Household optical storage and charging micro-grid energy management method based on event driving

Abstract

The invention discloses an event-driven household optical storage and charging micro-grid energy management method. Comprising the following steps: before the start of a scheduling day, solving a day-ahead curve of the household optical storage and charging micro-grid on the scheduling day and issuing the day-ahead curve to an energy storage battery; checking whether an emergency occurs in the household optical storage and charging micro-grid in each time period of the dispatching day, and if the emergency occurs, recalculating the operation curve of the remaining time period of the day according to the latest real-time operation condition of the household optical storage and charging micro-grid and sending the operation curve to the energy storage battery; and if the emergency does not occur, continuing to run according to the day-ahead curve until the emergency occurs. According to the method, the problem of day-ahead curve optimization loss caused by emergencies such as source load prediction deviation out-of-limit or electric vehicle access and departure under the condition that the computing resources of the household micro-grid terminal are limited is solved. According to the method, the economic effect close to rolling optimization is obtained with extremely few solving times, economic operation of the household micro-grid is guaranteed, and the requirement of light-weight terminal equipment for saving computing resources is also considered.

Description

Technical Field

[0001] This invention belongs to the field of residential photovoltaic-storage-charging microgrid optimization, and in particular relates to an event-driven energy management method for residential photovoltaic-storage-charging microgrids. Background Technology

[0002] Currently, existing research mainly focuses on energy management of photovoltaic-storage-charging microgrids in industrial parks or commercial buildings. Research on energy management of household photovoltaic-storage-charging microgrids accounts for a large proportion of individual electric vehicle charging loads, while research on energy management of household photovoltaic-storage-charging microgrids with limited computing resources for terminal control equipment is relatively scarce.

[0003] In residential photovoltaic-storage-charging microgrids, the power of AC charging piles for electric vehicles typically exceeds the basic load power of households, resulting in a very high proportion of charging power in the load. This characteristic means that the connection or disconnection of a single electric vehicle can immediately cause a drastic change in the original power balance of the microgrid, severely undermining the effectiveness of day-ahead-based optimized operation schemes. Traditional day-ahead optimization methods quickly become inefficient in this dynamic scenario, leading to a significant decline in operational economics.

[0004] To address these issues, researchers proposed a rolling optimization method with fixed time intervals. However, this method requires periodically repeating the optimization problem, which continuously consumes significant computational resources. Residential microgrids typically use resource-constrained embedded control devices as their operating terminals. These devices are characterized by small memory capacity and limited processor performance. In actual operation, the terminal devices must simultaneously handle multiple tasks, including data monitoring, human-computer interaction, and control command execution. Frequent optimization calculations can easily lead to excessive CPU utilization, response latency, and even system crashes, affecting the stable operation of the residential microgrid. Summary of the Invention

[0005] To address the real-time mismatch of day-ahead optimized operating curves caused by unforeseen events such as electric vehicle access or prediction deviation exceeding limits in residential scenarios, and the limited computing resources of lightweight residential terminal devices, this invention provides an event-driven energy management method for residential photovoltaic-storage-charging microgrids.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] I. An event-driven energy management method for residential photovoltaic-storage-charging microgrids

[0008] Before the start of the dispatch day, the whole day is divided into multiple time periods. Based on the predicted values ​​of photovoltaic output and load power in the household photovoltaic-storage-charging microgrid, the fixed parameters of the photovoltaic-storage-charging equipment configured by the user, the preset default charging habit data of electric vehicles, and the power market demand response control instructions, the day-ahead curve of the household photovoltaic-storage-charging microgrid on the dispatch day is solved and sent to the energy storage battery.

[0009] During each time period of the scheduling day, check whether any emergencies occur in the residential photovoltaic-storage-charging microgrid. If an emergency occurs, recalculate the operating curve for the remaining time period of the day based on the latest real-time operating status of the residential photovoltaic-storage-charging microgrid and send it to the energy storage battery. If no emergency occurs, continue to operate according to the previous day's curve until an emergency occurs.

[0010] The emergencies include: grid connection of residential electric vehicles, off-grid events of residential electric vehicles, user modification of expected charging target SOC events, photovoltaic power generation and load power prediction deviation exceeding limits events, grid-side update of demand response control instructions events, and microgrid grid connection and islanding operation mode switching events.

[0011] The inspection method for photovoltaic power generation and load power prediction deviation exceeding the limit event is as follows:

[0012] The overall prediction deviation between photovoltaic power output and load power coupling within the time window is obtained by using a sliding time window, and the moving average deviation rate within the time window is calculated. If the moving average of the deviation rate over L consecutive time periods If the deviation rate threshold is exceeded, a photovoltaic power generation and load power prediction deviation exceeding the limit event will occur; otherwise, it will not occur.

[0013] Calculate the moving average of the deviation rate within the time window using the following formula. :

[0014]

[0015] Where t is the current runtime segment; i is the indexed time period; and M is the number of time periods within the time window; Let be the deviation rate for time period i.

[0016] The calculation process for the deviation rate threshold is as follows:

[0017] During the scheduling day, the actual photovoltaic output, predicted photovoltaic output, actual load power, and predicted load power are collected in real time for each time period. Then, the photovoltaic deviation and load deviation are calculated. Subsequently, the difference between the photovoltaic deviation and the load deviation is used as the coupled overall deviation, and the normalized relative deviation rate is calculated based on the coupled overall deviation. Next, the mean and standard deviation are calculated based on the historical normalized relative deviation rate data. Finally, the deviation rate threshold is calculated based on the mean and standard deviation.

[0018] The method for solving the day-ahead curve of the residential photovoltaic-storage-charging microgrid on the dispatch day specifically includes:

[0019] A mixed-integer linear programming model and its constraints are constructed, and the day-ahead curve of a residential photovoltaic-storage-charging microgrid on the dispatch day is solved based on this mixed-integer linear programming model. The objective function f of the mixed-integer linear programming model satisfies the following formula:

[0020]

[0021] in, , These represent the purchased and sold power of a residential photovoltaic-storage-charging microgrid during time period t, respectively. , These represent the electricity purchase price and the electricity sales price during time period t, respectively. , These represent the charging status of the energy storage battery and the electric vehicle during time period t, respectively. , These represent the charging status of the energy storage battery and the electric vehicle during the time period t-1, respectively. This represents the penalty factor for charge / discharge state transitions.

[0022] II. An event-driven energy management system for residential photovoltaic-storage-charging microgrids

[0023] The day-ahead curve generation unit is used to solve the day-ahead curve of the residential photovoltaic-storage-charging microgrid on the dispatch day based on the predicted values ​​of photovoltaic output and load power in the residential photovoltaic-storage-charging microgrid, the fixed parameters of the photovoltaic-storage-charging equipment configured by the user, the preset default charging habit data of electric vehicles, and the power market demand response control instructions.

[0024] The emergency judgment unit is used to check whether any emergencies occur in the household photovoltaic-storage-charging microgrid at different times during the scheduling day;

[0025] The operation curve generation unit is used to recalculate the operation curves for the current period and subsequent periods based on the latest real-time operation status of the residential photovoltaic-storage-charging microgrid.

[0026] III. A computer device

[0027] The device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the event-driven energy management method for residential photovoltaic-storage-charging microgrids.

[0028] IV. A computer-readable storage medium

[0029] The medium stores a computer program, which, when executed by a processor, implements the steps of the event-driven energy management method for a residential photovoltaic-storage-charging microgrid.

[0030] V. A computer program product

[0031] The product includes a computer program / instruction that, when executed by a processor, implements the steps of the event-driven energy management method for residential photovoltaic-storage-charging microgrids.

[0032] The beneficial effects of this invention are as follows:

[0033] Based on the previously optimized operating curve, this invention updates the operating curve only under the latest state driven by sudden events.

[0034] The method proposed in this invention can effectively cope with sudden events such as electric vehicle access and prediction deviation exceeding the limit, ensuring the economy of optimization results, and significantly reducing the consumption of computing resources, adapting to the computing capabilities of lightweight household terminal devices.

[0035] This invention effectively solves the problem of real-time mismatch of the day-ahead optimized operation curve caused by sudden events such as electric vehicle access or prediction deviation exceeding the limit in residential scenarios, as well as the problem of limited computing resources for lightweight residential terminal devices. Attached Figure Description

[0036] Figure 1 This is a flowchart illustrating the event-driven energy management method for residential photovoltaic-storage-charging microgrids provided in this embodiment of the invention.

[0037] Figure 2 This is the topology of the residential photovoltaic-storage-charging microgrid provided in the embodiments of the present invention.

[0038] Figure 3 This refers to the photovoltaic and load forecasting deviation data provided in the embodiments of the present invention.

[0039] Figure 4 This is an example of an event-driven running curve update of the microgrid running results provided in an embodiment of the present invention.

[0040] Figure 5 This is a currently optimized solution provided by the embodiments of the present invention.

[0041] Figure 6 This is the intraday timed rolling optimization scheme provided in the embodiments of the present invention.

[0042] Figure 7 This is a scheme for intraday event-driven optimization provided in the embodiments of the present invention. Detailed Implementation

[0043] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0044] This invention proposes an event-driven energy management method for residential photovoltaic-storage-charging microgrids, targeting home-scale microgrids equipped with photovoltaic power generation units, energy storage batteries, and residential electric vehicles. Figure 1 As shown, the method specifically includes the following steps:

[0045] Before the start of the dispatch day, the entire day is divided into multiple time periods. Based on the predicted values ​​of photovoltaic output and load power in the residential photovoltaic-storage-charging microgrid, the fixed parameters of the user-configured photovoltaic-storage-charging equipment, the preset default charging habits data of electric vehicles, and the power market demand response control instructions, the day-ahead curve of the residential photovoltaic-storage-charging microgrid on the dispatch day is calculated and sent to the energy storage battery. The energy storage battery operates according to the day-ahead curve. Among them, the fixed parameters of the photovoltaic-storage-charging equipment include the energy storage battery capacity, the maximum charging and discharging power of the energy storage battery, the charging and discharging efficiency of the energy storage battery, the battery capacity of the residential electric vehicle, the maximum charging and discharging power of the charging pile, and the charging and discharging efficiency of the charging pile. The default charging habits data of electric vehicles include the daily default access time, the daily default disconnection time, the daily default initial SOC upon access, and the daily default expected SOC upon disconnection.

[0046] During each time period of the scheduling day, the system checks whether any emergencies occur in the residential photovoltaic-storage-charging microgrid. If an emergency occurs, the operating curve for the remaining time period of the day is recalculated based on the latest real-time operating status of the microgrid and sent to the energy storage batteries. The energy storage batteries then operate according to the operating curve. Specifically, the remaining time period from the time of the emergency to the end of the day is used as a new optimization interval. Based on the actual values ​​of photovoltaic output and load power in the current time period, the predicted values ​​of photovoltaic output and load power in the remaining time period of the day, and the latest electric vehicle status, a mixed-integer linear programming model is re-solved to form the operating curve for the remaining time period. This updates the original day-ahead operating curve and sends it to the energy storage batteries for execution. If no emergency occurs, the system continues to operate according to the day-ahead curve until an emergency occurs.

[0047] The method for detecting photovoltaic power generation and load power prediction deviations exceeding limits is based on a dynamic threshold detection mechanism, as follows:

[0048] The prediction deviation of photovoltaic power output and the overall prediction deviation of load power coupled within the time window are obtained by using a sliding time window, and the moving average of the deviation rate within the time window is calculated. To avoid false triggers caused by instantaneous fluctuations, it is necessary to determine the value over consecutive time periods. This involves considering the moving average of the deviation rate over L consecutive time periods. If the deviation rate threshold is exceeded, a photovoltaic power generation and load power prediction deviation exceeding the limit event will occur; otherwise, it will not occur.

[0049] Calculate the moving average of the deviation rate within the time window using the following formula. :

[0050]

[0051] Where t is the current runtime segment; i is the indexed time period; and M is the number of time periods within the time window; Let be the deviation rate for time period i.

[0052] The calculation process for the deviation rate threshold is as follows:

[0053] During the dispatch day, the microgrid system collects the actual photovoltaic output value for each time period in real time. Photovoltaic power output forecast Actual load power and load power forecast Next, the photovoltaic deviation is calculated. With load deviation The formula is as follows:

[0054]

[0055]

[0056] Subsequently, the photovoltaic deviation With load deviation The difference is used as the overall coupling deviation. This reflects the net effect of system-level prediction errors. The formula is as follows:

[0057]

[0058] This method effectively solves the problem of mutual cancellation between photovoltaic and load deviations, ensuring that event detection is more in line with the actual operating status of the microgrid.

[0059] To eliminate the influence of the power baseline on the deviation assessment, the overall deviation is normalized. A baseline value is set. It is set to a very small value to prevent division by zero errors. Then, based on the overall coupling deviation... Calculate the normalized relative deviation rate The formula is as follows:

[0060]

[0061] in, This indicates the absolute value operation.

[0062] This formula is normalized using a differential power benchmark, highlighting the relative significance of the photovoltaic-load coupling deviation.

[0063] Next, the mean is calculated based on historical normalized relative deviation rate data (i.e., normalized relative deviation rate over a past period). and standard deviation The formula is as follows:

[0064]

[0065]

[0066] in, This refers to historical data points, typically set to 7 days of data. This represents the normalized relative bias rate data for the i-th historical period.

[0067] Finally, based on the mean and standard deviation Calculate the deviation rate threshold The formula is as follows:

[0068]

[0069] The deviation rate threshold can be adjusted by adjusting the sensitivity coefficient k according to actual conditions such as weather and day type.

[0070] Solving for the day-ahead curve of a residential photovoltaic-storage-charging microgrid on the dispatch date specifically includes:

[0071] A mixed-integer linear programming model and its constraints are constructed, and the day-ahead curve of a residential photovoltaic-storage-charging microgrid on the dispatch day is solved based on this mixed-integer linear programming model. The objective function f of the mixed-integer linear programming model satisfies the following formula:

[0072]

[0073] in, , These represent the purchased and sold power of a residential photovoltaic-storage-charging microgrid during time period t, respectively. , These represent the electricity purchase price and the electricity sales price during time period t, respectively. , These represent the charging status of the energy storage battery and the electric vehicle during time period t, respectively, with 1 indicating that it is charging and 0 indicating that it is not charging. , These represent the charging status of the energy storage battery and the electric vehicle during the time period t-1, respectively. This represents the penalty factor for charge / discharge state transitions, and is usually set to a very small value. This indicates the number of time periods within the optimization time.

[0074] The constraints are as follows:

[0075] 1) The power balance constraint satisfies the following formula:

[0076]

[0077] in, , These represent the predicted values ​​for photovoltaic power and load, respectively. , These represent the charging and discharging power of the energy storage battery, respectively. , These represent the charging and discharging power of the electric vehicle, respectively.

[0078] 2) The maximum charging power constraint of the energy storage battery satisfies the following formula:

[0079]

[0080] in, Maximum charging power of energy storage.

[0081] 3) The maximum discharge power constraint of the energy storage battery satisfies the following formula:

[0082]

[0083] in, This indicates the maximum discharge power of the stored energy.

[0084] 4) The mutual exclusion constraint of the charge and discharge states of the energy storage battery satisfies the following formula:

[0085]

[0086] 5) The upper and lower limits of the SOC of the energy storage battery satisfy the following formula:

[0087]

[0088] To ensure that energy storage batteries do not suffer from shortened lifespan due to overcharging and discharging, it is usually necessary to set upper and lower limits for State of Charge (SOC). Indicates the lower limit of SOC; This indicates the upper limit of SOC.

[0089] 6) The SOC (State of Charge) definition constraint of the energy storage battery satisfies the following formula:

[0090]

[0091] in, Indicates the capacity of the energy storage battery. This represents the SOC of the energy storage battery at the beginning of time period t; This indicates the time granularity, i.e., the length of a time period; , These represent the charging and discharging efficiencies of the energy storage battery, respectively.

[0092] 7) The mutual exclusion constraint between power purchase and sale states satisfies the following formula:

[0093]

[0094] in, , These represent the states of electricity purchase and sale, respectively, and are represented by 0-1 variables.

[0095] 8) The maximum charging power constraint for electric vehicles satisfies the following formula:

[0096]

[0097] in, This indicates the connection status of the electric vehicle, with 0 indicating no connection and 1 indicating connection.

[0098] 9) The upper and lower limits of the State of Charge (SOC) of electric vehicles satisfy the following formula:

[0099]

[0100] Like energy storage batteries, electric vehicle batteries need to have upper and lower limits set for their State of Charge (SOC) to extend their lifespan. Indicates the lower limit of the State of Charge (SOC) of electric vehicles; This indicates the upper limit of the State of Charge (SOC) for electric vehicles.

[0101] 10) The SOC definition constraints for electric vehicles satisfy the following formula:

[0102]

[0103] in, Indicates the battery capacity of an electric vehicle; , These represent the charging and discharging efficiencies of electric vehicle batteries, respectively.

[0104] The mutual exclusion constraint for the charging and discharging states of electric vehicles satisfies the following formula:

[0105]

[0106] The expected SOC constraint for off-grid electric vehicles satisfies the following formula:

[0107]

[0108] in, This indicates the user-defined desired off-grid SOC (State of Charge) for electric vehicles. This indicates the status of electric vehicles accessing the microgrid during time period t, where 1 indicates that the electric vehicle is connected to the microgrid and 0 indicates that the electric vehicle is disconnected from the grid; it also indicates the status of electric vehicles accessing the microgrid during time period t+1.

[0109] The power grid demand response constraint satisfies the following formula:

[0110] When the grid issues a demand response command to the microgrid, the microgrid needs to limit the power it purchases from the main grid.

[0111]

[0112] in, This represents the maximum power purchase in time period t of the grid demand response command. This indicates the demand response status: 1 indicates demand response is in progress, and 0 indicates normal operation.

[0113] Please see Figure 2 One embodiment of the present invention provides a topology diagram of a residential photovoltaic-storage-charging microgrid and describes its fixed parameters. Specifically, this residential photovoltaic-storage-charging microgrid mainly consists of photovoltaic panels, energy storage batteries, and V2G AC charging piles for electric vehicles. It is a household-scale system, and its fixed parameters are shown in Table 1.

[0114] Table 1. Fixed parameter values ​​for the embodiments

[0115]

[0116] Unexpected events include grid-connected events for residential electric vehicles, grid-off events for residential electric vehicles, events where users modify their expected target SOC for charging, events where photovoltaic power generation and load power prediction deviations exceed limits, events where the grid side updates demand response control instructions, and events where microgrids switch between grid-connected and islanded operation modes.

[0117] Please see Figure 3 One embodiment of the present invention provides predicted deviation data for photovoltaic output and load power of the aforementioned residential photovoltaic-storage-charging microgrid on a typical operating day. In a residential microgrid, residents typically return home to their electric vehicles after get off work and leave during work hours. To demonstrate the complete charging process, this embodiment uses a 24-hour period from 8:00 AM on the first day to 8:00 AM on the second day as the operating example. An event-driven energy management method is employed for optimized operation.

[0118] Before the start of the scheduling day, the entire 24 hours are divided into 96 equal time periods, with a time granularity of 15 minutes. Based on the predicted values ​​of photovoltaic output and load power in the residential photovoltaic-storage-charging microgrid, the fixed parameters of the photovoltaic-storage-charging equipment provided in Table 1, the preset default charging habits data of electric vehicles, and the electricity market demand response control instructions, the day-ahead curve of the residential photovoltaic-storage-charging microgrid on the scheduling day is calculated and sent to the energy storage battery.

[0119] During the dispatch day, the microgrid system calculates the overall coupling deviation based on the collected predicted and actual power of photovoltaic and load data using the following formula:

[0120]

[0121]

[0122]

[0123] Subsequently, based on the overall coupling deviation Calculate the normalized relative deviation rate using the following formula. :

[0124]

[0125] In this embodiment, the reference value Set to 0.5kW.

[0126] Next, the mean is calculated based on historical normalized relative deviation rate data (i.e., normalized relative deviation rate over a past period). and standard deviation The formula is as follows:

[0127]

[0128]

[0129] In this embodiment, the number of historical data points Set it to 96, which represents the data from the past day. Calculate the mean. The standard deviation is 0.355. It is 0.382.

[0130] Subsequently, the deviation rate threshold is calculated. The formula is as follows:

[0131]

[0132] In this embodiment, the sensitivity coefficient k is set to 1.5, and the deviation rate threshold can be calculated. It is 0.929.

[0133] Next, the moving average of the deviation rate within the time window is calculated using the following formula. If the moving average of the deviation rate exceeds the deviation rate threshold over L consecutive time periods, a photovoltaic power generation and load power prediction deviation exceeding the limit event will occur; otherwise, it will not occur.

[0134]

[0135] In this embodiment, M is set to 2 and L is set to 1. The results of the sudden events are shown in Table 2. During this operating day, the residential photovoltaic-storage-charging microgrid experienced a total of 5 photovoltaic output and load power prediction deviation exceeding the limit events, occurring in time periods 29, 35, 36, 39, and 50, as detailed in Table 2. The system will update the day-ahead curve driven by the sudden events at the beginning of these time periods.

[0136] Table 2 List of Contingencies on Operating Days

[0137]

[0138] Please see Figure 4 An embodiment of the present invention provides specific operational results. In the grid interaction power, positive values ​​represent the power purchased, and negative values ​​represent the power sold; in the energy storage charging and discharging power, positive values ​​represent the discharging power, and negative values ​​represent the charging power; in the electric vehicle charging and discharging power, positive values ​​represent the discharging power, and negative values ​​represent the charging power. According to Figure 4 It can be seen that the maximum load power of the residential photovoltaic-storage-charging microgrid occurred at 20:00 on this operating day, reaching 1.35kW. Table 1 shows that the rated charging power of the AC charging piles for residential electric vehicles within this microgrid is 7kW, far exceeding the household load. Therefore, the connection or disconnection of a single electric vehicle will immediately cause a drastic change in the total power supply and demand pattern of the residential microgrid, by orders of magnitude.

[0139] In this embodiment, the user sets the default arrival time of the home electric vehicle to 18:15 every day, with a default SOC of 30% upon arrival; the default departure time is 7:30 the next morning, with a desired SOC of 80% upon departure.

[0140] As shown in Table 2, in this embodiment, the actual arrival time of the home electric vehicle on the operating day is 17:00 on the same day, with an actual SOC of 20% upon arrival; the actual departure time is 6:30 the next morning. Compared to the set default time, the user returns home earlier and leaves home earlier the following day. Therefore, it is necessary to drive the update of the day-ahead curve when the charging status changes twice: when the user arrives home and the vehicle connects to the microgrid, and when the user leaves home and the vehicle leaves the microgrid the following day.

[0141] In this embodiment, in addition to using intraday event-driven optimization, day-ahead optimization and intraday timed rolling optimization were also used for comparative analysis.

[0142] The recently optimized solution is as follows: Figure 5 As shown, this method involves performing an optimization calculation based on the predicted values ​​of photovoltaic output and load power and the default charging habits of electric vehicles before the start of the operating day, and then running according to the curve throughout the day without updating.

[0143] Intraday timed rolling optimization solution such as Figure 6 As shown, this method is based on the day-ahead operating curve. At the beginning of each time period, the actual values ​​of the time period and photovoltaic output, load power and electric vehicle data are used, combined with the predicted values ​​for the remaining time period of the day, to resolve and update the operating curve.

[0144] Intraday event-driven optimization solutions, such as Figure 7 As shown, the running curve is updated based on the latest state only when driven by a sudden event.

[0145] Table 3 shows a comparison of the daily operating costs and computing resource consumption of the three optimization methods.

[0146] Table 3 Comparison of Results of Three Optimization Methods

[0147]

[0148] As shown in Table 3, the day-ahead optimization method only requires 0.35 seconds for each solution, meeting the need for saving computing resources for lightweight terminal equipment in residential microgrids. However, due to significant discrepancies between actual and predicted data on photovoltaic output, load power, and electric vehicle charging behavior, these discrepancies need to be compensated for through energy interaction with the grid, resulting in a daily operating cost as high as 7.625 yuan.

[0149] If a daily rolling optimization method is adopted, with a 15-minute time window, a total of 96 solutions are performed within 24 hours. The predicted values ​​are corrected using actual values ​​in each time period, maximizing operational efficiency. The resulting daily operating cost is only 6.409 yuan. However, the total time consumed by the 96 solutions is 21.408 seconds, significantly impacting the daily monitoring, interaction, and control processes of the lightweight terminal equipment in the residential microgrid.

[0150] By employing intraday event-driven optimization, only 1.81 seconds are needed to solve the problem 7 times during sudden events, resulting in an operating cost of 6.787 yuan. Compared to day-ahead optimization, this only consumes 1.460 seconds more in solution time, saving 10.99% of the operating cost. Compared to intraday timed rolling optimization, with only a 5.90% loss in operating cost, the number of solutions is reduced from 96 to 7, and the solution time is shortened from 21.408 seconds to 1.810 seconds, saving 91.55% of the computation time. The method proposed in this invention ensures both the economical operation of residential microgrids and the need for lightweight terminal devices in residential microgrids to save computing resources.

[0151] Therefore, by implementing the embodiments of the present invention, the method proposed in this invention has the following beneficial effects:

[0152] (1) Improved the economic efficiency of residential photovoltaic-storage-charging microgrids. Compared with day-ahead optimization, the event-driven energy management method for residential photovoltaic-storage-charging microgrids greatly eliminates the phenomenon of day-ahead optimization curve economic inefficiency caused by sudden events by capturing and updating key sudden events, saving 10.99% of operating costs.

[0153] (2) It saves computing resources for lightweight terminal equipment in residential microgrids. Compared with rolling optimization, the event-driven energy management method for residential photovoltaic-storage-charging microgrids only solves and updates during critical bursts. With only a 5.90% loss in operating costs, it reduces the number of solutions from 96 to 7 and the solution time from 21.408 seconds to 1.810 seconds, saving 91.55% of the computing time. This ensures the normal operation of daily monitoring, interaction, and control functions of lightweight terminal equipment in residential microgrids to the greatest extent.

Claims

1. An event-driven energy management method for residential photovoltaic-storage-charging microgrids, characterized in that, Includes the following steps: Before the start of the dispatch day, the whole day is divided into multiple time periods. Based on the predicted values ​​of photovoltaic output and load power in the household photovoltaic-storage-charging microgrid, the fixed parameters of the photovoltaic-storage-charging equipment configured by the user, the preset default charging habit data of electric vehicles, and the power market demand response control instructions, the day-ahead curve of the household photovoltaic-storage-charging microgrid on the dispatch day is solved and sent to the energy storage battery. During each time period of the scheduling day, check whether any emergencies occur in the residential photovoltaic-storage-charging microgrid. If an emergency occurs, recalculate the operating curve for the remaining time period of the day based on the latest real-time operating status of the residential photovoltaic-storage-charging microgrid and send it to the energy storage battery. If no unforeseen event occurs, the system will continue to operate according to the previous day's curve until an unforeseen event occurs.

2. The event-driven energy management method for residential photovoltaic-storage-charging microgrids according to claim 1, characterized in that, The emergencies include: grid connection of residential electric vehicles, off-grid events of residential electric vehicles, user modification of expected charging target SOC events, photovoltaic power generation and load power prediction deviation exceeding limits events, grid-side update of demand response control instructions events, and microgrid grid connection and islanding operation mode switching events.

3. The event-driven energy management method for residential photovoltaic-storage-charging microgrids according to claim 2, characterized in that, The inspection method for photovoltaic power generation and load power prediction deviation exceeding the limit event is as follows: The overall prediction deviation between photovoltaic power output and load power coupling within the time window is obtained by using a sliding time window, and the moving average deviation rate within the time window is calculated. If the moving average of the deviation rate over L consecutive time periods If the deviation rate threshold is exceeded, a photovoltaic power generation and load power prediction deviation exceeding the limit event will occur; otherwise, it will not occur.

4. The event-driven energy management method for residential photovoltaic-storage-charging microgrids according to claim 3, characterized in that, Calculate the moving average of the deviation rate within the time window using the following formula. : ； Where t is the current runtime segment; i is the indexed time period; and M is the number of time periods within the time window; Let be the deviation rate for time period i.

5. The event-driven energy management method for residential photovoltaic-storage-charging microgrids according to claim 3, characterized in that, The calculation process for the deviation rate threshold is as follows: During the scheduling day, the actual photovoltaic output, predicted photovoltaic output, actual load power, and predicted load power are collected in real time for each time period. Then, the photovoltaic deviation and load deviation are calculated. Subsequently, the difference between the photovoltaic deviation and the load deviation is used as the coupled overall deviation, and the normalized relative deviation rate is calculated based on the coupled overall deviation. Next, the mean and standard deviation are calculated based on the historical normalized relative deviation rate data. Finally, the deviation rate threshold is calculated based on the mean and standard deviation.

6. The event-driven energy management method for residential photovoltaic-storage-charging microgrids according to claim 1, characterized in that, The method for solving the day-ahead curve of the residential photovoltaic-storage-charging microgrid on the dispatch day specifically includes: A mixed-integer linear programming model and its constraints are constructed, and the day-ahead curve of a residential photovoltaic-storage-charging microgrid on the dispatch day is solved based on this mixed-integer linear programming model. The objective function f of the mixed-integer linear programming model satisfies the following formula: ； in, , These represent the purchased and sold power of a residential photovoltaic-storage-charging microgrid during time period t, respectively. , These represent the electricity purchase price and the electricity sales price during time period t, respectively. , These represent the charging status of the energy storage battery and the electric vehicle during time period t, respectively. , These represent the charging status of the energy storage battery and the electric vehicle during the time period t-1, respectively. This represents the penalty factor for charge / discharge state transitions.

7. An event-driven energy management system for residential photovoltaic-storage-charging microgrids, characterized in that, include: The day-ahead curve generation unit is used to solve the day-ahead curve of the residential photovoltaic-storage-charging microgrid on the dispatch day based on the predicted values ​​of photovoltaic output and load power in the residential photovoltaic-storage-charging microgrid, the fixed parameters of the photovoltaic-storage-charging equipment configured by the user, the preset default charging habit data of electric vehicles, and the power market demand response control instructions. The emergency judgment unit is used to check whether any emergencies occur in the household photovoltaic-storage-charging microgrid at different times during the scheduling day; The operation curve generation unit is used to recalculate the operation curves for the current period and subsequent periods based on the latest real-time operation status of the residential photovoltaic-storage-charging microgrid.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the event-driven energy management method for residential photovoltaic-storage-charging microgrids as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the event-driven energy management method for a residential photovoltaic-storage-charging microgrid as described in any one of claims 1 to 6.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the event-driven energy management method for a residential photovoltaic-storage-charging microgrid as described in any one of claims 1 to 6.

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