Micro-source capacity optimization configuration and collaborative control method for weak power grid

Abstract

The application discloses a micro power capacity optimization configuration and collaborative control method for a weak power grid, and belongs to the technical field of power systems, and comprises the following steps: generating a capacity matching and a day-ahead output plan containing each micro power source based on a multi-objective optimization algorithm; monitoring system operation data in real time, and calculating a multi-energy complementary coupling coefficient representing system regulation margin; dynamically correcting virtual synchronous machine control parameters of a network type energy storage device according to real-time monitored wind and light penetration rates, so as to adaptively adjust system equivalent damping; controlling energy storage output to support bus voltage frequency according to the corrected virtual synchronous machine control parameters, and when the multi-energy complementary coupling coefficient is lower than a preset threshold or the frequency deviation exceeds a limit, micro pumped storage devices are collaboratively triggered to perform power compensation, so that power balance and stability control of the micro grid are completed. Through dynamic parameter correction and multi-energy collaborative compensation, the frequency stability, power supply reliability and clean energy consumption rate of the weakly interconnected micro grid are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and more specifically, to a method for optimizing the capacity configuration and coordinated control of micro-sources for weak power grids. Background Technology

[0002] With the deepening of the global energy transition, the penetration rate of distributed renewable energy, represented by photovoltaics and wind power, in the power system is continuously rising. Especially in remote mountainous areas, islands, or at the end of the grid, where large grid coverage is difficult or power quality is unstable, building microgrids integrating multiple energy forms such as wind, solar, energy storage, and micro-hydropower has become a key way to solve local power supply problems and improve energy self-sufficiency. These microgrids typically operate in a "weakly interconnected" or even off-grid state, relying on the spatiotemporal complementarity between micro-power sources to maintain power balance within the system. Currently, microgrid technology is gradually evolving from single energy access to multi-energy complementarity and intelligent collaboration. The application of grid-based energy storage technology enables microgrids to establish voltage and frequency references and maintain independent and stable operation even when disconnected from the main grid. To ensure the safety and efficiency of microgrids, the industry typically establishes a multi-level control system including weather forecasting, power planning, and real-time output. This system aims to address the inherent volatility and intermittency challenges of renewable energy by comprehensively managing the output characteristics of different energy sources, ensuring the continuity and reliability of power supply.

[0003] However, existing microgrid control technologies still exhibit significant limitations in extreme scenarios with high penetration and weak interconnection. On the one hand, conventional energy storage virtual synchronous machine control strategies typically employ fixed inertia and damping parameters, making it difficult to adapt to rapid frequency shifts caused by drastic fluctuations in wind and solar power output. Especially in weak grid environments, the lack of dynamic adjustment mechanisms can easily lead to system instability or even grid disconnection due to frequency exceeding limits. On the other hand, the utilization of micro-hydropower resources often remains at a rudimentary "power whenever water is available" level, lacking the flexible on-site regulation capabilities of micro-pumped storage systems similar to large-scale pumped storage. This makes it impossible to effectively coordinate with distributed wind and solar power fluctuations for on-site peak shaving and valley filling, resulting in the waste of valuable regulation resources. Summary of the Invention

[0004] The purpose of this invention is to provide a method for optimizing the configuration and coordinated control of micro-power supply capacity for weak power grids. It realizes the adaptive adjustment of energy storage control parameters according to wind and solar penetration rates, and innovatively utilizes water towers to construct micro pumped storage. Through multi-timescale coordinated control, it effectively solves the problems of voltage and frequency stability and long-term power supply in distributed clean energy high-penetration weak interconnected microgrids.

[0005] This invention is achieved through the following technical solution:

[0006] A method for optimizing the capacity configuration and coordinated control of micro-sources in weak power grids, comprising the following steps:

[0007] Meteorological data and historical load data of the microgrid area are collected. For the physical equipment layer consisting of photovoltaic power generation, micro wind turbines, micro hydropower generation devices and grid-type energy storage, a day-ahead prediction model of the power generation capacity of micro power sources is established. Based on a multi-objective optimization algorithm, a day-ahead power output plan containing the power output instructions of each micro power source is generated as a power reference benchmark for system operation.

[0008] During the operation of the microgrid, the actual output of each micro-power source is monitored in real time, and the deviation between it and the power reference benchmark in the day-ahead output plan is calculated. Combined with the real-time state of charge of the energy storage system, the multi-energy complementary coupling coefficient is calculated to quantify the system's ability to compensate for wind and solar fluctuations and load gaps at the current moment.

[0009] Based on real-time monitored wind and solar permeability data, the control parameters of the virtual synchronous machine of the grid-type energy storage are dynamically corrected, and the multi-energy complementary coupling coefficient is used as a judgment threshold to determine whether the system enters the emergency coordination mode.

[0010] The grid-type energy storage output is controlled according to the modified virtual synchronous machine control parameters to support the microgrid bus voltage frequency. When the multi-energy complementary coupling coefficient is lower than the preset threshold or the frequency deviation exceeds the limit, the micro pumped storage device is triggered to perform power compensation based on the water tower potential energy reserved in the daytime output plan, so as to complete the power balance and stability control of the microgrid.

[0011] Optionally, the method for generating a day-ahead power output plan containing the power output instructions of each micro-power source based on a multi-objective optimization algorithm specifically involves filtering the original forecast data using the following calculation formula when calculating the planned power output of the micro-hydropower generation device:

[0012]

[0013] in, To be included in the current contribution plan After smoothing out the time, the planned output of the micro-hydropower project is as follows: for Real-time predicted output of micro-hydropower plants, constantly affected by water flow fluctuations. For the smooth output of the previous moment, For smoothing coefficients, ∈[0,1] (typical value 0.3~0.5).

[0014] Optionally, the specific calculation formula of the multi-objective optimization algorithm is as follows:

[0015]

[0016] in, These are the weighting coefficients. For the total lifecycle operating cost of a microgrid, This refers to the state of charge of the energy storage at the end of the output cycle. This represents the maximum energy storage capacity.

[0017] Optionally, the multi-objective optimization algorithm further includes power balance constraints and energy storage SOC dynamic constraints;

[0018] The specific calculation formula for the power balance constraint is as follows:

[0019]

[0020] in, Provide real-time power for photovoltaics. To ensure the wind turbine outputs power in real time, For energy storage discharge power, For energy storage charging power, For load demand during period t;

[0021] The specific calculation formula for the dynamic constraints of the energy storage SOC is as follows:

[0022]

[0023]

[0024] in, , For energy storage charging and discharging efficiency, , For energy storage, the minimum and maximum safe states of charge, This represents the current state of charge of the energy storage system. For time step.

[0025] Optionally, the calculation of the multi-energy complementary coupling coefficient is based on theoretical data and real-time monitoring data from the day-ahead power output plan, and is calculated using the following formula. Multi-energy complementary coupling coefficient at time :

[0026]

[0027] in, Contribute to the micro-hydropower project included in the recent power generation plan; To contribute the most to the theory of being glorious at the present moment; To determine the actual power output of wind power at the current moment; The current discharge power of the energy storage; The photovoltaic theory can generate power at the current moment; To contribute to the actual operation of photovoltaic power at this moment; This represents the maximum discharge power of the energy storage system. For energy storage regulation weighting coefficient, ∈[0,1] (typical value 0.7~0.9); minimal positive number =10 -6 kW, to avoid the formula becoming invalid due to a denominator of 0.

[0028] Optionally, the dynamic correction of the virtual synchronous machine control parameters for grid-type energy storage specifically employs an improved droop control strategy, calculated using the following formula:

[0029]

[0030] in, The actual active power command executed by the energy storage system. This is the power reference value given in the recent power output plan. For real-time frequency deviation, Based on the basic damping coefficient, and This refers to the real-time penetration rate of wind power and photovoltaic power monitored in step two. This is the adjustment coefficient.

[0031] Optionally, after establishing the day-ahead prediction model for the micro-power generation capacity, the system further includes prediction logic based on weather forecasts, specifically:

[0032] If it is predicted that there will be at least 6 consecutive hours of no effective sunshine and an average wind speed of less than 2 m / s within the next 24 hours, then the daytime power output plan will be revised as follows:

[0033] Set the upper limit of micro-hydropower output to the maximum value, and generate pumped storage commands during off-peak hours or when wind and solar power are in surplus, so as to increase the water storage capacity of the water tower to a set ratio that meets the evening peak load demand. At the same time, lock the SOC target value of the energy storage system to no less than 85% to ensure the adjustment capability when the micro pumped storage device is triggered in coordination for power compensation.

[0034] Optionally, the collaborative triggering of the micro pumped storage device for power compensation also includes real-time emergency response logic, which specifically includes:

[0035] When the microgrid is in off-grid mode and frequency deviation At that time, the grid-type energy storage enters a fast response mode based on the corrected virtual synchronous machine control parameters;

[0036] If frequency deviation If the duration exceeds the set threshold, it is determined that the balance cannot be maintained by relying solely on energy storage, and the micro pumped storage device is immediately triggered to control the water in the water tower to impact the micro water turbine through the pipeline for emergency power replenishment.

[0037] If the frequency does not recover after power restoration, then disconnect part of the adjustable load.

[0038] Optionally, the physical implementation of the collaboratively triggered micro pumped storage device is as follows:

[0039] A pipeline system connecting the water network and the elevated water tower is used to install water pumps and reversible micro-hydro turbine generators in the pipeline.

[0040] During off-peak hours in the daily power output plan or when wind and solar power curtailment is detected, the water pump is controlled to pump water into the water tower to store potential energy.

[0041] When power compensation is needed, the pipeline valves are opened to use the potential energy of the water tower to drive the micro turbine generator to generate electricity and feed the electrical energy into the bus.

[0042] Optionally, it also includes based on multi-energy complementary coupling coefficients. The external interaction logic is as follows:

[0043] Establish a communication interface with neighboring microgrids to broadcast the calculated data in real time. value;

[0044] when At this time, the system is in a high regulation margin state and accepts power support requests from adjacent microgrids;

[0045] when Furthermore, when the system continues to operate for two consecutive periods, it issues an energy shortage warning and requests external power assistance or the activation of a backup diesel generator.

[0046] The technical solution of the present invention has at least the following advantages and beneficial effects:

[0047] This invention significantly improves the stability and robustness of microgrids by constructing a day-ahead-real-time closed-loop collaborative mechanism. It not only introduces a multi-energy complementary coupling coefficient to quantify the system's regulation margin in real time and achieves dynamic adaptive adjustment of virtual synchronous machine control parameters based on wind and solar penetration rates, thus providing precise inertia support during system frequency fluctuations, but also creatively proposes a scheme to construct a micro-pumped storage system using domestic water towers. This transforms previously uncontrollable micro-hydropower into a flexible and regulated resource with energy storage properties, significantly expanding the system's long-term regulation capability at extremely low cost. Furthermore, this invention establishes a predictive and micro-power source collaborative emergency response logic for extreme weather conditions, achieving full coverage from millisecond-level frequency support to hourly-level energy balance, effectively solving the power supply reliability problem caused by distributed power source fluctuations in weak grid environments with high penetration of distributed clean energy. Attached Figure Description

[0048] Figure 1This is a schematic diagram of the structure of the collaborative control system provided by the present invention;

[0049] Figure 2 A schematic diagram of the principle of the collaborative control system provided by the present invention;

[0050] Figure 3 This is a flowchart illustrating the micro-power supply capacity optimization configuration and coordinated control method for weak power grids provided by the present invention. Detailed Implementation

[0051] 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, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0052] Reference Figures 1-2 As shown, this invention provides a method for optimizing the configuration and coordinated control of micro-power sources for weak power grids. This scheme aims to construct a standardized energy station unit that is ready to use, operates autonomously, and responds flexibly. By scientifically selecting and integrating various advanced micro-power sources, and relying on a digital intelligent power source control platform for refined coordination, it achieves long-term energy self-sufficiency with clean green electricity as the main source, and possesses the ability to interconnect and mutually support external energy systems.

[0053] In one specific embodiment, the collaborative control system adopts a three-layer architecture design to form an integrated system of collaborative aggregation and intelligent management and control, specifically including a physical equipment layer, a digital control layer, and an application layer.

[0054] The physical equipment layer is the physical foundation for energy generation, conversion, storage and distribution, and includes power generation units, energy storage units, power distribution units and load connection ports.

[0055] The power generation unit integrates various types of micro-power sources to achieve spatiotemporal complementarity. Specifically, it selects and integrates a "360° tracking photovoltaic panel," which uses a single-axis or dual-axis tracking bracket to maximize the capture of sunlight throughout the day and continue generating electricity even under low-light conditions such as cloudy days or dawn / dusk. Simultaneously, it integrates a "high-sensitivity micro-wind generator," which uses models with starting wind speeds as low as 1.5 to 2 meters per second, effectively utilizing the low-wind-speed resources common in remote areas as an important supplement to power generation at night and during periods without sunlight. Furthermore, it integrates a "micro-hydropower generation device," designed for scenarios with natural streams, piped water flow, or small drops in elevation. This device uses a low-head, low-flow turbine generator to achieve intermittent or long-term stable supplementary power, serving as a reliable baseload power source or backup power source for the system. In particular, this embodiment also includes an innovative micro pumped storage device. This device connects a water pipeline network to an elevated water tower. During off-peak hours or when there is surplus power from wind and solar power, it uses surplus electricity to drive a water pump to pump water into the water tower for storage as potential energy. When the system needs electricity, it controls the water in the water tower to flow down through the water pipes, driving a reversible micro turbine to rotate and drive a generator to generate electricity, thus completing the conversion of potential energy into electrical energy and providing flexible and adjustable power support for the system.

[0056] The energy storage unit is based on a grid-connected energy storage system. Unlike traditional grid-connected energy storage, the grid-connected energy storage system can actively establish a stable and reliable voltage and frequency reference when the microgrid is disconnected from the main grid, i.e., when operating off-grid, providing a fundamental guarantee for the autonomous operation of the entire microgrid.

[0057] The power distribution unit includes AC busbars, DC busbars, synchronous detection grid connection devices, and various protection devices, responsible for collecting, converting, and distributing the electrical energy generated by each unit. The load connection port connects to various electrical equipment within the station and is divided into non-adjustable loads and adjustable loads. For example, water pumps, air conditioners, and charging piles are all adjustable loads, which can be adjusted by the control platform when the system needs them.

[0058] The intelligent digital control layer is the nerve center of the entire system, specifically a digital intelligent power source control platform. This platform connects to all devices in the physical equipment layer, including photovoltaic inverters, wind turbine controllers, hydropower rectifiers / inverters, and energy storage converters, via standard industrial communication networks such as the Modbus TCP protocol. This standardized interface design enables "plug-and-play" operation and power coordination among the various micro-power sources. The intelligent digital control platform is responsible for the real-time acquisition, in-depth analysis, accurate prediction, and closed-loop collaborative control of all station operation data.

[0059] The application layer provides a human-machine interface and advanced application functions, including local or remote monitoring and operation and maintenance platforms. Users can view power generation, energy storage, and load data in real time through mobile apps and other terminals, and remotely control the system's operating mode. In addition, the application layer also reserves a virtual power plant aggregation interface, enabling individual energy stations to participate as resources in a wider range of demand-side response or virtual power plant operations, thereby obtaining additional economic benefits.

[0060] like Figure 3 As shown, in this embodiment, the collaborative control method proposed in this invention is implemented through a two-stage, multi-timescale collaborative output strategy. This strategy is divided into a day-ahead output optimization stage and a real-time collaborative control stage. The two stages are closely linked through a digital intelligent energy control platform to ensure that the system maintains power balance under various operating scenarios and maximizes the utilization rate of clean energy.

[0061] Phase 1: Current efforts focused on optimization.

[0062] The day-ahead power optimization phase involves rolling planning on a 24-hour cycle with a time granularity of 15 minutes. Its core objective is to generate a day-ahead power plan that balances economy and reliability based on forecast information, serving as a power reference benchmark for the system's operation the following day.

[0063] The first step in this phase is to initiate the data acquisition and forecasting process. The intelligent power source control platform collects weather forecast data for the next 24 hours for the area where the microgrid is located, including sunlight intensity, wind speed, and water flow conditions. Combined with historical load curves, it establishes day-ahead forecasting models and load forecasting models for the power generation capacity of each microgrid.

[0064] After obtaining the forecast data, the platform initiates a multi-objective optimization algorithm to generate the optimal day-ahead power output plan. The objective function of this optimization model aims to find the best balance between economic efficiency and supply capacity, specifically by minimizing a weighted combination function:

[0065]

[0066] In this function, It is a weighting coefficient set by the user, with a value between 0 and 1. For example, when Setting it to 0.6 indicates a greater emphasis on economic efficiency; setting it to 0.4 indicates a greater emphasis on supply capacity. This represents the total lifecycle operating cost of all devices in a microgrid, including micro wind power, photovoltaics, micro hydropower, and energy storage. (Expression) This is used to quantify the supply capacity margin of the energy storage system at the end of the power output planning cycle, where It is the final state of charge of the energy storage system at the end of the power output planning cycle. This is its maximum capacity. By minimizing this objective function, the system can reduce operating costs while ensuring sufficient reserve energy remains at the end of the planned output period.

[0067] A series of strict constraints must be satisfied when solving this optimization problem.

[0068] The primary constraint is power balance, meaning that at every time t, the sum of the planned output of all generating units must be greater than or equal to the predicted load demand at that time. Its mathematical expression is:

[0069]

[0070] in, , These are the real-time outputs of photovoltaic and wind power, respectively. and These are the planned discharge and charging power of the energy storage, respectively. This refers to the load demand during time period t.

[0071] It is worth noting that the planned output of micro-hydropower The original predicted value is not used directly, but rather filtered through a micro-hydropower output smoothing algorithm. This is because the water flow in natural streams or water pipe networks may experience short-term fluctuations, and directly using this data for output control would introduce uncertainty. The formula for this smoothing algorithm is:

[0072]

[0073] in, for Real-time predicted output of micro-hydropower plants, constantly affected by water flow fluctuations. To ensure a smooth output in the previous moment, and λ is a smoothing coefficient ranging from 0.3 to 0.5. This algorithm effectively filters out high-frequency disturbances by weighted averaging of the current real-time predicted value and historical smoothed values, resulting in a smoother and more reliable micro-hydropower output curve included in the day-ahead output plan. The value range of λ is the optimal interval determined through extensive empirical data, effectively balancing response speed and stability.

[0074] Secondly, there is the dynamic constraint of the energy storage SOC. The change of the energy storage system's state of charge at every moment must obey the law of conservation of energy, the expression of which is:

[0075]

[0076] At the same time, the state of charge must be maintained within the safe operating range at all times, that is... .in, , These are the charging and discharging efficiencies of energy storage. It is the time step. It is the minimum safe state of charge set to protect battery life, such as 20% of the maximum capacity.

[0077] In addition, the recent optimization phase also incorporated intelligent judgment logic for specific scenarios.

[0078] For example, criterion 1: If weather forecasts indicate that there will be at least 6 consecutive hours of no effective sunlight and an average wind speed of less than 2 m / s in the next 24 hours, the system will automatically activate the micro-hydropower priority mode. When generating the daytime output plan, the upper limit of the micro-hydropower output will be set to its maximum technical output. At the same time, the system will be instructed to start pumped storage in advance during off-peak hours or periods of wind and solar surplus, increasing the water storage capacity of the water tower to 120% sufficient to meet the evening peak load demand. Furthermore, the SOC target value of the energy storage system will be locked at no less than 85%, prohibiting it from deep discharge, thereby reserving sufficient regulating resources for the upcoming energy shortage period.

[0079] Ultimately, the output of the current power optimization phase is a detailed 24-hour rolling power output plan with 15-minute intervals. This plan includes the planned power output curves of each micro-power source, the charging and discharging plan of energy storage, and the reserved backup capacity. This plan will serve as the execution benchmark and reference for the real-time control phase.

[0080] Phase Two: Real-time Collaborative Control.

[0081] The time scale of the real-time collaborative control stage is from seconds to minutes. Its core objective is to dynamically track the day-ahead power output plan and to quickly and accurately compensate for power deviations caused by prediction errors, load changes, etc., thereby maintaining the dynamic power balance and frequency stability of the system.

[0082] The first step in this phase is real-time monitoring, deviation quantification, and adjustment capability assessment. The intelligent power source control platform collects data in real time, at a frequency of seconds, including the actual output of each micro-power source, the actual power of the load, the SOC status of energy storage, and the bus frequency of the microgrid. The platform compares the actual output of each micro-power source with the corresponding power reference benchmark in the day-ahead output plan to calculate the real-time power deviation.

[0083] To dynamically evaluate the system's comprehensive compensation capability for wind and solar power fluctuations and load gaps at the current moment, this embodiment introduces and calculates a key indicator: the multi-energy complementary coupling coefficient. This coefficient is a dimensionless index, and its calculation formula is as follows:

[0084]

[0085] The numerator of this formula represents the total available upside margin of the system. This represents the current remaining adjustable capacity of micro-hydropower. This represents the total power output deficit of wind and solar power. The min function ensures that the compensation capacity of micro-hydropower will not exceed its own remaining capacity, nor will it exceed the actual deficit demand. This represents the current available discharge margin of the energy storage system, where, It is an adjustment weighting coefficient that reflects the effectiveness of energy storage response. The denominator is the total theoretical power output of wind and solar power, which serves as a normalization benchmark to ensure that the coupling coefficient is comparable across different time periods.

[0086] Calculated multi-energy complementary coupling coefficient This will serve as an important basis for subsequent control decisions. Based on engineering practice, when... A value greater than or equal to 0.4 indicates that the system has sufficient regulation capability; when If the value is less than 0.3 and persists for a specific period of time, an energy shortage warning will be triggered, and the system will need to activate emergency measures.

[0087] The second step in this phase is parameter correction and coordinated execution. When the system detects a deviation in the operating state, it will dynamically correct the control parameters based on real-time data and coordinate with each unit to perform compensation actions.

[0088] A core execution action is the control of the virtual synchronous machine (VSG) in the grid-connected energy storage system. This occurs when the microgrid is in off-grid operation mode and frequency deviation is detected. At this time, the system will immediately activate the fast response mode of the grid-type energy storage. In this mode, the active power output of the energy storage no longer simply follows a fixed reference value, but is dynamically adjusted using an improved droop control strategy. The control law is as follows:

[0089]

[0090] In this control law It is the actual active power command ultimately executed by the energy storage system. This is the power reference value given by the power output plan recently. It is the droop control term in a traditional virtual synchronizer, providing basic frequency support. and This refers to the real-time penetration rate of wind power and photovoltaic power as monitored in step two. This is the adjustment coefficient, which is optimized and tuned according to the actual system characteristics, typically ranging from 2 to 6, to achieve a balance between rapid frequency stabilization and good dynamic performance. The key improvement of this invention lies in the introduction of the last term. .in, and This represents the real-time penetration rate of wind and solar power, i.e., the proportion of their output to total power generation. The physical significance of this term is that the higher the penetration rate of fluctuating power sources like wind and solar, the more dynamically the system's equivalent damping is increased, thus more effectively suppressing frequency fluctuations. This allows the frequency support function of energy storage to adaptively adjust according to the real-time operating conditions of the system, making it particularly effective in high-penetration, weak-grid environments.

[0091] At the collaborative execution level, the system also includes a tiered emergency response logic.

[0092] If frequency deviation is detected In the event of a severe frequency drop, the VSG response alone may not be sufficient to stabilize the system. In this situation, the system immediately triggers a micro-pumped hydroelectric storage device, controlling water in the tower to flow through pipes and impact a micro turbine for emergency power compensation. If the frequency still does not return to a safe range after power replenishment, the system will further reduce some pre-set adjustable loads, such as temporarily reducing the charging pile power or adjusting the air conditioning temperature setting, as a final line of defense for stability.

[0093] Another emergency scenario is that when the SOC of the energy storage system falls below the warning line of 20%, and the micro-hydropower is currently unable to output effective power due to factors such as low water levels, the system will automatically switch to the "supply priority" mode. In this mode, the photovoltaic and micro-wind generators will operate at maximum power, while all adjustable loads will be limited to operate at reduced power until the energy storage SOC recovers to a safe level of over 25%.

[0094] Finally, this invention also supports multi-energy complementary coupling coefficients. External interaction. The system broadcasts real-time calculated data to adjacent microgrids or distribution networks via a communication interface. Value. When A value greater than or equal to 0.4 indicates that the system has sufficient regulation margin and can respond to external power support requests, participating in the aggregate response of the virtual power plant. Conversely, when... When the value is less than 0.3 and remains so for a period of time, the system can also send out an energy shortage warning signal when it activates its internal emergency response, requesting external power assistance, thereby achieving a resilience improvement from the autonomous operation of a single energy station to the "grouping together for warmth" of a microgrid group.

[0095] In summary, this embodiment constructs a three-layer architecture consisting of a physical equipment layer, a digital control layer, and an application layer. It implements a multi-timescale strategy combining day-ahead optimized output planning with real-time collaborative control, achieving refined and intelligent collaborative management of various micro-power sources, including solar-powered photovoltaics, micro-wind, micro-hydro, and innovative micro-pumped storage. This method not only improves the economy and foresight of day-ahead planning through multi-objective optimization and intelligent prediction logic, but also effectively solves the problems of frequency stability and long-term power supply balance in microgrids with high penetration and weak interconnection of distributed clean energy by introducing a multi-energy complementary coupling coefficient for state assessment at the real-time level and utilizing an improved virtual synchronous machine control strategy and a hierarchical emergency response mechanism. This significantly improves the system's autonomous operation capability, energy utilization efficiency, and overall power supply reliability.

[0096] The above are merely preferred embodiments of the present invention and are not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing the capacity configuration and coordinated control of micro-sources for weak power grids, characterized in that, The steps of this method include: Meteorological data and historical load data of the microgrid area are collected. For the physical equipment layer consisting of photovoltaic power generation, micro wind turbines, micro hydropower generation devices and grid-type energy storage, a day-ahead prediction model of the power generation capacity of micro power sources is established. Based on a multi-objective optimization algorithm, a day-ahead power output plan containing the power output instructions of each micro power source is generated as a power reference benchmark for system operation. During the operation of the microgrid, the actual output of each micro-power source is monitored in real time, and the deviation between it and the power reference benchmark in the day-ahead output plan is calculated. Combined with the real-time state of charge of the energy storage system, the multi-energy complementary coupling coefficient is calculated to quantify the system's ability to compensate for wind and solar fluctuations and load gaps at the current moment. Based on real-time monitored wind and solar permeability data, the control parameters of the virtual synchronous machine of the grid-type energy storage are dynamically corrected, and the multi-energy complementary coupling coefficient is used as a judgment threshold to determine whether the system enters the emergency coordination mode. The grid-type energy storage output is controlled according to the modified virtual synchronous machine control parameters to support the microgrid bus voltage frequency. When the multi-energy complementary coupling coefficient is lower than the preset threshold or the frequency deviation exceeds the limit, the micro pumped storage device is triggered to perform power compensation based on the water tower potential energy reserved in the daytime output plan, so as to complete the power balance and stability control of the microgrid. The specific calculation formula of the multi-objective optimization algorithm is as follows: in, These are the weighting coefficients. For the total lifecycle operating cost of a microgrid, This refers to the state of charge of the energy storage at the end of the output cycle. This represents the maximum energy storage capacity. The calculation of the multi-energy complementary coupling coefficient is based on theoretical data and real-time monitoring data from the day-ahead power output plan, and is performed using the following formula. Multi-energy complementary coupling coefficient at time : in, Contribute to the micro-hydropower project included in the recent power generation plan; To contribute the most to the theory of being glorious at the present moment; To determine the actual power output of wind power at the current moment; The current discharge power of the energy storage; The photovoltaic theory can generate power at the current moment; To contribute to the actual operation of photovoltaic power at this moment; This represents the maximum discharge power of the energy storage system. For energy storage regulation weighting coefficient, To avoid positive numbers with a denominator of 0.

2. The method for optimized configuration and coordinated control of micro-source capacity for weak power grids according to claim 1, characterized in that, The method for generating a day-ahead power output plan based on a multi-objective optimization algorithm, which includes the power output commands of each micro-power source, specifically filters the original forecast data when calculating the planned power output of the micro-hydropower unit using the following calculation formula: in, To be included in the current contribution plan After smoothing out the time, the planned output of the micro-hydropower project is as follows: for Real-time predicted output of micro-hydropower plants, constantly affected by water flow fluctuations. For the smooth output of the previous moment, For smoothing coefficients, ∈[0,1].

3. The method for micro-source capacity optimization and coordinated control for weak power grids according to claim 2, characterized in that, The multi-objective optimization algorithm also includes power balance constraints and energy storage SOC dynamic constraints. The specific calculation formula for the power balance constraint is as follows: in, Provide real-time power for photovoltaics. To ensure the wind turbine outputs power in real time, For energy storage discharge power, For energy storage charging power, For the load demand during time period t, for Real-time micro-hydropower output prediction values ​​that are constantly affected by water flow fluctuations; The specific calculation formula for the dynamic constraints of the energy storage SOC is as follows: in, , For energy storage charging and discharging efficiency, , For energy storage, the minimum and maximum safe states of charge, This represents the current state of charge of the energy storage system. For time step.

4. The method for micro-source capacity optimization and coordinated control for weak power grids according to claim 1, characterized in that, The dynamic correction of the virtual synchronous machine control parameters for grid-type energy storage specifically employs an improved droop control strategy, and the calculation formula is as follows: in, The actual active power command executed by the energy storage system. This is the power reference value given in the recent power output plan. For real-time frequency deviation, Based on the basic damping coefficient, and This refers to the real-time penetration rate of wind power and photovoltaic power monitored in step two. This is the adjustment coefficient.

5. The method for micro-source capacity optimization and coordinated control for weak power grids according to claim 1, characterized in that, After establishing the day-ahead prediction model for the micro-power generation capacity, the system also includes prediction logic based on weather forecasts, which specifically includes: If it is predicted that there will be at least 6 consecutive hours of no effective sunshine and an average wind speed of less than 2 m / s within the next 24 hours, then the daytime power output plan will be revised as follows: Set the upper limit of micro-hydropower output to the maximum value, and generate pumped storage commands during off-peak hours or when wind and solar power are in surplus, so as to increase the water storage capacity of the water tower to a set ratio that meets the evening peak load demand. At the same time, lock the SOC target value of the energy storage system to no less than 85% to ensure the adjustment capability when the micro pumped storage device is triggered in coordination for power compensation.

6. The method for micro-source capacity optimization and coordinated control for weak power grids according to claim 1, characterized in that, The power compensation for the collaboratively triggered micro pumped storage device also includes real-time emergency response logic, which specifically includes: When the microgrid is in off-grid mode and frequency deviation At that time, the grid-type energy storage enters a fast response mode based on the corrected virtual synchronous machine control parameters; If frequency deviation If the duration exceeds the set threshold, it is determined that the balance cannot be maintained by relying solely on energy storage, and the micro pumped storage device is immediately triggered to control the water in the water tower to impact the micro water turbine through the pipeline for emergency power replenishment. If the frequency does not recover after power restoration, then disconnect part of the adjustable load.

7. The method for micro-source capacity optimization and coordinated control for weak power grids according to claim 1, characterized in that, The specific physical implementation of the aforementioned collaboratively triggered micro pumped storage device is as follows: A pipeline system connecting the water network and the elevated water tower is used to install water pumps and reversible micro-hydro turbine generators in the pipeline. During off-peak hours in the daily power output plan or when wind and solar power curtailment is detected, the water pump is controlled to pump water into the water tower to store potential energy. When power compensation is needed, the pipeline valves are opened to use the potential energy of the water tower to drive the micro turbine generator to generate electricity and feed the electrical energy into the bus.

8. The method for micro-source capacity optimization and coordinated control for weak power grids according to claim 1, characterized in that, It also includes multi-energy complementary coupling coefficients The external interaction logic is as follows: Establish a communication interface with neighboring microgrids to broadcast the calculated data in real time. value; when At this time, the system is in a high regulation margin state and accepts power support requests from adjacent microgrids; when Furthermore, when the system continues to operate for two consecutive periods, it issues an energy shortage warning and requests external power assistance or the activation of a backup diesel generator.

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