Off-grid micro-grid comprehensive energy management method and device in extreme scene

Abstract

The invention discloses an off-grid micro-grid comprehensive energy management method and device in an extreme scene, and the method comprises the steps: obtaining the fuel cost according to the output power of a diesel engine, and obtaining the operation and maintenance cost according to the photovoltaic output power, the output power of the diesel engine, the output power of a fan, and the output power of a storage battery; according to the fuel cost and the operation and maintenance cost, an objective function with the minimum total operation cost is established, according to the extreme scene parameters, a photovoltaic output power correction model is established, and photovoltaic output power is output based on the photovoltaic output power correction model. The extreme scene parameters comprise a battery panel simulation temperature, a simulation dust shielding coefficient and a simulation plateau low pressure influence coefficient, obtaining fan output power, solving the target function based on the constraint condition, the photovoltaic output power and the fan output power, and outputting diesel engine output power and storage battery output power. According to the invention, extreme characteristics are deeply fused, and all links of source network load storage are comprehensively coordinated, so that comprehensive energy management has intelligent decision-making and self-healing capabilities.

Description

Technical Field

[0001] This invention relates to the field of energy management technology, specifically to a method and apparatus for integrated energy management of off-grid microgrids under extreme scenarios. Background Technology

[0002] As the global energy structure gradually shifts towards cleaner and lower-carbon energy, renewable energy sources, represented by wind and solar power, are developing rapidly, providing a crucial pathway to address the increasingly severe energy crisis and environmental challenges. Off-grid microgrids, as a new type of energy network system capable of self-control, protection, and management, are an important solution for efficiently integrating distributed renewable energy and achieving regional energy self-sufficiency. They have irreplaceable application value in extreme scenarios such as plateaus, islands, border areas, and uninhabited regions (which can be summarized as "high, sea, border, and uninhabited").

[0003] While off-grid microgrids are ideal for the aforementioned scenarios, the unique characteristics of these scenarios present unprecedented technical challenges to their energy management. Existing off-grid microgrid energy management systems are mostly designed for relatively favorable urban or grid-connected scenarios. When directly applied to "high-altitude, coastal, and remote" scenarios, they exhibit numerous limitations and shortcomings: weak ability to cope with extreme climates and highly volatile renewable energy sources, leading to poor power supply reliability; energy management strategies are mostly limited to electricity, lacking coordinated optimization of multiple energy flows such as electricity, heat, cooling, and storage, resulting in overall low energy efficiency; simultaneously, existing methods do not adequately consider environmental specificities (such as low temperature and low oxygen in high-altitude areas, and high salinity and humidity in islands), resulting in a severe disconnect between equipment performance degradation models and control strategies, and poor system robustness; furthermore, the methods generally have low levels of intelligence, lacking predictive, diagnostic, and self-healing capabilities, making it difficult to achieve efficient and low-cost autonomous operation in extremely difficult "high-altitude, coastal, and remote" scenarios. In summary, existing off-grid microgrid energy management technologies are insufficient to meet the stringent requirements of high reliability, high energy efficiency, strong robustness, high intelligence, and low operation and maintenance costs in extreme scenarios such as "high altitude, seaside, and no grid connection." Therefore, there is an urgent need for a comprehensive energy management system that can deeply integrate scenario characteristics, comprehensively coordinate all aspects of the power generation, grid, load, and storage systems, and possess intelligent decision-making and self-healing capabilities. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides an integrated energy management method and device for off-grid microgrids under extreme scenarios. It deeply integrates the characteristics of extreme scenarios and comprehensively coordinates the various links of source, grid, load and storage, enabling integrated energy management to have intelligent decision-making and self-healing capabilities. It can perform collaborative optimization of multiple energy flows and has high reliability, high energy efficiency, strong robustness, high intelligence and low operation and maintenance costs.

[0005] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0006] According to a first aspect of this application, a comprehensive energy management method for off-grid microgrids under extreme scenarios is provided, comprising: Fuel costs are obtained based on diesel engine output power, and operation and maintenance costs are obtained based on photovoltaic output power, diesel engine output power, wind turbine output power, and battery output power. An objective function for minimizing total operating costs is established based on fuel costs and operation and maintenance costs. The constraints of the objective function are power balance constraints, equipment operation constraints, and battery constraints. A photovoltaic output power correction model is established based on extreme scenario parameters. The photovoltaic output power is output based on the photovoltaic output power correction model. The extreme scenario parameters include the simulated temperature of the solar panel, the simulated dust shading coefficient, and the simulated low-pressure influence coefficient of the plateau. Obtain the wind turbine output power, and solve the objective function that minimizes the total operating cost based on the constraints, the photovoltaic output power, and the wind turbine output power, and output the diesel engine output power and the battery output power.

[0007] In some embodiments of this application, based on the foregoing scheme, power balance constraints are established according to photovoltaic output power, diesel engine output power, wind turbine output power, battery output power, and total power; Establish equipment operation constraints for diesel engines based on the upper and lower limits of diesel engine output power and the gradeability. Establish equipment operation constraints for photovoltaics based on the upper and lower limits of photovoltaic power output and the ramp rate; Establish equipment operation constraints for the fan based on the upper and lower limits of the fan's output power and the ramp rate; Establish equipment operation constraints for the battery based on the upper and lower limits of the battery's output power and the ramp rate; Battery constraints are established based on charging efficiency, discharging efficiency, rated capacity, minimum battery state of charge, and maximum battery state of charge. The extreme scenario parameters include photovoltaic power, actual irradiance, simulated panel temperature, simulated dust shading coefficient, and simulated low-pressure influence coefficient at high altitudes under standard test conditions.

[0008] In some embodiments of this application, based on the foregoing scheme, the method for obtaining the simulated temperature of the solar panel is as follows: The first relationship between the convective heat transfer coefficient, the area of ​​the solar panel, the ambient temperature, and the simulated temperature of the solar panel is established. A second relationship between the radiative heat dissipation power of the solar panel and the sky is established based on emissivity, Stefan-Boltzmann constant, equivalent sky temperature, and simulated solar panel temperature. A third relationship is established based on the absorptivity of the solar panel, the actual irradiance, and the area of ​​the solar panel to determine the solar radiation power absorbed by the solar panel. Establish the steady-state thermal equilibrium equation based on the first, second, and third relations; Solving the steady-state thermal balance equation, an analytical expression for the simulated temperature of the solar panel is derived. Based on the analytical expression of the simulated temperature of the solar panel, a first linear regression expression is established with the key parameters to be identified as the first regression coefficients. Based on the collected actual temperature of the solar panel, ambient temperature, and wind speed, the least squares fitting algorithm is used to solve the key parameters to be identified in the first linear regression expression to obtain a well-fitted first linear regression expression. The simulated temperature of the solar panel is then obtained based on the well-fitted first linear regression expression.

[0009] In some embodiments of this application, based on the foregoing scheme, the method for obtaining the simulated sandstorm obstruction coefficient is as follows: The pollutant accumulation index is obtained based on pollutant concentration and relative humidity. An exponential decay model for the simulated dust cover coefficient is established based on the baseline value, decay rate coefficient, and pollutant accumulation index under clean conditions. The theoretical cleaning power is obtained based on the actual irradiance and the simulated temperature of the solar panel. The real-time dust blocking coefficient is obtained based on the theoretical cleaning power and the measured cleaning power. Using the pollutant accumulation index as the independent variable and the natural logarithm of the quotient of the real-time dust obstruction coefficient and the baseline value under clean conditions as the dependent variable, a second linear regression expression is established with the attenuation rate coefficient as the second regression coefficient. The attenuation rate coefficient in the second linear regression expression is solved by the least squares fitting algorithm to obtain the well-fitted second linear regression expression. The simulated sandstorm blocking coefficient is obtained based on the well-fitted second linear regression expression. Statistical analysis determines the real-time dust obstruction coefficient that yields the highest economic benefits from cleaning as the obstruction coefficient threshold. If the simulated dust obstruction coefficient is lower than the obstruction coefficient threshold, a cleaning warning signal is issued.

[0010] In some embodiments of this application, based on the foregoing scheme, the method for obtaining the simulated plateau low-pressure influence coefficient is as follows: Using the real-time plateau low-pressure influence coefficient as the independent variable and the difference between the local average air pressure and the photovoltaic power under standard test conditions as the dependent variable, a third linear regression expression with the air pressure influence coefficient as the third regression coefficient is established. The least squares fitting algorithm is used to solve the air pressure influence coefficient in the third linear regression expression to obtain the well-fitted third linear regression expression. Based on the well-fitted second linear regression expression, the simulated sandstorm shielding coefficient is obtained.

[0011] In some embodiments of this application, based on the foregoing scheme, the diesel engine output power and battery output power are further corrected using a fuzzy control method, specifically as follows: The system frequency deviation, system bus voltage deviation, current battery capacity, and battery state of charge are used as fuzzy control input variables, while the diesel engine output power and battery output power are used as fuzzy control output variables. The fuzzy control input variables are converted into input quantities and then into fuzzy linguistic variables. Establish a fuzzy rule base based on IF-THEN rules; The centroid method is used for defuzzification to obtain the fuzzy output quantities of the diesel engine power output and the battery power output, which are then converted into precise control commands and sent to the underlying equipment.

[0012] In some embodiments of this application, based on the foregoing scheme, the method for obtaining the system rated frequency and system rated voltage is as follows: The instantaneous three-phase voltage signal of the busbar is acquired in real time through a voltage transformer; The system frequency deviation is obtained by using the zero-crossing detection method or Fourier analysis to detect the instantaneous system frequency and by the difference between the instantaneous system frequency and the rated system frequency. The instantaneous voltage signal is collected and the root mean square is calculated within one cycle to obtain the instantaneous voltage of the system bus. The system bus voltage deviation is obtained based on the difference between the instantaneous voltage of the system bus and the rated voltage of the system bus. A positive system frequency deviation indicates that the frequency is too high and the system generates more power; a negative system frequency deviation indicates that the frequency is too low and the system generates less power. A positive system bus voltage deviation indicates that the voltage is too high, while a negative system bus voltage deviation indicates that the voltage is too low.

[0013] According to a second aspect of this application, an off-grid microgrid integrated energy management device for extreme scenarios is provided, comprising: The objective function establishment module is used to obtain fuel cost based on diesel engine output power, operation and maintenance cost based on photovoltaic output power, diesel engine output power, wind turbine output power, and battery output power, and establish an objective function that minimizes total operating cost based on fuel cost and operation and maintenance cost. The objective function is constrained by power balance constraint, equipment operation constraint, and battery constraint. The first output module is used to establish a photovoltaic output power correction model based on extreme scenario parameters, and output photovoltaic output power based on the photovoltaic output power correction model. The extreme scenario parameters include the simulated temperature of the solar panel, the simulated dust shading coefficient, and the simulated low-pressure influence coefficient of the plateau. The second output module is used to obtain the wind turbine output power, solve the objective function of minimizing the total operating cost based on the constraints, the photovoltaic output power and the wind turbine output power, and output the diesel engine output power and the battery output power.

[0014] According to a third aspect of this application, a computer-readable storage medium is provided that stores a computer program thereon, the computer program including executable instructions that, when executed by a processor, implement the method described above.

[0015] According to a fourth aspect of this application, an electronic device is provided, comprising: One or more processors; A memory for storing executable instructions of the processor, which, when executed by the one or more processors, cause the one or more processors to implement the method described above.

[0016] The beneficial effects of this application are as follows: (1) The off-grid microgrid integrated energy management method and device for extreme scenarios provided in this application deeply integrates the characteristics of extreme scenarios, considers the simulated temperature of the solar panel, the simulated dust shading coefficient and the simulated low air pressure influence coefficient of the plateau, and comprehensively coordinates the various links of source, grid, load and storage, so that the integrated energy management has intelligent decision-making and self-healing capabilities, and can optimize the multi-energy flow collaboratively, and meet the requirements of high reliability, high energy efficiency, strong robustness, high intelligence and low operation and maintenance costs in the extreme scenario of "high seas and no power supply".

[0017] (2) The off-grid microgrid integrated energy management method and device provided in this application under extreme scenarios takes into account system frequency deviation, system bus voltage deviation, current battery capacity and battery state of charge, and corrects the diesel engine output power and battery output power. It can adaptively adjust the control strategy, suppress the impact of disturbances on the system, further improve robustness, and cope with dynamic disturbances.

[0018] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and do not limit this application. Attached Figure Description

[0019] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and are intended to explain the invention, but do not constitute an undue limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the off-grid microgrid integrated energy management method under extreme scenarios according to the present invention; Figure 2 This is a schematic diagram of the off-grid microgrid integrated energy management device under extreme scenarios according to the present invention; Figure 3 This is a schematic diagram of an electronic device according to the present invention. Detailed Implementation

[0020] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0021] It should be understood that the terms "comprising" and other similar expressions in the specification, claims, and accompanying drawings of this invention are intended to cover a non-exclusive inclusion, such as a process, method, system, or apparatus that includes a series of steps or units and is not limited to the listed steps or units. Furthermore, "first" and "second" are used to distinguish different objects and are not intended to describe a specific order.

[0022] According to the first aspect of this application, Figure 1 As shown in this embodiment, the integrated energy management method for off-grid microgrids in extreme scenarios includes: Step S101: Obtain the fuel cost based on the diesel engine output power, and obtain the operation and maintenance cost based on the photovoltaic output power, diesel engine output power, wind turbine output power, and battery output power. Establish an objective function that minimizes the total operating cost based on the fuel cost and operation and maintenance cost. The constraints of the objective function are power balance constraints, equipment operation constraints, and battery constraints.

[0023] In some embodiments of this example, the step of establishing an objective function to minimize the total operating cost based on photovoltaic output power, diesel engine output power, wind turbine output power, and battery output power specifically involves: Fuel costs are determined based on the diesel engine's output power. The operation and maintenance costs are determined based on the photovoltaic output power, wind turbine output power, and battery output power. An objective function is established to minimize total operating costs based on fuel costs and maintenance costs.

[0024] In this embodiment, the formula for calculating the objective function that minimizes the total operating cost is:

[0025] in, To set the time The output power of the diesel engine, To set the time The total power output of photovoltaic power, wind turbine power, and battery power. For fuel costs, For operation and maintenance costs.

[0026] In some embodiments of this example, a power balance constraint is established based on the photovoltaic output power, diesel engine output power, wind turbine output power, battery output power, and total power. The calculation formula is as follows:

[0027] in, The total active power consumed by all electricity users. For photovoltaic output power, This refers to the output power of the wind turbine. This refers to the output power of the storage battery.

[0028] In some embodiments of this example, equipment operation constraints for diesel engines, photovoltaics, wind turbines, and batteries are established based on the upper and lower limits of diesel engine output power, photovoltaic output power, wind turbine output power, and battery output power, as well as the ramp rate.

[0029] In some embodiments of this example, battery constraints are established based on charging efficiency, discharging efficiency, rated capacity, minimum battery state of charge, and maximum battery state of charge, and the calculation formula is as follows:

[0030] in, To set the time The state of charge of the battery. To set the time The state of charge of the battery. For charging efficiency, For discharge efficiency, The sampling time interval, To set the time The battery pack absorbs active power from the microgrid bus to charge itself; in this case, the battery acts as a load. To set the time The active power released by the battery pack to the microgrid bus is used to support the system power supply. At this time, the battery acts as a power source.

[0031] Thus, by introducing robust optimization theory, wind and solar power output and load are modeled as a bounded uncertain set, and the optimization objective is to ensure that the system can still operate stably in the worst case, thereby enhancing the anti-interference capability of the scheduling scheme.

[0032] Step S102: Based on the extreme scenario parameters, establish a photovoltaic output power correction model, and output the photovoltaic output power based on the photovoltaic output power correction model. The extreme scenario parameters include the simulated temperature of the solar panel, the simulated dust shading coefficient, and the simulated low-pressure influence coefficient of the plateau.

[0033] In some embodiments of this example, the extreme scenario parameters include photovoltaic power under standard test conditions, actual irradiance, simulated panel temperature, simulated dust shading coefficient, and simulated low-pressure influence coefficient at high altitudes. The step of establishing a photovoltaic output power correction model based on these extreme scenario parameters includes: Based on the simulated temperature of the solar panel, the simulated dust shading coefficient, and the simulated low-pressure influence coefficient at high altitudes, a photovoltaic output power correction model is established, and the calculation formula is as follows:

[0034] in, Photovoltaic power under standard test conditions For actual irradiance, To simulate the temperature of the solar panel, For power temperature coefficient, To simulate the dust obstruction coefficient, To simulate the influence coefficient of low air pressure at high altitudes, This refers to the output power of photovoltaics.

[0035] In this embodiment, the method for obtaining the simulated temperature of the solar panel is as follows: Based on the convective heat transfer coefficient, solar panel area, ambient temperature, and simulated solar panel temperature, a first relationship is established between the convective heat dissipation power of the solar panel and the air. The calculation formula is as follows:

[0036] in, This refers to the convective heat dissipation power between the solar panel and the air. The convective heat transfer coefficient is denoted by , and the wind speed is denoted by . The function, The area of ​​the solar panel. To simulate the temperature of the solar panel, The ambient temperature.

[0037] Based on emissivity, Stefan-Boltzmann constant, equivalent sky temperature, and simulated solar panel temperature, a second relationship is established between the radiative heat dissipation power of the solar panel and the sky. The calculation formula is as follows:

[0038] in, The radiative heat dissipation power of the solar panel and the sky. For emission rate, It is the Stefan-Boltzmann constant. For the equivalent sky temperature, it can usually be approximated as: .

[0039] Based on the absorptivity of the solar panel, the actual irradiance, and the area of ​​the solar panel, a third relationship is established for the solar radiation power absorbed by the solar panel. The calculation formula is as follows:

[0040] in, The solar radiation power absorbed by the solar panels. This refers to the surface absorption rate of the solar panel.

[0041] Based on the first, second, and third relations, the steady-state heat balance equation is established, and the calculation formula is as follows: .

[0042] Solving the steady-state thermal balance equation, an analytical expression for the simulated temperature of the solar panel is derived:

[0043] in, These are the key parameters to be identified.

[0044] Based on the analytical expression of the simulated temperature of the solar panel, a first linear regression expression is established, using the key parameters to be identified as the first regression coefficients. The calculation formula is as follows:

[0045] in, For the first Each solar panel simulates temperature. No. An ambient temperature, No. wind speed, For the first The actual irradiance.

[0046] Based on the collected actual temperature of the solar panel, ambient temperature, and wind speed, the least squares fitting algorithm is used to solve the key parameters to be identified in the first linear regression expression to obtain a well-fitted first linear regression expression. The simulated temperature of the solar panel is then obtained based on the well-fitted first linear regression expression.

[0047] In some implementations of this embodiment, when using the least squares fitting algorithm to solve for the key parameters to be identified in the first linear regression expression, on-site data collection and preparation are carried out. In the target application scenario (such as a plateau station, island station, border station, and uninhabited area station), a monitoring system is deployed to synchronously collect high-frequency data (per minute or per 5 minutes) over a period of time (such as at least one complete season). Input variable (feature): Ambient temperature (Weather station), wind speed (Weather station), actual irradiance (Irradiation instrument); Target variable (label): Actual temperature of the photovoltaic panel backsheet or solar cells (Use an attached temperature sensor or infrared thermometer for accurate measurement).

[0048] In some embodiments of this example, when solving the first linear regression expression based on the least squares fitting algorithm, let .

[0049] Using the first linear regression expression Fit the coefficients and .

[0050] The physical parameters are obtained by reverse calculation: , Among them, absorption rate Typical values ​​can be obtained from the component datasheet, or they can be fitted as a whole coefficient.

[0051] In some implementations of this embodiment, verification is performed by using another set of measured data that were not involved in the fitting to verify the simulated temperature of the solar panel calculated by the model. With the actual temperature of the solar panel Errors (such as root mean square error RMSE). Ensure the model accuracy meets engineering requirements (typically, the error should be within ±3°C).

[0052] Application (online operation): The identified parameters , and The information is embedded into the environmental adaptive correction module of the energy management system. During system operation, it is read in real time. , and The formula can be used The real-time simulated temperature of the solar panel is calculated.

[0053] In some implementations of this embodiment, The simulated dust shading coefficient is a time-varying coefficient that characterizes the decrease in optical transmittance caused by the accumulation of pollutants such as dust, smoke, and snow on the surface of photovoltaic panels. Its value ranges from 0 (complete shading) to 1 (complete cleanliness).

[0054] In some embodiments of this example, the method for obtaining the simulated sandstorm obstruction coefficient is as follows: The pollutant accumulation index is obtained based on pollutant concentration and relative humidity. The calculation formula is as follows:

[0055] in, For time The cumulative amount of pollutants within the region. for PM10 concentration at any given time (measurement of dust). for Relative humidity at any given time (humidity affects the adhesion of pollutants) An exponential decay model for the simulated dust cover coefficient is established based on the baseline value, decay rate coefficient, and pollutant accumulation index under clean conditions. The calculation formula is as follows:

[0056] in, This is the baseline value under clean conditions (usually ≈1). When artificial cleaning or precipitation (such as rainfall >10mm) occurs, Reset to 0, This is the decay rate coefficient.

[0057] The theoretical cleaning power is obtained based on the actual irradiance and the simulated temperature of the solar panel. The calculation formula is as follows:

[0058] in, Theoretical cleaning power; The real-time dust obstruction coefficient is obtained based on the theoretical and measured cleaning power, and the calculation formula is as follows:

[0059] in, This represents the real-time dust obstruction coefficient. This represents the actual cleaning power.

[0060] Record the time of all cleaning and effective precipitation events. Based on the cleaning / precipitation events, divide the historical data into multiple independent "pollution accumulation periods." Within each period, sort the data chronologically. As the independent variable, As the dependent variable, a second linear regression expression is established with the decay rate coefficient as the second regression coefficient, and the decay rate coefficient for the target application scenario is obtained by fitting the expression. ,in: The second linear regression expression , , .

[0061] Determine the cleaning threshold: Statistical analysis determines the real-time dust obstruction coefficient (e.g., 0.85) that yields the highest economic benefits from cleaning as the obstruction coefficient threshold, and uses this threshold as the threshold to trigger operation and maintenance alarms.

[0062] Real-time calculation during runtime And using the fitted Value, according to the formula Real-time output of simulated sandstorm obstruction coefficient. When the simulated sandstorm obstruction coefficient... When the occlusion coefficient threshold is lower than the specified threshold, a cleaning warning signal will be issued.

[0063] In some embodiments of this example, the influence coefficient of low-pressure air at high altitudes is simulated. It is a relatively stable coefficient that mainly reflects the combined effect of low air pressure on the heat dissipation conditions and spectral response of photovoltaic modules. It is obtained by fitting the coefficient by comparing the systematic deviation between the measured clean power at high altitude and the theoretical power under standard air pressure at the same solar radiation and ambient temperature.

[0064] The method for obtaining the influence coefficient of low air pressure in simulated high-altitude areas is as follows: Based on the local average air pressure, photovoltaic power under standard test conditions, and the air pressure influence coefficient, a third linear regression expression is established with the simulated plateau low air pressure influence coefficient as the third regression coefficient. The calculation formula is as follows:

[0065] in, The local average air pressure Photovoltaic power under standard test conditions, This is the air pressure influence coefficient.

[0066] Data was collected at high-altitude stations and then rigorously screened. Filtering criteria: Select data from periods of clear skies without clouds, stable wind speeds, and clean surfaces (i.e., Kdust ≈ 1). This is to isolate the effects of sandstorms and sudden weather changes, focusing on barometric pressure effects.

[0067] Input variable: Actual irradiance Ambient temperature Wind speed Local average air pressure .

[0068] Tag data: Actual cleaning power: .

[0069] Standard model power calculations: using component pre-test conditions (STC) parameters and actual irradiance. Ambient temperature Wind speed Substituting the standard plate temperature model and output model (which do not consider pressure correction) into the model, the theoretical photovoltaic power under standard test conditions is calculated. .

[0070] The measured real-time high-altitude low-pressure influence coefficient is obtained based on the measured clean power and the theoretical photovoltaic power under standard test conditions. The calculation formula is as follows:

[0071] by The x-axis is... Use the vertical axis to plot a scatter plot; Using the third linear regression expression The pressure influence coefficient was obtained by fitting. ; Finally, a third linear regression expression for the influence coefficient of low-pressure air in high-altitude areas, specific to the target application scenario, is obtained:

[0072] Based on the measured real-time low-pressure influence coefficient at high altitude, the local average air pressure, and the photovoltaic power under standard test conditions, the least squares fitting algorithm is used to solve for the air pressure influence coefficient in the third linear regression expression, and a well-fitted third linear regression expression is obtained.

[0073] In this way, by deeply integrating the characteristics of extreme scenarios, considering the simulated temperature of solar panels, the simulated dust shading coefficient, and the simulated low-pressure influence coefficient of high-altitude areas, and comprehensively coordinating all aspects of the source, grid, load, and storage, the integrated energy management has intelligent decision-making and self-healing capabilities. It can optimize the synergistic operation of multiple energy flows and meet the requirements of high reliability, high energy efficiency, strong robustness, high intelligence, and low operation and maintenance costs in extreme scenarios such as "high altitude, seaside, and no power supply".

[0074] Step S103: Obtain the wind turbine output power, solve the objective function of minimizing the total operating cost based on the constraints, the photovoltaic output power and the wind turbine output power, and output the diesel engine output power and the battery output power.

[0075] In some embodiments of this example, the output power of the diesel engine and the output power of the battery are also corrected based on a fuzzy control method.

[0076] Specifically, the system frequency deviation, system bus voltage deviation, current battery capacity, and battery state of charge are used as fuzzy control input variables, and the diesel engine output power and battery output power are used as fuzzy control output variables. Among them, the system in the system frequency deviation and system bus voltage deviation refers to the off-grid microgrid system, including photovoltaic, diesel engine, wind turbine and battery. The fuzzy control input variables are converted into fuzzy linguistic variables such as "negative large (NB)", "negative small (PS)", "zero (ZE)", "positive small (PS)" and "positive large (PB)".

[0077] A fuzzy rule base is established based on the "IF-THEN" rule.

[0078] The centroid method is used for defuzzification to obtain the fuzzy output of diesel engine power and the fuzzy control output variables of battery power, which are then converted into precise control commands and sent to the underlying equipment.

[0079] Thus, by taking into account system frequency deviation, system bus voltage deviation, current battery capacity, and battery state of charge, the output power of the diesel engine and the output power of the battery can be corrected. This enables adaptive adjustment of the control strategy, suppressing the impact of disturbances on the system, further improving robustness, and coping with dynamic disturbances.

[0080] In some embodiments of this example, the current battery capacity (SOH) reflects the degree of capacity decay and internal resistance increase of the battery relative to its brand-new state (e.g., SOH=80% means the current battery capacity is only 80% of the rated capacity). Its core function is to transform the long-term, slow physical aging of the battery into a key parameter that the energy management system can read and quantify in real time. When formulating charge and discharge plans, the optimized scheduling of diesel engine output power and battery output power must be calculated based on the actual available capacity rather than the nominal capacity. Otherwise, the plan will become impractical, either leading to battery overcharging and over-discharging (damaging battery life) or causing system power imbalance (impairing safety). Using the current battery capacity (SOH), online estimation provides dynamic and accurate capacity constraints for the optimized scheduling of diesel engine output power and battery output power.

[0081] The extended Kalman filter (EKF) algorithm is used to estimate the battery internal resistance in real time. and battery state of charge Its state-space equation is:

[0082] Among them, state variables Observed variables Terminal voltage, , This includes process noise and observation noise. Real-time updates provide more accurate SOC and SOH readings, which are then fed back to the scheduling module to correct the battery's charging and discharging power commands.

[0083] In some embodiments of this example, the system frequency deviation System bus voltage deviation These two variables are the difference between the instantaneous measured value and the system's rated value.

[0084]

[0085]

[0086] in, and These are the system's rated frequency and the system's rated bus voltage. for The instantaneous frequency of the system at a given moment. for The instantaneous voltage of the system bus at a given moment.

[0087] In some embodiments of this example, the method for obtaining the system rated frequency and system rated voltage is as follows: Voltage signal acquisition: Real-time acquisition of the three-phase voltage instantaneous signal of the busbar via voltage transformer. , , ; Frequency measurement: using zero-crossing detection or Fourier analysis (more accurate); Zero-crossing detection method: Accurately detect the moment when the voltage sine wave crosses zero from negative to positive. The time interval between two adjacent zero points is half a cycle, from which the instantaneous frequency of the system can be obtained. ; Fourier analysis: A Fast Fourier Transform (FFT) is performed on the voltage sampling sequence within a time window (e.g., 20ms, i.e., one power frequency cycle) to extract the phase angle of the 50Hz component. By continuously calculating the rate of change of the phase angle, the phase angle can be obtained. ,in It is the phase angle of the fundamental voltage; Calculate the RMS voltage value: Perform root mean square (RMS) calculation on the acquired instantaneous voltage signal over one period to obtain the instantaneous voltage of the system bus. The calculation formula is:

[0088] A positive system frequency deviation indicates that the frequency is too high and the system generates more power; a negative system frequency deviation indicates that the frequency is too low and the system generates less power. A positive system bus voltage deviation indicates that the voltage is too high, while a negative system bus voltage deviation indicates that the voltage is too low.

[0089] In some embodiments of this example, based on the photovoltaic output power correction model, the photovoltaic output power is cyclically output at a set time, and the wind turbine output power is cyclically obtained at a set time. Then, based on the constraints, the photovoltaic output power, and the wind turbine output power, the objective function for minimizing the total operating cost is solved, and the diesel engine output power and battery output power are cyclically output at a set time.

[0090] In some implementations of this embodiment, the off-grid microgrid system adopts a "three-layer, two-plane" overall architecture: Planning layer: Based on ultra-short-term wind and solar load forecast data, formulate the optimal economic operation plan on a daily / weekly basis, which is the objective function of minimizing total operating costs.

[0091] Dispatch layer: The planning layer instructions are continuously revised in hourly / 15-minute increments, and the output power of the diesel engine and the battery are output to balance the power in real time.

[0092] Control layer: Executes scheduling layer instructions in seconds / milliseconds to maintain the stability of system bus voltage and system frequency.

[0093] Two planes: Management plane: Contains the above three layers and is responsible for energy decisions.

[0094] Data plane: Responsible for the collection and interaction of data (environmental parameters, equipment status, power quality) for the entire system.

[0095] According to the second aspect of this application, such as Figure 2 As shown in this embodiment, an off-grid microgrid integrated energy management device for extreme scenarios includes: The objective function establishment module 201 is used to obtain fuel cost based on diesel engine output power, operation and maintenance cost based on photovoltaic output power, diesel engine output power, wind turbine output power, and battery output power, and establish an objective function that minimizes total operating cost based on fuel cost and operation and maintenance cost. The objective function is constrained by power balance constraint, equipment operation constraint, and battery constraint. The first output module 202 is used to establish a photovoltaic output power correction model based on extreme scenario parameters, and output photovoltaic output power based on the photovoltaic output power correction model. The extreme scenario parameters include the simulated temperature of the solar panel, the simulated dust shading coefficient, and the simulated low-pressure influence coefficient of the plateau. The second output module 203 is used to obtain the wind turbine output power, solve the objective function of minimizing the total operating cost based on the constraints, the photovoltaic output power and the wind turbine output power, and output the diesel engine output power and the battery output power.

[0096] Specifically, this embodiment corresponds one-to-one with the above method embodiments. The functions of each module have been described in detail in the corresponding method embodiments, so they will not be repeated here.

[0097] According to a third aspect of this application, this embodiment provides a computer-readable storage medium having a computer program stored thereon, the computer program including executable instructions that, when executed by a processor, implement the method described above.

[0098] The present invention can implement all or part of the processes in the above methods, or it can be accomplished by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or system capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0099] According to the fourth aspect of this application, such as Figure 3 As shown, an electronic device is provided, comprising: One or more processors; Memory is used to store executable instructions for the processor, which, when executed by one or more processors, cause one or more processors to implement the methods described above.

[0100] Electronic devices are manifested in the form of general-purpose computing devices. Components of an electronic device may include, but are not limited to: at least one processor, at least one memory, and a bus connecting different system components (including memory and processor).

[0101] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of a computer system, connecting all parts of the computer system through various interfaces and lines.

[0102] Memory can be used to store computer programs and / or modules. The processor implements various functions of the computer system by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory. Memory can mainly include a program storage area and a data storage area. The program storage area can store the operating system and at least one application program required for a function (e.g., sound playback, image playback, etc.); the data storage area can store data created based on the use of the mobile phone (e.g., audio data, video data, etc.). Furthermore, memory can include high-speed random access memory, and can also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, SmartMedia Cards (SMC), Secure Digital (SD) cards, Flash Cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0103] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, servers, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and memory) containing computer-usable program code.

[0104] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), servers, and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0105] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0106] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0107] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0108] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0109] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A comprehensive energy management method for off-grid microgrids under extreme scenarios, characterized in that, include: Fuel costs are obtained based on diesel engine output power, and operation and maintenance costs are obtained based on photovoltaic output power, diesel engine output power, wind turbine output power, and battery output power. An objective function for minimizing total operating costs is established based on fuel costs and operation and maintenance costs. The constraints of the objective function are power balance constraints, equipment operation constraints, and battery constraints. A photovoltaic output power correction model is established based on extreme scenario parameters. Based on the photovoltaic output power correction model, the photovoltaic output power is output. The extreme scenario parameters include the simulated temperature of the solar panel, the simulated dust shading coefficient, and the simulated low-pressure influence coefficient of the plateau. Obtain the wind turbine output power, and solve the objective function that minimizes the total operating cost based on the constraints, the photovoltaic output power, and the wind turbine output power, and output the diesel engine output power and the battery output power.

2. The method according to claim 1, characterized in that: A power balance constraint is established based on the output power of photovoltaics, the output power of diesel engines, the output power of wind turbines, the output power of batteries, and the total power. Establish equipment operation constraints for diesel engines based on the upper and lower limits of diesel engine output power and the gradeability. Establish equipment operation constraints for photovoltaics based on the upper and lower limits of photovoltaic power output and the ramp rate; Establish equipment operation constraints for the fan based on the upper and lower limits of the fan's output power and the ramp rate; Establish equipment operation constraints for the battery based on the upper and lower limits of the battery's output power and the ramp rate; Battery constraints are established based on charging efficiency, discharging efficiency, rated capacity, minimum battery state of charge, and maximum battery state of charge.

3. The method according to claim 1, characterized in that, The method for obtaining the simulated temperature of the solar panel is as follows: The first relationship between the convective heat transfer coefficient, the area of ​​the solar panel, the ambient temperature, and the simulated temperature of the solar panel is established. A second relationship between the radiative heat dissipation power of the solar panel and the sky is established based on emissivity, Stefan-Boltzmann constant, equivalent sky temperature, and simulated solar panel temperature. A third relationship is established based on the absorptivity of the solar panel, the actual irradiance, and the area of ​​the solar panel to determine the solar radiation power absorbed by the solar panel. Establish the steady-state thermal equilibrium equation based on the first, second, and third relations; Solving the steady-state thermal balance equation, an analytical expression for the simulated temperature of the solar panel is derived. Based on the analytical expression of the simulated temperature of the solar panel, a first linear regression expression is established with the key parameters to be identified as the first regression coefficients. Based on the collected actual temperature of the solar panel, ambient temperature, and wind speed, the least squares fitting algorithm is used to solve the key parameters to be identified in the first linear regression expression to obtain a well-fitted first linear regression expression. The simulated temperature of the solar panel is then obtained based on the well-fitted first linear regression expression.

4. The method according to claim 1, characterized in that, The method for obtaining the simulated sandstorm shielding coefficient is as follows: The pollutant accumulation index is obtained based on pollutant concentration and relative humidity. An exponential decay model for the simulated dust cover coefficient is established based on the baseline value, decay rate coefficient, and pollutant accumulation index under clean conditions. The theoretical cleaning power is obtained based on the actual irradiance and the simulated temperature of the solar panel. The real-time dust blocking coefficient is obtained based on the theoretical cleaning power and the measured cleaning power. Using the pollutant accumulation index as the independent variable and the natural logarithm of the quotient of the real-time dust obstruction coefficient and the baseline value under clean conditions as the dependent variable, a second linear regression expression is established with the attenuation rate coefficient as the second regression coefficient. The attenuation rate coefficient in the second linear regression expression is solved by the least squares fitting algorithm to obtain the well-fitted second linear regression expression. The simulated sandstorm blocking coefficient is obtained based on the well-fitted second linear regression expression. Statistical analysis determines the real-time dust obstruction coefficient that yields the highest economic benefits from cleaning as the obstruction coefficient threshold. If the simulated dust obstruction coefficient is lower than the obstruction coefficient threshold, a cleaning warning signal is issued.

5. The method according to claim 1, characterized in that, The method for obtaining the simulated plateau low-pressure influence coefficient is as follows: Using the real-time plateau low-pressure influence coefficient as the independent variable and the difference between the local average air pressure and the photovoltaic power under standard test conditions as the dependent variable, a third linear regression expression with the air pressure influence coefficient as the third regression coefficient is established. The least squares fitting algorithm is used to solve the air pressure influence coefficient in the third linear regression expression to obtain the well-fitted third linear regression expression. Based on the well-fitted second linear regression expression, the simulated sandstorm shielding coefficient is obtained.

6. The method according to claim 1, characterized in that, It also includes correcting the diesel engine output power and battery output power based on fuzzy control methods, specifically: The system frequency deviation, system bus voltage deviation, current battery capacity, and battery state of charge are used as fuzzy control input variables, while the diesel engine output power and battery output power are used as fuzzy control output variables. The fuzzy control input variables are converted into input quantities and then into fuzzy linguistic variables. Establish a fuzzy rule base based on IF-THEN rules; The centroid method is used for defuzzification to obtain the fuzzy output quantities of the diesel engine power output and the battery power output, which are then converted into precise control commands and sent to the underlying equipment.

7. The method according to claim 6, characterized in that, The method for obtaining the system's rated frequency and rated voltage is as follows: The instantaneous three-phase voltage signal of the busbar is acquired in real time through a voltage transformer; The system frequency deviation is obtained by using the zero-crossing detection method or Fourier analysis to detect the instantaneous system frequency and by the difference between the instantaneous system frequency and the rated system frequency. The instantaneous voltage signal is collected and the root mean square is calculated within one cycle to obtain the instantaneous voltage of the system bus. The system bus voltage deviation is obtained based on the difference between the instantaneous voltage of the system bus and the rated voltage of the system bus. A positive system frequency deviation indicates that the frequency is too high and the system generates more power; a negative system frequency deviation indicates that the frequency is too low and the system generates less power. A positive system bus voltage deviation indicates that the voltage is too high, while a negative system bus voltage deviation indicates that the voltage is too low.

8. An off-grid microgrid integrated energy management device for extreme scenarios, characterized in that, include: The objective function establishment module is used to obtain fuel cost based on diesel engine output power, operation and maintenance cost based on photovoltaic output power, diesel engine output power, wind turbine output power, and battery output power, and establish an objective function that minimizes total operating cost based on fuel cost and operation and maintenance cost. The objective function is constrained by power balance constraint, equipment operation constraint, and battery constraint. The first output module is used to establish a photovoltaic output power correction model based on extreme scenario parameters, and output photovoltaic output power based on the photovoltaic output power correction model. The extreme scenario parameters include the simulated temperature of the solar panel, the simulated dust shading coefficient, and the simulated low-pressure influence coefficient of the plateau. The second output module is used to obtain the wind turbine output power, solve the objective function of minimizing the total operating cost based on the constraints, the photovoltaic output power and the wind turbine output power, and output the diesel engine output power and the battery output power.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program includes executable instructions that, when executed by a processor, implement the method of any one of claims 1-7.

10. An electronic device, characterized in that, include: One or more processors; A memory for storing executable instructions of the processor, which, when executed by the one or more processors, cause the one or more processors to perform the method according to any one of claims 1-7.

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