Microgrid safety optimization method

Abstract

Provided in the present invention is a microgrid safety optimization method, comprising the following steps: determining whether a specific scenario change occurs in a current microgrid; after determining that a specific scenario change has occurred in the current microgrid, performing reactive power optimization on the microgrid; after completing the reactive power optimization on the microgrid, optimizing energy storage units in the microgrid; and after completing the optimization on the energy storage units in the microgrid, improving the overall power quality control of an energy storage system. In the present invention, the power flow distribution of a microgrid can be regulated by means of reactive power optimization of the microgrid, such that it ensured that the voltages at nodes in a system are stable, and the power quality is up to standard, thereby making the system operate safely and stably, and making a load operate reliably.

Description

A microgrid security optimization method

[0001] Cross-references to related applications

[0002] This application claims priority to Chinese Patent Application No. 2024114404127, filed on October 16, 2024, entitled "A Microgrid Security Optimization Method", the entire contents of which are incorporated herein by reference. Technical Field

[0003] This invention relates to the field of microgrid security, specifically a microgrid security optimization method. Background Technology

[0004] With the development of industry and agriculture and the improvement of electrification levels, the demand for electricity in the whole society is increasing. However, the depletion of fossil energy and the requirement for a low-carbon economy have forced countries to vigorously develop renewable energy sources such as wind and solar power. However, the output of renewable energy is uncertain, making it impossible to determine the reactive power compensation amount when traditional microgrids absorb renewable energy, leading to increased network losses and voltage deviations. Therefore, research on power security optimization for systems containing microgrids is of great significance. Summary of the Invention

[0005] This invention provides a microgrid safety optimization method. By optimizing the reactive power of the microgrid, the power flow distribution of the microgrid can be adjusted to ensure the voltage stability of each node in the system and the power quality meets the standards, so that the system can operate safely and stably and the load can work reliably.

[0006] A microgrid security optimization method includes the following steps:

[0007] Determine whether specific scenario changes have occurred in the current microgrid;

[0008] After identifying changes in a specific scenario within the current microgrid, reactive power optimization is performed on the microgrid.

[0009] After optimizing the reactive power of the microgrid, the energy storage units in the microgrid are then optimized.

[0010] After optimizing the energy storage units in the microgrid, the overall power quality control of the energy storage system is improved.

[0011] Furthermore, the determination of whether a specific scenario change has occurred in the current microgrid specifically includes:

[0012] Determine whether a high proportion of renewable energy is integrated into the microgrid;

[0013] If a high proportion of renewable energy is connected to the microgrid, determine whether a power quality event has occurred in the microgrid;

[0014] After determining that a high proportion of renewable energy is connected to the microgrid and that a power quality event has occurred in the microgrid, it is determined whether demand-side management needs to be implemented in the microgrid.

[0015] After determining that demand-side management needs to be implemented in the microgrid, specific changes in the current microgrid scenario are identified.

[0016] Furthermore, determining whether a high proportion of renewable energy is integrated into the microgrid specifically includes:

[0017] Real-time data collection of power generation data from various energy sources in the microgrid; comparison of real-time power generation data with historical data; analysis of the changing trend of renewable energy power generation; if the proportion of renewable energy power generation in the total power generation continues to rise and reaches the preset power generation threshold, it is judged that a high proportion of renewable energy has been connected.

[0018] Alternatively, if the proportion of renewable energy installed capacity to total installed capacity exceeds a preset proportion, it is judged that there is a high proportion of renewable energy connected to the grid.

[0019] Furthermore, the determination of whether a power quality event has occurred in the microgrid specifically includes:

[0020] Determine whether voltage fluctuations occur at key nodes of the microgrid;

[0021] Summarize the number of times voltage fluctuations and harmonic interference occur simultaneously at each key node of the microgrid;

[0022] Determine whether the number of simultaneous voltage fluctuations and harmonic interferences at key nodes of the microgrid exceeds a set ratio and a set duration. If so, determine that a power quality event has occurred in the current microgrid.

[0023] Furthermore, the reactive power optimization of the microgrid after determining that a specific scenario change has occurred in the current microgrid specifically includes:

[0024] Set the comprehensive optimization objective F for reactive power optimization of the microgrid:

[0025] In the formula: ω1 and ω2 are the weighting coefficients for reducing voltage deviation and network loss in the microgrid, respectively; F1 represents the minimum node voltage deviation in the microgrid, and F2 represents the minimum network loss in the microgrid. F1 and F2 are expressed by the following formulas:

[0026] F2=min(P loss )

[0027] Among them, U i UN U imax U imin These are the microgrid node voltage, node reference voltage, maximum allowable node voltage, and minimum allowable node voltage, respectively; P loss G represents the network loss of the microgrid system; N represents the number of nodes in the microgrid; G represents the network loss of the microgrid system. ij U represents the electrical conductance between nodes i and j in the microgrid. j Let θ be the voltage at node j; ij The voltage phase angle difference between nodes i and j in the microgrid;

[0028] Based on the comprehensive optimization objective F of reactive power optimization in microgrids, reactive power optimization is performed on distributed power sources in microgrids to achieve overall reactive power optimization of microgrids.

[0029] Furthermore, after completing the reactive power optimization of the microgrid, the energy storage units in the microgrid are optimized, including:

[0030] In the voltage and current dual closed-loop control of the inverters of each distributed power source in the microgrid, a dynamic virtual impedance balancing strategy is introduced to achieve accurate and even distribution of the active power output of each distributed power source inverter.

[0031] Equalization control is performed on the current state of charge of each distributed energy storage unit to achieve equalization of the current state of charge among the distributed energy storage units;

[0032] Furthermore, a dynamic virtual impedance balancing strategy is introduced into the voltage and current dual closed-loop control of the inverters of each distributed power source in the microgrid to achieve precise and even distribution of the active power output of each distributed power source's inverter. Specifically, this includes:

[0033] Determine the virtual negative inductance value to be introduced in the parallel control module of each inverter in the microgrid;

[0034] Determine the virtual resistance values ​​to be added to the parallel control modules of each inverter in the microgrid;

[0035] The dynamic virtual complex impedance added to the parallel control module of each inverter in the microgrid is determined based on the virtual negative inductance value and the virtual resistance value.

[0036] The dynamic virtual complex impedance is added to the parallel control module of each inverter in the microgrid to introduce a dynamic virtual impedance balancing strategy, so as to achieve accurate and even distribution of the active power output of each distributed power source inverter.

[0037] Furthermore, the step of balancing the current state of charge of each distributed energy storage unit to achieve a balance of the current state of charge among the distributed energy storage units specifically includes:

[0038] In the formula: sgn is the symbolic function used to determine the DESU operating mode, S oc,i S is the current SOC state value of the i-th DESU. oc,ave P represents the mean SOC of each DESU. i Q i Let U and n be the active and reactive power outputs of the inverter corresponding to the i-th DESU, respectively, and m and n be the active-voltage droop coefficient and the reactive-frequency droop coefficient, respectively; N U is the rated output voltage of the inverter; i This represents the voltage amplitude output by the inverter corresponding to the i-th DESU during operation.

[0039] The working process of the method for equalizing the SOC of each DESU is as follows:

[0040] First, the running status of each DESU is determined using the sgn function. Then, the SOC state value S of the i-th DESU is obtained using the average consensus algorithm. oc,i The mean SOC of each DESU oc,ave The difference is used to make adaptive adjustments.

[0041] Furthermore, after optimizing the energy storage units in the microgrid, the overall power quality control of the energy storage system is improved, specifically including:

[0042] The charging and discharging of the energy storage system is controlled according to the set strategy to improve the power quality of the microgrid;

[0043] Determine the magnitude of the current command that can suppress power fluctuations in the energy storage unit, and incorporate the current command to suppress power fluctuations into the energy storage unit for control. Specifically, this includes:

[0044] Let P be the active power consumed by the user side. load The active power generated by distributed energy sources is P. DG The active power at the beginning of the microgrid is P. ref Then the active power required by the energy storage system to smooth out power fluctuations is P = P load -P ref -P DG If the rotating vector synthesized from the three-phase grid voltages is aligned with the d-axis of the synchronous coordinate system, then P is represented as:

[0045] In the formula e d Let i be the voltage on the d-axis. q For the current component on the q-axis in a synchronously rotating coordinate system, i d To output active current to the energy storage unit, and control the output active current i of the energy storage unit. dThis allows for control of its active power. In the harmonic detection stage, i q Set to 0, and add the active power current used to smooth out power fluctuations to the obtained compensation current to finally form the required command current.

[0046] Furthermore, the established strategy includes:

[0047] When the SOC of the energy storage unit is less than 5%, the corresponding IGBT of the energy storage unit is turned off, and the energy storage unit cannot perform active power regulation and power quality management of the microgrid.

[0048] When the SOC is between 5% and 95%, the energy storage unit charges and discharges based on the difference between the grid-connected power of the distributed energy source and the reference value of the active power of the distribution area. If the difference is positive, the energy storage unit charges to store the grid-connected energy exceeding the reference value and simultaneously performs reactive power compensation and harmonic mitigation. If the difference is close to 0, the capacity of the energy storage unit tends to stabilize, and only the power quality of the distribution area is managed. If the difference is negative, the energy storage unit discharges to compensate for the grid-connected energy that is less than the reference value and simultaneously participates in the power quality management of the distribution area.

[0049] When the SOC is greater than or equal to 95%, the corresponding IGBT of the energy storage unit is turned off to keep the bus voltage at the reference value. A chopper resistor is connected in parallel on the bus voltage output side and controlled by the IGBT so that the bus voltage is not affected when the energy storage unit exceeds the upper limit.

[0050] The present invention has the following beneficial effects:

[0051] (1) In this invention, reactive power optimization of the microgrid can adjust the power flow distribution of the microgrid, ensure the voltage stability of each node in the system and meet the power quality standards, so that the system can operate safely and stably and the load can work reliably. In addition, reactive power optimization can also reduce network losses and improve the economic efficiency of system operation.

[0052] (2) This invention achieves precise equal distribution of the system output active power by introducing a virtual impedance module; it adopts resistive control based on SOC equalization, adaptively adjusts the droop coefficient according to the output SOC value of each DESU, and sets voltage boundaries to ensure system stability during the SOC adjustment process.

[0053] (3) By combining microgrid reactive power optimization and resistive control based on SOC balance, the system can ensure stable voltage and power quality at each node, and further improve system stability on the basis of achieving system SOC balance.

[0054] (4) It can control the power and energy input and output of the microgrid at different time scales, effectively utilize the grid-connected power of distributed energy, stabilize the active power fluctuation of the microgrid, improve the stability and operating characteristics of the microgrid, and combine energy storage with power quality management devices. It can not only compensate for harmonics and reactive current in the microgrid and manage the power quality problems of the microgrid, but also give full play to the advantages of energy storage units in stabilizing the active power of the microgrid when the grid-connected power of distributed energy fluctuates. Attached Figure Description

[0055] Figure 1 is a topology diagram of a microgrid connected to the power grid;

[0056] Figure 2 is a diagram of an isolated AC microgrid structure;

[0057] Figure 3 is a block diagram of the dual closed-loop control introduced by the dynamic virtual impedance balance in an embodiment of the present invention;

[0058] Figure 4 is a schematic diagram of the working principle of SOC equalization based on improved droop control in an embodiment of the present invention.

[0059] Figure 5 is a topology diagram of an energy storage system connected in parallel at the beginning of a microgrid. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] Figure 1 shows the topology of a microgrid connected to the grid. In Figure 1, Vw is the wind speed at the location of the wind turbine installation, I is the solar irradiance at the location of the photovoltaic power generation device installation, Pw is the active power output of the wind turbine, Pv is the active power output of the photovoltaic system, and P+jQ is the output of the grid-connected inverter.

[0062] This invention provides a microgrid security optimization method, comprising the following steps:

[0063] Step 1: Determine whether there are any changes in specific scenarios in the current microgrid. If there are changes in specific scenarios, it is necessary to initiate the subsequent reactive power optimization, power quality control and overall improvement of energy storage units in this invention.

[0064] To determine whether a specific scenario has occurred in the current microgrid, the following steps are taken:

[0065] Step 1.1: Determine whether there is a high proportion of renewable energy connected to the microgrid.

[0066] When a high proportion of renewable energy sources such as wind and solar power is integrated into a microgrid, the volatility and uncertainty of these energy sources may necessitate improvements in reactive power optimization and energy storage unit optimization to enhance the system's regulation and stability. Simultaneously, power quality control becomes particularly crucial to ensure stable power supply.

[0067] The microgrid is determined to have a high proportion of renewable energy sources such as wind and solar power, specifically including:

[0068] Real-time data collection of power generation data from various energy sources in the microgrid, including wind power, solar power, energy storage devices, and traditional energy sources, is collected and transmitted to the central control system via sensors, smart meters, and other devices.

[0069] By comparing real-time power generation data with historical data, the changing trends of renewable energy power generation can be analyzed. If the proportion of power generation from renewable energy sources such as wind and solar power in the total power generation continues to rise and reaches a preset power generation threshold, it can be determined that a high proportion of renewable energy has been connected to the grid.

[0070] By calculating the proportion of renewable energy generation, such as wind and solar power, in the total power generation of a microgrid, we can intuitively understand the integration of renewable energy. For example, if wind and solar power generation accounts for more than 50% of the total power generation (this threshold can be set according to actual conditions), it can be considered that a high proportion of renewable energy has been integrated.

[0071] In addition to the proportion of electricity generation, the proportion of renewable energy installed capacity can also be considered. This reflects the potential power generation capacity of renewable energy in the microgrid. If the proportion of renewable energy installed capacity to total installed capacity is also high, then in actual operation, renewable energy power generation is also very likely to account for a high proportion.

[0072] Step 1.2: If a high proportion of renewable energy is connected to the microgrid, determine whether a power quality event has occurred in the microgrid.

[0073] If a microgrid experiences power quality issues, such as voltage fluctuations or harmonic interference, it may require emergency operations such as reactive power optimization, energy storage unit configuration, and power quality control optimization to improve the overall performance of the system.

[0074] First, power quality monitoring devices, such as power quality analyzers and harmonic monitors, need to be installed at key nodes of the microgrid (e.g., grid connection points and important load points) to monitor key parameters such as voltage, current, frequency, and harmonics in real time. The specific implementation steps are as follows:

[0075] Step 1.2.1: Determine whether voltage fluctuations occur at each critical node of the microgrid.

[0076] Voltage fluctuation refers to the continuous variation of the effective voltage value within a series of instantaneous values. To clarify the definition and allowable range of voltage fluctuation, it is usually measured by the voltage fluctuation rate (the ratio of the change in the effective voltage value to the rated voltage), which is typically determined according to national or industry standards.

[0077] Calculating whether voltage fluctuations occur at critical nodes of a microgrid includes: real-time monitoring of the current voltage value of each critical node and obtaining the voltage value at the previous monitoring time; calculating the change in the effective voltage value of each critical node based on the current voltage value and the voltage value at the previous monitoring time; calculating the voltage fluctuation rate of each critical node based on the ratio of the change in the effective voltage value to the rated voltage; and comparing the voltage fluctuation rate with the pre-set standard voltage fluctuation rate limit to determine whether there is a voltage fluctuation problem at each critical node.

[0078] Assuming the national standard stipulates that voltage fluctuation rate should not exceed ±5%, at a certain moment the monitored voltage fluctuation rate at the grid connection point is +6%, the voltage fluctuation rate at important load point A is -4%, and the voltage fluctuation rate at important load point B is +3%. In this case, it can be determined that there are voltage fluctuation problems at the grid connection point and important load point A, while the voltage fluctuation at important load point B is within the allowable range.

[0079] Step 1.2.2: Determine whether harmonic interference occurs at each critical node of the microgrid.

[0080] Harmonics refer to sinusoidal electrical components whose frequencies are integer multiples of the fundamental frequency. They can have adverse effects on the normal operation of the power system and the safety of equipment.

[0081] Calculating whether harmonic interference occurs at each critical node of the microgrid includes:

[0082] Obtain the predetermined permissible limits for each harmonic (such as the 3rd, 5th, 7th, etc.), i.e., the standard limits;

[0083] Analyze the harmonic spectrum of each key node and calculate the content of each harmonic.

[0084] By comparing the harmonic content rate with the standard limit, it can be determined whether there is harmonic interference at each key node.

[0085] Assuming the national standard stipulates that the fifth harmonic voltage content should not exceed 4%, at a certain moment the monitored fifth harmonic voltage content at the grid connection point is 5.5%, the fifth harmonic voltage content at important load point A is 3.8%, and the fifth harmonic voltage content at important load point B is 2.5%. In this case, it can be determined that there is harmonic interference at the grid connection point, while the harmonic content at important load points A and B is within the allowable range.

[0086] Step 1.2.3: Summarize the number of simultaneous voltage fluctuations and harmonic interferences at each critical node of the microgrid.

[0087] The analysis results of voltage fluctuations and harmonic interference at each key node are summarized to determine the number of key nodes in the microgrid that simultaneously experience voltage fluctuations and harmonic interference.

[0088] Step 1.2.4: Determine whether the number of simultaneous voltage fluctuations and harmonic interferences at each critical node of the microgrid exceeds the set ratio and the set duration. If so, determine that a power quality event has occurred in the current microgrid.

[0089] If multiple critical nodes experience voltage fluctuations or harmonic interference simultaneously, exceeding a set ratio and lasting for an extended period (exceeding the set duration), it can be determined that there is a problem with the power quality of the microgrid, and a power quality event has occurred in the current microgrid.

[0090] Suppose a microgrid system in a certain region consists of photovoltaic power generation, wind power generation, energy storage systems, and multiple critical load nodes. The system aims to provide a stable and reliable power supply to meet the daily life needs of local residents and industrial production. Recently, the microgrid monitoring system has shown that multiple critical nodes (such as grid connection points and important load points) are simultaneously experiencing voltage fluctuations and harmonic interference.

[0091] Voltage fluctuations at several key nodes exceeded ±5%, far exceeding the ±2% voltage fluctuation limit recommended by the International Electrotechnical Commission (IEC).

[0092] Among the harmonic content detected at multiple key nodes, the 3rd, 5th, and 7th harmonic content rates increased significantly, reaching 5%, 3%, and 2% respectively, far exceeding the preset harmonic limits for general microgrid systems (usually the harmonic content rate of each order should not exceed 1%).

[0093] The analysis results show that there are 3 critical nodes with simultaneous voltage fluctuations and harmonic interference (assuming a total of 10 critical nodes are detected). The proportion of critical nodes with simultaneous voltage fluctuations and harmonic interference is 3 / 10 = 30%, which exceeds the set ratio of 20%. Moreover, the duration of each is about 1 minute. Therefore, it can be determined that there is a problem with the power quality of the current microgrid, and a power quality event has occurred in the current microgrid.

[0094] Step 1.3: After determining that a high proportion of renewable energy is connected to the microgrid and that a power quality event has occurred in the microgrid, determine whether demand-side management needs to be implemented in the microgrid.

[0095] If it is determined that the microgrid needs to implement demand-side management strategies, such as demand response or load control, then it is determined that the microgrid needs reactive power optimization, optimized energy storage units, and power quality control to respond more flexibly to load changes.

[0096] The method for determining whether a microgrid needs demand-side management can be based on a request from the manager, or the manager can pre-set a management strategy. When it is determined that a high proportion of renewable energy is connected to the microgrid and a power quality event occurs, the system automatically determines whether demand-side management is necessary. This determines whether a specific scenario has occurred in the microgrid, necessitating the initiation of subsequent reactive power optimization, power quality control, and overall energy storage unit upgrades as described in this invention.

[0097] Step 2: After determining the specific scenario changes in the current microgrid in Step 1, perform reactive power optimization on the microgrid.

[0098] Reactive power optimization primarily focuses on improving voltage stability and reducing transmission losses in microgrids. Reactive power optimization directly impacts the basic operating conditions and efficiency of the power grid, and is the first step in this optimization approach.

[0099] When traditional microgrids do not absorb the power output of other microgrids, reactive power compensation is typically achieved using devices such as static var compensators (SVCs), electrostatic capacitors (ESCs), and transformer tap changers. The main principles followed are a combination of local and grid-wide balancing, as well as a combination of distributed and centralized compensation. However, when a microgrid absorbs the power output of another microgrid, the uncertainty of that output affects the power flow distribution, causing traditional discrete reactive power compensation methods to fail to respond quickly. Therefore, there is an urgent need to propose novel reactive power optimization compensation strategies for microgrids connected to traditional microgrids.

[0100] Therefore, in order to more accurately describe the fluctuations in microgrid output, this invention uses reactive power optimization to adjust the power flow distribution of the microgrid, ensuring stable voltage at each node and meeting power quality standards, thus enabling the system to operate safely and stably and the loads to work reliably. Furthermore, reactive power optimization can reduce network losses and improve the economic efficiency of system operation.

[0101] Step 2 specifically includes:

[0102] Step 2.1: First, set the comprehensive optimization objective F for reactive power optimization of the microgrid.

[0103] The overall optimization objective F for reactive power optimization of microgrids is as follows:

[0104] In the formula: ω1 and ω2 are the weighting coefficients for reducing voltage deviation and network loss in the microgrid, respectively; different control objectives can be achieved for each microgrid by adjusting the values ​​of ω1 and ω2.

[0105] F1 represents the minimum node voltage deviation in the microgrid, and F2 represents the minimum network loss in the microgrid. F1 and F2 are expressed by the following formulas:

[0106] F2=min(P loss )

[0107] Among them, U i U N U imax U imin These are the microgrid node voltage, node reference voltage, maximum allowable node voltage, and minimum allowable node voltage, respectively; P loss G represents the network loss of the microgrid system; N represents the number of nodes in the microgrid; G represents the network loss of the microgrid system. ij U represents the electrical conductance between nodes i and j in the microgrid. j Let θ be the voltage at node j; ij Let be the voltage phase angle difference between nodes i and j in the microgrid.

[0108] To differentiate the voltage requirements of the beginning and end nodes in a microgrid, this invention sets different lower limits for the voltage at different nodes. Considering voltage drops along the line, the lower limit for the voltage at the beginning node is set relatively high. For example, U... 1min U 2min and U 5min Set it to 1.05, and set U 18min U 22min and U 33min The lower limit of the voltage for the remaining nodes is set to 0.85, and the lower limit of the voltage for the remaining nodes is set to 0.9.

[0109] In the reactive power optimization process of distributed power sources in a microgrid, the reactive power output of photovoltaic power sources and wind turbine power sources must also meet the following constraints:

[0110] In the formula: Q PV、 Q w Q represents the reactive power output of photovoltaic power and wind turbine power in the microgrid, respectively. PV_max Q PV_min Q w_max Q w_min These represent the maximum and minimum reactive power outputs of photovoltaic power and wind turbine power in the microgrid, respectively.

[0111] Step 2.2: Based on the comprehensive optimization objective F of reactive power optimization of the microgrid, perform reactive power optimization on the distributed power sources in the microgrid to achieve overall reactive power optimization of the microgrid.

[0112] For example, the above formula can be used to optimize the reactive power of photovoltaic power and wind turbine power in a microgrid, respectively.

[0113] As shown in Figure 1, the photovoltaic power supply and wind turbine power supply are connected to the microgrid through a grid-connected inverter. Let the rated power of the grid-connected inverter be S. Then, the active power P and reactive power Q output by the grid-connected inverter for both the wind turbine and photovoltaic power supply must satisfy P... 2 +Q 2 ≤S 2 Therefore, even in windless or dark weather, the grid-connected inverter can still output reactive power for reactive power optimization.

[0114] In this invention, reactive power optimization of the microgrid ensures stable voltage and meets power quality standards at each node in the system, enabling safe and stable system operation and reliable load function. Furthermore, reactive power optimization reduces network losses and improves the economic efficiency of system operation.

[0115] Step 3: After completing the reactive power optimization of the microgrid, optimize the energy storage units in the microgrid.

[0116] Effective use of energy storage units requires a stable power grid. When microgrids need to improve their adaptability to special scenarios (such as the specific scenario in step 1, the volatility of renewable energy, etc.), energy storage unit optimization becomes particularly important. In this invention, after reactive power optimization of the microgrid, energy storage units in the microgrid are optimized to balance supply and demand, while further reducing dependence on the external power grid and lowering costs.

[0117] Due to the intermittency, uncertainty, and randomness of renewable energy sources, it is difficult to maintain stable output power. Therefore, energy storage systems need to be added to distributed microgrids. Energy storage systems can include multiple distributed energy storage units (DESUs) to improve power supply reliability.

[0118] Currently, AC microgrids are typically connected to low-voltage distribution systems. In islanded low-voltage AC microgrids, the DC power output by each microgrid unit needs to be converted into AC power by an inverter to supply power to the load, and multiple DESUs are configured to provide continuous power supply to the microgrid.

[0119] However, due to the impedance differences in the output lines of each inverter, the output power distribution is unreasonable, and the initial state of charge (SOC) of each DESU is inconsistent. Traditional resistive droop control is insufficient to achieve SOC balancing among microgrid units, leading to overcharging or over-discharging of DESUs and reducing their lifespan and capacity utilization. If multiple DESUs fail to exit the system due to SOC imbalance, the reliability of the microgrid power supply will be significantly reduced, and in severe cases, even cause system collapse. Therefore, researching SOC balancing strategies for DESUs in low-voltage microgrids is of significant strategic importance.

[0120] To address the SOC imbalance problem of DESUs in isolated low-voltage microgrids, this invention first ensures the decoupling of droop control power and the precise equal distribution of output active power, providing the necessary conditions for achieving SOC balance. Then, an SOC adjustment term is added to the droop control, allowing the droop coefficient to adaptively adjust according to the SOC of each DESU, ensuring SOC balance among DESUs. Voltage and frequency compensation control is also introduced to further improve system stability. Finally, the effectiveness of the proposed strategy is verified through simulation models.

[0121] As shown in Figure 2, the isolated AC microgrid generates DC power from photovoltaic and wind turbine distributed units, which then supplies power to the loads through inverters and output lines. Furthermore, each DESU is connected in parallel with the other through interface inverters and is connected to the common AC bus to provide power support for the isolated microgrid loads.

[0122] Due to differences in line impedance, active power cannot be evenly distributed, leading to SOC imbalance in the DEUS. This invention proposes an improved SOC balancing strategy to optimize energy storage units in microgrids.

[0123] In this invention, the strategy for optimizing energy storage units in a microgrid is divided into three parts: the first part is to introduce dynamic virtual complex impedance in the voltage and current dual closed-loop control to achieve accurate and even distribution of active power output by the inverter; the second part is to improve the traditional resistive droop control to achieve SOC balance among each DESU.

[0124] Step 3 specifically includes:

[0125] Step 3.1: Introduce dynamic virtual impedance balancing strategies in the voltage and current dual closed-loop control of the inverters of each distributed power source in the microgrid to achieve accurate and even distribution of the active power output of each distributed power source's inverter.

[0126] To achieve SOC balance among distributed power sources (DESUs) in the microgrid, the uneven distribution of active power output from the inverters must first be addressed. To solve this problem, a dynamic virtual impedance balancing strategy is proposed, which introduces dynamic virtual complex impedance, including virtual negative inductance and virtual resistance. The virtual negative inductance eliminates the inductive component in the inverter output impedance and line impedance, thus resolving the power coupling problem in resistive droop control. The virtual resistance balances the differences in line impedance between inverters, ensuring a reasonable and even distribution of output active power. The control block diagram for introducing dynamic virtual impedance balancing into the inverter parallel control module is shown in Figure 3.

[0127] In Figure 3, K pu With K pi These are the control coefficients for the voltage outer loop and current inner loop in the inverter parallel control module, respectively, K. pwmU is the gain coefficient of the dual closed-loop control. ref with u ref * represents the reference voltage before and after the introduction of the dynamic virtual impedance balancing strategy in the inverter parallel control module, u0 and i0 represent the output voltage and output current values, respectively, and R... f L f and C f These are the filter resistor, inductor, and capacitor, respectively. Z v This introduces a dynamic virtual complex impedance (including virtual negative inductance and virtual resistance).

[0128] Step 3.1 specifically includes:

[0129] Step 3.1.1: Determine the virtual negative inductance value to be introduced in the parallel control module of each inverter in the microgrid; specifically, step 3.1.1 includes:

[0130] Step 3.1.1.1: Calculate the output impedance Z of each inverter parallel control module in the microgrid after introducing the dynamic virtual complex impedance. eq (s)

[0131] Z eq The expression for (s) is:

[0132] in:

[0133] Step 3.1.1.2: Based on the output impedance Z eq (s) Determine the inductive component of the inverter output impedance.

[0134] Output impedance will be expressed in a complex form, with its real part representing the resistive component and its imaginary part representing the inductive component.

[0135] Step 3.1.1.3: Set the line inductive reactance of each inverter in the parallel control module of the microgrid after the introduction of the virtual negative inductance to be equal to the inductive component of the inverter output impedance, and determine the value of the virtual negative inductance to be introduced.

[0136] To ensure a reasonable and even distribution of active power in resistive droop control output, the power coupling problem caused by the inductive component in the inverter output impedance needs to be addressed. A virtual negative inductor is introduced to cancel the inductive component in the output impedance, thereby making the output impedance resistive.

[0137] The expression for the introduced virtual negative inductance is:

[0138] in:

[0139] In the formula: X li This refers to the line inductive reactance of the inverter.

[0140] By introducing a virtual negative inductance value that is adaptively selected based on the inductive component of the line impedance, the inductive component of the inverter output impedance can be eliminated, thus solving the power coupling problem in resistive droop control and making the inverter output impedance approximately purely resistive.

[0141] Step 3.1.2: Determine the virtual resistance value to be added to the parallel control module of each inverter in the microgrid.

[0142] By introducing virtual resistors into the parallel control modules of each inverter in the microgrid, the active power output of each inverter is reasonably and evenly distributed. The expression for the resistance value of the virtual resistors introduced into the parallel control modules of each inverter in the microgrid is as follows:

[0143] In the formula: R v0 The initial value of the virtual resistance is typically set to 0.8 to 3.5 times the output line impedance; K R This is the set virtual resistance adjustment factor.

[0144] The introduced dynamic virtual resistance expression is a drooping equation with virtual resistance and active power as control variables. By setting P1, P2...Pi equal, the virtual resistance of different distributed power sources in the microgrid can be determined, and then the virtual resistance can be dynamically adjusted to achieve accurate and even distribution of output active power.

[0145] Step 3.1.3: Determine the dynamic virtual complex impedance added to the parallel control module of each inverter in the microgrid based on the virtual negative inductance value and the virtual resistance value, that is, the combination of virtual negative inductance and virtual resistance;

[0146] Step 3.1.4: Add dynamic virtual complex impedance to the parallel control module of each inverter in the microgrid to introduce a dynamic virtual impedance balancing strategy and achieve accurate and even distribution of the active power output of each distributed power source inverter.

[0147] Step 3.2: Perform equalization control on the current state of charge (SOC) of each distributed energy storage unit (DESU) to achieve SOC balance among DESUs.

[0148] Based on the introduction of dynamic virtual complex impedance to achieve basic equal distribution of active power, the system SOC is difficult to balance due to the unequal initial SOC states of each DESU. Therefore, a method for balancing the SOC of each DESU is proposed, as shown in the following equation:

[0149] In the formula: sgn is the symbolic function used to determine the DESU operating mode, S oc,i S is the current SOC state value of the i-th DESU. oc,ave P represents the mean SOC of each DESU. i Qi Let U and n be the active and reactive power outputs of the inverter corresponding to the i-th DESU, respectively, and m and n be the active-voltage droop coefficient and the reactive-frequency droop coefficient, respectively; N U is the rated output voltage of the inverter; i This represents the voltage amplitude output by the inverter corresponding to the i-th DESU during operation.

[0150] The working process of the method for equalizing the SOC of each DESU is as follows:

[0151] First, the running status of each DESU is determined using the sgn function. Then, the SOC state value S of the i-th DESU is obtained using the average consensus algorithm. oc,i The mean SOC of each DESU oc,ave The difference is used to make adaptive adjustments. The working principle of the method for equalizing the SOC of each DESU is shown in Figure 4.

[0152] The SOC state value S of the i-th DESU is obtained using the average consensus algorithm. oc,i The mean SOC of each DESU oc,ave The difference is used to make adaptive adjustments, and the specific implementation method is as follows:

[0153] As shown in Figure 4, in discharge mode, if the S of the DESU oc,i Greater than S oc,ave If the active power-voltage curve shifts upward, it increases the output active power of the DESU; if the S of the DESU oc,i Less than S oc,ave This causes its active-voltage curve to shift downwards, reducing the output active power rate of the DESU. In charging mode, if the S of the DESU... oc,i Greater than S oc,ave If the active power-voltage curve shifts upward, the output active power of the DESU will decrease, and vice versa. Furthermore, voltage boundary limits are set during the adjustment process to ensure that system voltage variations are within ±5% U. N Within. When each DESU's S oc,i equals S oc,ave Once the system's SOC is balanced, traditional resistive droop control is reverted to its original state.

[0154] The average SOC of each DESU oc,ave The results are obtained through a dynamic consensus algorithm. An energy storage system in a microgrid can be represented by n nodes G and n edges E, with each DESU considered as a node G. i 、E i The communication status between each node, i.e. (G i E iFirst, each DESU samples its local SOC information and exchanges SOC status information with neighboring DESUs using low-bandwidth communication. Then, the SOC status is solved using equation (1). oc,ave And for the desired S oc,ave Convergence is determined according to equation (2). If equation (2) is not satisfied, an update iteration is performed, and S is recalculated. oc,ave The process continues until the convergence requirement is met. Finally, if the convergence condition is satisfied, the solution process ends, and the final value S is output. oc,ave result. |S OC,j (k)-S OC,i (k)|<ε (2)

[0155] In the formula: S oc,ave (k+1) represents the average SOC of the i-th DESU after k+1 iterations; S oc,ave (k) represents the SOC value of the i-th DESU after k iterations; δ ij (k) represents the difference in the average SOC between adjacent DESUs after k iterations, and δ ij (0) = 0; X ij Let ε represent the connection state between the i-th and j-th DESUs, and let ε be the edge weight of the DESU.

[0156] Step 4: After optimizing the energy storage units in the microgrid, improve the overall power quality control of the energy storage system.

[0157] In this invention, energy storage technology can control the power and energy input and output of a microgrid at different time scales, effectively utilize the grid-connected power of distributed energy sources, stabilize the active power fluctuations of the microgrid, and improve the stability and operating characteristics of the microgrid. By combining energy storage with power quality management devices, it can not only compensate for harmonics and reactive currents in the microgrid and manage the power quality problems of the microgrid, but also leverage the advantage of energy storage systems in stabilizing the active power of the microgrid when the grid-connected power of distributed energy sources fluctuates.

[0158] This invention utilizes the similarity between the topology of energy storage systems and active power filters (APFs) to improve control strategies and power regulation of energy storage systems while also providing power quality regulation functions.

[0159] As a common power quality management device, APF can generate command current from the detected reactive current and harmonics through the computing circuit, then transmit it to the control circuit, and after modulating the PWM signal, send it to the drive circuit. The drive signal is then sent to the converter control switch, and finally filtered by the filter to achieve the effect of power quality management. However, APF can only achieve the effect of power quality management and cannot stabilize the power fluctuations caused by the randomness and intermittency of distributed energy sources in microgrids.

[0160] To effectively address the power quality issues generated by microgrids, an energy storage system is connected in parallel at the head end of the microgrid, as shown in Figure 5. The energy storage lithium battery first undergoes DC / DC conversion via a bidirectional half-bridge, then connects to a four-arm inverter. This provides energy to the inverter while also stabilizing the bus voltage. After passing through an L filter, it is connected to the head end of a 10kV / 380V microgrid. L and R are the filter inductance and parasitic resistance, respectively. The energy storage system can effectively improve the power quality of the microgrid and provide a guarantee for the operation of the microgrid under distributed energy grid connection.

[0161] Step 4 specifically includes:

[0162] Step 4.1: Control the charging and discharging of the energy storage system according to the set strategy to improve the power quality of the microgrid.

[0163] It can charge and discharge based on the difference between the current state of charge (SOC) of each energy storage unit in the energy storage system and the active power at the beginning of the microgrid.

[0164] When the SOC of the energy storage unit is less than 5%, the insulated gate bipolar transistor (IGBT) corresponding to the energy storage unit in Figure 2 is turned off, and the energy storage unit cannot perform active power regulation and power quality management of the microgrid.

[0165] When the SOC is between 5% and 95%, the energy storage unit can charge and discharge according to the difference between the grid-connected power of the distributed energy and the reference value of the active power of the distribution area. If the difference is positive, the energy storage unit charges to store the grid-connected energy exceeding the reference value and simultaneously performs reactive power compensation and harmonic mitigation. If the difference is close to 0, the capacity of the energy storage unit tends to stabilize and only performs power quality management of the distribution area. If the difference is negative, the energy storage unit discharges to compensate for the grid-connected energy that is less than the reference value and simultaneously participates in power quality management of the distribution area.

[0166] When the SOC is greater than or equal to 95%, the IGBT is turned off to prevent the energy storage unit from charging and discharging beyond the limit, so that the bus voltage is kept at the reference value. To prevent a short charging process when the SOC is greater than or equal to 95%, a chopper resistor is connected in parallel on the bus voltage output side and controlled by the IGBT so that the bus voltage is not affected when the energy storage unit exceeds the upper limit.

[0167] Step 4.2: Determine the magnitude of the current command that can suppress power fluctuations in the energy storage unit, and add the current command to suppress power fluctuations to the energy storage unit for control.

[0168] In order for the energy storage system to stabilize the active power of the microgrid, a current command that can suppress power fluctuations is added while controlling harmonic currents.

[0169] Assume the active power consumed by the user is P. load The active power generated by distributed energy sources such as photovoltaics, wind power, and hydropower is P. DG The active power at the beginning of the microgrid is P. ref Then the active power required by the energy storage system to smooth out power fluctuations is P = P load -P ref -P DG If the rotating vector synthesized from the three-phase grid voltages is aligned with the d-axis of the synchronous coordinate system, then P can also be expressed as:

[0170] In the formula e d Let i be the voltage on the d-axis. q For the current component on the q-axis in a synchronously rotating coordinate system, i d This provides the active current output to the energy storage unit. From the formula above, it can be seen that controlling the active current i output by the energy storage unit... d This allows for control of its active power. In the harmonic detection stage, i q Set to 0, and add the active power current used to smooth out power fluctuations to the obtained compensation current to finally form the required command current.

[0171] Smoothing power fluctuations corresponds to a 50Hz time scale, while harmonic control corresponds to higher harmonics, such as the 5th and 7th harmonics, which correspond to a 5×50Hz or 7×50Hz time scale. Therefore, adopting a reasonable control strategy can unify the two in terms of time scale.

[0172] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A microgrid security optimization method, characterized in that, Includes the following steps: Determine whether specific scenario changes have occurred in the current microgrid; After identifying changes in a specific scenario within the current microgrid, reactive power optimization is performed on the microgrid. After optimizing the reactive power of the microgrid, the energy storage units in the microgrid are then optimized. After optimizing the energy storage units in the microgrid, the overall power quality control of the energy storage system is improved.

2. The microgrid security optimization method of claim 1, wherein: The determination of whether a specific scenario change has occurred in the current microgrid specifically includes: Determine whether a high proportion of renewable energy is integrated into the microgrid; If a high proportion of renewable energy is connected to the microgrid, determine whether a power quality event has occurred in the microgrid; After determining that a high proportion of renewable energy is connected to the microgrid and that a power quality event has occurred in the microgrid, it is determined whether demand-side management needs to be implemented in the microgrid. After determining that demand-side management needs to be implemented in the microgrid, specific changes in the current microgrid scenario are identified.

3. The microgrid security optimization method of claim 2, wherein: The determination of whether a high proportion of renewable energy is connected to the microgrid specifically includes: Real-time data collection of power generation data from various energy sources in the microgrid; comparison of real-time power generation data with historical data; analysis of the changing trend of renewable energy power generation; if the proportion of renewable energy power generation in the total power generation continues to rise and reaches the preset power generation threshold, it is judged that a high proportion of renewable energy has been connected. Alternatively, if the proportion of renewable energy installed capacity to total installed capacity exceeds a preset proportion, it is judged that there is a high proportion of renewable energy connected to the grid.

4. The microgrid security optimization method of claim 2, wherein: The determination of whether a power quality event has occurred in the microgrid specifically includes: Determine whether voltage fluctuations occur at key nodes of the microgrid; Summarize the number of times voltage fluctuations and harmonic interference occur simultaneously at each key node of the microgrid; Determine whether the number of simultaneous voltage fluctuations and harmonic interferences at key nodes of the microgrid exceeds a set ratio and a set duration. If so, determine that a power quality event has occurred in the current microgrid.

5. The microgrid security optimization method of claim 1, wherein: The process of optimizing the reactive power of the microgrid after determining that a specific scenario has occurred in the current microgrid includes: A comprehensive optimization objective F for reactive power optimization of the microgrid is set: In the formula, ω1 and ω2 are weight coefficients for reducing voltage deviation in the microgrid and reducing network loss, respectively; F1 is the minimum voltage deviation in the microgrid, and F2 is the minimum network loss in the microgrid, and F1 and F2 are respectively represented by the following formulae: F2 = min(P loss ) Among them, U i U N U imax U imin These are the microgrid node voltage, node reference voltage, maximum allowable node voltage, and minimum allowable node voltage, respectively; P loss G represents the network loss of the microgrid system; N represents the number of nodes in the microgrid; G represents the network loss of the microgrid system. ij U represents the electrical conductance between nodes i and j in the microgrid. j Let θ be the voltage at node j; ij The voltage phase angle difference between nodes i and j in the microgrid; Based on the comprehensive optimization objective F of reactive power optimization in microgrids, reactive power optimization is performed on distributed power sources in microgrids to achieve overall reactive power optimization of microgrids.

6. The microgrid security optimization method of claim 1, wherein: After completing the reactive power optimization of the microgrid, the energy storage units in the microgrid are optimized, specifically including: In the voltage and current dual closed-loop control of the inverters of each distributed power source in the microgrid, a dynamic virtual impedance balancing strategy is introduced to achieve accurate and even distribution of the active power output of each distributed power source inverter. The current state of charge of each distributed energy storage unit is balanced to achieve a balance of the current state of charge among the distributed energy storage units.

7. The microgrid security optimization method of claim 6, wherein: The introduction of dynamic virtual impedance balancing strategies in the voltage and current dual closed-loop control of the inverters of each distributed power source in the microgrid aims to achieve precise and even distribution of the active power output of the inverters of each distributed power source. Specifically, this includes: Determine the virtual negative inductance value to be introduced in the parallel control module of each inverter in the microgrid; Determine the virtual resistance values ​​to be added to the parallel control modules of each inverter in the microgrid; The dynamic virtual complex impedance added to the parallel control module of each inverter in the microgrid is determined based on the virtual negative inductance value and the virtual resistance value. The dynamic virtual complex impedance is added to the parallel control module of each inverter in the microgrid to introduce a dynamic virtual impedance balancing strategy, so as to achieve accurate and even distribution of the active power output of each distributed power source inverter.

8. The microgrid security optimization method of claim 6, wherein: The balancing control of the current state of charge of each distributed energy storage unit realizes the balance of the current state of charge among the distributed energy storage units, and specifically includes: In the formula: sgn is the symbolic function used to determine the DESU operating mode, S oc,i S is the current SOC state value of the i-th DESU. oc,ave P represents the mean SOC of each DESU. i Q i Let U be the active power and reactive power output of the inverter corresponding to the i-th DESU, respectively, and m and n be the active power-voltage droop coefficient and reactive power-frequency droop coefficient, respectively; N U is the rated output voltage of the inverter; i This represents the voltage amplitude output by the inverter corresponding to the i-th DESU during operation. The working process of the method for equalizing the SOC of each DESU is as follows: Firstly, the running state of each DESU is judged by the sgn function, and then the SOC state value S of the i th DESU is obtained by the average consistency algorithm oc,i The difference between the SOC average value S of each DESU oc,ave , so as to perform adaptive adjustment.

9. The microgrid security optimization method of claim 1, wherein: After optimizing the energy storage units in the microgrid, the overall power quality control of the energy storage system is improved, specifically including: The charging and discharging of the energy storage system is controlled according to the set strategy to improve the power quality of the microgrid; Determine the magnitude of the current command that can suppress power fluctuations in the energy storage unit, and incorporate the current command to suppress power fluctuations into the energy storage unit for control. Specifically, this includes: Let the active power consumed by the user side be P load , the active power generated by the distributed energy be P DG , and the active power at the head of the micro-grid be P ref , then the active power provided by the energy storage system for power fluctuation suppression is P=P load -P ref -P DG If the rotating vector composed of the three-phase grid voltage coincides with the d-axis of the synchronous coordinate system, then P at this time is expressed as: where e d is the voltage on d-axis, i q is the current component on q-axis in synchronous rotating coordinate system, i d is the active current output by the energy storage unit, control the active current i d The active power control can be realized, in the harmonic detection link, i q is set to 0, on the basis of the obtained compensation current, plus the active power current used to suppress power fluctuation, finally constitute the required command current.

10. The microgrid security optimization method of claim 9, wherein: The strategies set include: When the SOC of the energy storage unit is less than 5%, the corresponding IGBT of the energy storage unit is turned off, and the energy storage unit cannot perform active power regulation and power quality management of the microgrid. When the SOC is between 5% and 95%, the energy storage unit charges and discharges based on the difference between the grid-connected power of the distributed energy source and the reference value of the active power of the distribution area. If the difference is positive, the energy storage unit charges to store the grid-connected energy exceeding the reference value and simultaneously performs reactive power compensation and harmonic mitigation. If the difference is close to 0, the capacity of the energy storage unit tends to stabilize, and only the power quality of the distribution area is managed. If the difference is negative, the energy storage unit discharges to compensate for the grid-connected energy that is less than the reference value and simultaneously participates in the power quality management of the distribution area. When the SOC is greater than or equal to 95%, the corresponding IGBT of the energy storage unit is turned off to keep the bus voltage at the reference value. A chopper resistor is connected in parallel on the bus voltage output side and controlled by the IGBT so that the bus voltage is not affected when the energy storage unit exceeds the upper limit.

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