A microgrid planning and design method and system

Abstract

The application provides a micro-grid planning and designing method, comprising: obtaining historical load and voltage of a power distribution network node and historical load and module performance parameters of each micro-grid; inputting the historical load and voltage of the power distribution network node into an outer power distribution network operation control model to obtain upper and lower limits of exchanged power of each micro-grid and the power distribution network and a first optimal power curve; inputting the upper and lower limits of exchanged power of each micro-grid and the historical load and module performance parameters of each micro-grid into each inner micro-grid optimization configuration model to obtain the number of each module of each micro-grid and a second optimal power curve of the access point of each micro-grid and the power distribution network; comparing the first optimal power curve and the second optimal power curve corresponding to each micro-grid to determine the number of each module of each micro-grid; and the implementation of the application can simplify the micro-grid planning and designing, procurement, manufacturing and assembly process, improve the safety and reliability of the micro-grid and the power distribution network, improve the comprehensive utilization rate of internal energy of the micro-grid and reduce the operation network loss of the power distribution network.

Description

Technical Field

[0001] This invention belongs to the field of microgrid planning and optimization configuration technology, specifically relating to a microgrid planning and design method and system. Background Technology

[0002] With energy depletion and increasingly serious issues of energy conservation and environmental protection, distributed generation technologies are being applied more and more widely. Microgrids can effectively improve the utilization efficiency of distributed power sources, representing an important development trend in distributed generation and a key to meeting users' personalized electricity needs and improving power supply reliability. Microgrids are an important component of the distribution network, undertaking regional power supply tasks and directly addressing user needs; their design level directly affects the construction effect. When a microgrid is connected to the grid, it can be considered a power-controllable source / load of the distribution network, effectively mitigating the negative impact of high-penetration distributed power source access on the grid. While meeting local user needs, it improves the distribution network's acceptance capacity for distributed power sources and provides necessary support to the distribution network.

[0003] Current research on microgrid engineering design is limited, and several problems exist in the planning and design process: microgrid demonstration projects often lack unified technical principles and construction models, resulting in highly individualized design schemes with poor universality and difficulty in guaranteeing design quality. This leads to numerous problems during the implementation of some projects, including inefficient utilization and local consumption of renewable energy, low overall efficiency of energy system utilization, and the inability of existing energy and power system planning technologies to adapt to engineering practice. Furthermore, most microgrid planning and optimization methods only consider the microgrid's own objectives and constraints, neglecting the interconnections and influences between grid-connected microgrids and distribution networks. This can lead to low operating efficiency of the distribution network and, in severe cases, voltage exceeding limits. Therefore, addressing the shortcomings of current microgrid planning and design methods is a problem that those skilled in the art need to solve. Summary of the Invention

[0004] To overcome the shortcomings of the prior art, the present invention provides a microgrid planning and design method, comprising:

[0005] Obtain historical load and voltage information of each node in the distribution network related to the area where the planned microgrid is located, as well as historical load information within the coverage area of ​​each microgrid and pre-set performance parameters of each module;

[0006] The historical load and voltage information of each node of the distribution network are input into the pre-set outer distribution network operation control model for calculation, so as to obtain the upper and lower limits of the exchange power between each microgrid and the distribution network access point and the first optimal power curve.

[0007] The upper and lower limits of the exchange power of each microgrid and the distribution network access point, the historical load information within the coverage area of ​​each microgrid, and the performance parameters of each module are input into the pre-set optimization configuration model of each inner microgrid for calculation, so as to obtain the number of each module of each microgrid and the second optimal power curve of each microgrid and the distribution network access point.

[0008] The first optimal power curve and the second optimal power curve corresponding to each microgrid are compared to determine the number of each module in each microgrid.

[0009] Preferably, each module includes:

[0010] Distributed power supply modules, energy storage battery modules, and inverter modules;

[0011] The distributed power module includes: a wind turbine module and a photovoltaic module;

[0012] The inverter module includes: a wind turbine inverter module, a photovoltaic inverter module, and an energy storage battery inverter module.

[0013] Preferably, the performance parameters of each module include:

[0014] Parameters of distributed power supply module, energy storage battery module, and inverter module;

[0015] The parameters of the distributed power module include: rated power, operating life, and voltage level;

[0016] The parameters of the energy storage battery module include: rated capacity, rated voltage, maximum charge / discharge current, and charge / discharge efficiency.

[0017] The inverter module parameters include: rated power, inverter efficiency, rated voltage, and maximum input current.

[0018] Preferably, the setting of the optimal configuration model for each inner microgrid includes:

[0019] Based on the module settings within each microgrid, the operating efficiency of each inverter module, and the interaction power between each microgrid and the distribution network access point, an objective function is constructed with the goal of maximizing the comprehensive utilization rate of energy within each microgrid.

[0020] Set the constraints for the optimal configuration of the inner microgrid and construct the optimal configuration model of the inner microgrid;

[0021] The constraints include: module quantity constraints, module power constraints, energy storage battery charge / discharge power constraints, and energy storage battery state of charge constraints.

[0022] Preferably, the formula for calculating the operating efficiency of each inverter module is as follows:

[0023]

[0024] In the formula: η invr For inverter module operating efficiency, P out P is the output power of the inverter module. in P is the input power for the inverter module. rate The nominal rated power of the inverter module, P * K0, K1, and K2 are the per-unit values ​​of the output power at rated power, and K0, K1, and K2 are the efficiency coefficients of the inverter module.

[0025] Preferably, the objective function is as follows:

[0026]

[0027] In the formula, P in_wt P is the input power of the wind turbine module inverter. in_pv P represents the input power of the photovoltaic module inverter. in_ba P is the input power of the energy storage battery module inverter module. out_wt P represents the output power of the wind turbine module inverter. out_pv P represents the output power of the photovoltaic module inverter. out_ba η represents the output power of the energy storage battery module inverter. sys This refers to the overall utilization rate of energy within a microgrid.

[0028] Preferably, the module number constraint is as shown in the following formula:

[0029]

[0030] In the formula, N wt N represents the number of wind turbine modules. pv N represents the number of photovoltaic modules. bess N represents the number of energy storage battery modules. invr N represents the number of inverter modules. wt-max N represents the maximum number of wind turbine modules. pv-max N represents the upper limit of the number of photovoltaic modules. bess-max N represents the upper limit of the number of energy storage battery modules. invr-max This represents the maximum number of inverter modules.

[0031] Preferably, the module power constraint is as shown in the following formula:

[0032]

[0033] In the formula, P out_pv (t) represents the output power of the wind turbine module inverter at time t, P out_wt (t) represents the output power of the photovoltaic module inverter at time t, Pout_ba (t) represents the output power of the energy storage battery module inverter at time t, P pcc (t) represents the interaction power between the microgrid and the distribution network at time t, P load (t) represents the load power within the microgrid, P pcc max P represents the maximum value of the interaction power between the microgrid and the distribution network. pcc min P represents the minimum power exchange between the microgrid and the distribution network. in_wt P is the input power of the wind turbine module inverter. in_pv P represents the input power of the photovoltaic module inverter. in_ba P is the input power of the energy storage battery module inverter module. out_wt P represents the output power of the wind turbine module inverter. out_pv P represents the output power of the photovoltaic module inverter. out_ba N represents the output power of the energy storage battery module inverter. wt N represents the number of wind turbine modules. pv N represents the number of photovoltaic modules. bess N represents the number of energy storage battery modules. invr_wt N represents the number of inverters in the wind turbine module. invr_pv N represents the number of photovoltaic module inverters. invr_ba B represents the number of inverters in the energy storage module. wt B represents the capacity of a single wind turbine module. pv For the capacity of a single photovoltaic module, B bess P represents the maximum output power of a single energy storage battery. rate_wt P is the rated capacity of the wind turbine module inverter. rate_pv P represents the rated capacity of the photovoltaic module inverter. rate_ba R is the rated capacity of the energy storage battery module inverter; self The self-balancing rate of the microgrid. For a given value.

[0034] Preferably, the charging and discharging power constraint of the energy storage battery is as shown in the following formula:

[0035]

[0036] In the formula, k bi k represents the charging conversion efficiency of the energy storage battery. bo For the discharge conversion efficiency of energy storage batteries, f bi (t) represents the charging flag of the energy storage battery at time t, f bo (t) represents the discharge flag of the energy storage battery at time t, P in_ba-in (t) represents the internal charging power of a single energy storage battery module at time t, P in_ba-out(t) represents the internal discharge power of a single energy storage battery module at time t, B bess This represents the maximum output power of a single energy storage battery.

[0037] Preferably, the state of charge constraint of the energy storage battery is as follows:

[0038]

[0039] In the formula, S(t) is the state of charge (SOC) of the energy storage battery at time t, S(t+Δt) is the SOC of the energy storage battery at time t+Δt, and k bi k represents the charging conversion efficiency of the energy storage battery. bo For the discharge conversion efficiency of energy storage batteries, P in_ba-in (t) represents the internal charging power of a single energy storage battery module at time t, P in_ba-out (t) represents the internal discharge power of a single energy storage battery module at time t, C bess S represents the total capacity of a single energy storage battery module. min S represents the minimum state of charge of the energy storage battery. max This represents the maximum state of charge (SOC) of the energy storage battery.

[0040] Preferably, the setting of the outer distribution network operation control model includes:

[0041] Based on the control variables of the distribution network operation and the interaction power between each microgrid and the distribution network, an objective function is constructed with the goal of minimizing the distribution network operation loss.

[0042] Define the operation and control constraints of the outer distribution network and construct the operation and control model of the outer distribution network;

[0043] The constraints include: branch active power constraints, branch reactive power constraints, branch terminal voltage constraints, branch current constraints, voltage square constraints, and current square constraints.

[0044] Preferably, the objective function is as follows:

[0045]

[0046] In the formula, r ij Let F be the resistance on the ij-th branch. ij,h Let be the square of the current in branch ij during the time interval h.

[0047] Preferably, the constraints are as follows:

[0048] Branch line functional constraints:

[0049] Branch reactive power constraint:

[0050] Branch terminal voltage constraints:

[0051] Branch current constraints:

[0052] Voltage square constraint: V min ≤V h ≤V max

[0053] Current square constraint: F min ≤F h ≤F max

[0054] In the formula, P ij,h line Let r be the active power flowing through branch ij during the time interval h. ij Let F be the resistance on the ij-th branch. ij,h P is the square of the current in branch ij during the time interval h. j,h D For the active power load at node j during time period h, P j,h pcc P is the active power injected into the microgrid connected to node j during the time period h. h grid Q represents the power absorbed from the grid during the time period h. ij,h line Let x be the reactive power flowing through branch ij during the time interval h. ij Let Q be the reactance on the ij-th branch. j,h D Q represents the reactive load at node j during time interval h. j,h pcc Reactive power is injected into the microgrid connected to node j during time period h, V j,h V is the square of the voltage at node j during the time interval h. i,h Let V be the square of the voltage at node i during the time interval h. h V is a vector composed of the squares of the voltages at each node during the time interval h. min V is the minimum value of the square of the node voltage. max F is the maximum value of the square of the node voltage. h F is a vector composed of the squares of the currents in each branch over a time interval h. min F is the minimum value of the square of the current in the branch. max This represents the maximum value of the square of the current in the branch.

[0055] Preferably, the first optimal power curve and the second optimal power curve corresponding to each microgrid are compared to determine the number of each module in each microgrid, including:

[0056] The first optimal power curve and the second optimal power curve corresponding to each microgrid are compared:

[0057] When all errors are within the set range, the microgrids are planned and designed based on the number of modules of each microgrid calculated in the first calculation; otherwise, the upper and lower limits of the exchange power between each microgrid and the distribution network access point and the first optimal power curve calculated by the outer layer distribution network operation control model are updated. The updated upper and lower limits of the exchange power between each microgrid and the distribution network access point are input into the pre-set optimization configuration model of each inner layer microgrid for calculation to obtain the updated number of modules of each microgrid and the second optimal power curve between each microgrid and the distribution network access point. The comparison ends when the error between the first optimal power curve and the second optimal power curve corresponding to each microgrid is within the set range. The microgrids are then planned and designed based on the number of modules of each microgrid calculated in the last calculation.

[0058] Based on the same inventive concept, the present invention also provides a microgrid planning and design system, comprising:

[0059] The data acquisition module is used to acquire historical load information and voltage information of each node of the distribution network in the area where the planned microgrid is located, as well as historical load information within the coverage area of ​​each microgrid and the performance parameters of each module that are preset.

[0060] The outer layer calculation module is used to input the historical load information and voltage information of each node of the distribution network into the pre-set outer layer distribution network operation control model for calculation, and obtain the upper and lower limits of the exchange power and the first optimal power curve of each microgrid and the distribution network access point.

[0061] The inner layer calculation module is used to input the upper and lower limits of the exchange power of each microgrid and the distribution network access point, the historical load information within the coverage area of ​​each microgrid, and the performance parameters of each module into the pre-set inner layer microgrid optimization configuration model for calculation, so as to obtain the number of each module of each microgrid and the second optimal power curve of each microgrid and the distribution network access point.

[0062] The output module is used to compare the first optimal power curve and the second optimal power curve corresponding to each microgrid to determine the number of each module in each microgrid.

[0063] Compared with the closest existing technology, the present invention has the following beneficial effects:

[0064] This invention provides a microgrid planning and design method, comprising: acquiring historical load and voltage information of each node of the distribution network in the area where the planned microgrid is located, historical load information within the coverage area of ​​each microgrid, and pre-set performance parameters of each module; inputting the historical load and voltage information of each node of the distribution network into a pre-set outer layer distribution network operation control model for calculation to obtain the upper and lower limits of the exchange power between each microgrid and the distribution network access point and a first optimal power curve; inputting the upper and lower limits of the exchange power between each microgrid and the distribution network access point, the historical load information within the coverage area of ​​each microgrid, and the performance parameters of each module into a pre-set inner layer microgrid optimization configuration model for calculation to obtain the number of each module of each microgrid and a second optimal power curve between each microgrid and the distribution network access point; comparing the first optimal power curve and the second optimal power curve corresponding to each microgrid to determine the number of each module of each microgrid; this invention considers the interaction and influence between the microgrid and the distribution network, improves the safety and reliability of the microgrid and the distribution network, and at the same time improves the comprehensive utilization rate of energy within the microgrid and reduces the network loss of the distribution network.

[0065] The advantage of modular design within a microgrid lies in the reuse of similar modules. When system conditions permit, multiple modules can be cascaded and combined to expand into a larger capacity microgrid. At the same time, because modular design adopts factory production and assembly, it facilitates overall transportation and simplifies the planning, design, procurement, manufacturing and assembly processes throughout the entire microgrid life cycle, providing convenient conditions for the construction of microgrids. Attached Figure Description

[0066] Figure 1 A schematic diagram illustrating a microgrid planning and design method provided by the present invention;

[0067] Figure 2 A schematic diagram of a microgrid planning and design system provided by the present invention;

[0068] Figure 3 This is a schematic diagram of the topology of a distribution network system with multiple microgrids provided in an embodiment of the present invention;

[0069] Figure 4 This is a schematic diagram illustrating the solution process of the inner microgrid optimization configuration model in an embodiment of the present invention. Detailed Implementation

[0070] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0071] Example 1:

[0072] This invention provides a microgrid planning and design method based on the topological characteristics of a distribution network system with multiple microgrids, as shown in Figure 3. A schematic diagram is shown below. Figure 1 As shown, a two-layer optimization configuration method is proposed for this model: the decision variables of the inner layer model are the number of distributed power generation modules, energy storage battery modules, and inverter modules installed, and the objective function is to maximize energy utilization considering the microgrid's self-balancing rate; the outer layer is the distribution network operation control model, with decision variables being the power interaction with the microgrid and various distribution network operation control variables, and the objective being to minimize the distribution network operation loss. A method combining commercial solvers and artificial intelligence algorithms is used for solving this problem. Specific methods include:

[0073] S1 acquires historical load and voltage information of each node of the distribution network in the area where the planned microgrid is located, as well as historical load information within the coverage area of ​​each microgrid and the performance parameters of each module set in advance;

[0074] S2 inputs the historical load information and voltage information of each node of the distribution network into the pre-set outer layer distribution network operation control model for calculation, and obtains the upper and lower limits of the exchange power and the first optimal power curve of each microgrid and the distribution network access point.

[0075] S3 inputs the upper and lower limits of the exchange power of each microgrid and the distribution network access point, the historical load information within the coverage area of ​​each microgrid, and the performance parameters of each module into the pre-set optimization configuration model of each inner microgrid for calculation, and obtains the number of each module of each microgrid and the second optimal power curve of each microgrid and the distribution network access point.

[0076] S4 compares the first optimal power curve and the second optimal power curve corresponding to each microgrid to determine the number of modules in each microgrid.

[0077] S1 acquires historical load and voltage information of each node in the distribution network related to the area where the planned microgrid is located, as well as historical load information within the coverage area of ​​each microgrid and pre-set performance parameters of each module. The performance parameters of each module are as follows:

[0078] Distributed power module parameters include rated power, operating life, voltage level, etc.

[0079] Energy storage battery module parameters include rated capacity, rated voltage, maximum charge / discharge current, charge / discharge efficiency, etc.

[0080] Inverter module parameters include rated power, inverter efficiency, rated voltage, and maximum input current.

[0081] S2 inputs the historical load and voltage information of each node in the distribution network into a pre-set outer distribution network operation control model for calculation, obtaining the upper and lower limits of the exchange power between each microgrid and the distribution network access point, and the first optimal power curve. The outer distribution network operation control model setting includes:

[0082] S2-1 Determine decision variables

[0083] Node voltage squared:

[0084] Branch current square:

[0085] The side road has contributed to the trend:

[0086] Branch road without contribution to current:

[0087] Power absorbed from the power grid:

[0088] Active power interacting with the microgrid:

[0089] Interactive power exchanged with microgrids:

[0090] Where NB represents the total number of nodes in the distribution network, i represents the node number in the distribution network, NL represents the total number of branches in the distribution network, ij represents the ij-th branch, and N h The total number of runtime points is represented by h, which represents the runtime point. i,h Let v be the square of the voltage at node i during the time interval h. i,h F represents the actual voltage of node i during the time period h. ij,h f is the square of the current in branch ij during the time interval h. ij,h P represents the actual current on branch ij during the time interval h. ij,h line Q represents the active power flowing through branch ij during the time interval h. ij,h line P represents the reactive power flowing through branch ij during the time interval h. h grid P represents the power absorbed from the grid during the time period h. i,h pcc Injecting active power Q into the microgrid connected to node i during the time period h. i,h pcc Inject reactive power into the microgrid connected to node i during the time period h;

[0091] S2-2 Setting the objective function

[0092] Consider the objective function for minimizing the network loss in the distribution network, as shown in the following formula:

[0093]

[0094] In the formula, r ijLet F be the resistance on the ij-th branch. ij,h Let be the square of the current in branch ij during the time interval h;

[0095] S2-3 Determine the constraints

[0096] Branch line functional constraints:

[0097] Branch reactive power constraint:

[0098] Branch terminal voltage constraints:

[0099] Branch current constraints:

[0100] Voltage square constraint: V min ≤V h ≤V max

[0101] Current square constraint: F min ≤F h ≤F max

[0102] In the formula, P ij,h line Let r be the active power flowing through branch ij during the time interval h. ij Let F be the resistance on the ij-th branch. ij,h P is the square of the current in branch ij during the time interval h. j,h D For the active power load at node j during time period h, P j,h pcc P is the active power injected into the microgrid connected to node j during the time period h. h grid Q represents the power absorbed from the grid during the time period h. ij,h line Let x be the reactive power flowing through branch ij during the time interval h. ij Let Q be the reactance on the ij-th branch. j,h D Q represents the reactive load at node j during time interval h. j,h pcc Reactive power is injected into the microgrid connected to node j during time period h, V j,h V is the square of the voltage at node j during the time interval h. i,h Let V be the square of the voltage at node i during the time interval h. h V is a vector composed of the squares of the voltages at each node during the time interval h. min V is the minimum value of the square of the node voltage. max F is the maximum value of the square of the node voltage.h F is a vector composed of the squares of the currents in each branch over a time interval h. min F is the minimum value of the square of the current in the branch. max This represents the maximum value of the square of the current in the branch.

[0103] S3 inputs the upper and lower limits of the exchange power of each microgrid and the distribution network access point, the historical load information within the coverage area of ​​each microgrid, and the performance parameters of each module into a pre-set inner-layer microgrid optimization configuration model for calculation, to obtain the number of each module in each microgrid and the second optimal power curve of each microgrid and the distribution network access point. The setting of the inner-layer microgrid optimization configuration model includes:

[0104] S3-1 Define the objective function

[0105] The configuration of inner-layer modules in a microgrid primarily considers equipment operating efficiency and energy utilization. The main equipment considered includes wind turbines (WT), photovoltaic arrays (PV), battery energy storage systems (BESS), and inverters (INVR). The goal of inner-layer microgrid module configuration is to maximize the utilization of energy within the grid through optimized combinations of various modules, while basically meeting local load requirements. With the objective of maximizing the overall energy utilization rate within the microgrid, the optimization model expression is as follows:

[0106] maxη sys

[0107] In the formula, η sys The comprehensive utilization rate of energy within a microgrid is calculated using the following formula:

[0108]

[0109] In the formula, P in_wt P is the input power of the wind turbine module inverter. in_pv P represents the input power of the photovoltaic module inverter. in_ba P is the input power of the energy storage battery module inverter module. out_wt P represents the output power of the wind turbine module inverter. out_pv P represents the output power of the photovoltaic module inverter. out_ba K0, K1, and K2 are the output power of the energy storage battery module inverter, and K0, K1, and K2 are the inverter module efficiency coefficients.

[0110] The calculation process for the inverter module efficiency coefficient is as follows:

[0111] Calculating inverter module efficiency is crucial for the microgrid's configuration scheme, as the inverter module serves as the primary interface for distributed power generation in the system. Its operating efficiency determines resource utilization. The expression for inverter operating efficiency is:

[0112]

[0113] In the formula: η invr For inverter module operating efficiency, P out P is the output power of the inverter module. in P is the input power for the inverter module. rate The nominal rated power of the inverter module, P * This represents the per-unit value of the output power at rated power. K0, K1, and K2 are the inverter module efficiency coefficients.

[0114] Based on the output power-efficiency curve provided by the inverter manufacturer, the efficiency equation shown above is fitted to determine K0, K1, and K2. This method is more accurate than the linearized efficiency model and is easier to implement in engineering.

[0115] S3-2 Determine the constraints

[0116] 1) Decision variable constraints

[0117] Select the number of wind turbine units N wt Photovoltaic capacity N pv Number of energy storage cells N bess And the number of inverters N invr As optimization decision variables, when performing optimization calculations, a certain range needs to be set to restrict the decision variables to a valid and reasonable range, which can be expressed as:

[0118]

[0119] In the formula, N wt N represents the number of wind turbine modules. pv N represents the number of photovoltaic modules. bess N represents the number of energy storage battery modules. invr N represents the number of inverter modules. wt-max N represents the maximum number of wind turbine modules. pv-max N represents the upper limit of the number of photovoltaic modules. bess-max N represents the upper limit of the number of energy storage battery modules. invr-max This represents the maximum number of inverter modules. The maximum number of modules can be set based on factors such as the actual investment budget and installation footprint. Note that N... invr This includes the number N of wind turbine module inverters. invr_wt Number of photovoltaic module inverters N invr_pv Number of energy storage module inverters N invr_ba ;

[0120] 2) Module power constraints

[0121] The system operation must meet the power constraint condition, which can be expressed as:

[0122]

[0123] In the formula: P out_pv (t) represents the output power of the wind turbine module inverter at time t, P out_wt (t) represents the output power of the photovoltaic module inverter at time t, P out_ba (t) represents the output power of the energy storage battery module inverter at time t, P pcc (t) represents the interaction power between the microgrid and the distribution network at time t, P load (t) represents the load power within the microgrid, P pcc max P represents the maximum value of the interaction power between the microgrid and the distribution network. pcc min P represents the minimum power exchange between the microgrid and the distribution network. in_wt P is the input power of the wind turbine module inverter. in_pv P represents the input power of the photovoltaic module inverter. in_ba P is the input power of the energy storage battery module inverter module. out_wt P represents the output power of the wind turbine module inverter. out_pv P represents the output power of the photovoltaic module inverter. out_ba N represents the output power of the energy storage battery module inverter. wt N represents the number of wind turbine modules. pv N represents the number of photovoltaic modules. bess N represents the number of energy storage battery modules. invr_wt N represents the number of inverters in the wind turbine module. invr_pv N represents the number of photovoltaic module inverters. invr_ba B represents the number of inverters in the energy storage module. wt B represents the capacity of a single wind turbine module. pv For the capacity of a single photovoltaic module, B bess P represents the maximum output power of a single energy storage battery. rate_wt P is the rated capacity of the wind turbine module inverter. rate_pv P represents the rated capacity of the photovoltaic module inverter. rate_ba R is the rated capacity of the energy storage battery module inverter; self The self-balancing rate of the microgrid. For a given value;

[0124] The formula for calculating the self-balancing rate is as follows:

[0125]

[0126] In the formula, R self E is the self-balance rate index of a microgrid. self Etotal For the load supply and total load of the microgrid;

[0127] 3) Energy storage battery charging and discharging power constraints

[0128] The charging and discharging power of energy storage batteries must not exceed their maximum allowable value, which can be expressed as:

[0129]

[0130] In the formula, k bi k represents the charging conversion efficiency of the energy storage battery. bo For the discharge conversion efficiency of energy storage batteries, f bi (t) represents the charging flag of the energy storage battery at time t, f bo (t) represents the discharge flag of the energy storage battery at time t, P in_ba-in (t) represents the internal charging power of a single energy storage battery module at time t, P in_ba-out (t) represents the internal discharge power of a single energy storage battery module at time t, B bess This represents the maximum output power of a single energy storage battery.

[0131] 4) State of Charge (SOC) Constraints

[0132] Considering the impact of the battery's state of charge (SOC), i.e., the SOC value of an energy storage battery, on battery life, the SOC value should be less than its maximum allowable value and greater than its minimum allowable value, which can be expressed as:

[0133]

[0134] In the formula, S(t) is the state of charge (SOC) of the energy storage battery at time t, S(t+Δt) is the SOC of the energy storage battery at time t+Δt, and k bi k represents the charging conversion efficiency of the energy storage battery. bo For the discharge conversion efficiency of energy storage batteries, P in_ba-in (t) represents the internal charging power of a single energy storage battery module at time t, P in_ba-out (t) represents the internal discharge power of a single energy storage battery module at time t, C bess S represents the total capacity of a single energy storage battery module. min S represents the minimum state of charge of the energy storage battery. max This represents the maximum state of charge (SOC) of the energy storage battery.

[0135] S4 compares the first optimal power curve and the second optimal power curve corresponding to each microgrid to determine the number of each module in each microgrid, including:

[0136] S4-1: Input the historical load and voltage information of each node in the distribution network into the outer distribution network operation control model, and calculate the upper and lower limits P of the switching power at each microgrid access point using the Jacobian matrix. pcc max and P pcc min and the optimal switching power P at each microgrid access point pcc_op_1 This upper and lower limits of the switching power are then sent down to the inner microgrid optimization configuration layer;

[0137] S4-2: Input the historical load information within each microgrid, the performance parameters of each module, and the upper and lower limits of the exchange power between each microgrid and the distribution network connection point into the optimization configuration model of each inner microgrid, and calculate the number of wind turbine modules N using the genetic algorithm described above. wt Number of photovoltaic modules N pv Number of energy storage battery modules N bess and the number of inverter modules N invr Simultaneously, while optimizing the module configuration, the optimal power curve at the microgrid grid connection point, P, is calculated. pcc_op_2 ;

[0138] S4-3: P calculated by the outer model pcc_op_1 With P pcc_op_2 The comparison is performed. If the values ​​are within the error range, the iteration ends. If they are not equal, the outer model updates the upper and lower limits of the switching power of each microgrid access point through the Jacobian matrix and proceeds to step 1.

[0139] S4-4: Determine the optimal configuration scheme within each microgrid based on the optimal calculation results.

[0140] The inner-layer mathematical model established in this invention is a typical hybrid nonlinear model. Therefore, a genetic algorithm can effectively solve this type of problem. The decimal genetic algorithm avoids the problem of excessively long chromosomes caused by the binary genetic algorithm, and can effectively improve the speed of crossover and mutation processing. A schematic diagram of the solution process of the inner-layer microgrid optimization configuration model is shown below. Figure 4 As shown, the individual gene defined in this paper is N. wt N pv N bess N invr The chromosome arrangement structure is represented as {N} wt |N pv |N bess |N invr The specific calculation steps are as follows:

[0141] Step a: Initialization, read simulation duration, scheduling cycle, and natural resources (wind and solar power output parameters P). wt P pv ), load (P) load ) and other required parameters;

[0142] Step b: Generate an initial population P of size N. The individuals in the initial population P are represented as {N}. wt |N pv |N bess |N invr}, that is, decision variables;

[0143] Step c: Based on the constraints (decision variable constraints, power balance constraints, charging and discharging power constraints, and state of charge constraints), use the penalty function method to calculate the objective function value fitness (i.e., the comprehensive energy utilization rate of the microgrid) for each individual, and find the initial optimal individual (gbest) and the optimal population (zbest).

[0144] Step d: Monitor and assess individual fitness, update the population, select the appropriate ratio selection operator for the selection operation, and use the single-point crossover operator for the crossover operation. c P represents the crossover probability; the mutation operation uses the single-point mutation operator. m The mutation probability;

[0145] Step e: Repeat step c to find and record the best individual and the best population discovered so far in the evolutionary population.

[0146] Step f: Determine if the maximum number of iterations has been reached. If the maximum number of iterations has been reached, terminate the calculation and output the optimal result; otherwise, increment the iteration count by 1 and proceed to step d.

[0147] Example 2:

[0148] Based on the same inventive concept, the present invention also provides a microgrid planning and design system, comprising:

[0149] The data acquisition module is used to acquire historical load information and voltage information of each node of the distribution network in the area where the planned microgrid is located, as well as historical load information within the coverage area of ​​each microgrid and the performance parameters of each module that are preset.

[0150] The outer layer calculation module is used to input the historical load information and voltage information of each node of the distribution network into the pre-set outer layer distribution network operation control model for calculation, and obtain the upper and lower limits of the exchange power and the first optimal power curve of each microgrid and the distribution network access point.

[0151] The inner layer calculation module is used to input the upper and lower limits of the exchange power of each microgrid and the distribution network access point, the historical load information within the coverage area of ​​each microgrid, and the performance parameters of each module into the pre-set inner layer microgrid optimization configuration model for calculation, so as to obtain the number of each module of each microgrid and the second optimal power curve of each microgrid and the distribution network access point.

[0152] The output module is used to compare the first optimal power curve and the second optimal power curve corresponding to each microgrid to determine the number of each module in each microgrid.

[0153] Preferably, each module includes:

[0154] Distributed power supply modules, energy storage battery modules, and inverter modules;

[0155] The distributed power module includes: a wind turbine module and a photovoltaic module;

[0156] The inverter module includes: a wind turbine inverter module, a photovoltaic inverter module, and an energy storage battery inverter module.

[0157] Preferably, the performance parameters of each module include:

[0158] Parameters of distributed power supply module, energy storage battery module, and inverter module;

[0159] The parameters of the distributed power module include: rated power, operating life, and voltage level;

[0160] The parameters of the energy storage battery module include: rated capacity, rated voltage, maximum charge / discharge current, and charge / discharge efficiency.

[0161] The inverter module parameters include: rated power, inverter efficiency, rated voltage, and maximum input current.

[0162] Preferably, a microgrid planning and design system further includes:

[0163] The inner microgrid optimization configuration model setting module is used to set the optimization configuration model for each inner microgrid.

[0164] The outer distribution network operation control model setting module is used to set the outer distribution network operation control model.

[0165] Preferably, the inner microgrid optimization configuration model setting module includes:

[0166] The inner objective function construction module is used to construct an objective function based on the module settings within each microgrid, the operating efficiency of each inverter module, and the interaction power between each microgrid and the distribution network access point, with the goal of maximizing the comprehensive utilization rate of energy within each microgrid.

[0167] The inner constraint construction module is used to set the constraints for the optimal configuration of the inner microgrid and to construct the optimal configuration model of the inner microgrid.

[0168] The constraints include: module quantity constraints, module power constraints, energy storage battery charge / discharge power constraints, and energy storage battery state of charge constraints.

[0169] Preferably, the outer distribution network operation control model setting module includes:

[0170] The outer objective function construction module is used to construct an objective function based on the distribution network operation control variables and the interaction power between each microgrid and the distribution network, with the goal of minimizing the distribution network operation loss;

[0171] The outer constraint construction module is used to set the outer distribution network operation control constraints and construct the outer distribution network operation control model;

[0172] The constraints include: branch active power constraints, branch reactive power constraints, branch terminal voltage constraints, branch current constraints, voltage square constraints, and current square constraints.

[0173] Preferably, the output module includes:

[0174] The comparison module is used to compare the first optimal power curve with the second optimal power curve corresponding to each microgrid:

[0175] The calculation module is used to plan and design each microgrid based on the number of modules of each microgrid calculated in the first calculation when the errors are all within the set range; otherwise, it updates the upper and lower limits of the exchange power between each microgrid and the distribution network access point and the first optimal power curve calculated by the outer layer distribution network operation control model. The updated upper and lower limits of the exchange power between each microgrid and the distribution network access point are input into the pre-set optimization configuration model of each inner layer microgrid for calculation to obtain the updated number of modules of each microgrid and the second optimal power curve between each microgrid and the distribution network access point. The comparison ends when the error between the first optimal power curve and the second optimal power curve corresponding to each microgrid is within the set range. The microgrid is then planned and designed based on the last calculated number of modules of each microgrid.

[0176] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0177] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0178] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0179] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0180] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and not to limit its protection scope. Although this application has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading this application, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the application, but these changes, modifications or equivalent substitutions are all within the protection scope of the claims pending approval.

Claims

1. A microgrid planning and design method, characterized in that, include: Obtain historical load and voltage information of each node in the distribution network related to the area where the planned microgrid is located, as well as historical load information within the coverage area of ​​each microgrid and pre-set performance parameters of each module; The historical load and voltage information of each node of the distribution network are input into the pre-set outer distribution network operation control model for calculation, so as to obtain the upper and lower limits of the exchange power between each microgrid and the distribution network access point and the first optimal power curve. The upper and lower limits of the exchange power of each microgrid and the distribution network access point, the historical load information within the coverage area of ​​each microgrid, and the performance parameters of each module are input into the pre-set optimization configuration model of each inner microgrid for calculation, so as to obtain the number of each module of each microgrid and the second optimal power curve of each microgrid and the distribution network access point. The first optimal power curve and the second optimal power curve corresponding to each microgrid are compared to determine the number of each module in each microgrid. The setting of the optimization configuration model for each inner microgrid includes: Based on the module settings within each microgrid, the operating efficiency of each inverter module, and the interaction power between each microgrid and the distribution network access point, an objective function is constructed with the goal of maximizing the comprehensive utilization rate of energy within each microgrid. The decision variables of the optimization configuration model for each inner microgrid are the number of distributed power modules, energy storage battery modules, and inverter modules installed. Set the constraints for the optimal configuration of the inner microgrid and construct the optimal configuration model of the inner microgrid; The constraints for the optimal configuration of the inner microgrid include: module quantity constraints, module power constraints, energy storage battery charging and discharging power constraints, and energy storage battery state of charge constraints. The setting of the outer distribution network operation control model includes: Based on the control variables of the distribution network operation and the interaction power between each microgrid and the distribution network, an objective function is constructed with the goal of minimizing the distribution network operation loss. Define the operation and control constraints of the outer distribution network and construct the operation and control model of the outer distribution network; The outer distribution network operation control constraints include: branch active power constraints, branch reactive power constraints, branch terminal voltage constraints, branch current constraints, voltage square constraints, and current square constraints. The step of comparing the first optimal power curve and the second optimal power curve corresponding to each microgrid to determine the number of each module in each microgrid includes: The first optimal power curve and the second optimal power curve corresponding to each microgrid are compared: When all errors are within the set range, the microgrids are planned and designed based on the number of modules of each microgrid calculated in the first calculation; otherwise, the upper and lower limits of the exchange power between each microgrid and the distribution network access point and the first optimal power curve calculated by the outer layer distribution network operation control model are updated. The updated upper and lower limits of the exchange power between each microgrid and the distribution network access point are input into the pre-set optimization configuration model of each inner layer microgrid for calculation to obtain the updated number of modules of each microgrid and the second optimal power curve between each microgrid and the distribution network access point. The comparison ends when the error between the first optimal power curve and the second optimal power curve corresponding to each microgrid is within the set range. The microgrids are then planned and designed based on the number of modules of each microgrid calculated in the last calculation.

2. The method as described in claim 1, characterized in that, The modules include: Distributed power supply modules, energy storage battery modules, and inverter modules; The distributed power module includes: a wind turbine module and a photovoltaic module; The inverter module includes: a wind turbine inverter module, a photovoltaic inverter module, and an energy storage battery inverter module.

3. The method as described in claim 1, characterized in that, The performance parameters of each module include: Parameters of distributed power supply module, energy storage battery module, and inverter module; The parameters of the distributed power module include: rated power, operating life, and voltage level; The parameters of the energy storage battery module include: rated capacity, rated voltage, maximum charge / discharge current, and charge / discharge efficiency. The inverter module parameters include: rated power, inverter efficiency, rated voltage, and maximum input current.

4. The method as described in claim 1, characterized in that, The formula for calculating the operating efficiency of each inverter module is as follows: In the formula: To improve the operating efficiency of the inverter module, For the output power of the inverter module, Input power to the inverter module, The inverter module is labeled with its nominal rated power. This is the per-unit value of the output power at rated power. , , This represents the efficiency coefficient of the inverter module.

5. The method as described in claim 1, characterized in that, The objective function of each inner microgrid optimization configuration model is shown in the following equation: In the formula, P in_wt This refers to the input power of the wind turbine module inverter. P in_ pv The input power of the photovoltaic module inverter. P in_ba This refers to the input power of the energy storage battery module inverter module. P out_wt This refers to the output power of the wind turbine module inverter. P out_ pv This refers to the output power of the photovoltaic module inverter. P out_ba For the output power of the energy storage battery module inverter, η sys This refers to the overall utilization rate of energy within a microgrid.

6. The method as described in claim 1, characterized in that, The constraint on the number of modules is shown in the following formula: In the formula, N wt For the number of wind turbine modules, N pv For the number of photovoltaic modules, N bess For the number of energy storage battery modules, N invr The number of inverter modules. N wt-max This represents the maximum number of wind turbine modules. N pv-max This represents the upper limit for the number of photovoltaic modules. N bess-max This represents the upper limit for the number of energy storage battery modules. N invr-max This represents the maximum number of inverter modules.

7. The method as described in claim 1, characterized in that, The module power constraint is shown in the following formula: In the formula, P out_ pv ( t Let t be the output power of the photovoltaic module inverter at time t. P out_wt ( t Let t be the output power of the wind turbine module inverter. P out_ ba ( t Let t be the output power of the energy storage battery module inverter. P pcc ( t Let t be the power exchanged between the microgrid and the distribution network at time t. P load ( t ) represents the load power within the microgrid. P pcc max This represents the maximum value of the interaction power between the microgrid and the distribution network. P pcc min This represents the minimum power exchange between the microgrid and the distribution network. P in_wt This refers to the input power of the wind turbine module inverter. P in_ pv The input power of the photovoltaic module inverter. P in_ba This refers to the input power of the energy storage battery module inverter module. P out_wt This refers to the output power of the wind turbine module inverter. P out_ pv This refers to the output power of the photovoltaic module inverter. P out_ba This refers to the output power of the energy storage battery module inverter. N wt For the number of wind turbine modules, N pv For the number of photovoltaic modules, N bess For the number of energy storage battery modules, This refers to the number of inverters in the wind turbine module. The number of photovoltaic module inverters. This refers to the number of inverters in the energy storage module. B wt The capacity of a single wind turbine module. B pv For the capacity of a single photovoltaic module, B bess This represents the maximum output power of a single energy storage battery. The rated capacity of the wind turbine module inverter. This refers to the rated capacity of the photovoltaic module inverter. This refers to the rated capacity of the energy storage battery module inverter. The self-balancing rate of the microgrid. For a given value.

8. The method as described in claim 1, characterized in that, The charging and discharging power constraint of the energy storage battery is shown in the following formula: In the formula, k bi The charging conversion efficiency of energy storage batteries. k bo For the discharge conversion efficiency of energy storage batteries, f bi ( t () represents the charging flag of the energy storage battery at time t. f bo ( t () represents the discharge flag of the energy storage battery at time t. P in_ba-in ( t Let t be the internal charging power of a single energy storage battery module at time t. P in_ba-out ( t Let t be the internal discharge power of a single energy storage battery module at time t. B bess This represents the maximum output power of a single energy storage battery.

9. The method as described in claim 1, characterized in that, The state of charge constraint of the energy storage battery is shown in the following formula: In the formula, S ( t )for t The state of charge (SOC) value of the energy storage battery at all times. S ( t+∆t )for t+∆t The state of charge (SOC) value of the energy storage battery at all times. k bi The charging conversion efficiency of energy storage batteries. k bo For the discharge conversion efficiency of energy storage batteries, P in_ba-in ( t Let t be the internal charging power of a single energy storage battery module at time t. P in_ba-out ( t Let t be the internal discharge power of a single energy storage battery module at time t. C bess This refers to the total capacity of a single energy storage battery module. This represents the minimum state of charge (SOC) of the energy storage battery. This represents the maximum state of charge (SOC) of the energy storage battery.

10. The method as described in claim 1, characterized in that, The objective function of the outer distribution network operation control model is shown in the following equation: In the formula, r ij For the first ij The resistance on the branch, F ij,h for h Branch roads within the time period ij The square of the current.

11. The method as described in claim 1, characterized in that, The operation control constraints of the outer distribution network are shown in the following formula: In the formula, P ij,h line for h Branch roads within the time period ij The active power flowing through the middle, r ij For the first ij The resistance on the branch, F ij,h for h Branch roads within the time period ij The square of the medium current, P j,h D For nodes j superior h Active load within a time period P j,h pcc for h Access nodes within the time period j Injecting active power into the microgrid P h grid for h Power absorbed from the grid during the time period Q ij,h line for h Branch roads within the time period ij The reactive power flowing through the middle, x ij For the first ij Reactance on the branch, Q j,h D For nodes j superior h Reactive load during the time period Q j,h pcc for h Access nodes within the time period j Injecting reactive power into the microgrid, V j,h for h Nodes within the time period j The square of the voltage, V i,h for h Nodes within the time period i The square of the voltage, for h A vector composed of the squares of the voltages at each node within a time period. This represents the minimum value of the square of the node voltage. This represents the maximum value of the square of the node voltage. for h The vector composed of the squares of the currents in each branch over a given time period. This represents the minimum value of the square of the current in the branch. This represents the maximum value of the square of the current in the branch.

12. A microgrid planning and design system, characterized in that, include: The data acquisition module is used to acquire historical load information and voltage information of each node of the distribution network in the area where the planned microgrid is located, as well as historical load information within the coverage area of ​​each microgrid and the performance parameters of each module that are preset. The outer layer calculation module is used to input the historical load information and voltage information of each node of the distribution network into the pre-set outer layer distribution network operation control model for calculation, and obtain the upper and lower limits of the exchange power and the first optimal power curve of each microgrid and the distribution network access point. The inner layer calculation module is used to input the upper and lower limits of the exchange power of each microgrid and the distribution network access point, the historical load information within the coverage area of ​​each microgrid, and the performance parameters of each module into the pre-set inner layer microgrid optimization configuration model for calculation, so as to obtain the number of each module of each microgrid and the second optimal power curve of each microgrid and the distribution network access point. The output module is used to compare the first optimal power curve and the second optimal power curve corresponding to each microgrid to determine the number of each module in each microgrid. The system is used to execute a microgrid planning and design method as described in any one of claims 1-11.

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