end nodes of the outgoing / incoming lines corresponding to node i respectively, and are the injected active power and reactive power of node i respectively; and are the active powers of the microgrid and the load on node i at time t respectively; and are the reactive powers of the microgrid and the load on node i at time t respectively; is the power of the energy storage on node i at time t, and are the charging power and discharging power of the energy storage on node i at time t respectively; U li and U ui are the lower voltage limit value and upper voltage limit value of node i respectively, I lmax is the maximum current on branch ij; ESS i,t and ESS i,t+Δt represents the remaining capacity values of the energy storage on node i at time t and t+Δt, η i,min and η i,max are the minimum and maximum values of the state of charge of the energy storage on node i respectively; S ess,i represents the capacity of the energy storage on node i, k bi and k bo are the charging efficiency and discharging efficiency of the energy storage respectively; U i,ch,t and U i,dis,t are the charging state and discharging state of node i at time t respectively, and are the maximum charging power and maximum discharging power of the energy storage on node i respectively.

9. A flexible interconnected microgrid networking planning device, characterized in that, Including: A building module, which is used to establish a two - layer model for the networking planning of a flexible interconnected micro - grid group. The two - layer model for the networking planning of the flexible interconnected micro - grid group includes an outer - layer model and an inner - layer model. The outer - layer model is established with the goal of minimizing the investment cost of the micro - grid group within the planning scope while ensuring the optimal networking indicators of the micro - grid group. The inner - layer model is established with the goals of minimizing the power grid loss, the total power reverse transmission amount of the distribution network, the total operating cost of the micro - grid group, and the number of configured flexible interconnected switches; A solving module, which is used to use the particle swarm optimization algorithm combined with a solver to solve the two - layer model for the networking planning of the flexible interconnected micro - grid group, and obtain a networking planning scheme for the flexible interconnected micro - grid; A networking planning module, which is used to use the obtained networking planning scheme for the flexible interconnected micro - grid to carry out the networking and planning of the micro - grid group.

10. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, it realizes the steps of the flexible interconnected micro - grid networking planning method as described in any one of claims 1 - 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it realizes the steps of the flexible interconnected micro - grid networking planning method as described in any one of claims 1 - 8.

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