Highway micro-grid dynamic energy control instruction generation and control method

Abstract

The application discloses a kind of highway micro-grid dynamic energy control instruction generation and control method, comprising: according to the environmental data and traffic flow data of real-time acquisition, respectively through photovoltaic output model and turbulent wind power output model calculation photovoltaic output data and turbulent wind power output data.Combining traffic flow data, photovoltaic output data, turbulent wind power output data and energy state data, update the feasible region of each partition current control period.Combined with traffic flow data and energy state data, the objective function is solved by rolling optimization based on the feasible region, and the control instruction of highway micro-grid in the current control period is obtained.Traffic flow is embedded in energy scheduling state space, and traffic flow-energy double-state vector is formed for highway micro-grid control decision, which realizes the deep coupling of traffic information and energy decision, and adopts the adaptive dynamic penalty coefficient mechanism based on virtual queue backlog, which overcomes the defects of fixed weight method priority rigidity and insufficient robustness.

Description

Technical Field

[0001] This invention relates to the field of system resource allocation or scheduling technology, specifically to a method for generating and controlling dynamic energy control commands for highway microgrids. Background Technology

[0002] The highway network includes numerous electrical installations such as service areas, toll stations, tunnel lighting, traffic monitoring cameras, and roadside sensing facilities, almost entirely reliant on the municipal power grid. This poses a significant safety hazard of widespread power outages during extreme disasters or regional power outages. Simultaneously, the highway network possesses abundant renewable energy resources, including rooftop solar power, sound barrier solar power, and vehicle wake turbulence wind power, providing natural conditions for constructing a wind-solar-storage multi-energy complementary microgrid.

[0003] However, existing technologies fail to incorporate traffic flow patterns into the energy optimization framework when converting these resources into reliable power supply. Highway loads differ fundamentally from ordinary distribution network loads. For example, tunnel lighting loads are jointly determined by visibility and traffic flow, exhibiting rigid temporal constraints and cannot be treated as ordinary interruptible loads. Furthermore, the dispatchable potential of flexible loads such as service area charging stations and air conditioning is directly related to peak and off-peak traffic volumes.

[0004] Existing microgrid energy management methods treat highway loads as ordinary time-varying load curves, which cannot use traffic flow forecast information to pre-determine the rigid demand for tunnel lighting, nor can they quantify the real-time scheduling space of service area elastic loads. This results in a serious disconnect between scheduling decisions and actual electricity consumption physical laws, and low efficiency in the time-series allocation of energy storage resources.

[0005] Moreover, highway microgrids simultaneously face three competing optimization objectives: minimizing electricity purchase costs, maximizing renewable energy integration, and protecting energy storage lifespan. Existing methods for multi-objective scheduling generally employ a weighted summation method with pre-set fixed weights to scalarize the multiple objectives. This method suffers from two serious drawbacks: first, when the dimensions and magnitudes of the objectives differ significantly, the fixed weights cannot guarantee the balance of the Pareto optimal solution; second, when the system state undergoes drastic changes, such as when the energy storage charge state approaches the emergency lower bound, the fixed weights cannot adaptively adjust the relative priorities of the objectives, leading to erroneous decisions in critical operating conditions where safety constraints are sacrificed for economic efficiency. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention proposes a dynamic energy control command generation and control method for highway microgrids. This method achieves deep coupling between traffic physics and energy scheduling decisions, and overcomes the inherent defects of the fixed-weight method, such as rigid priority and insufficient robustness under strong constraints. The specific technical solution is as follows: In the first aspect, a method for generating dynamic energy control commands for highway microgrids is provided. In the first implementable mode of the first aspect, it includes: Acquire current environmental data, traffic flow data, and energy state data for the highway; Based on the environmental data and traffic flow data, the photovoltaic power output data and turbulent wind power output data are calculated using the constructed photovoltaic power output model and turbulent wind power output model. Based on the traffic flow data, photovoltaic power output data, turbulent wind power output data, and energy state data, update the feasible domain corresponding to the current control cycle; Based on the constructed feasible region, the control command of the highway microgrid in the current control cycle is obtained by performing rolling optimization on the objective function constructed based on the Lyapunov virtual queue mechanism. The objective function is: ; in, For control vectors, For the number of partitions, For the penalty weight parameter, , and These are the penalty coefficients corresponding to electricity purchase cost, wind and solar curtailment, and energy storage loss, respectively. , and These represent the virtual queue values ​​corresponding to electricity purchase cost, wind and solar curtailment, and energy storage loss, respectively. The rated energy capacity of the zoned energy storage system. The power purchased from the grid for each zone, For the curtailment of wind and solar power in different zones, For the charging power or charging pulse of the zone, The discharge power of the zone.

[0007] In conjunction with the first feasible method of the first aspect, in the second feasible method of the first aspect, the photovoltaic output model includes a rooftop photovoltaic module output model, the specific calculation formula of which is as follows: ; in, Contribute to rooftop solar modules For reference to photovoltaic module efficiency, The rooftop photovoltaic area for each zone. The irradiance of the inclined plane, For temperature coefficient, The operating temperature of photovoltaic modules. For reference temperature, These are the linearization coefficients.

[0008] In conjunction with the first or second implementable method of the first aspect, in the third implementable method of the first aspect, the photovoltaic output model includes the photovoltaic module output model of the sound barrier, and the specific calculation formula is as follows: ; in, Contribute to the photovoltaic modules for sound barriers The photovoltaic area of ​​the sound barrier zone. Vertical irradiance, For mutual occlusion correction factor, For inverter efficiency, The unit temperature for the partition.

[0009] In conjunction with the first feasible method of the first aspect, the fourth feasible method of the first aspect constructs the turbulent wind power output model, including: The turbulent wind power output model is constructed using the Weibull distribution. The specific turbulent wind power output model is as follows: ; ; ; ; in, For turbulent wind power output, The number of fans for each zone. , These are the upper and lower limits for wind speed. This is a standard three-segment piecewise power curve function. Let be the probability density function of wind speed. For scale parameters, For effective wind speed, Let be the shape parameter of the Weibull distribution. Natural wind speed, For turbulent wind speed, For vehicle model coefficient, Traffic flow density, Average vehicle speed Lateral spacing This represents the power term of the average vehicle speed in the turbulent wind speed. This is the interval power term.

[0010] In conjunction with the first feasible method of the first aspect, the fifth feasible method of the first aspect updates the feasible domain corresponding to the current control cycle, including: Based on photovoltaic power output data and turbulent wind power output data, nodal power balance constraints are constructed, and the specific expressions are as follows: ; in, Contribute power to the photovoltaic modules of the noise barriers along the roadside. For adjacent partition indexes, The set of adjacent partitions that are adjacent to the partition. For the transmission power of the tie line, For the rigid load power requirements of the zone, The flexible load power demand for the zone.

[0011] In conjunction with the first feasible method of the first aspect, the sixth feasible method of the first aspect updates the feasible domain corresponding to the current control cycle, including: Based on traffic flow data and energy status data, calculate the total power demand of rigid loads and the real-time dispatchable power range, respectively. The rigid power supply hard constraint is determined based on the total power demand of the rigid load, and the specific expression is as follows: ; The schedulable boundary constraints for elastic loads are determined based on the real-time schedulable power range, and the specific expression is as follows: ; in, For rigid power supply, This represents the total power demand of the rigid load.

[0012] In conjunction with the first implementable method of the first aspect, the seventh implementable method of the first aspect also includes: The traffic flow mutation rate in the traffic flow data is compared with the mutation threshold in real time. In response to a traffic flow mutation exceeding a mutation threshold, the feasible domain is immediately updated, and control commands for the highway microgrid in the current control cycle are generated by combining traffic flow data, environmental data, and energy state data. Alternatively, the visibility in the environmental data can be compared with a visibility threshold; In response to visibility falling below a visibility threshold, the feasible region is immediately updated, and control commands for the highway microgrid in the current control cycle are generated by combining traffic flow data, environmental data, and energy state data.

[0013] Secondly, a dynamic energy control method for highway microgrids is provided. In the first implementable mode of the second aspect, it includes: The control command for the highway microgrid in the current control cycle is generated by adopting the dynamic energy control command generation method for highway microgrids as described in any of the first to seventh implementable methods of the first aspect. Adjust the energy storage system and load of the highway microgrid according to the generated control commands.

[0014] In conjunction with the first implementable method of the second aspect, the second implementable method of the second aspect also includes: Calculate the three-phase voltage imbalance, fundamental frequency deviation, and effective voltage deviation at the point of common coupling; The overall power grid early warning index is calculated by weighting the three-phase voltage imbalance, fundamental frequency deviation, and effective voltage deviation. Compare the comprehensive power grid early warning index with the early warning index threshold; In response to the mains power comprehensive early warning index being greater than the early warning index threshold, the energy storage converter in the energy storage system maintains normal operation in grid-connected current source mode, while preloading the reference voltage amplitude, frequency, and virtual impedance parameters required for islanded voltage source control into the control register.

[0015] In conjunction with the first implementable method of the second aspect, the third implementable method of the second aspect also includes: When the mains power fails, the energy storage converter in the energy storage system switches to voltage source mode and disconnects the tertiary loads in the highway microgrid; The secondary loads in the highway microgrid are reduced proportionally based on the power deficit.

[0016] Beneficial Effects: The dynamic energy control command generation and control method for highway microgrids of this invention explicitly embeds real-time traffic flow data into the energy scheduling state space, forming a traffic flow-energy dual-state vector together with the energy storage state of charge. Within each control cycle, the complete dual-state information drives the rolling optimization solution of the objective function, fundamentally achieving deep coupling between traffic physics and energy scheduling decisions, eliminating time lag and information loss. Furthermore, the static fixed-weight weighted summation is improved into an adaptive dynamic penalty coefficient mechanism based on virtual queue backlog, with the corresponding virtual queue value as the adaptive dynamic penalty coefficient for each optimization objective embedded in the objective function in real time. This ensures that when an optimization objective continuously deteriorates, the queue backlog of that objective automatically increases, and the equivalent weight automatically rises. When the deviation is eliminated, the queue backlog automatically falls back. This achieves online, unmanned adaptive adjustment of multi-objective priorities, fundamentally overcoming the inherent defects of the fixed-weight method in strong-constraint scenarios, such as rigid priorities and insufficient robustness. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention, the accompanying drawings used in the specific embodiments will be briefly described below. In all the drawings, the elements or parts are not necessarily drawn to scale.

[0018] Figure 1 This is a flowchart of a method for generating dynamic energy control commands for a highway microgrid, as provided in an embodiment of the present invention. Detailed Implementation

[0019] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. These embodiments are merely illustrative of the technical solution of the present invention and are therefore intended to limit the scope of protection of the present invention.

[0020] Example 1 like Figure 1 The flowchart shown illustrates a method for generating dynamic energy control commands for highway microgrids. This method includes: Step 1: Obtain current environmental data, traffic flow data, and energy state data for the highway; Step 2: Based on the environmental data and traffic flow data, calculate the photovoltaic power output data and turbulent wind power output data using the constructed photovoltaic power output model and turbulent wind power output model; Step 3: Update the feasible domain corresponding to the current control cycle based on the traffic flow data, photovoltaic power output data, turbulent wind power output data, and energy state data; Step 4: Based on the constructed feasible region, the control command of the highway microgrid in the current control cycle is obtained by performing rolling optimization on the objective function constructed based on the Lyapunov virtual queue mechanism. The objective function is: ; in, For control vectors, For the number of partitions, For the penalty weight parameter, , and These are the penalty coefficients corresponding to electricity purchase cost, wind and solar curtailment, and energy storage loss, respectively. , and These represent the virtual queue values ​​corresponding to electricity purchase cost, wind and solar curtailment, and energy storage loss, respectively. Let k be the rated energy capacity of the energy storage system in the k-th zone. Let be the power purchased from the grid by the k-th partition at time t. Let be the power of wind and solar power curtailed in the k-th partition at time t. For the charging power or charging pulse of the zone, The discharge power of the zone.

[0021] Specifically, firstly, environmental data, traffic flow data, and energy state data of the highway microgrid can be acquired in real time for each zone of the highway. Then, based on the acquired environmental and traffic flow data, pre-built photovoltaic (PV) output models and turbulent wind power output models can be invoked to calculate PV output data and turbulent wind power output data, respectively. Next, the feasible region corresponding to the current control cycle of different zones can be updated by combining the traffic flow data, PV output data, turbulent wind power output data, and energy state data. Finally, based on the updated feasible region, the objective function pre-built according to the Lyapunov virtual queue mechanism is solved through rolling optimization based on real-time traffic flow data and energy state data, thereby obtaining the control command for the highway microgrid in the current control cycle.

[0022] In this way, traffic flow can be explicitly embedded into the energy scheduling state space, forming a traffic flow-energy dual state vector for use in highway microgrid control decisions. This achieves deep coupling between traffic information and energy decision-making, eliminating the unavoidable time lag and information loss during the conversion of traffic information by the prediction module. Furthermore, the conventional static fixed-weight weighted summation in multi-objective optimization mechanisms is upgraded to an adaptive dynamic penalty coefficient mechanism based on virtual queue backlog. Independent virtual queues are established for the three types of optimization objectives, with the current backlog of each queue serving as the adaptive dynamic penalty coefficient for the optimization objective, embedded in the linear term of the objective function in real time. When an optimization objective continuously deteriorates, its queue backlog automatically increases, and its equivalent weight automatically rises. When the deviation is eliminated, the queue automatically falls back, achieving online, unmanned adaptive adjustment of multi-objective priorities. This fundamentally overcomes the inherent defects of the fixed-weight method in strong-constraint scenarios, such as rigid priorities and insufficient robustness.

[0023] In this embodiment, the penalty weight parameter is used to balance the trade-off between the Lyapunov drift term and the objective function penalty term. A larger penalty weight parameter results in a more aggressive optimization of the objective function, but also a lower tolerance for constraint violations. A smaller penalty weight parameter results in a more conservative system, prioritizing constraint satisfaction.

[0024] The penalty weight parameter can be determined jointly based on the system's time-averaged performance requirements and constraint margin. Specifically, it can be determined through the drift penalty trade-off theory based on Lyapunov optimization, and its value range is

[10] . 2 10 5 The units are consistent with the dimensions of the system objective function. Preferably, the penalty weight parameter is set to

[10] . 3 10 4 This approach achieves a good balance between economic optimization and system constraint stability. When the system's rated energy storage capacity is in the 100kWh range, the penalty weight parameter is preferably set to 5×10. 3 .

[0025] The penalty coefficient corresponding to the electricity purchase cost can be determined based on the peak electricity price in the local industrial electricity price or time-of-use electricity price system, reflecting the economic cost of a unit of electricity purchased. The value range is [0.3, 1.5] yuan / kWh, and the specific value is determined based on the peak time price of the time-of-use electricity price in the region.

[0026] The penalty coefficient for the amount of wind and solar power curtailment can be determined based on local renewable energy subsidy policies or the carbon emission equivalent of curtailed electricity, reflecting the resource waste cost per unit of curtailed electricity. It is generally taken as 0.5 to 2 times the penalty coefficient for electricity purchase cost to reflect the policy orientation of renewable energy consumption. Its value range is [0.1, 1.0] yuan / kWh.

[0027] The penalty coefficient corresponding to energy storage losses can be determined based on the total life cycle cost and equivalent number of cycles of the energy storage system. The specific calculation formula is as follows: ; in, The initial investment cost of the energy storage system, The nominal cycle life is the number of cycles. This represents the rated energy capacity. The penalty coefficient for energy storage loss ranges from [0.05, 0.5] yuan / kWh.

[0028] In this embodiment, environmental data of the highway, such as atmospheric visibility and background brightness outside tunnels, as well as traffic flow data, such as average vehicle speed and traffic flow density, can be collected through relevant sensors. The active power of loads at various levels and the state of charge of energy storage devices can also be directly obtained from the highway microgrid. After acquiring the environmental data, traffic flow data, and energy state data of the microgrid, these data can be preprocessed, such as removing outliers and aligning timestamps.

[0029] In this embodiment, optionally, the photovoltaic output model includes a rooftop photovoltaic module output model, the specific calculation formula of which is as follows: ; in, Contribute to rooftop solar modules For reference to photovoltaic module efficiency, The rooftop photovoltaic area for each zone. The irradiance of the inclined plane, For temperature coefficient, The operating temperature of photovoltaic modules. For reference temperature, These are the linearization coefficients.

[0030] For the temperature coefficient, the power temperature coefficient value provided by the photovoltaic module manufacturer in the product datasheet can be directly adopted. The temperature coefficient value range is... For common monocrystalline and polycrystalline silicon photovoltaic modules, the preferred temperature coefficient is [value missing]. For thin-film photovoltaic modules, the optimal temperature coefficient is preferred. .

[0031] In this embodiment, the irradiance of the inclined plane can be calculated according to the isotropic sky scattering model, and the specific calculation formula is as follows: ; in, The equivalent component of direct irradiance on the inclined plane is calculated using the following formula: ; Horizontal irradiance, This represents diffuse reflectance irradiance. The tilt angle of the rooftop photovoltaic modules. It is the surface albedo. The correction factor for direct irradiation on an inclined plane is given by the following formula: ; The angle of incidence, It is the zenith angle.

[0032] The operating temperature of photovoltaic modules can be estimated using the NOCT model, with the specific calculation formula as follows: ; in, Ambient air temperature, The reference irradiance under NOCT conditions. This refers to the module temperature of a photovoltaic module under NOCT conditions. NOCT, or Nominal Operating Cell Temperature, is a standard condition used to define the operating temperature of a photovoltaic module in a typical outdoor environment and is used to evaluate the thermal performance of the photovoltaic module.

[0033] In this embodiment, optionally, the photovoltaic output model includes the photovoltaic module output model of the sound barrier, and the specific calculation formula is as follows: ; in, Contribute to the photovoltaic modules for sound barriers The photovoltaic area of ​​the sound barrier zone. Vertical irradiance, For mutual occlusion correction factor, For inverter efficiency, The unit temperature for the partition.

[0034] Specifically, the horizontal equivalent irradiance model of rooftop photovoltaics used to treat sound barrier photovoltaics neglects the systematic differences in direct radiation components and mutual shading effects during morning and evening hours caused by the vertical installation of sound barrier modules, introducing significant prediction biases during complementary morning and evening hours. Therefore, this embodiment optimizes and improves the output model of the sound barrier photovoltaic modules by introducing vertical irradiance correction and mutual shading correction factors, thereby accurately capturing the complementary relationship between the sound barrier photovoltaic modules and rooftop photovoltaic modules during morning and evening hours and improving the accuracy of control decisions.

[0035] The photovoltaic modules of the sound barrier are installed vertically, requiring correction for the irradiance on the vertical surface. The specific calculation formula is as follows: ; in, Let cosine be the angle between the vertical plane normal of the photovoltaic module of the sound barrier and the direction of sunlight. It is the cosine of the zenith angle.

[0036] The introduced mutual shading correction factor can be calculated based on the installation height, equivalent spacing, and solar altitude angle of the photovoltaic modules of the sound barrier. The specific calculation formula is as follows: ; in, For equivalent spacing, For installation height, This is the solar altitude angle.

[0037] The aforementioned power output models for sound barrier photovoltaic modules and rooftop photovoltaic modules characterize the complementary power output of sound barrier photovoltaic modules and rooftop photovoltaic modules during morning and evening hours, providing accurate power output benchmark input for subsequent joint scheduling.

[0038] In this embodiment, optionally, constructing the turbulent wind power output model includes: The turbulent wind power output model is constructed using the Weibull distribution. The specific turbulent wind power output model is as follows: ; ; ; ; in, For turbulent wind power output, The number of fans for each zone. , These are the upper and lower limits for wind speed. This is a standard three-segment piecewise power curve function. Let be the probability density function of wind speed. For scale parameters, For effective wind speed, represents the shape parameter of the Weibull distribution, used to control the shape of the Weibull probability density distribution. Natural wind speed, For turbulent wind speed, For vehicle model coefficient, Traffic flow density, Average vehicle speed Lateral spacing This represents the power term of the average vehicle speed in the turbulent wind speed. This is the interval power term.

[0039] Specifically, existing technologies typically drive wind power output prediction using pure meteorological wind speed data during the decision-making process, completely ignoring the contribution of vehicle wake turbulence in the effective wind speed field of the central median wind turbines, leading to distorted predictions of wind power availability. To address this, this embodiment establishes a turbulent wind power output model that uses traffic flow density and vehicle speed as real-time driving parameters based on the Weibull distribution scale. This results in wind power output prediction accuracy significantly outperforming traditional pure meteorological models during peak traffic hours, eliminating systematic modeling biases in highway scenarios from the source of output prediction.

[0040] In this embodiment, after calculating the photovoltaic power output data and turbulent wind power output data for the current control cycle using the above model, the feasible region for the current control cycle can be determined based on the photovoltaic power output data and turbulent wind power output data. This allows traffic flow to be embedded into the energy state space of the highway microgrid, constructing a traffic flow-energy dual state vector. Within each control step, the complete traffic flow-energy dual state vector information drives the rolling optimization solution, ensuring that the generation of all constraints in the feasible region is completed within the scheduling layer. This fundamentally achieves deep coupling between traffic physics and energy scheduling decisions, eliminating the unavoidable time lag and information loss during the conversion of traffic information by the prediction module.

[0041] In this embodiment, the vehicle type coefficient can characterize the difference in wake turbulence intensity generated by different types of vehicles during operation, and is used to weighted correct the comprehensive turbulent wind speed caused by different vehicle types in traffic flow. The vehicle type coefficient can be determined based on a measured calibration method.

[0042] Specifically, the proportions of different vehicle types, including small passenger cars, medium-sized buses, large trucks, and extra-long trucks, are statistically analyzed on the target highway section. Turbulent wind speeds under different traffic flow densities and vehicle composition conditions are measured using an anemometer. The residuals of these measurements are then regressed and fitted to the natural wind speeds to obtain the vehicle coefficients for each vehicle composition. The vehicle coefficients range from [0.01, 0.30] and are dimensionless. Preferably, for typical mixed traffic flow compositions on highways, the vehicle coefficients can be set to 0.08~0.12, corresponding to the comprehensive contribution coefficient of vehicle wakes to turbulent wind speed in typical highway scenarios. This effectively captures the enhancing effect of traffic flow on wind power output without introducing significant calculation errors.

[0043] In this embodiment, the feasible region includes rigid power supply hard constraints, specifically expressed as: ; in, To control the rigid power supply of the zones within a control period. The total power demand of the rigid loads in a given zone refers to the rigid loads along the highway, which include traffic monitoring equipment, toll collection systems, lighting, roadside base stations, and other equipment that cannot be powered off. Therefore, the total power demand of the rigid loads in a given zone can be calculated by adding up the individual components, as shown in the following formula: ; in, To meet the power supply needs of traffic monitoring equipment, For the power requirements of the toll collection system, To meet the power requirements of tunnel lighting, Power requirements for roadside base stations.

[0044] The feasible region also includes the flexible load dispatchable boundary constraint. In this embodiment, the flexible load along the highway includes charging pile clusters and non-core air conditioning clusters. The upper bound of the maximum dispatchable power of the charging pile clusters can be determined by the number of electric vehicles on site, and the number of electric vehicles can be jointly determined by traffic flow density and service area dwell characteristics, as shown in the following formula: ; ; in, The current number of electric vehicles in the partition. The current traffic flow density for the zone, The average speed of vehicles in the current zone. The average dwell time of vehicles in each zone. For conversion factors, The maximum charging power for the vehicle. The current maximum schedulable power limit for the partition.

[0045] The conversion coefficient is used to convert the macroscopic traffic flow state, jointly described by traffic flow density and vehicle speed, into the real-time number of electric vehicles on site within the service area. It is a key parameter connecting the traffic flow state with the upper limit of the schedulable power of the charging piles' elastic load. In this embodiment, the conversion coefficient can be obtained by performing joint statistical analysis on historical traffic flow data and charging pile access records of the target service area, and by regression fitting the actual number of electric vehicles on site under different time periods' traffic flow density-vehicle speed combinations. In this embodiment, the conversion coefficient ranges from [0.001, 0.05], and the unit is vehicles·h / km·vehicle, that is, the proportion of electric vehicles on site corresponding to each unit of traffic flow density × vehicle speed × dwell time.

[0046] For non-core air conditioning groups, the dispatchable power range can be adjusted according to outdoor temperature levels. Combining the charging pile group and the non-core air conditioning group, the real-time dispatchable power range for the overall flexible load is as follows: ; in, For the minimum comprehensive elastic load, This represents the maximum comprehensive elastic load.

[0047] The real-time schedulable power range can be updated based on the traffic flow density and vehicle speed in the current traffic flow data, thereby eliminating the systematic disconnect between the traditional fixed boundary and the actual physical feasible domain.

[0048] In this embodiment, optionally, updating the feasible domain corresponding to the current control cycle includes: Based on photovoltaic power output data and turbulent wind power output data, nodal power balance constraints are constructed, and the specific expressions are as follows: ; in, Contribute power to the photovoltaic modules of the noise barriers along the roadside. For adjacent partition indexes, The set of adjacent partitions that are adjacent to the partition. For the transmission power of the tie line, For the rigid load power requirements of the zone, The flexible load power demand for the zone.

[0049] Specifically, the feasible domain provided by this invention also includes node power balance constraints. When updating the feasible domain corresponding to the current control cycle, the current photovoltaic power output data and turbulent wind power output data of each partition can be calculated based on real-time environmental data and traffic flow data, and then the node power balance constraints can be updated based on real-time photovoltaic power output data and turbulent wind power output data.

[0050] In this embodiment, the feasible domain provided by the present invention also includes dynamic emergency standby charge state bounding constraints, energy storage state equations and static boundary constraints, charge / discharge mutual exclusion and power limiting constraints, and inter-regional tie-line power constraints. These constraints can be updated based on real-time energy state data of the highway microgrid. The emergency standby charge state bounding constraint refers to the requirement that the energy storage system of the highway microgrid must retain sufficient emergency power to support the primary load for a certain period of time in the future. The specific expression is as follows: ; in, The state of charge of the partition. Based on the baseline value of the emergency state of charge, For the power demand of the primary load, Duration For simulation or calculation, time variables The time interval is specified.

[0051] The energy storage state equation and static boundary constraints are calculated as follows: ; The specific calculation formulas for charge / discharge mutual exclusion and power limiting constraints are as follows: ; ; ; .

[0052] in, , Let be the binary variables representing the charging state and discharging state of the k-th partition at time t, respectively. , These represent the maximum discharge power limit and the maximum charging power limit of the energy storage system in the k-th partition, respectively.

[0053] The specific power constraints for inter-regional tie lines are as follows: ; in, This represents the upper limit of power.

[0054] In this embodiment, traffic flow state vectors can be used. and energy state vector Together, they construct a traffic flow-energy dual-state vector. : ; ; in, The state of charge of the energy storage system in the zone. The total power passing through the PCC. Traffic flow density for each zone Let K be the average vehicle speed in zone K.

[0055] In this embodiment, a traffic flow-energy dual state vector can be used. To provide an information foundation, the objective function provided by this invention is continuously optimized. Existing technologies typically employ a weighted summation method with preset fixed weights to quantify three types of objectives: electricity purchase cost, wind and solar curtailment, and energy storage losses. When critical operating conditions occur, such as when the energy storage state of charge approaches the emergency lower limit, the fixed weight mechanism cannot adaptively raise the equivalent priority of safety objectives, potentially leading to erroneous decisions that sacrifice emergency reserve capacity for short-term economic gains. Furthermore, the differences in the dimensions and orders of magnitude of the three types of objectives cause significant fluctuations in the effectiveness of the fixed weights under different seasonal and climatic conditions, requiring frequent manual recalibration.

[0056] To this end, this invention establishes independent virtual queues for the three types of optimization objectives, and uses the current backlog of each queue as the adaptive dynamic penalty coefficient for the corresponding objective, embedding it in real time into the linear term of the objective function. Specifically, independent virtual queues are established for the three types of optimization objectives: electricity purchase cost, wind and solar curtailment, and energy storage loss, and their corresponding update equations are as follows: ; ; ; in, The virtual queue corresponding to the electricity purchase cost. This refers to the virtual queue corresponding to the amount of wind and solar power curtailment. This is the virtual queue corresponding to energy storage losses. , and These are the target drift rate benchmarks corresponding to electricity purchase cost, wind and solar curtailment, and energy storage loss, respectively.

[0057] Define the Lyapunov function as the quadratic form of all virtual queues: ; Minimize the upper bound of the drift penalty in each control cycle, and after expanding the offset term and ignoring constant terms that are irrelevant to the control variables, the objective function described above can be solved equivalently.

[0058] By solving the above objective function, when a certain objective continues to deteriorate, its queue backlog automatically increases and its equivalent weight automatically rises. When the deviation is eliminated, the queue automatically falls back, realizing online adaptive adjustment of the priority of multiple objectives without human intervention. This fundamentally overcomes the inherent defects of the fixed weight method in strong constraint scenarios, such as rigid priority and insufficient robustness.

[0059] Example 2 Example 2 is largely the same as Example 1, except that in this example, it further includes: The traffic flow mutation rate in the traffic flow data is compared with the mutation threshold in real time. In response to a traffic flow mutation exceeding a mutation threshold, the feasible domain is immediately updated, and control commands for the highway microgrid in the current control cycle are generated by combining traffic flow data, environmental data, and energy state data. Alternatively, the visibility in the environmental data can be compared with a visibility threshold; In response to visibility falling below a visibility threshold, the feasible region is immediately updated, and control commands for the highway microgrid in the current control cycle are generated by combining traffic flow data, environmental data, and energy state data.

[0060] Specifically, existing technologies typically update microgrid dispatch plans on a fixed-cycle basis, with the maximum response delay equal to one complete update cycle. Even if the system state undergoes drastic changes within two adjacent update time windows, the dispatch layer cannot respond in time. In highway scenarios, sudden fog can cause a sharp drop in visibility and a dramatic increase in rigid power demand for lighting within tens of seconds, while traffic accidents can significantly narrow the dispatchable space for flexible loads within minutes. Purely periodic triggering mechanisms are powerless to cope with these highly time-sensitive disturbances.

[0061] To this end, in addition to the conventionally set control cycle triggering channel, two asynchronous triggering mechanisms can be set up: a traffic-meteorological event triggering channel and an energy storage safety triggering channel. These mechanisms will immediately refresh the dispatch plan when traffic flow density suddenly exceeds the threshold, visibility drops sharply to the level of fog, or the state of charge approaches the dynamic emergency lower limit. This will compress the maximum response delay of emergencies and ensure that the dispatch plan responds effectively and in real time to the specific emergencies on highways.

[0062] Specifically, the traffic flow mutation rate of each zone in the traffic flow data can be compared with a mutation threshold in real time. If the traffic flow mutation rate of any zone exceeds the mutation threshold, rolling optimization is immediately triggered to obtain the corresponding control command for the highway microgrid. Alternatively, the visibility in the environmental data can be compared with a visibility threshold in real time. If the visibility is lower than the visibility threshold, rolling optimization is immediately triggered to obtain the corresponding control command for the highway microgrid.

[0063] Example 3 A dynamic energy control method for highway microgrids, comprising: The method for generating dynamic energy control commands for highway microgrids as described in Example 1 is used to generate control commands for the highway microgrid in the current control cycle. Adjust the energy storage system and load of the highway microgrid according to the generated control commands.

[0064] Specifically, firstly, the aforementioned method for generating dynamic energy control commands for highway microgrids can be used to generate control commands corresponding to the current control cycle. These commands include energy storage charging / discharging command sequences and adjustable load control sequences for each zone. The energy storage charging / discharging execution unit and the flexible load coordination control unit in the highway microgrid can adjust the energy storage system and load based on the energy storage charging / discharging command sequences and the adjustable load control sequences. Specifically, the energy storage charging / discharging execution unit converts the energy storage charging / discharging command sequences into converter current loop reference values, driving the energy storage systems in each zone to perform charging and discharging operations. The flexible load coordination control unit decomposes the adjustable load control sequences into orderly charging signals for the charging pile group and setpoint adjustment commands for the air conditioning group, completing refined power regulation of the flexible load while maintaining the minimum service quality lower bound.

[0065] Example 4 Example 4 is largely the same as Example 3, with the main difference being that, optionally, it also includes: Calculate the three-phase voltage imbalance, fundamental frequency deviation, and effective voltage deviation at the point of common coupling; The overall power grid early warning index is calculated by weighting the three-phase voltage imbalance, fundamental frequency deviation, and effective voltage deviation. Compare the comprehensive power grid early warning index with the early warning index threshold; In response to the mains power comprehensive early warning index being greater than the early warning index threshold, the energy storage converter in the energy storage system maintains normal operation in grid-connected current source mode, while preloading the reference voltage amplitude, frequency, and virtual impedance parameters required for islanded voltage source control into the control register.

[0066] Specifically, it can also calculate the three-phase voltage imbalance, fundamental frequency deviation, and effective voltage deviation at each point of common coupling in the highway microgrid in real time. The specific calculation formula is as follows: ; ; ; in, This refers to the three-phase voltage imbalance. The magnitude of the negative sequence voltage. This is the positive sequence voltage amplitude. For fundamental frequency deviation, This is the actual frequency value. For the deviation of the effective value of voltage, This is the actual effective voltage value. This is the rated voltage.

[0067] Then, by combining the three-phase voltage imbalance, fundamental frequency deviation, and voltage RMS deviation, the comprehensive power grid early warning index can be evaluated. The specific calculation formula is as follows: ; in, , and These are the weighting coefficients corresponding to the three-phase voltage imbalance, fundamental frequency deviation, and effective voltage deviation, respectively. The weighting coefficients for these three-phase voltage imbalance, fundamental frequency deviation, and effective voltage deviation satisfy the normalization constraint, i.e.: ; ; In this embodiment, the fundamental frequency deviation is most detrimental to highway microgrid scenarios containing a large number of frequency converters and motor loads, such as charging pile converters and tunnel fans, and therefore has the highest weight. The effective voltage deviation directly affects the grid-connected control stability and overvoltage protection operation of the energy storage converter, and is given the second highest weight. Three-phase voltage imbalance has a significant impact on charging pile groups that are mainly connected by single phases, but its detrimental effect is slightly lower than the previous two categories, and therefore has the lowest weight.

[0068] Verification showed that the range of values ​​for the weighting coefficient corresponding to the three-phase voltage imbalance is as follows: The range of values ​​for the weighting coefficient corresponding to the fundamental frequency deviation is: The range of values ​​for the weighting coefficient corresponding to the effective voltage value is: .

[0069] The comprehensive power grid early warning index can detect multi-dimensional composite degradation of the power grid in advance, and has a stronger predictive ability compared to single over / under voltage or over / under frequency protection.

[0070] Finally, the comprehensive early warning index of the mains power can be compared with the early warning index threshold. When the comprehensive early warning index of the mains power is greater than the early warning index threshold, the energy storage converter in the energy storage charging and discharging execution unit will preload the reference voltage amplitude, frequency and virtual impedance parameters required for island voltage source control into the control register while maintaining normal operation of the grid-connected current source mode. This completes zero-disturbance soft preset, compressing the switching response time from hundreds of milliseconds to the order of milliseconds.

[0071] Example 5 Example 5 is largely the same as Example 3, with the main difference being that, optionally, in this example, it also includes: When the mains power fails, the energy storage converter in the energy storage system switches to voltage source mode and disconnects the tertiary loads in the highway microgrid; The secondary loads in the highway microgrid are reduced proportionally based on the power deficit.

[0072] Specifically, during the control process of the highway microgrid, upon confirming a mains power outage, the microgrid can first disconnect the static switch and control the energy storage converter to switch to voltage source mode with preset parameters. Simultaneously, it disconnects tertiary loads in the microgrid, such as landscape lighting, non-critical air conditioning, and charging piles. Then, it proportionally reduces secondary loads in the microgrid, such as non-critical air conditioning, some charging piles, and roadside information screens, based on the power deficit. For primary loads, such as monitoring centers, toll collection systems, and tunnel emergency lighting, power is maintained until mains power is restored.

[0073] After the mains power is restored, the islanded frequency and phase angle can be driven to converge towards the mains power using PI control. The specific control formula is as follows: ; This is a frequency reference value. For standard power frequency, For frequency deviation, This is the phase angle deviation. The gain coefficient is the frequency deviation. This is the cumulative gain coefficient representing past frequency deviations. This is the gain coefficient of the phase damping term.

[0074] The gain coefficients for frequency deviation, the accumulation of past frequency deviation, and the phase damping term can be determined by frequency domain design or time domain simulation tuning of the frequency control loop of the islanded inverter. Alternatively, they can be directly assigned based on engineering experience in highway microgrid scenarios with a rated frequency of 50Hz and an inverter capacity of 100-300kW.

[0075] In this embodiment, the gain coefficient for frequency deviation ranges from 0.05 to 2.0, preferably 0.3; the gain coefficient for accumulated past frequency deviations ranges from 0.01 to 1.0s. -1 0.1s is preferred. -1 The gain coefficient of the phase damping term ranges from 0.02 to 2.0 Hz / rad, with 0.2 Hz / rad being preferred.

[0076] To verify the technical effectiveness of this invention, a simulation experimental platform was built using a wind-solar-storage multi-energy complementary microgrid demonstration project on a highway as the subject. The highway is approximately 120km long and divided into 6 dispatch zones. Each zone is equipped with rooftop photovoltaic (rated power 200kW), sound barrier photovoltaic (rated power 80kW), three central median wind turbines (each with a rated power of 30kW), and lithium iron phosphate energy storage units (200kWh / 100kW). The system is connected to the 10kV distribution network via a point of common coupling (PCC). The simulation data is based on 5-minute annual meteorological data from a province and a typical highway traffic flow measurement dataset, with a driving duration of 365 consecutive days (5-minute time step).

[0077] The experiment employed a controlled experimental method, comprehensively comparing the method of this invention with the following two types of benchmark control methods: Comparison with Method A: Traditional fixed-period rolling optimization + fixed-weight multi-objective weighted summation (current mainstream engineering practice). Comparison with Method B: Traditional fixed-cycle rolling optimization + traffic flow prediction module transformation (a typical scheme that introduces traffic flow prediction in existing academic solutions).

[0078] (1) Comparison of prediction accuracy of turbulent wind power output models This invention establishes a turbulent wind power output model using traffic flow density and average vehicle speed as Weibull distribution scale parameters for real-time driving forces, effectively capturing the contribution of vehicle wake turbulence in the wind speed field where the wind turbines are located in the median strip. Using measured wind power output as a benchmark, the root mean square error (RMSE) and mean absolute percentage error (MAPE) of each model are calculated. The comparison results are shown in Table 1 below: Table 1 Comparison of Prediction Accuracy for Turbulent Wind Power Output As shown in Table 1 above, traditional pure meteorological models completely ignore the contribution of vehicle wake turbulence, resulting in prediction biases as high as 25.4%-29.1% during peak traffic periods and systematically underestimating wind power availability during holidays and accident-related congestion. The turbulent wind power output model of this invention reduces the annual weighted MAPE by 44.6% compared to traditional methods, and improves prediction accuracy by over 60% under peak conditions, eliminating systematic modeling biases in highway scenarios from the source of output prediction.

[0079] (2) Comparison of the accuracy of photovoltaic power output models for sound barriers This invention addresses the unique characteristic of vertically installed photovoltaic modules in sound barriers by introducing vertical surface irradiance correction and mutual shading correction factors to accurately capture the complementary relationship between the module and rooftop photovoltaic systems during morning and evening hours. The comparison results are shown in Table 2 below: Table 2 Comparison of Prediction Accuracy of Photovoltaic Output Model for Noise Barriers During Morning and Evening Times As shown in Table 2 above, the traditional horizontal equivalent irradiance model ignores the advantage of direct radiation component caused by the vertical installation of the sound barrier during the early morning and late evening (when the solar altitude angle is below 25°), resulting in a MAPE as high as 38.7%-41.2%, introducing significant prediction bias. The modified model of this invention reduces the weighted MAPE by 66.5% throughout the day, especially during the periods when the complementary gains are strongest in the early morning and late evening, improving accuracy by over 76%, effectively supporting precise scheduling decisions for wind-solar combined power output.

[0080] (3) The impact of deep coupling of traffic flow and energy on scheduling accuracy This experiment evaluates the impact of explicitly embedding traffic flow into the energy scheduling state space (the method of this invention) on the accuracy of rigid load power supply guarantee and flexible load scheduling compared to indirect transformation through a traffic flow prediction module (control method B). The comparison results are shown in Tables 3 and 4 below: Table 3 Comparison of calculation errors for rigid load demand (annual statistics) Table 4 Comparison of Estimated Dispatchable Power Ranges for Flexible Loads (Service Area Charging Pile Clusters, Annual Statistics) As can be seen from Tables 3 and 4 above, Comparative Method A, which uses fixed boundaries, has a scheduling power range error as high as ±50.3%, resulting in scheduling decisions being severely out of sync with the actual physical feasible region. Comparative Method B, which introduces time lag through prediction module transformation, still has a range error of ±23.1%. The method of this invention directly embeds traffic flow into the state space, compressing the overall range error to ±7.2%, an improvement of 68.8% compared to Comparative Method B, fundamentally eliminating the systematic disconnect between traditional fixed boundaries and the actual physical feasible region.

[0081] (4) The impact of the virtual queue adaptive dynamic penalty mechanism on the multi-objective optimization effect This experiment focuses on verifying the improvement effect of the adaptive dynamic penalty coefficient mechanism based on Lyapunov virtual queue backlog in this invention on the balance of the three optimization objectives and the robustness of key operating conditions compared with the traditional fixed weight method. The comparison results are shown in Tables 5 and 6 below: Table 5 Comparison of Comprehensive Indicators for the Three Types of Optimization Targets Throughout the Year Table 6 Comparison of decision robustness under critical operating conditions (SOC approaching the emergency lower limit) As shown in Tables 5 and 6 above, the fixed-weight method, unable to detect the strong constraint scenario where the State of Charge (SOC) approaches the emergency lower bound, resulted in 31 emergency lower bound default events throughout the year, accumulating a duration of 4.6 hours, and generating 16.6% erroneous decisions (sacrificing emergency reserve capacity for short-term electricity purchase cost savings). The virtual queue mechanism of this invention, by automatically increasing the equivalent weight of energy storage safety objectives, reduced the number of emergency defaults to zero throughout the year. Simultaneously, it reduced annual electricity purchase costs by 15.7%, wind and solar curtailment rates by 52.7%, and energy storage cycle counts by 23.0%, achieving simultaneous optimization of safety constraints and multi-objective economics without any manual weighting intervention.

[0082] 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 or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for generating dynamic energy control commands for highway microgrids, characterized in that, include: Acquire current environmental data, traffic flow data, and energy state data for the highway; Based on the environmental data and traffic flow data, the photovoltaic power output data and turbulent wind power output data are calculated using the constructed photovoltaic power output model and turbulent wind power output model. Based on the traffic flow data, photovoltaic power output data, turbulent wind power output data, and energy state data, update the feasible domain corresponding to the current control cycle; Based on the updated feasible region, the control command for the highway microgrid in the current control cycle is obtained by performing rolling optimization on the objective function constructed based on the Lyapunov virtual queue mechanism. The objective function is: ； in, For control vectors, For the number of partitions, For the penalty weight parameter, , and These are the penalty coefficients corresponding to electricity purchase cost, wind and solar curtailment, and energy storage loss, respectively. , and These represent the virtual queue values ​​corresponding to electricity purchase cost, wind and solar curtailment, and energy storage loss, respectively. The rated energy capacity of the zoned energy storage system. The power purchased from the grid for each zone, The amount of wind and solar power curtailed in each zone. The charging power of the zone, The discharge power of the zone.

2. The method for generating dynamic energy control commands for highway microgrids according to claim 1, characterized in that, The photovoltaic power output model includes a rooftop photovoltaic module power output model, and the specific calculation formula is as follows: ； in, Contribute to rooftop solar modules For reference to photovoltaic module efficiency, The rooftop photovoltaic area for each zone. The irradiance of the inclined plane, For temperature coefficient, The operating temperature of photovoltaic modules. For reference temperature, These are the linearization coefficients.

3. The method for generating dynamic energy control commands for highway microgrids according to claim 1 or 2, characterized in that, The photovoltaic output model includes the photovoltaic module output model for the sound barrier, and the specific calculation formula is as follows: ； in, Contribute to the photovoltaic modules for sound barriers The photovoltaic area of ​​the sound barrier zone. Vertical irradiance, For mutual occlusion correction factor, For inverter efficiency, For the unit temperature of the partition, For reference to photovoltaic module efficiency, For temperature coefficient, This is a reference temperature.

4. The method for generating dynamic energy control commands for highway microgrids according to claim 1, characterized in that, Constructing the turbulent wind power output model includes: The turbulent wind power output model is constructed using the Weibull distribution. The specific turbulent wind power output model is as follows: ； ； ； ； in, For turbulent wind power output, The number of fans for each zone. , These are the upper and lower limits for wind speed. This is a standard three-segment piecewise power curve function. Let be the probability density function of wind speed. For scale parameters, For effective wind speed, Let be the shape parameter of the Weibull distribution. Natural wind speed, For turbulent wind speed, For vehicle model coefficient, Traffic flow density, Average vehicle speed Lateral spacing This represents the power term of the average vehicle speed in the turbulent wind speed. This is the interval power term.

5. The method for generating dynamic energy control commands for highway microgrids according to claim 1, characterized in that, Update the feasible domain corresponding to the current control cycle, including: Based on photovoltaic power output data and turbulent wind power output data, nodal power balance constraints are constructed, and the specific expressions are as follows: ； in, Contribute power to the photovoltaic modules of the noise barriers along the roadside. For adjacent partition indexes, The set of adjacent partitions that are adjacent to the partition. For the transmission power of the tie line, For the rigid load power requirements of the zone, For the flexible load power demand of the zone, Contribute to rooftop solar modules It provides power for turbulent wind power.

6. The method for generating dynamic energy control commands for highway microgrids according to claim 1, characterized in that, Update the feasible domain corresponding to the current control cycle, including: Based on traffic flow data and energy status data, calculate the total power demand of rigid loads and the real-time dispatchable power range, respectively. The rigid power supply hard constraint is determined based on the total power demand of the rigid load, and the specific expression is as follows: ； The schedulable boundary constraints for elastic loads are determined based on the real-time schedulable power range, and the specific expression is as follows: ； in, For rigid power supply, This represents the total power demand of the rigid load. For the minimum comprehensive elastic load, This represents the maximum comprehensive elastic load.

7. The method for generating dynamic energy control commands for highway microgrids according to claim 1, characterized in that, Also includes: The traffic flow mutation rate in the traffic flow data is compared with the mutation threshold in real time. In response to a traffic flow mutation exceeding a mutation threshold, the feasible domain is immediately updated, and control commands for the highway microgrid in the current control cycle are generated by combining traffic flow data, environmental data, and energy state data. Alternatively, the visibility in the environmental data can be compared with a visibility threshold; In response to visibility falling below a visibility threshold, the feasible region is immediately updated, and control commands for the highway microgrid in the current control cycle are generated by combining traffic flow data, environmental data, and energy state data.

8. A dynamic energy control method for highway microgrids, characterized in that, include: The control command for the highway microgrid in the current control cycle is generated using the dynamic energy control command generation method for highway microgrids as described in any one of claims 1-7. Adjust the energy storage system and load of the highway microgrid according to the generated control commands.

9. The dynamic energy control method for highway microgrids according to claim 8, characterized in that, Also includes: Calculate the three-phase voltage imbalance, fundamental frequency deviation, and effective voltage deviation at the point of common coupling; The overall power grid early warning index is calculated by weighting the three-phase voltage imbalance, fundamental frequency deviation, and effective voltage deviation. Compare the comprehensive power grid early warning index with the early warning index threshold; In response to the mains power comprehensive early warning index being greater than the early warning index threshold, the energy storage converter in the energy storage system maintains normal operation in grid-connected current source mode, while preloading the reference voltage amplitude, frequency, and virtual impedance parameters required for islanded voltage source control into the control register.

10. The dynamic energy control method for highway microgrids according to claim 8, characterized in that, Also includes: When the mains power fails, the energy storage converter in the energy storage system switches to voltage source mode and disconnects the tertiary loads in the highway microgrid; The secondary loads in the highway microgrid are reduced proportionally based on the power deficit.

Images