A light storage collaborative dynamic power dispatching method for sharing electric bicycle charging and swapping stations

Abstract

The application is suitable for the technical field of power dispatching, and provides a light storage collaborative dynamic power dispatching method for a shared electric bicycle charging and swapping station, which comprises the following steps: acquiring the instantaneous power generation of a photovoltaic power generation unit, the real-time state of charge of an energy storage unit, the time-of-use electricity price information of a current power grid, and the real-time state data of batteries in each charging compartment in all charging and swapping cabinets; taking the lowest total operation cost of the station end, the highest battery turnover efficiency, and the lowest charging safety risk as the comprehensive optimization target, and constructing a power dispatching optimization model; solving the power dispatching optimization model to generate decision variables; converting the decision variables into control instructions and issuing the control instructions to the energy storage converter in the energy storage unit and each charging and swapping cabinet for execution. The application realizes the significant improvement of the overall economic benefit of the charging station and the user battery swapping experience under the premise of guaranteeing the safety of the batteries, especially in high-temperature and high-humidity regional environments.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power dispatching, in particular to a light-storage collaborative dynamic power dispatching method for a shared electric bicycle charging and swapping station. BACKGROUND

[0002] At present, the shared electric bicycle integrated charging and swapping station integrating photovoltaic, energy storage, charging and swapping functions has become an important development direction of new urban infrastructure. The power dispatching of such a power station usually adopts a control strategy based on simple rules or independent optimization, for example, preferentially using photovoltaic power generation, storing the remaining electricity in the energy storage, and purchasing electricity from the power grid when insufficient; or only carrying out peak-valley arbitrage with the lowest electricity cost as the target. However, in a special geographical environment such as Guangxi with high temperature and high humidity, the single economic-oriented strategy may ignore the safety state of the battery temperature, health degree and other factors, blindly pursue low-cost high-power charging, and easily aggravate the risk of battery thermal runaway and shorten the battery life; at the same time, the power distribution priority is not dynamically adjusted according to the real-time swapping demand prediction, resulting in low battery turnover efficiency and affecting user experience.

[0003] In view of this, a light-storage collaborative dynamic power dispatching method for a shared electric bicycle charging and swapping station is proposed. SUMMARY

[0004] The present application provides a light-storage collaborative dynamic power dispatching method for a shared electric bicycle charging and swapping station, which is used to solve the problem of low battery turnover efficiency and affect user experience.

[0005] The present application provides a light-storage collaborative dynamic power dispatching method for a shared electric bicycle charging and swapping station, which is applied to an integrated charging and swapping station composed of one photovoltaic power generation unit, one energy storage unit and multiple charging and swapping cabinets, and includes the following steps:

[0006] Obtain the instantaneous power generation of the photovoltaic power generation unit, the real-time state of charge of the energy storage unit, the time-of-use electricity price information of the current power grid, and the real-time state data of the batteries in each charging compartment of all charging and swapping cabinets;

[0007] Based on the obtained data, a power dispatching optimization model is constructed with the lowest total station operation cost, the highest battery turnover efficiency and the lowest charging safety risk as the comprehensive optimization targets;

[0008] Solve the power dispatching optimization model to generate decision variables; the decision variables include the real-time working mode and corresponding charging and discharging power value of the energy storage unit, the total input power limit value of the multiple charging and swapping cabinets, the charging priority sequence of each charging compartment and the real-time power allocation value of each charging compartment;

[0009] The decision variable is converted into a control instruction and is issued to the corresponding device for execution; wherein, based on the charge and discharge power value, an instruction is generated and issued to the energy storage converter in the energy storage unit; based on the total input power limit value and the real-time power distribution value of each charging bin, an instruction is generated and issued to each charging and swapping cabinet.

[0010] Further, the total cost of the station end operation The corresponding calculation formula is as follows:

[0011]

[0012] Wherein: is the total scheduling time, is the interaction power of the period with the power grid, and the power purchase is positive and the power sale is negative; is the time-of-use price information of the period; is the time interval of scheduling; is the reference battery loss coefficient; is the charge and discharge power of the energy storage in the period; is the regional climate influence factor, wherein is the ambient temperature of the period, is the reference temperature, is the fitting coefficient; , are respectively the demand response incentive price and the power reduction of the period; is an indicator function, indicating whether the station participates in the demand response of the period.

[0013] Further, the battery turnover efficiency The corresponding calculation formula is as follows:

[0014]

[0015] Wherein: is the total number of charging and swapping cabinets in the station; is the total number of charging bins contained in the th charging and swapping cabinet; is a prediction time variable, is a prediction time domain; is the state of charge of the th block battery in the th charging and swapping cabinet at the th moment; , are weight coefficients; is a hyperbolic tangent function,​ This is the scaling factor. For prediction At any given time, the intensity of battery swapping demand at this site; For the first The first charging and swapping cabinet The health status of the battery.

[0016] Furthermore, the aforementioned charging safety risks The corresponding calculation formula is as follows:

[0017]

[0018] in: for Time period assigned to the The first charging and swapping cabinet The charging power of each charging case; For the first The first charging and swapping cabinet The maximum charging power of each charging compartment; These are the fitting coefficients; for Time period The first charging and swapping cabinet Real-time temperature of each charging compartment; It is the minimum value; Health risk coefficient; Based on the basic humidity risk factor; Risk factor for relative humidity; for The relative humidity of the environment during a given time period.

[0019] Furthermore, the objective function of the power dispatch optimization model is expressed as finding decision variables that minimize the following scalar function:

[0020]

[0021] in: , , These are the weighting coefficients corresponding to the total operating cost of the station, battery turnover efficiency, and charging safety risks, respectively, to meet the following requirements. .

[0022] Furthermore, the power dispatch optimization model is equipped with constraints, including power balance constraints, energy storage system operation constraints, charging and swapping cabinet power constraints, grid interaction power constraints, battery state safety constraints, and non-negativity constraints of decision variables.

[0023] Furthermore, the calculation formulas for the constraints are as follows:

[0024] Power balance constraints:

[0025]

[0026] Energy storage system operation constraints:

[0027]

[0028]

[0029] Charging and swapping cabinet power constraints:

[0030]

[0031]

[0032] Grid interaction power constraints:

[0033]

[0034] Battery state safety constraints:

[0035]

[0036]

[0037] Non-negativity constraints on decision variables:

[0038]

[0039] In the above equations: is the real-time output power of the photovoltaic power generation unit in the , , are the charging power and discharging power of the energy storage unit in the , is the total input power of the th charging and swapping cabinet in the , is the total power consumption of the auxiliary equipment in the station in the , , are the real-time state of charge of the energy storage unit in the , , , are the minimum and maximum state of charge safety thresholds allowed by the energy storage unit, , are the maximum allowed charging and discharging power of the energy storage unit, , indicates that the energy storage unit is in the charging state in the total energy actually stored in the energy storage unit within the time period, , represents the total energy actually taken out from the energy storage unit within the time period, , , are the charging efficiency and discharging efficiency of the energy storage unit, respectively, representing the energy loss in the charging process and discharging process, is the rated capacity of the energy storage unit; is the power allocated to the i-th charging compartment in the j-th charging and swapping cabinet within the time period, is the maximum charging power allowed for the i-th charging compartment in the j-th charging and swapping cabinet, determined by the power of the charging module configured in the i-th charging compartment in the j-th charging and swapping cabinet, is the upper limit of the total input power of the j-th charging and swapping cabinet; is the maximum value of the power allowed to be fed to the power grid, is the maximum value of the power allowed to be obtained from the power grid; is the temperature alarm threshold of the i-th charging compartment in the j-th charging and swapping cabinet, once the temperature exceeds this value, the compartment must stop charging, is the real-time state of charge of the battery in the i-th charging compartment in the j-th charging and swapping cabinet within the time period, , are the minimum state of charge and maximum state of charge allowed for the battery in the i-th charging compartment in the j-th charging and swapping cabinet. ,

[0040] Further, the generating instructions based on the charging and discharging power value and issuing to the energy storage converter in the energy storage unit comprises:

[0041] generating an energy storage scheduling control instruction frame, the energy storage scheduling control instruction at least containing a frame header, a working mode identifier, a power setting value, a time stamp and a check code; wherein the working mode identifier is used to instruct the energy storage converter to switch to a charging mode, a discharging mode or a standby mode; the power setting value is the absolute value of the charging and discharging power value;

[0042] issuing the energy storage scheduling control instruction frame to the energy storage converter in the energy storage unit through a preset industrial communication protocol; and receiving the instruction confirmation signal and real-time operating parameters fed back by the energy storage converter. ​​​​​​​​​​​​​

[0043] Further, the generating instructions based on the total input power limit value and the real-time power allocation value of each charging compartment and issuing the instructions to each charging and swapping cabinet comprises:

[0044] For each charging and swapping cabinet, a cabinet-level power constraint instruction and a set of compartment-level power allocation instructions are generated; the cabinet-level power constraint instruction is used to set the upper limit of the total input power of the charging and swapping cabinet to the total input power limit value corresponding to the charging and swapping cabinet; the set of compartment-level power allocation instructions includes the identifier of each charging compartment in the charging and swapping cabinet and the corresponding real-time power allocation value;

[0045] After the cabinet-level power constraint instruction and the set of compartment-level power allocation instructions are packaged, they are issued to the charging and swapping cabinet.

[0046] Further, the set of compartment-level power allocation instructions is a power allocation matrix , which is mathematically represented as:

[0047]

[0048] wherein the row vector index of the matrix corresponds to the charging and swapping cabinet number, the column vector index corresponds to the charging compartment number in the cabinet, and the matrix element value is the real-time power allocation value allocated to the i th cabinet and the j th charging compartment. For idle or faulty compartments, the corresponding matrix element value is zero.

[0049] As can be seen from the above technical solution, the present application has the following advantages:

[0050] The present application constructs a comprehensive optimization model with the goals of simultaneously achieving the lowest total cost of station operation, the highest battery turnover efficiency, and the lowest charging safety risk by real-time collection of photovoltaic power generation, energy storage state of charge, grid price, and detailed state data of batteries in each charging compartment; by solving the model, decision variables covering energy storage charging and discharging, cabinet total power limitation, and fine-grained power allocation of each charging compartment are generated and converted into control instructions to be issued to energy storage converters and each charging and swapping cabinet for execution, thereby solving the problem of mutual isolation of economic efficiency, safety, and operation efficiency targets in the prior art, and achieving significant improvement in overall economic efficiency of the charging station and user battery swapping experience while ensuring battery safety, especially in high-temperature and high-humidity regional environments. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 is an embodiment flowchart of a photovoltaic storage collaborative dynamic power dispatching method for a shared electric bicycle charging and swapping station in the present application; ​​​​​

[0052] Figure 2 FIG. 2 is a temperature time curve comparison result diagram of the charging and battery swapping cabinet under two strategies in the case simulation of the present application;

[0053] Figure 3 FIG. 3 is a power time curve comparison result diagram of the charging and battery swapping cabinet under two strategies in the case simulation of the present application. DETAILED DESCRIPTION

[0054] The terms "first", "second", "third", "fourth" and the like in the description of this application and the above drawings, if any, are used to distinguish similar objects, and do not necessarily have to be described in a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "correspond to" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0055] Embodiment one

[0056] The method implemented in this embodiment can be implemented in a system, and can be implemented in a server or a terminal, and the specific implementation is not limited. From the perspective of system implementation, the method in the present application will be introduced below. Please refer to Figure 1 The method provided by the embodiment of the present application includes the following steps:

[0057] S11. Obtain the instantaneous power generation of the photovoltaic power generation unit, the real-time state of charge of the energy storage unit, the time-of-use electricity price information of the current power grid, and the real-time state data of the batteries in all charging compartments of the charging and battery swapping cabinet;

[0058] In this embodiment, the local controller or data collector in the photovoltaic power generation unit extracts the instantaneous power generation value from the data frame through the preset communication protocol analysis program. The obtained data is the actual output active power of the photovoltaic power generation unit at the current moment, which reflects the comprehensive situation of light intensity, environmental temperature, photovoltaic panel state and the like. The energy storage unit obtains the comprehensive state information of the energy storage battery pack in real time through the monitoring interface provided by the energy storage converter or the battery management system. The communication mode is similar to that of the photovoltaic unit, and data exchange is performed through a standard industrial protocol. The obtained core data is the state of charge of the energy storage unit at the current moment, which represents the remaining available energy of the energy storage unit at the current moment and is a key state quantity for determining the charging and discharging behavior of the energy storage unit. The time-of-use price information of the current power grid is obtained from the energy management cloud platform API interface provided by the power grid company or the local price configuration database through a communication link such as 4G / 5G or Ethernet, and the time-of-use price table in a future scheduling period is obtained. The obtained data is time-divided price information, which usually includes peak, valley, flat periods and their corresponding prices, and is a core external incentive signal for optimizing system operation economy. Each charging and swapping cabinet is equipped with an intelligent management unit, which communicates with the battery management system of each charging compartment through the cabinet bus, and periodically polls or subscribes detailed state data of all batteries. Then, the intelligent management unit of each cabinet aggregates the data and uploads it to the local central controller through the local area network. This part of data specifically includes the real-time power, health, real-time temperature and charging time of the battery in each charging compartment.

[0059] S12. Based on the obtained data, a power dispatching optimization model is constructed with the lowest total cost, the highest battery turnover efficiency and the lowest charging safety risk as the comprehensive optimization objectives.

[0060] This step is to convert the multi-source data collected in S11 into a solvable mathematical optimization problem, thereby providing accurate quantitative basis for realizing the coordinated dispatching of photovoltaic storage and charging. Specifically, by comprehensively considering the economy, operation efficiency and safety of the three targets, and strictly following the physical operation constraints of the system, a multi-objective dynamic power dispatching optimization model is constructed. The implementation principle is as follows: first, based on real-time and predicted data, the total cost of station operation, battery turnover efficiency and charging safety risk are quantified respectively; the multi-objective optimization problem is converted into a single objective scalar function by using linear weighting method; finally, complete constraint condition set is set combined with energy balance, equipment limit value and safe operation boundary of the system, to form a complete optimization model. The mathematical expression of the model is as follows:

[0061]

[0062] Wherein: 、 、 are the weight coefficients corresponding to the total cost of station-side operation, battery turnover efficiency and charging safety risk, respectively, satisfying .

[0063] Total cost of station-side operation The corresponding calculation formula is as follows:

[0064]

[0065] wherein: is the total scheduling time, is the interaction power between the time period and the power grid, and the power purchase is positive and the power sale is negative; is the time-of-use electricity price information of the time period; is the time interval of scheduling; is the reference battery loss coefficient; is the charging and discharging power of the energy storage in the time period; is the regional climate influence factor, wherein is the ambient temperature of the time period, is the reference temperature, is the fitting coefficient; , are the demand response incentive price and the power reduction of the time period, respectively; is an indicator function, indicating whether the station participates in the demand response of the time period.

[0066] Battery turnover efficiency The corresponding calculation formula is as follows:

[0067]

[0068] wherein: is the total number of charging and swapping cabinets in the station; is the total number of charging bays contained in the charging and swapping cabinet; is the prediction time variable, is the prediction time domain; is the state of charge of the block battery in the charging and swapping cabinet at the moment; , are weight coefficients; is a hyperbolic tangent function, is a scaling coefficient, is the swapping demand intensity of the station at the predicted moment; The health degree of the first battery in the first charging and replacing cabinet in the first time period. The health degree of the first battery in the first charging and replacing cabinet in the first time period.

[0069] Charging safety risk The corresponding calculation formula is as follows:

[0070]

[0071] Among them: The charging power of the first charging and replacing cabinet in the first time period is allocated to the first charging bin. The charging power of the first charging and replacing cabinet in the first time period is allocated to the first charging bin. The maximum charging power of the first charging and replacing cabinet in the first time period is allocated to the first charging bin. The maximum charging power of the first charging and replacing cabinet in the first time period is allocated to the first charging bin. The fitting coefficient is: The fitting coefficient is: The fitting coefficient is: The fitting coefficient is: The real-time temperature of the first charging and replacing cabinet in the first time period is allocated to the first charging bin. The real-time temperature of the first charging and replacing cabinet in the first time period is allocated to the first charging bin. The real-time temperature of the first charging and replacing cabinet in the first time period is allocated to the first charging bin. The minimum value is: The health degree risk coefficient is: The base humidity risk coefficient is: The relative humidity risk coefficient is: The relative humidity risk coefficient is: The environmental relative humidity in the first time period is: The environmental relative humidity in the first time period is:

[0072] In this embodiment, the power dispatching optimization model is provided with constraint conditions, including power balance constraint, energy storage system operation constraint, charging and replacing cabinet power constraint, grid interaction power constraint, battery state safety constraint and decision variable non-negativity constraint.

[0073] The calculation formula of the constraint condition is as follows:

[0074] Power balance constraint:

[0075]

[0076] Energy storage system operation constraint:

[0077]

[0078]

[0079] Charging and replacing cabinet power constraint:

[0080]

[0081] ​

[0082] Power grid interaction constraints:

[0083]

[0084] Battery state safety constraints:

[0085]

[0086]

[0087] Nonnegativity constraint on decision variables:

[0088]

[0089] In the above formula: for Real-time output power of the photovoltaic power generation unit during the time period. , They are respectively The charging and discharging power of the time-limited energy storage unit. for Time period Total input power of each charging and swapping cabinet for Total power consumption of auxiliary equipment within the station during the time period; , They are respectively Time period and Real-time state of charge of the time-segment energy storage unit , These are the minimum and maximum permissible state-of-charge safety thresholds for energy storage units, respectively. , These are the maximum allowable charging power and discharging power of the energy storage unit, respectively. , indicating in The total energy actually stored in the energy storage unit during the period. , indicating in The total energy actually extracted from the energy storage unit during the time period. , These represent the charging efficiency and discharging efficiency of the energy storage unit, respectively, and indicate the energy loss during the charging and discharging processes. This refers to the rated capacity of the energy storage unit; for Time period assigned to the The first charging and swapping cabinet The power of each charging case For the first The first charging and swapping cabinet The maximum charging power allowed by each charging compartment is determined by the first one. The first charging and swapping cabinet a charging module power decision configured by the charging bin, an upper limit of the total input power of the first an upper limit of the total input power of the first a maximum value allowed to feed power to the power grid, a maximum value allowed to obtain power from the power grid; a temperature alarm threshold of the first charging bin in the first a temperature alarm threshold of the first charging bin in the first a real-time state of charge of the battery in the first charging bin in the first a real-time state of charge of the battery in the first charging bin in the first a real-time state of charge of the battery in the first charging bin in the first a real-time state of charge of the battery in the first charging bin in the first a minimum state of charge and a maximum state of charge allowed for the battery in the first charging bin in the first a minimum state of charge and a maximum state of charge allowed for the battery in the first charging bin in the first a minimum state of charge and a maximum state of charge allowed for the battery in the first charging bin in the first a minimum state of charge and a maximum state of charge allowed for the battery in the first charging bin in the first a minimum state of charge and a maximum state of charge allowed for the battery in the first charging bin in the first

[0090] S13. Solve the power dispatching optimization model to generate decision variables; the decision variables include the real-time working mode and corresponding charging and discharging power values of the energy storage unit, the total input power limit value of the plurality of charging and battery swapping cabinets, the charging priority sequence of each charging bin, and the real-time power allocation value of each charging bin.

[0091] In this embodiment, an efficient optimization algorithm is used to solve the complex nonlinear programming model constructed in S12 in real time, and the multi-dimensional optimization target is converted into a directly executable control instruction set. The specific implementation process is as follows:

[0092] Based on the current system state data, the preset intelligent optimization algorithm is called to solve the power dispatching optimization model in rolling, and the optimal solution set that minimizes the objective function is calculated under the premise of meeting all the constraint conditions. The solution set is the decision variable for driving the system to run, mainly including: the real-time working mode and corresponding accurate charging and discharging power values of the energy storage unit, the maximum total input power limit value allowed for each charging and battery swapping cabinet, the charging priority sequence of each charging bin dynamically sorted according to the urgency and health degree, and the real-time power setting value finally allocated to each charging bin. This process ensures that the system can automatically generate an optimal dispatching scheme that takes into account economy, efficiency and safety at each dispatching period.

[0093] S14. Convert the decision variables into control instructions and issue them to the corresponding devices for execution; wherein, based on the charging and discharging power values, instructions are generated and issued to the energy storage converters in the energy storage unit; based on the total input power limit value and the real-time power allocation value of each charging bin, instructions are generated and issued to each charging and battery swapping cabinet.

[0094] In this embodiment, generating instructions based on the charge / discharge power value and sending them to the energy storage converter in the energy storage unit includes:

[0095] 1. Generate an energy storage dispatch control command frame. The energy storage dispatch control command frame includes at least a frame header, a working mode identifier, a power setting value, a timestamp, and a checksum. The working mode identifier is used to indicate whether the energy storage converter switches to charging mode, discharging mode, or standby mode. The power setting value is the absolute value of the charging and discharging power value.

[0096] 2. Through a pre-set industrial communication protocol, the energy storage scheduling and control command frame is sent to the energy storage converter in the energy storage unit; and the command confirmation signal and real-time operating parameters fed back by the energy storage converter are received.

[0097] Specifically, based on the operating mode and charge / discharge power values ​​in the decision variables, a structured energy storage scheduling control command frame is generated. This command frame is a data packet containing multiple fields, including at least: a frame header (used to identify the command start and protocol type), an operating mode identifier (using a specific code to indicate that the energy storage converter switches to charging, discharging, or standby mode), a power setpoint (the absolute value of the charge / discharge power value), a timestamp (identifying the command generation time), and a checksum (used to ensure the integrity of data transmission). The encapsulated energy storage scheduling control command frame is sent to the energy storage converter in the energy storage unit via a pre-set industrial communication protocol; simultaneously, the communication interface is monitored to receive command confirmation signals and real-time operating parameters (such as actual output power, DC-side voltage, etc.) from the energy storage converter to verify whether the command has been correctly executed.

[0098] In this embodiment, instructions are generated based on the total input power limit and the real-time power allocation value of each charging compartment and sent to each charging and swapping cabinet, including:

[0099] 1. For each charging and swapping cabinet, generate cabinet-level power constraint instructions and compartment-level power allocation instruction sets; cabinet-level power constraint instructions are used to set the total input power limit of the charging and swapping cabinet to the total input power limit corresponding to the charging and swapping cabinet; compartment-level power allocation instruction sets contain the identifier of each charging compartment in the charging and swapping cabinet and the corresponding real-time power allocation value.

[0100] 2. Package the cabinet-level power constraint instructions and the warehouse-level power allocation instruction set and send them to the charging and swapping cabinet.

[0101] Among them, the warehouse-level power allocation instruction set is a power allocation matrix. Mathematically, it is represented as:

[0102]

[0103] Where: row vector index of the matrix Corresponding charging / swapping cabinet number, column vector index corresponding to the charging bin number in the cabinet, the value of the matrix element is the real-time power allocation value assigned to the thcharging bin of the thcabinet ; for the matrix element corresponding to the idle or fault bin position, the value is zero.

[0104] Specifically, for each charging and battery swapping cabinet, two types of instructions are generated respectively. First, generate a cabinet-level power constraint instruction, which is used to set the upper limit of the total input power of the target charging and battery swapping cabinet to the corresponding total input power limit value in the decision variable. Second, generate a set of bin-level power allocation instructions, which includes the unique identifier (such as the physical address or logical ID of the bin position) of each charging bin in the cabinet and its corresponding real-time power allocation value. Package the above cabinet-level power constraint instruction and bin-level power allocation instruction set into a data packet and issue it to the corresponding charging and battery swapping cabinet through the local area network. After receiving and analyzing the packet, the intelligent management unit in the cabinet first constrains the total power of the cabinet according to the cabinet-level instruction, and then drives the power allocation circuit of each bin position according to the bin-level instruction set to allocate specific power values to each charging bin.

[0105] The above method will be described in detail in combination with a case simulation:

[0106] 1. Case background:

[0107] This simulation is for a typical shared electric bicycle integrated charging and battery swapping station located in Nanning, Guangxi. The station configuration includes: a 50kWp photovoltaic power generation unit, a 100kWh / 50kW energy storage unit, and 3 charging and battery swapping cabinets, each containing 12 charging bins, a total of 36 charging bin positions. The simulation duration is set to 48 hours, simulating a typical scenario including high temperature and high humidity weather and peak electricity consumption.

[0108] Regional parameter settings:

[0109] Electricity price policy: Adopt Nanning City's time-of-use electricity price policy for industrial and commercial users, with peak time (10:00-12:00, 14:00-19:00) electricity price of 1.2 yuan / kWh, flat time (08:00-10:00, 12:00-14:00, 19:00-24:00) of 0.8 yuan / kWh, and valley time (00:00-08:00) of 0.4 yuan / kWh. Climate data: simulate a high temperature and high humidity weather process from hour 12 to hour 36, with environmental temperature rising from 28°C to 36°C and relative humidity rising from 65% to 92%.

[0110] Comparison method:

[0111] Traditional economic scheduling strategy: optimize for the single objective of minimizing total electricity cost, ignoring battery safety risks and turnover efficiency. The multi-objective collaborative scheduling strategy of the present application: dynamically balance economy, safety and efficiency by using the method of the present application.

[0112] 2. Simulation process and results:

[0113] Normal scenario (0-12 hours): good weather conditions, stable battery replacement demand. Both strategies prefer to use photovoltaic power and charge the energy storage during the valley time electricity price period. The traditional strategy pursues the lowest cost and charges all batteries at maximum power during the valley time electricity price period (early morning). The strategy of the present application also charges during the valley time, but differentiates power allocation according to battery health, and uses trickle charging for batteries with lower health to protect their lifespan.

[0114] High temperature and high humidity mixed scenario (12-36 hours): the environment temperature and humidity continue to rise, the risk of battery thermal runaway increases, and the demand for battery replacement increases during the evening peak.

[0115] Traditional strategy: to meet the demand for battery replacement during the evening peak, it continuously charges at high power, causing the temperature of some batteries with lower health or poor heat dissipation to rise sharply. By the 24th hour, 2 battery positions in cabinet No. 3 have triggered forced power-off due to temperature exceeding the 45°C alarm threshold, not only failing to be fully charged, but also reducing the number of available batteries due to failure.

[0116] Strategy of the present application: the safety risk weight in the model dynamically increases as the environment temperature rises. The algorithm actively limits the charging power of high-risk positions and prioritizes energy allocation to batteries with high health and normal temperature. As shown in Figure 2 , although the total charging power has decreased slightly, all battery temperatures are controlled below the safety line. At the same time, the algorithm predicts the demand during the evening peak and schedules energy storage discharge in advance, ensuring that there are enough fully charged batteries available during the peak period.

[0117] Extreme peak scenario (36-42 hours): the evening peak superimposes high temperature and high humidity, and the system faces the greatest pressure.

[0118] Traditional strategy: due to the failure of some positions, the total number of available batteries decreases, and the demand during the peak cannot be met, prolonging the waiting time for battery replacement by users. The system purchases electricity from the grid during the peak electricity price period to charge the remaining batteries, significantly increasing the electricity cost.

[0119] Strategy of the present application: under the premise of safety, the algorithm outputs the optimal power allocation matrix, ensuring that all 36 positions operate at full load and efficiently. At the same time, it prioritizes energy storage discharge during the peak electricity price period, greatly reducing expensive grid purchases.

[0120] 3. Performance comparison results:

[0121] The following table compares the key performance indicators of the two strategies within a 48-hour simulation period:

[0122]

[0123] The simulation results show that although the strategy of the present application has slight differences in valley charging strategy, the total cost is reduced by 11.4% due to the avoidance of concentrated electricity purchase during peak hours and the loss of fault shutdown. Through intelligent prediction and priority sorting, the battery turnover rate is improved by 33%, significantly shortening the user waiting time and improving the user experience. There is no high temperature alarm throughout the process, effectively prolonging the overall life of the battery pack, especially protecting the battery assets with lower health.

[0124] As shown in Figure 2 , the maximum temperature in the traditional strategy cabinet continuously rises with the increase of environmental temperature and humidity, reaching a peak of 46°C at the 24th hour, which seriously exceeds the safety threshold of 45°C, triggering system alarm and causing forced power-off. Although the maximum temperature in the cabinet of the present application also shows an upward trend, the temperature curve is smooth and is effectively suppressed below the safety line of 45°C through early intervention of the algorithm, and no temperature alarm is triggered throughout the process.

[0125] As shown in Figure 3 , the power curve of the traditional strategy truly reflects the consequences of temperature out of control, and the over-temperature at the 24th hour directly leads to a sharp drop in the total input power of the cabinet body to zero (forced power-off). After restoring power supply, the power curve fluctuates sharply, showing the unstable state of the charging- overheating power-off-recharging process, and the system runs intermittently and inefficiently; the power curve of the present application shows high stability, and the algorithm actively and smoothly limits the total charging power during the high temperature and humidity period (about 12-36 hours) to obtain safety margin. Therefore, its power has no interruption, showing a continuous and stable running state, ensuring the reliability of the charging process.

[0126] It can be understood that those skilled in the art can combine various embodiments in the above embodiments under the guidance of the above embodiments to obtain technical solutions of various embodiments.

[0127] The above is only a preferred embodiment of the present application and is not intended to limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A light storage collaborative dynamic power dispatching method for sharing electric bicycle charging and swapping stations, applied to an integrated charging and swapping station composed of a photovoltaic power generation unit, an energy storage unit and multiple charging and swapping cabinets, characterized in that, The method comprises: acquiring the instantaneous power generation of a photovoltaic power generation unit, the real-time state of charge of an energy storage unit, the time-of-use electricity price information of a current power grid, and the real-time state data of the batteries in each charging compartment of all charging and swapping cabinets; based on the acquired data, constructing a power dispatching optimization model with the lowest total cost, the highest battery turnover efficiency, and the lowest charging safety risk as the comprehensive optimization objectives; The battery turnover efficiency The corresponding calculation formula is as follows: Wherein: is the total number of charging and battery swapping cabinets in the station; is the total number of charging and battery swapping cabinets in the station; is the total number of charging bays in the is the prediction time variable, is the current scheduling time, is the prediction time domain; is the predicted time, the state of charge of the block battery in the charging and battery swapping cabinet; , is the weight coefficient; is the hyperbolic tangent function, is the scaling coefficient, is the station identifier, is the predicted time, the battery swapping demand intensity of the station; is the health degree of the block battery in the charging and battery swapping cabinet; The charging safety risk The corresponding calculation formula is as follows: wherein: is the charging power of the first charging bin in the first charging and battery swapping cabinet in the time period; is the charging power of the first charging bin in the first charging and battery swapping cabinet in the time period; is the maximum charging power of the first charging bin in the first charging and battery swapping cabinet; is a fitting coefficient; is the real-time temperature of the first charging bin in the first charging and battery swapping cabinet in the time period; is the real-time temperature of the first charging bin in the first charging and battery swapping cabinet in the time period; is the ambient temperature in the time period; is a minimum value; is a risk coefficient of relative humidity; is the relative humidity in the time period; solving the power dispatching optimization model to generate decision variables; the decision variables include the real-time working mode and corresponding charging and discharging power value of the energy storage unit, the total input power limit value of the multiple charging and swapping cabinets, the charging priority sequence of each charging compartment, and the real-time power allocation value of each charging compartment; converting the decision variables into control instructions and issuing them to the corresponding devices for execution; wherein, based on the charging and discharging power value, an instruction is generated and issued to the energy storage converter in the energy storage unit; based on the total input power limit value and the real-time power allocation value of each charging compartment, an instruction is generated and issued to each charging and swapping cabinet.

2. The method for dynamic power dispatch of optical storage coordination of shared electric bicycle charging and swapping station according to claim 1, characterized in that, The total cost of running the station end The corresponding calculation formula is as follows: wherein: is the total scheduling time, is the interaction power of the time period with the grid, positive for buying power, negative for selling power; is the time-of-use price information of the time period; is the time interval of the scheduling; is the reference battery loss coefficient; is the charge and discharge power of the energy storage of the time period; is the regional climate impact factor, wherein is the ambient temperature of the time period, is the reference temperature, is the fitting coefficient; , are respectively the demand response incentive price and the curtailment power of the time period; is the indicator function, indicating whether the site participates in the demand response of the time period.

3. The method for dynamic power dispatch of optical storage in coordination with shared electric bicycle charging and swapping station according to claim 2, characterized in that, The objective function of the power dispatching optimization model is expressed as finding the decision variables that minimize the following scalar function: Wherein: , , respectively are weight coefficients corresponding to the total cost of station end operation, battery turnover efficiency and charging safety risk, satisfying .

4. The method for dynamic power dispatch of optical storage in coordination with shared electric bicycle charging and swapping station according to claim 1, characterized in that, The power dispatching optimization model is provided with constraint conditions, including power balance constraints, energy storage system operation constraints, charging and swapping cabinet power constraints, power grid interaction power constraints, battery state safety constraints, and decision variable non-negativity constraints.

5. The method for dynamic power dispatch of optical storage in coordination with shared electric bicycle charging and swapping station according to claim 4, characterized in that, The calculation formulas of the constraint conditions are as follows: Power balance constraint: Energy storage system operation constraint: Charging and swapping cabinet power constraint: Power grid interaction power constraint: Battery state safety constraint: Decision variable non-negativity constraint: In the above formula: This refers to the total number of charging and battery swapping cabinets within the station. For the first The total number of charging compartments contained in each charging and swapping cabinet. for Real-time output power of the photovoltaic power generation unit during the time period. for Interaction power between time period and power grid, , They are respectively The charging and discharging power of the time-limited energy storage unit. for Time period Total input power of each charging and swapping cabinet for Total power consumption of auxiliary equipment within the station during the time period; , They are respectively Time period and Real-time state of charge of the time-segment energy storage unit , These are the minimum and maximum permissible state-of-charge safety thresholds for energy storage units, respectively. , These are the maximum allowable charging power and discharging power of the energy storage unit, respectively. , indicating in The total energy actually stored in the energy storage unit during the period. , indicating in The total energy actually extracted from the energy storage unit during the time period. , These represent the charging efficiency and discharging efficiency of the energy storage unit, respectively, and indicate the energy loss during the charging and discharging processes. This refers to the rated capacity of the energy storage unit; for Time period assigned to the The first charging and swapping cabinet The power of each charging case For the first The first charging and swapping cabinet The maximum charging power allowed by each charging compartment is determined by the first one. The first charging and swapping cabinet The power of the charging module configured in each charging compartment is determined by its own power. For the first The upper limit of the total input power of a single charging and swapping cabinet; To the maximum allowable power feed to the grid, To determine the maximum power that can be drawn from the grid; for Time period the real-time temperature of the first charging compartment in the first charging and replacing cabinet the real-time temperature of the first charging compartment in the first charging and replacing cabinet the temperature alarm threshold of the first charging compartment in the first charging and replacing cabinet the temperature alarm threshold of the first charging compartment in the first charging and replacing cabinet the temperature alarm threshold of the first charging compartment in the first charging and replacing cabinet the real-time temperature of the first charging compartment in the first charging and replacing cabinet the real-time temperature of the first charging compartment in the first charging and replacing cabinet the real-time temperature of the first charging compartment in the first charging and replacing cabinet the real-time temperature of the first charging compartment in the first charging and replacing cabinet , the minimum allowable state of charge and the maximum allowable state of charge of the battery in the first charging compartment in the first charging and replacing cabinet the minimum allowable state of charge and the maximum allowable state of charge of the battery in the first charging compartment in the first charging and replacing cabinet the minimum allowable state of charge and the maximum allowable state of charge of the battery in the first charging compartment in the first charging and replacing cabinet 6. The method for dynamic power dispatch of optical storage in coordination with shared electric bicycle charging and swapping station according to claim 1, characterized in that, The instruction generated based on the charging and discharging power value and issued to the energy storage converter in the energy storage unit comprises: generating an energy storage dispatching control instruction frame, which at least contains a frame header, a working mode identifier, a power setting value, a timestamp, and a check code; wherein, the working mode identifier is used to instruct the energy storage converter to switch to a charging mode, a discharging mode, or a standby mode; the power setting value is the absolute value of the charging and discharging power value; issuing the energy storage dispatching control instruction frame to the energy storage converter in the energy storage unit through a pre-set industrial communication protocol; and receiving the instruction confirmation signal and real-time operation parameters fed back by the energy storage converter.

7. The method for dynamic power dispatch of optical storage in coordination with shared electric bicycle charging and swapping station according to claim 1, characterized in that, The instruction generated based on the total input power limit value and the real-time power allocation value of each charging compartment and issued to each charging and swapping cabinet comprises: for each charging and swapping cabinet, generating a cabinet-level power constraint instruction and a set of compartment-level power allocation instructions; the cabinet-level power constraint instruction is used to set the upper limit of the total input power of the charging and swapping cabinet to the total input power limit value corresponding to the charging and swapping cabinet; the set of compartment-level power allocation instructions contains the identifier of each charging compartment in the charging and swapping cabinet and the corresponding real-time power allocation value; after packaging the cabinet-level power constraint instruction and the set of compartment-level power allocation instructions, issuing them to the charging and swapping cabinet.

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