Energy storage optimal configuration method for improving power supply quality of photovoltaic high-permeability microgrid

Abstract

According to the energy storage optimal configuration method for improving the power supply quality of the photovoltaic high-permeability micro-grid, a double-layer particle swarm optimization algorithm with node voltage fluctuation, network loss and optimal economical efficiency as targets is used for achieving energy storage locating and sizing of a photovoltaic high-permeability zone area, and the first-layer algorithm is used for determining a total daily operation plan of energy storage with economical efficiency as a target; and the second layer of algorithm takes node voltage and network loss as double targets, and optimizes the energy storage access position and quantity by adopting a particle swarm algorithm of embedded load flow calculation. The method can plan the energy storage configuration strategy from the overall perspective of the micro-grid, improves the power supply quality of the whole power distribution network, and reduces the network loss. According to the method, a double-layer multi-target optimal configuration algorithm is adopted, the upper layer carries out capacity fixing on the stored energy and determines the energy storage capacity and the operation plan, the lower layer determines the access position and quantity of the stored energy with the node voltage and the network loss as targets, an energy storage configuration strategy can be planned from the overall angle of the micro-grid, the power supply quality of the whole power distribution network is improved, and the network loss is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to an energy storage optimization configuration method for improving the power supply quality of a photovoltaic high-penetration microgrid. Background Art

[0002] With the continuous development of energy storage technology, the application of energy storage is becoming more and more extensive. The emergence of energy storage equipment connected to the power grid has broken the bottleneck of the traditional power system that generation, transmission, distribution and use must be balanced in real time, and significantly enhanced the flexibility of the power system. The connection of energy storage equipment to the power grid can effectively alleviate the problem of accommodating renewable energy such as photovoltaics and improve the economic benefits of the power grid. Due to the uncertainty of load and photovoltaic output, in order to meet load demand, improve grid stability, alleviate peak power grid pressure, and absorb more renewable energy such as photovoltaics, energy storage capacity configuration planning has become a hot topic of research.

[0003] At present, there are many goals and methods for energy storage planning, such as taking optimal operating economy as the planning goal, considering the uncertainty of the output power of renewable energy sources such as wind and solar power, and using mixed integer linear programming methods to solve the optimal energy storage capacity; taking the optimal system operating performance as the optimization goal, and the energy storage state of charge SOC as a constraint, constructing a mathematical model for energy storage capacity configuration with the minimum active power fluctuation of distributed power sources, so that the fluctuation of the output power of distributed power generation units is within the allowable range of grid access.

[0004] The above methods have studied the capacity configuration planning problem of energy storage equipment connected to the distribution network from different angles, but have not considered the impact of each charge and discharge depth of energy storage on the cycle life of energy storage, which in turn affects energy storage. The energy storage capacity is mostly determined based on the maximum output power at a certain moment or the charge / discharge amount in a certain period of time, and such planning results are not accurate enough. In addition, the above energy storage site selection and capacity planning platform software has certain limitations and cannot be applied to a variety of scenarios.

[0005] Currently, energy storage access to the grid is mostly targeted at nodes with the largest voltage gaps and heaviest loads. It does not start from the grid topology of the entire region to try to optimize the voltage and system network losses of all nodes as a whole, and comprehensively consider the capacity and location of energy storage access. Summary of the invention

[0006] The present invention proposes a method for optimizing energy storage configuration for improving the power supply quality of a photovoltaic high-penetration microgrid, which solves the problem that the existing energy storage access scheme for a substation cannot take into account both voltage quality improvement and economic optimization at the same time.

[0007] To achieve the above object, the present invention adopts the following technical solutions: A method for optimizing energy storage configuration to improve the power supply quality of a photovoltaic high penetration microgrid comprises the following steps: A two-layer multi-objective optimization configuration algorithm is used. The upper layer determines the energy storage capacity, and the lower layer determines the energy storage site selection and the operation plan of each unit. Step 1: The upper optimization algorithm determines the capacity of energy storage with the goal of optimal economic cost. For the investment cost of energy storage, it does not simply convert the investment cost into an annual equivalent investment cost based on the service life of the energy storage, but considers the impact of the charge and discharge depth on the cycle life of the energy storage equipment. Shallow charge and discharge will greatly increase the cycle life of energy storage compared to deep charge and discharge. The optimization algorithm includes two operating scenarios: island and grid-connected. Under island operation, the objective function is: (1) Where C is the system comprehensive cost of a typical day; t is the number of hours, T=24; C1 is the energy storage equipment cost, including the energy storage operation and maintenance cost C y_ESS and the energy storage depreciation cost C w_ESS ; C2 is the cost of photovoltaic equipment, including photovoltaic operation and maintenance cost C y_PV and photovoltaic depreciation cost C w_PV ; C3 is the cost of the removable load.

[0008] Energy storage cost C1: (2) Daily operation and maintenance cost of energy storage C y_ESS for: (3) In the formula, C a is the annual operation and maintenance cost per unit capacity of energy storage, E ESS For the energy storage capacity.

[0009] The cycle life of lithium batteries is related to the depth of charge and discharge (DOD), so the cycle life depreciation loss cost of energy storage batteries should be considered in energy storage planning. The relationship between the cycle life of lithium batteries and the depth of charge and discharge is as follows: Figure 1 As shown, the cycle life of lithium battery is: (4) Where N is the cycle life of the lithium battery; a and b are coefficients; DOD is the depth of discharge of the battery.

[0010] Daily depreciation cost of battery C w_ESS for: (5) A is the initial investment cost of energy storage, A=C e ×E ESS , C e is the investment cost per unit capacity of energy storage, E ESS For the energy storage capacity.

[0011] 2) Photovoltaic cost C2: (6) Daily operation and maintenance cost of photovoltaic power generation C y_PV for: (7) In the formula, C b is the annual operation and maintenance cost per unit capacity of photovoltaic power, P PV Installed capacity for photovoltaic equipment.

[0012] Daily depreciation cost of photovoltaic power generation C w_PV for: (8) Photovoltaic annual equivalent investment cost C PV for: (9) In the formula, r is the discount rate, y is PV is the service life of photovoltaic power generation, C p is the photovoltaic unit capacity investment cost, P PV Installed capacity for photovoltaic equipment.

[0013] The optimization algorithm must meet the actual operating constraints. The following three constraints are considered under isolated operation: (1) Power balance constraints: Where P PV (t) is the photovoltaic output power at time t; P ESS (t) is the energy storage charging and discharging power at time t; P load (t) is the load power at time t.

[0014] (2) ESS state of charge SOC constraint: In the formula, SOC min is the lower limit of energy storage discharge, SOC max The upper limit of energy storage charging.

[0015] (3) Removable load constraints: Where P cut_max The upper limit of the removable load.

[0016] The objective function in the grid-connected mode is: (10) Where C1 and C2 are the same as those in the objective function of the grid-connected mode, and C3 is the total electricity cost.

[0017] The electricity transaction amount C3 is: (11) In the formula, C BUY (t) is the real-time electricity price of the power purchased by the distribution network from the large power grid in the t period; P g (t) is the interaction power between the distribution network and the large power grid during period t, Pg (t)>0 Purchase electricity from the large power grid, and sell electricity to the large power grid is not allowed.

[0018] In the grid-connected mode, the upper and lower limits of the interactive power between the distribution network and the large power grid must also be considered: , where P g (t) is the power injected into the large power grid at time t, P g_max The upper limit of the power allowed to be injected into the large power grid.

[0019] Assuming that the energy exchanged between ESS, MG and the large power grid and the energy distribution of adjustable load in each hour of 24 hours is a particle, the particle swarm optimization algorithm is used to optimize the energy storage capacity for one day. In order to meet the needs of the two operating scenarios, the larger value obtained by optimizing in the island and grid-connected modes is selected as the final energy storage capacity determination result.

[0020] Step 2: The lower-level optimization algorithm selects the location for the energy storage installation, and the objective function is: 1) Node voltage fluctuation (12) Where: U ij is the node voltage amplitude of node i at the jth moment, U n The standard voltage is 380V, N is the total number of system nodes, and T=24.

[0021] 2) System network loss (13) Where: P loss,lj is the system network loss of the lth branch at the jth moment, N l is the total number of system branches.

[0022] Taking into account the voltage fluctuations at system nodes and the active power network losses of the system, the multi-objective optimization function for energy storage site selection in the second stage is as follows: (14) The lower-level optimization algorithm must satisfy the following four constraints: 1) Substation power balance constraints: , where P g is the grid input power, P ESS is the energy storage charging and discharging power, P PVg is the photovoltaic output, P load The grid load.

[0023] 2) Node voltage constraints: , where U min , U max They are the lower and upper limits of the system node voltage respectively. That is, the voltage deviation between the node voltage and the standard voltage shall not exceed ±7%.

[0024] 3) Branch transmission power constraints: , where P l_max is the upper limit of branch transmission power.

[0025] 4) Grid flow constraints

[0026] Where: P i and Q i are the active injection power and reactive injection power of node i at time t, G ij and B ij are the real and imaginary parts of the node admittance matrix in row i and column j, respectively. i is the voltage amplitude of node i at time t, δ ij is the phase difference between the two ends of branch ij at time t, and n is the total number of nodes in the system.

[0027] The optimization results of the first stage are used as input, and node voltage fluctuations and system active network losses are used as objective functions for multi-objective optimization. By calculating the optimal power flow, a multi-objective optimization algorithm embedded in the power flow calculation is used to obtain the optimal energy storage site selection result that meets the constraints. The charging and discharging states of energy storage in the power grid have different equivalent models. In the power flow calculation process, it is necessary to determine the specific model of the energy storage according to its charging and discharging state. The present invention determines the energy storage operation plan based on the planning results of the first stage, that is, determines the charging and discharging stages of the energy storage at each moment. In the second stage, when the energy storage is charging, it can be connected to the microgrid as a load equivalent to a positive load; when the energy storage is discharging, it can be connected to the microgrid as a load equivalent to a negative load.

[0028] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0029] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0030] It can be seen from the above technical scheme that the energy storage optimization configuration method for improving the power supply quality of photovoltaic high-penetration microgrids of the present invention realizes the energy storage site selection and capacity determination of photovoltaic high-penetration areas with a double-layer particle swarm optimization algorithm with node voltage fluctuation, network loss, and economic optimization as the target. The optimization is divided into two stages. The first stage is based on the collected area load rate curve, power grid flow data and power grid parameters, with comprehensive economic optimization as the objective function. The economic costs involved include the cost of exchanging electricity with the large power grid, the investment and operation and maintenance costs of energy storage. Using the particle swarm algorithm, the energy exchange between ESS, MG and the large power grid and the energy distribution of adjustable loads in each hour of 24 hours are set as a particle to optimize the energy storage operation plan for one day. The second stage optimizes the access location and quantity of energy storage, and performs multi-objective optimization with node voltage fluctuation and system active network loss as the objective function. By calculating the optimal power flow, a multi-objective optimization algorithm embedded in power flow calculation is used to obtain the optimal energy storage site selection result that meets the constraints.

[0031] In general, the present invention uses a double-layer particle swarm optimization algorithm with node voltage fluctuation, network loss, and economic optimization as the goal to achieve energy storage site selection and capacity determination in photovoltaic high penetration areas. The first layer of the algorithm determines the total daily operation plan of energy storage with economic efficiency as the goal, and the second layer of the algorithm uses node voltage and network loss as dual goals, and uses a particle swarm algorithm embedded in power flow calculation to optimize the energy storage access location and quantity. The present invention can plan energy storage configuration strategies from the overall perspective of the microgrid, improve the power supply quality of the entire distribution network, and reduce network losses.

[0032] Specifically, the beneficial effects of the present invention are as follows: The present invention adopts a two-layer multi-objective optimization configuration algorithm. The upper layer determines the capacity of energy storage and determines the energy storage capacity and operation plan. The lower layer determines the access location and quantity of energy storage based on node voltage and network loss. From the overall perspective of the microgrid, the energy storage configuration strategy can be planned to improve the power supply quality of the entire distribution network and reduce network losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of the two-stage optimization algorithm of the present invention; Figure 2 This is a graph showing the relationship between the cycle life and charge and discharge depth of the lithium battery of the present invention; Figure 3 It is a flow chart of energy storage site selection planning of an energy storage optimization configuration algorithm for improving the power supply quality of a photovoltaic high penetration rate microgrid according to the present invention; Figure 4 This is a diagram of a comprehensive variable model of a substation in an embodiment of the present invention; Figure 5 is a 24-hour load rate variation diagram of the microgrid in an embodiment of the present invention; Figure 6It is a 24-hour energy storage operation plan diagram in an embodiment of the present invention; Figure 7a It is a 24-hour operation plan diagram of an energy storage connected to two energy storages in an embodiment of the present invention; Figure 7b It is a 24-hour operation plan diagram of energy storage 2 connected to 2 energy storages in an embodiment of the present invention; Figure 8 It is a comparison diagram of node voltages before and after energy storage is connected in an embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0035] like Figure 1 As shown, the energy storage optimization configuration method for improving the power supply quality of a photovoltaic high penetration microgrid described in this embodiment is shown in FIG. Figure 1 As shown in the figure, a double-layer particle swarm optimization algorithm with node voltage fluctuation, network loss and economic optimization as the target is used to realize the energy storage site selection and capacity determination in photovoltaic high penetration area. The optimization is divided into two stages. In the first stage, the comprehensive economic optimization is used as the objective function according to the collected load rate curve of the area, the power grid flow data and the power grid parameters. The economic costs involved are the cost of exchanging electricity with the large power grid, the investment and operation and maintenance costs of energy storage. Using the particle swarm algorithm, the energy exchange between ESS, MG and the large power grid and the energy distribution of adjustable load in each hour of 24 hours are set as a particle to find the optimal energy storage operation plan for one day. In the second stage, the access location and quantity of energy storage are optimized, and the node voltage fluctuation and system active network loss are used as the objective function for multi-objective optimization. By calculating the optimal power flow, the multi-objective optimization algorithm embedded in the power flow calculation is used to obtain the optimal energy storage site selection result that meets the constraints.

[0036] Step 1: The upper optimization algorithm determines the capacity of energy storage with the goal of optimizing economic cost. For the investment cost of energy storage, it does not simply convert the investment cost into annual equivalent investment cost based on the service life of energy storage, but considers the impact of charge and discharge depth on the cycle life of energy storage equipment, such as Figure 2 As shown, shallow charge and discharge will greatly improve the cycle life of energy storage compared to deep charge and discharge. The optimization algorithm includes two operating scenarios: island operation and grid connection. The objective function under island operation is: (1) In the formula, C The overall system cost for a typical day; t is the number of hours, T =24;C 1 is the cost of energy storage equipment, including energy storage operation and maintenance costs C y_ESS and storage depreciation costs C w_ESS ; C 2 is the cost of photovoltaic equipment, including photovoltaic operation and maintenance costs C y_PV and photovoltaic depreciation costs C w_PV ; C3 is the cost of the removable load.

[0037] Energy storage cost C1: (2) Daily operation and maintenance costs of energy storage C y_ESS for: (3) In the formula, C a is the annual operation and maintenance cost per unit capacity of energy storage, E ESS For the energy storage capacity.

[0038] The cycle life of lithium batteries is related to the depth of charge and discharge (DOD), so the cycle life depreciation loss cost of energy storage batteries should be considered in energy storage planning. The relationship between the cycle life of lithium batteries and the depth of charge and discharge is as follows: Figure 1 As shown, the cycle life of lithium battery is: (4) In the formula, N is the cycle life of lithium battery; a, b is the coefficient; DOD is the battery discharge depth.

[0039] Daily depreciation cost of batteries C w_ESS for: (5) A is the initial investment cost of energy storage, A = C e ×E ESS ，C e is the investment cost per unit capacity of energy storage, E ESS For the energy storage capacity.

[0040] 2) Photovoltaic cost C2: (6) Daily operation and maintenance costs of photovoltaic C y_PVfor: (7) In the formula, C b is the annual operation and maintenance cost per unit capacity of photovoltaic power, P PV Installed capacity for photovoltaic equipment.

[0041] Daily depreciation cost of photovoltaics C w_PV for: (8) Photovoltaic annual equivalent investment cost C PV for: (9) In the formula, r is the discount rate, y PV is the service life of photovoltaic power generation, C p is the investment cost per unit capacity of photovoltaic power generation, P PV Installed capacity for photovoltaic equipment.

[0042] The optimization algorithm must meet the actual operating constraints. The following three constraints are considered under isolated operation: (1) Power balance constraints: In the formula, P PV ( t ) is the photovoltaic output power at time t; P ESS ( t ) is the energy storage charging and discharging power at time t; P load ( t ) is the load power at time t.

[0043] (2) ESS state of charge SOC constraint: In the formula, SOC min is the lower limit of energy storage discharge, SOC max The upper limit of energy storage charging.

[0044] (3) Removable load constraints: Where P cut_max The upper limit of the removable load.

[0045] The objective function in the grid-connected mode is: (10) In the formula, C1 and C2 are the same as those in the grid-connected mode objective function.C 3 is the total electricity bill.

[0046] The electricity transaction amount C3 is: (11) In the formula, C BUY ( t ) is the t The real-time electricity price of the electricity purchased by the distribution network from the large power grid during the period; P g ( t )for t The interactive power between the distribution network and the large power grid during the period, P g ( t )>0Buy electricity from the large power grid, and are not allowed to sell electricity to the large power grid.

[0047] In the grid-connected mode, the upper and lower limits of the interactive power between the distribution network and the large power grid must also be considered: , where P g ( t ) is the power injected into the large power grid at time t, P g_max The upper limit of the power allowed to be injected into the large power grid.

[0048] Assume that the energy exchanged between ESS, MG and the large power grid and the energy distribution of adjustable load in each hour of 24 hours is a particle, and use the particle swarm optimization algorithm to find the optimal energy storage operation plan for one day. Figure 6 shown.

[0049] Based on step 1, the lower-level optimization algorithm selects the location for the energy storage installation. The optimization process is as follows: Figure 3 As shown, the objective function is: 1) Node voltage fluctuation (12) Where: U ij for i The node is j The node voltage amplitude at the moment, U n The standard voltage is 380V. N is the total number of system nodes, T =24.

[0050] 2) System network loss (13) Where: P loss,lj For the l The branch road is jThe system network loss at a certain moment, N l is the total number of system branches.

[0051] Taking into account the voltage fluctuations at system nodes and the active power losses of the system, the multi-objective optimization function for energy storage site selection and capacity determination in the second stage is as follows: (14) The lower-level optimization algorithm must satisfy the following four constraints: 1) Substation power balance constraints: , where P g The input power to the grid is P ESS is the energy storage charging and discharging power, P PVg For photovoltaic power generation, P load The grid load.

[0052] 2) Node voltage constraints: , where U min , U max They are the lower and upper limits of the system node voltage respectively. That is, the voltage deviation between the node voltage and the standard voltage shall not exceed ±7%.

[0053] 3) Branch transmission power constraints: , where P l_max is the upper limit of branch transmission power.

[0054] 4) Grid flow constraints

[0055] Where: P i and Q i are the active injection power and reactive injection power of node i at time t, G ij and B ij are the real and imaginary parts of the node admittance matrix in row i and column j, respectively. i is the voltage amplitude of node i at time t, δ ij is the phase difference between the two ends of branch ij at time t, and n is the total number of nodes in the system.

[0056] The optimization results of the first stage are used as input, and node voltage fluctuations and system active network losses are used as objective functions for multi-objective optimization. By calculating the optimal power flow, a multi-objective optimization algorithm embedded in the power flow calculation is used to obtain the optimal energy storage site selection result that meets the constraints. The charging and discharging states of energy storage in the power grid have different equivalent models. In the power flow calculation process, it is necessary to determine the specific model of the energy storage according to its charging and discharging state. The present invention determines the energy storage operation plan based on the planning results of the first stage, that is, determines the charging and discharging stages of the energy storage at each moment. In the second stage, when the energy storage is charging, it can be connected to the microgrid as a load equivalent to a positive load; when the energy storage is discharging, it can be connected to the microgrid as a load equivalent to a negative load.

[0057] On the basis of step 2, in order to verify the effectiveness of the energy storage optimization configuration algorithm of the present invention for improving the power supply quality of photovoltaic high penetration microgrid, the following application is made: Figure 4 The comprehensive variable model shown in the figure is verified.

[0058] The voltage over-limit situation before connecting to the energy storage on a typical day in this embodiment is shown in Table 1: Table 1 Voltage limit nodes and corresponding voltages

[0059] Collect the power flow data and power parameters of the distribution network, and the 24-hour load rate data of a typical day, such as Figure 5 As shown, a two-layer optimization algorithm is run in Python to verify the effectiveness of the energy storage access plan given by the present invention in improving power supply quality and network loss.

[0060] The energy storage access location, planned capacity, and minimum voltage after access are shown in Table 2: Table 2 Energy storage access and minimum voltage changes

[0061] like Figure 7a and Figure 7b The figure shows the 24-hour operation plan of energy storage 1 and energy storage 2 obtained by the lower optimization algorithm. It can be seen that the energy storage is charged when the load rate is low, and the energy storage is discharged to raise the distribution network voltage when the load rate is high.

[0062] like Figure 8 The figure shows the comparison of node voltage changes at the 21st moment in a day when the system has a higher load rate. It can be seen that compared with before the energy storage was connected, the node voltage has increased significantly and the power supply quality has been greatly improved.

[0063] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0064] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0065] In another embodiment provided in the present application, a computer program product comprising instructions is also provided. When the computer is executed on the computer, the computer executes any of the energy storage optimization configuration methods for improving the power supply quality of a photovoltaic high-penetration microgrid in the above embodiments.

[0066] It is understandable that the system, device and storage medium provided in the embodiments of the present invention correspond to the method provided in the embodiments of the present invention, and the explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the above methods.

[0067] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive Solid State Disk (SSD)), etc.

[0068] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0069] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0070] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing energy storage configuration to improve the power supply quality of a photovoltaic high penetration microgrid, characterized in that: The following steps are included: A double-layer particle swarm optimization algorithm with node voltage fluctuation, network loss and economic optimization as the goal is to realize the energy storage site selection and capacity determination in photovoltaic high penetration area. The optimization is divided into two stages: In the first stage, based on the collected load rate curves, power grid flow data and power grid parameters, the comprehensive economic optimum is taken as the objective function. The economic costs involved include the cost of exchanging electricity with the large power grid, the investment and operation and maintenance costs of energy storage; Using the particle swarm algorithm, the energy exchange between ESS, MG and the large power grid and the energy distribution of adjustable loads in each hour of 24 hours are set as a particle to find the optimal energy storage operation plan for one day; The second stage optimizes the access location and quantity of energy storage, and performs multi-objective optimization with node voltage fluctuations and system active network losses as objective functions. By calculating the optimal power flow and adopting a multi-objective optimization algorithm embedded in power flow calculation, the optimal energy storage site selection result that meets the constraints is obtained.

2. The energy storage optimization configuration method for improving the power supply quality of a photovoltaic high penetration microgrid according to claim 1 is characterized in that: The double-layer particle swarm optimization algorithm is used to realize the energy storage site selection and capacity determination in the photovoltaic high penetration area. The double-layer multi-objective optimization configuration algorithm is used. The upper layer determines the energy storage capacity, and the lower layer determines the energy storage site selection and the operation plan of each unit. The upper optimization algorithm determines the capacity of energy storage with the goal of optimal economic cost. For the investment cost of energy storage, the impact of charge and discharge depth on the cycle life of energy storage equipment is considered, including two operating scenarios: island operation and grid connection. The objective function under island operation is: (1) In the formula, C The overall system cost for a typical day; t is the number of hours, T =24; C 1 is the cost of energy storage equipment, including energy storage operation and maintenance costs C y_ESS and storage depreciation costs C w_ESS ; C 2 is the cost of photovoltaic equipment, including photovoltaic operation and maintenance costs C y_PV and photovoltaic depreciation costs C w_PV ; C3 is the cost of the removable load; 1 (2) Daily operation and maintenance costs of energy storage C y_ESS for: (3) In the formula, C a is the annual operation and maintenance cost per unit capacity of energy storage, E ESS is the energy storage capacity; The cycle life depreciation cost of the energy storage battery is considered in energy storage planning; the cycle life of the lithium battery is: (4) In the formula, N is the cycle life of lithium battery; a, b is the coefficient; DOD is the battery discharge depth; Daily depreciation cost of batteries C w_ESS for: (5) A is the initial investment cost of energy storage, A = C e ×E ESS ，C e is the investment cost per unit capacity of energy storage, E ESS is the energy storage capacity; 2) Photovoltaic cost C2: (6) Daily operation and maintenance costs of photovoltaic C y_PV for: (7) In the formula, C b is the annual operation and maintenance cost per unit capacity of photovoltaic power, P PV Installed capacity for photovoltaic equipment; Daily depreciation cost of photovoltaics C w_PV for: (8) Photovoltaic annual equivalent investment cost C PV for: (9) In the formula, r is the discount rate, y PV is the service life of photovoltaic power generation, C p is the investment cost per unit capacity of photovoltaic power generation, P PV Installed capacity for photovoltaic equipment.

3. The energy storage optimization configuration method for improving the power supply quality of a photovoltaic high penetration rate microgrid according to claim 2 is characterized in that: The two-layer particle swarm optimization algorithm meets the actual operation constraints. The following three constraints are considered under isolated operation: (1) Power balance constraints: In the formula, P PV ( t ) is the photovoltaic output power at time t; P ESS ( t ) is the energy storage charging and discharging power at time t; P load ( t ) is the load power at time t; (2) ESS state of charge SOC constraint: In the formula, SOC min is the lower limit of energy storage discharge, SOC max Provides an upper limit for energy storage charging; (3) Removable load constraints: Where P cut_max The upper limit of the removable load; The objective function in the grid-connected mode is: (10) In the formula, C1 and C2 are the same as those in the grid-connected mode objective function. C 3 is the total electricity bill; The electricity transaction amount C3 is: (11) In the formula, C BUY ( t ) is the t The real-time electricity price of the electricity purchased by the distribution network from the large power grid during the period; P g ( t )for t The interactive power between the distribution network and the large power grid during the period, P g ( t )>0Buy electricity from the large power grid, and are not allowed to sell electricity to the large power grid.

4. The energy storage optimization configuration method for improving the power supply quality of a photovoltaic high penetration rate microgrid according to claim 3 is characterized by: In the grid-connected mode, the upper and lower limits of the interactive power between the distribution network and the large power grid must also be considered: , where P g ( t ) is the power injected into the large power grid at time t, P g_max The upper limit of the power allowed to be injected into the large power grid.

5. The energy storage optimization configuration method for improving the power supply quality of a photovoltaic high penetration microgrid according to claim 4 is characterized in that: The lower optimization algorithm selects the location of the energy storage installation, and the objective function is: 1) Node voltage fluctuation (12) Where: U ij is the node voltage amplitude of node i at the jth moment, U n The standard voltage is 380V, N is the total number of system nodes, T=24; 2) System network loss (13) Where: P loss,lj is the system network loss of the lth branch at the jth moment, N l is the total number of system branches.

6. The energy storage optimization configuration method for improving the power supply quality of a photovoltaic high penetration microgrid according to claim 5 is characterized in that: Taking into account the voltage fluctuations at system nodes and the active power network losses of the system, the multi-objective optimization function for energy storage site selection in the second stage is as follows: (14) The lower-level optimization algorithm must satisfy the following four constraints: 1) Substation power balance constraints: , where P g is the grid input power, P ESS is the energy storage charging and discharging power, P PVg is the photovoltaic output, P load is the grid load; 2) Node voltage constraints: , where U min , U max are the lower and upper limits of the system node voltage respectively; That is: the voltage deviation between the node voltage and the standard voltage shall not exceed ±7%; 3) Branch transmission power constraints: , where P l_max is the upper limit of branch transmission power; 4) Grid flow constraints Where: P i and Q i are the active injection power and reactive injection power of node i at time t, G ij and B ij are the real and imaginary parts of the node admittance matrix in row i and column j, respectively. i is the voltage amplitude of node i at time t, δ ij is the phase difference between the two ends of branch ij at time t, and n is the total number of nodes in the system.

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