Dual-time-scale optimization scheduling system and method considering diesel storage insurance supply and new energy consumption

Abstract

The invention provides a dual-time-scale optimization scheduling system and method considering diesel storage insurance supply and new energy consumption, and the method comprises the steps: collecting a plurality of data in real time, and outputting a historical data set and real-time monitoring data to a scheduling module; based on the wind and light output predicted values and load predicted values of the day before dispatching and the dispatching day, a multi-target optimization model is constructed, an energy storage charging and discharging plan, a diesel generator start-stop plan and an electric vehicle dispatching instruction are output, and cross-day energy transfer is achieved; on the basis of a model prediction control principle, 15 minutes are taken as a scheduling step length, a day-ahead scheduling plan is corrected in a rolling manner in combination with ultra-short-term source load prediction data, and prediction deviation is stabilized; controlling charging and discharging switching of the energy storage system, dynamic adjustment of the charging power of the electric vehicle and the operation state of the charging pile; through cooperative operation of day-ahead pre-scheduling and intra-day rolling optimization, source-load balance and low-carbon operation are realized. According to the scheduling system, long-period energy planning and short-period dynamic correction are both considered, and the self-consistency and low-carbon property of the off-grid charging station are improved.

Description

Technical Field

[0001] This invention relates to the field of new energy dispatch optimization technology, and in particular to a dual-time-scale optimization dispatch system and method that considers both diesel storage and supply security and new energy consumption. Background Technology

[0002] As carbon reduction goals are advanced, the widespread adoption of electric vehicles in western China relies on the construction of off-grid wind, solar, and energy storage charging stations. These charging stations need to independently cope with the intermittency of wind and solar power output and the randomness of charging load. The existing operating model has two major pain points: First, single-time-scale scheduling is difficult to balance energy over multiple days, and during periods of weak wind and solar power, it relies on diesel generators for power supply, resulting in higher carbon emissions; second, deviations in source and load forecasting lead to a disconnect between actual operation and plans, resulting in frequent instances of wind and solar power curtailment or power shortages.

[0003] In existing technologies, scheduling strategies mostly focus on single-day optimization, neglecting the potential for cross-day energy transfer and lacking adaptability to peak loads during holidays. For example, existing microgrid scheduling methods only cover intraday scales and cannot cope with continuous multi-day wind and solar power fluctuations; furthermore, existing energy storage control devices lack a coordination mechanism with electric vehicles. Therefore, there is an urgent need for a scheduling system that balances long-term energy planning with short-term dynamic correction to improve the self-sufficiency and low-carbon nature of off-grid charging stations. Summary of the Invention

[0004] This invention provides a dual-timescale optimized scheduling system and method that considers both diesel storage and supply security and new energy consumption. It effectively improves the system self-consistency under scenarios of weak wind and solar power and peak loads during holidays, reduces the amount of wind and solar curtailment and the proportion of diesel power generation, and provides technical support for the stable and low-carbon operation of off-grid charging stations in western China.

[0005] This invention provides a dual-timescale optimized scheduling system that considers both diesel storage and supply security and new energy consumption, comprising: a data acquisition module, a day-ahead pre-scheduling module, an intraday rolling optimization module, and an equipment control module;

[0006] The data acquisition module is used to collect wind power output data, photovoltaic power output data, electric vehicle charging demand data, energy storage charge status data and diesel generator operating status data in real time, and output historical datasets and real-time monitoring data to the day-ahead pre-schedule module.

[0007] The day-ahead pre-scheduling module constructs a multi-objective optimization model based on the wind and solar power output forecasts and load forecasts of the day before and the scheduling day, and outputs energy storage charging and discharging plans, diesel generator start-stop plans and electric vehicle scheduling instructions to realize cross-day energy transfer;

[0008] The intraday rolling optimization module is based on the model predictive control principle. It uses a preset time period as the scheduling step size and combines ultra-short-term source-load prediction data to continuously correct the day-ahead scheduling plan and smooth out prediction deviations.

[0009] The equipment control module is used to receive scheduling instructions and control the energy storage system's charging and discharging switching, the diesel generator's start-up and output adjustment, the electric vehicle's charging power dynamic adjustment, and the charging pile's operating status.

[0010] Through the coordinated operation of the day-ahead pre-scheduling module and the intraday rolling optimization module, source-load balance and low-carbon operation are achieved.

[0011] According to the present invention, the data acquisition module further includes a data preprocessing unit for normalizing and correcting the acquired wind and solar power output data, load data and energy storage state of charge data, wherein the load forecast data includes a holiday peak load correction factor.

[0012] According to the present invention, the data acquisition module further includes a data preprocessing unit. The multi-objective optimization model of the day-ahead pre-scheduling module takes minimizing system operation and maintenance costs and penalty costs as its objective function, and the objective function is expressed as:

[0013]

[0014] Wherein, C1 and C2 are the operation and maintenance cost and penalty cost at time t, respectively. The operation and maintenance cost includes the unit power cost coefficients of wind power generation, photovoltaic power generation, energy storage charging and discharging, diesel generator power generation and electric vehicle dispatching. The penalty cost includes the wind curtailment penalty coefficient, power shortage penalty coefficient and diesel generator penalty coefficient.

[0015] According to the present invention, the data acquisition module further includes a data preprocessing unit, and the multi-objective optimization model further includes power balance constraints, upper and lower limit constraints of wind and solar power generation and diesel generator power, energy storage charge state boundary constraints, and energy storage charging and discharging power constraints.

[0016] Among them, the power balance constraint ensures that the sum of wind power, photovoltaic power, energy storage discharge power, diesel generator power and electric vehicle discharge power equals the load demand.

[0017] Energy storage constraints include energy storage charge and discharge state variables and energy storage discharge state variables, and the energy storage state of charge is limited to preset upper and lower limits.

[0018] According to the present invention, the data acquisition module further includes a data preprocessing unit, and the day-ahead pre-scheduling module is further configured to execute a scheduling scheme for severe scenarios, including:

[0019] In cases of power shortage on the dispatch day and curtailment of wind and solar power the day before dispatch, increase energy storage charging and dispatch electric vehicles to improve the state of charge of energy storage.

[0020] In the event of a power shortage on the dispatch day and no power was wasted the previous day, only electric vehicles will be dispatched to reduce reliance on diesel generators.

[0021] If there is a surplus on the dispatch day and the average power curtailment on the previous day and the dispatch day is consistent, or if the dispatch day is self-consistent, the original dispatch strategy shall be maintained.

[0022] If there is a surplus on the dispatch day and no power was wasted the previous day, reduce energy storage charging to lower the state of charge.

[0023] The scheduling scheme is based on the scene identifier variables Z1 and Z2, which are then subjected to normal linearization. This indicates that the previous day saw curtailment of wind and solar power, and that there was a power shortage on the dispatch day. This indicates that there was a power shortage the previous day and that wind and solar power were curtailed on the scheduled day.

[0024] According to the present invention, the data acquisition module further includes a data preprocessing unit, and the model prediction control method of the intraday rolling optimization module includes: prediction model, rolling optimization and feedback correction;

[0025] The prediction model is based on state-space equations. The system state vector includes wind power, photovoltaic power, energy storage charging and discharging power, diesel generator power, load power, wind and solar curtailment power, power shortage power, and electric vehicle dispatch power. The control vector includes energy storage charging and discharging increment, diesel generator output increment, and electric vehicle dispatch increment. The disturbance input includes wind and solar power output prediction error and load prediction error.

[0026] Rolling optimization takes minimizing the adjustment amount as the objective function, optimizes the strategy within the control time domain, and updates it on a rolling basis within a preset time period;

[0027] Feedback correction corrects subsequent prediction errors by comparing the actual output with the predicted output.

[0028] According to the present invention, the data acquisition module further includes a data preprocessing unit, and the system further includes an electric vehicle scheduling access mechanism, wherein the electric vehicle charging is set with a 10% power safety redundancy, and the discharge needs to ensure the vehicle's subsequent travel needs.

[0029] The maximum schedulable power is limited to 10% of the theoretically adjustable power range, with the specific constraints as follows:

[0030]

[0031] in, Let be the maximum discharge power of the electric vehicle at time t; I and J are the total number of electric vehicles participating in the discharge and the total number of vehicles charging at the charging station at time t, respectively. The adjustable state of charge range for the i-th electric vehicle participating in the discharge; The adjustable state of charge range for the j-th electric vehicle currently charging; and Let be the battery capacities of the i-th and j-th vehicles, respectively. This refers to the charge / discharge efficiency.

[0032] According to the present invention, the data acquisition module further includes a data preprocessing unit. The system optimizes the energy storage cross-day energy transfer and constrains the energy storage state of charge by using a preset time point as a scheduling node.

[0033] This invention also provides a dual-time-scale optimized scheduling method considering both diesel storage and supply security and new energy consumption, comprising:

[0034] Real-time data collection of wind power output, photovoltaic power output, electric vehicle charging demand, energy storage charge status, and diesel generator operating status is provided, and historical datasets and real-time monitoring data are output to the day-ahead pre-scheduling module.

[0035] Based on the wind and solar power output forecasts and load forecasts for the day before and the day of dispatch, a multi-objective optimization model is constructed to output energy storage charging and discharging plans, diesel generator start-up and shutdown plans, and electric vehicle dispatch instructions to achieve cross-day energy transfer.

[0036] Based on the principle of model predictive control, the scheduling plan is rolled up and corrected with a preset time period as the scheduling step size, combined with ultra-short-term source and load forecast data, so as to smooth out the forecast deviation.

[0037] According to the dispatch instructions, control the switching of energy storage system charging and discharging, the start-up and shutdown and output adjustment of diesel generator, the dynamic adjustment of electric vehicle charging power and the operating status of charging piles;

[0038] Through the coordinated operation of day-ahead pre-scheduling and intraday rolling optimization, source-load balance and low-carbon operation are achieved.

[0039] The present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a dual-time-scale optimized scheduling method considering both diesel storage and supply security and new energy consumption as described above.

[0040] This invention provides a dual-timescale optimized scheduling system and method that considers both diesel energy storage and renewable energy consumption. It operates through a two-tiered architecture of day-ahead pre-scheduling and intraday rolling optimization. The day-ahead scheduling layer, based on multi-day source-load forecast data, formulates strategies for cross-day pre-scheduling of energy storage and electric vehicle participation, reducing reliance on diesel power generation through energy time shifting. The intraday scheduling layer introduces a model predictive control method to dynamically correct equipment output plans on a 15-minute scale, mitigating forecast bias. This effectively improves system self-consistency under weak wind and solar power conditions and peak holiday load scenarios, reduces wind and solar curtailment and the proportion of diesel power generation, and provides technical support for the stable and low-carbon operation of off-grid charging stations in western China. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0042] Figure 1 This is a schematic diagram of the module connection of the dual-time-scale optimized scheduling system that considers both diesel storage and supply security and new energy consumption, provided by the present invention.

[0043] Figure 2 This is a flowchart illustrating the dual-time-scale optimized scheduling method for considering both diesel storage and supply security and new energy consumption provided by this invention.

[0044] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention.

[0045] Figure label:

[0046] 110: Data acquisition module; 120: Day-ahead pre-scheduling module; 130: Intraday rolling optimization module; 140: Equipment control module;

[0047] 310: Processor; 320: Communication interface; 330: Memory; 340: Communication bus. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0049] This invention provides a dual-timescale optimized scheduling system that considers both diesel energy storage supply and renewable energy consumption. The day-ahead scheduling layer, based on multi-day source-load forecast data, formulates cross-day pre-scheduling strategies for energy storage and electric vehicle participation, reducing reliance on diesel power generation through energy time shifting. The intraday scheduling layer introduces Model Predictive Control (MPC) to dynamically correct equipment output plans on a 15-minute scale, mitigating forecast bias. This invention effectively improves system self-consistency under weak wind and solar power conditions and peak holiday load scenarios, reduces wind and solar curtailment and the proportion of diesel power generation, and provides technical support for the stable and low-carbon operation of off-grid charging stations in western China.

[0050] The following is combined Figure 1 The present invention describes a dual-timescale optimized scheduling system that considers both diesel storage and supply security and new energy consumption, comprising: a data acquisition module 110, a day-ahead pre-scheduling module 120, an intraday rolling optimization module 130, and an equipment control module 140.

[0051] The data acquisition module 110 is used to collect wind power output data, photovoltaic power output data, electric vehicle charging demand data, energy storage charge status data and diesel generator operating status data in real time, and output historical datasets and real-time monitoring data to the day-ahead pre-schedule module.

[0052] The day-ahead pre-scheduling module 120 constructs a multi-objective optimization model based on the wind and solar power output forecasts and load forecasts of the day before and the scheduling day, and outputs energy storage charging and discharging plans, diesel generator start-stop plans and electric vehicle scheduling instructions to realize cross-day energy transfer;

[0053] The intraday rolling optimization module 130 is based on the model predictive control principle. It uses a preset time period as the scheduling step size and combines ultra-short-term source-load prediction data to roll and correct the day-ahead scheduling plan, thereby smoothing out prediction deviations.

[0054] The equipment control module 140 is used to receive scheduling instructions and control the energy storage system charging and discharging switching, diesel generator start-stop and output adjustment, electric vehicle charging power dynamic adjustment and charging pile operation status.

[0055] Through the coordinated operation of the day-ahead pre-scheduling module and the intraday rolling optimization module, source-load balance and low-carbon operation are achieved.

[0056] Specifically, the day-ahead supply guarantee optimization scheduling strategy for severe scenarios:

[0057] Based on multi-day forecast data of wind and solar power output and load demand, a day-ahead optimization architecture for energy storage pre-scheduling and electric vehicle charging / discharging coordination is constructed. By judging the surplus / deficit status of wind and solar power output on the scheduling day (D-day) and the day before scheduling (D-1 day), five scheduling schemes are implemented: If there is a power shortage on D-day and wind / solar curtailment on D-1 day, energy storage is increased + electric vehicle scheduling to improve the energy storage SOC; if there is a power shortage on D-day and no curtailment on D-1 day, electric vehicle scheduling alone reduces diesel dependence; if there is a surplus on D-day and curtailment on both days or D-day is self-sufficient, the original strategy is maintained; if there is a surplus on D-day and no curtailment on D-1 day, energy storage is reduced to lower the SOC. Simultaneously, an electric vehicle charging / discharging access mechanism is established, with a 10% safety redundancy for charging and ensuring sufficient capacity for subsequent travel during discharging, exploring the potential for flexible adjustment.

[0058] Intraday rolling optimized scheduling:

[0059] To correct day-ahead forecast bias, an intraday rolling optimization layer based on Model Predictive Control (MPC) is embedded in the day-ahead framework to dynamically calibrate equipment output at a 15-minute resolution. MPC comprises three parts: a forecast model, rolling optimization, and feedback correction. It predicts system output through state-space equations and optimizes the strategy within the control time domain with the goal of minimizing adjustments. After executing only the first control step instruction, it feeds back the actual deviation to correct subsequent forecasts. The time domain is shifted forward every 15 minutes, forming a closed-loop control that improves adaptability to source load randomness and ensures the accuracy of the scheduling plan.

[0060] The day-ahead pre-scheduling module establishes a day-ahead supply optimization scheduling model based on multi-day source-load forecast data.

[0061] To optimize the supply scheduling for the day-ahead period, the objective function is determined as follows:

[0062] (1)

[0063] In the formula: and Let be the maintenance cost and penalty cost at time t, respectively. , , , , and The wind power generation, photovoltaic power generation, energy storage charging power, energy storage discharging power, diesel generator power, and electric vehicle discharge power of the charging station at time t; , , , and The unit power cost coefficients for wind power generation, photovoltaic power generation, energy storage charging and discharging, diesel generator power generation, and electric vehicle dispatching are as follows: , and These are the wind and solar curtailment penalty coefficients, power shortage penalty coefficients, and diesel power generation penalty coefficients at time t, respectively.

[0064] The constraints include power balance constraints:

[0065] (2)

[0066] The upper and lower limits for wind and solar power generation and diesel generator power are as follows:

[0067] (3)

[0068] In the formula: This represents the maximum power output of the wind power generation at this charging station at any given time. The maximum power output of the photovoltaic power generation at this charging station at a given time; This is the maximum output power of the diesel generator.

[0069] Constraints are imposed on the energy storage state of charge boundary and the upper and lower limits of energy storage charge and discharge power:

[0070] (4)

[0071] In the formula: and Let i and y be the energy storage charging and discharging state variables at time i, respectively. The sum of these two variables being less than or equal to 1 ensures that charging and discharging do not occur at the same time. and These are the upper and lower limits of energy storage charging power, respectively. and These are the upper and lower limits of energy storage discharge power, respectively. and These are the upper and lower limits of the energy storage's state of charge; if these limits are exceeded, the energy storage will stop operating.

[0072] To achieve coordinated optimization of energy transfer across days and wind and solar power consumption in the energy storage system, this model uses 24:00 as the scheduling node to further constrain the state of charge of the energy storage.

[0073] The scenario is defined as follows:

[0074] (5)

[0075] (6)

[0076] In the formula: and As a scene identifier variable, A value of 1 indicates that there was wind and solar power curtailment on the previous day, and that there was insufficient wind and solar power generation on the dispatch day. A value of 1 indicates that there was insufficient wind and solar power generation the previous day, and that there was curtailment of wind and solar power on the dispatch day; , and These represent the power outputs of wind power, solar power, and load at time t, respectively, from the previous day: , and These represent the power of wind power, photovoltaic power, and load at time t on a dispatch day without dispatching conditions.

[0077] The above scene identifiers are linearized into constraints using the Big M method as follows:

[0078] (7)

[0079] exist , In a scenario where there is wind and solar power curtailment the previous day and insufficient wind and solar power generation on the dispatch day, the energy storage pre-dispatch target is set as follows:

[0080] (8)

[0081] In the formula: This represents the amount of wind and solar power curtailed the previous day. To address the daily power shortage in the dispatching system; To meet the daily normal operation requirements of the energy storage system, the maximum energy storage capacity is required; To meet the energy storage state of charge requirements for normal operation during dispatch days; This represents the final energy storage state that can be achieved the previous day. This represents the initial energy storage under no-dispatch conditions.

[0082] With linearization constraints, the ideal energy storage state of charge is calculated as follows:

[0083] (9)

[0084] In the formula: This is a binary variable; a value of 1 indicates that the amount of wind and solar power curtailment on the previous day was lower than the daily load deficit.

[0085] exist , In a scenario where there is insufficient wind and solar power generation the previous day, and wind and solar curtailment occurs on the dispatch day, the energy storage pre-dispatch target is set as follows:

[0086] (10)

[0087] Similarly, by linearizing the constraints, we obtain the formula for calculating the target energy storage state of charge. The final energy storage state of charge constraints are as follows:

[0088] (11)

[0089] To ensure the effectiveness of wind, solar, and energy storage charging stations under optimized scheduling strategies, the system's self-consistency rate is used as a constraint, as shown in the following formula:

[0090] (12)

[0091] In the formula: This represents the system's autonomy rate under the day-ahead scheduling plan. The system's autonomy rate is given without prior-day scheduling; T is 48, indicating that the previous day and the scheduling day total 48 hours.

[0092] Given the current low actual proportion of electric vehicles participating in grid dispatch, the maximum dispatchable power is temporarily set to 10% of the theoretical adjustable power range in the optimization model. The specific constraints are as follows:

[0093] (13)

[0094] In the formula: Let be the maximum discharge power of the electric vehicle at time t; I and J are the total number of electric vehicles participating in the discharge and the total number of vehicles charging at the charging station at time t, respectively. The adjustable state of charge range for the i-th electric vehicle participating in the discharge; The adjustable state of charge range for the j-th electric vehicle currently charging; and Let be the battery capacities of the i-th and j-th vehicles, respectively. This refers to the charge / discharge efficiency.

[0095] The intraday rolling optimization module introduces the Model Predictive Control (MPC) method to establish intraday short-timescale optimization scheduling.

[0096] Based on the state-space prediction equation, the following prediction model for wind-solar-storage-charging stations was established:

[0097] (14)

[0098] In the formula: The state vector representing energy management is determined by the wind power at time t. Photovoltaic power Energy storage charging power Energy storage and discharge power Diesel generator power Load power Wind and solar power curtailment Power shortage and electric vehicle dispatch power composition; For energy management control vectors, energy storage charging increments Energy storage discharge increment Diesel generator output increment and electric vehicle scheduling increment composition; The disturbance input is the short-term predicted power increment of wind power. Short-term forecast of photovoltaic power increase and short-term forecast power increment of load composition; The output vector is the energy storage charging power. Energy storage and discharge power Diesel generator power and electric vehicle dispatch power composition.

[0099] In the prediction model, a 5% normal distribution error and a 1% random error are added to the daytime wind and solar power output scenario as the intraday short-term predicted power increment. The formula is as follows:

[0100] (15)

[0101] In the formula: and These are the short-term projected power increases for wind power and solar power, respectively. The error is a normal distribution with a standard deviation of 5% of the day-ahead forecast and a mean of 0. and The uniform distribution error is 1% for wind power and photovoltaic power.

[0102] The maximum error is incorporated to represent the short-term predicted power increment of the load, as shown in the following formula:

[0103] (16)

[0104] In the formula: This is the result of the intraday short-term load demand forecast; This represents the maximum error value of the load during time period t. To conform to uniform distribution Random numbers.

[0105] The objective function for minimizing the adjustment is shown below:

[0106] (17)

[0107] In the formula: , , and These represent the adjustment amounts for energy storage charging and discharging, diesel generator power, and vehicle discharge at time t, respectively. , , and These represent the intraday planned power of energy storage, diesel generators, and electric vehicles at time t, respectively.

[0108] First rolling optimization and output deviation constraints of diesel storage unit and electric vehicle in day-ahead scheduling plan:

[0109] (18)

[0110] In the formula: The deviation coefficient; , , , These represent the optimized energy storage charging power, energy storage discharging power, diesel generator output power, and electric vehicle dispatching power at time t, respectively, during the first rolling cycle.

[0111] The rolling optimization constraints for the power output deviation between the diesel storage unit and the electric vehicle are as follows:

[0112] (19)

[0113] In the formula: , , , , , , and The values ​​represent the energy storage charging power, energy storage discharging power, diesel generator output power, and electric vehicle dispatching power at time t for the kth and (k-1)th rolling optimizations, respectively.

[0114] Feedback correction:

[0115] (20)

[0116] In the formula: This is for system control error; This is the error coefficient matrix; This represents the actual output of the system at time t. This is the system input at time t+1.

[0117] Based on the dual-timescale optimized scheduling system disclosed in this invention, which considers both diesel energy storage and renewable energy consumption, a two-tiered architecture of "day-ahead pre-scheduling - intraday rolling optimization" operates collaboratively: the day-ahead scheduling layer formulates cross-day pre-scheduling strategies for energy storage and electric vehicle participation based on multi-day source-load forecast data, reducing reliance on diesel power generation through energy time shifting; the intraday scheduling layer introduces model predictive control (MPC) to dynamically correct equipment output plans on a 15-minute scale, mitigating forecast bias. This invention can effectively improve system self-consistency under weak wind and solar power conditions and peak load scenarios during holidays, reduce wind and solar curtailment and the proportion of diesel power generation, and provide technical support for the stable and low-carbon operation of off-grid charging stations in western China.

[0118] refer to Figure 2 The present invention also discloses a dual-time-scale optimization scheduling method for considering both diesel storage and supply security and new energy consumption, comprising:

[0119] Step 100: Collect wind power output data, photovoltaic power output data, electric vehicle charging demand data, energy storage charge status data, and diesel generator operating status data in real time, and output historical datasets and real-time monitoring data to the day-ahead pre-schedule module.

[0120] Step 200: Based on the wind and solar power output forecasts and load forecasts for the day before and the day of scheduling, construct a multi-objective optimization model, and output energy storage charging and discharging plans, diesel generator start-up and shutdown plans, and electric vehicle scheduling instructions to achieve cross-day energy transfer;

[0121] Step 300: Based on the principle of model predictive control, the day-ahead scheduling plan is rolled over and corrected using a preset time period as the scheduling step size, combined with ultra-short-term source-load forecast data, to smooth out forecast deviations.

[0122] Step 400: According to the dispatch instructions, control the energy storage system's charging and discharging switching, the diesel generator's start-up and output adjustment, the electric vehicle charging power dynamic adjustment, and the charging pile's operating status.

[0123] Step 500: Achieve source-load balance and low-carbon operation through the coordinated operation of day-ahead pre-scheduling and intraday rolling optimization.

[0124] The pre-scheduling module is also configured to execute scheduling schemes for severe scenarios, including:

[0125] In cases of power shortage on the dispatch day and curtailment of wind and solar power the day before dispatch, increase energy storage charging and dispatch electric vehicles to improve the state of charge of energy storage.

[0126] In the event of a power shortage on the dispatch day and no power was wasted the previous day, only electric vehicles will be dispatched to reduce reliance on diesel generators.

[0127] If there is a surplus on the dispatch day and the average power curtailment on the previous day and the dispatch day is consistent, or if the dispatch day is self-consistent, the original dispatch strategy shall be maintained.

[0128] If there is a surplus on the dispatch day and no power was wasted the previous day, reduce energy storage charging to lower the state of charge.

[0129] The scheduling scheme is based on the scene identifier variables Z1 and Z2, which are then subjected to normal linearization. This indicates that the previous day saw curtailment of wind and solar power, and that there was a power shortage on the dispatch day. This indicates that there was a power shortage the previous day and that wind and solar power were curtailed on the scheduled day.

[0130] The model predictive control method of the intraday rolling optimization module includes: predictive model, rolling optimization, and feedback correction;

[0131] The prediction model is based on state-space equations. The system state vector includes wind power, photovoltaic power, energy storage charging and discharging power, diesel generator power, load power, wind and solar curtailment power, power shortage power, and electric vehicle dispatch power. The control vector includes energy storage charging and discharging increment, diesel generator output increment, and electric vehicle dispatch increment. The disturbance input includes wind and solar power output prediction error and load prediction error.

[0132] Rolling optimization aims to minimize the adjustment amount, optimizes the strategy within the control time domain, and updates it every 15 minutes.

[0133] Feedback correction corrects subsequent prediction errors by comparing the actual output with the predicted output.

[0134] Based on the dual-timescale optimization scheduling method disclosed in this invention, which considers both diesel storage supply and renewable energy consumption, a two-tier architecture of day-ahead pre-scheduling and intraday rolling optimization operates collaboratively: the day-ahead scheduling layer formulates cross-day pre-scheduling strategies for energy storage and electric vehicle participation based on multi-day source-load forecast data, reducing reliance on diesel power generation through energy time shifting; the intraday scheduling layer introduces model predictive control (MPC) to dynamically correct equipment output plans on a 15-minute scale, mitigating forecast bias. This invention can effectively improve system self-consistency under weak wind and solar power conditions and peak load scenarios during holidays, reduce wind and solar curtailment and the proportion of diesel power generation, and provide technical support for the stable and low-carbon operation of off-grid charging stations in western China.

[0135] Figure 3 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 3As shown, the electronic device may include: a processor 310, a communications interface 320, a memory 330, and a communications bus 340, wherein the processor 310, the communications interface 320, and the memory 330 communicate with each other through the communications bus 340. The processor 310 can call logic instructions in the memory 330 to execute a dual-timescale optimization scheduling method that considers both diesel and energy storage supply and new energy consumption. This method includes: real-time acquisition of wind power output data, photovoltaic power output data, electric vehicle charging demand data, energy storage state of charge data, and diesel generator operating status data, and outputting historical datasets and real-time monitoring data to the day-ahead pre-scheduling module; constructing a multi-objective optimization model based on the wind and solar power output forecasts and load forecasts of the day before and the scheduling day, and outputting energy storage charging and discharging plans, diesel generator start-stop plans, and electric vehicle scheduling instructions to achieve cross-day energy transfer; based on the model predictive control principle, using a preset time period as the scheduling step size, and combining ultra-short-term source and load forecast data to continuously correct the day-ahead scheduling plan and smooth out forecast deviations; controlling the energy storage system charging and discharging switching, diesel generator start-stop and output adjustment, electric vehicle charging power dynamic adjustment, and charging pile operating status according to the scheduling instructions; and achieving source-load balance and low-carbon operation through the coordinated operation of day-ahead pre-scheduling and intraday rolling optimization.

[0136] Furthermore, the logical instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0137] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the dual-time-scale optimization scheduling method considering both diesel storage supply and new energy consumption provided by the above methods. This method includes: real-time acquisition of wind power output data, photovoltaic power output data, electric vehicle charging demand data, energy storage state of charge data, and diesel generator operating status data, and outputting historical datasets and real-time monitoring data to the day-ahead pre-scheduling module; constructing a multi-objective optimization model based on the wind and solar power output forecasts and load forecasts of the day before and the scheduling day, and outputting energy storage charging and discharging plans, diesel generator start-stop plans, and electric vehicle scheduling instructions to achieve cross-day energy transfer; based on the model predictive control principle, using a preset time period as the scheduling step size, and combining ultra-short-term source and load forecast data to continuously correct the day-ahead scheduling plan and smooth out forecast deviations; controlling the energy storage system charging and discharging switching, diesel generator start-stop and output adjustment, electric vehicle charging power dynamic adjustment, and charging pile operating status according to the scheduling instructions; and achieving source-load balance and low-carbon operation through the coordinated operation of day-ahead pre-scheduling and intraday rolling optimization.

[0138] On another front, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, this computer program implements a dual-timescale optimization scheduling method considering both diesel storage supply and new energy consumption, as provided by the methods described above. This method includes: real-time acquisition of wind power output data, photovoltaic power output data, electric vehicle charging demand data, energy storage state of charge data, and diesel generator operating status data, and outputting historical datasets and real-time monitoring data to the day-ahead pre-scheduling module; constructing a multi-objective optimization model based on the wind and solar power output forecasts and load forecasts for the day before and the scheduling day, and outputting energy storage charging and discharging plans, diesel generator start-stop plans, and electric vehicle scheduling instructions to achieve cross-day energy transfer; based on the model predictive control principle, using a preset time period as the scheduling step size, and combining ultra-short-term source and load forecast data to continuously correct the day-ahead scheduling plan and smooth out forecast deviations; controlling the energy storage system charging and discharging switching, diesel generator start-stop and output adjustment, electric vehicle charging power dynamic adjustment, and charging pile operating status according to the scheduling instructions; and achieving source-load balance and low-carbon operation through the coordinated operation of day-ahead pre-scheduling and intraday rolling optimization.

[0139] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0140] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0141] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A double time scale optimal scheduling system considering coal storage supply and new energy consumption, characterized in that, The application relates to a wind-photovoltaic-storage-diesel-electric vehicle (WPSEV) scheduling system, which comprises a data acquisition module, a day-ahead pre-scheduling module, an intra-day rolling optimization module and a device control module. The data acquisition module is used for collecting wind power output data, photovoltaic output data, electric vehicle charging demand data, energy storage state of charge data and diesel generator operating state data in real time, and outputs historical data sets and real-time monitoring data to the day-ahead pre-scheduling module. The day-ahead pre-scheduling module is based on wind and light output prediction values and load prediction values of a day before scheduling and a scheduling day, constructs a multi-objective optimization model, outputs energy storage charging and discharging plans, diesel generator start-stop plans and electric vehicle scheduling instructions, and realizes cross-day energy transfer. The intra-day rolling optimization module is based on a model prediction control principle, takes a preset time period as a scheduling step, combines super-short-term source and load prediction data to roll over and correct the day-ahead scheduling plan, and suppresses prediction deviation. The device control module is used for receiving scheduling instructions, controlling energy storage system charging and discharging switching, diesel generator start-stop and output adjustment, electric vehicle charging power dynamic adjustment and charging pile operating state. Through the cooperative operation of the day-ahead pre-scheduling module and the intra-day rolling optimization module, source and load balance and low-carbon operation are realized. The data acquisition module further comprises a data preprocessing unit which is used for normalizing and error correcting the collected wind and light output data, load data and energy storage state of charge data, wherein the load prediction data comprises holiday peak load correction factors. 2.The dual-time-scale optimal scheduling system considering the security of coal storage and supply and the consumption of new energy according to claim 1, wherein, The multi-objective optimization model of the day-ahead pre-scheduling module takes minimizing system operation and maintenance costs and penalty costs as objective functions, and the objective functions are expressed as follows: 3.The dual-time-scale optimal dispatching system considering the coal storage, supply assurance and new energy consumption according to claim 1, characterized in that, Wherein, C1 and C2 are operation and maintenance costs and penalty costs at t moment respectively, the operation and maintenance costs include unit power cost coefficients of wind power generation, photovoltaic power generation, energy storage charging and discharging, diesel generator power generation and electric vehicle scheduling, and the penalty costs include wind and light abandonment penalty coefficients, power shortage penalty coefficients and diesel generator power generation penalty coefficients. ； The multi-objective optimization model further comprises power balance constraints, wind and light power generation power upper and lower limit constraints, energy storage state of charge boundary constraints and energy storage charging and discharging power constraints.

4. The dual-time-scale optimal dispatching system considering the coal storage, supply guarantee and new energy consumption according to claim 3, characterized in that, Wherein, the power balance constraints ensure that the sum of wind power, photovoltaic power, energy storage discharging power, diesel generator power and electric vehicle discharging power is equal to load demand; The energy storage constraints include energy storage charging and discharging state variables, and the energy storage state of charge is limited within preset upper and lower limits. The day-ahead pre-scheduling module is further configured to execute a scheduling scheme for a severe scenario, which comprises the following steps: 5.The dual-time-scale optimal dispatching system considering the coal storage, supply assurance and new energy consumption according to claim 1, wherein, In the case that there is power shortage in the scheduling day and wind and light abandonment in the day before scheduling, the energy storage charging is increased and the electric vehicle is scheduled to improve the energy storage state of charge; In the case that there is power shortage in the scheduling day and no power shortage in the day before scheduling, only the electric vehicle is scheduled to reduce the dependence of the diesel generator; In the case that there is surplus in the scheduling day and power shortage in the day before scheduling and the scheduling day or the scheduling day is self-consistent, the original scheduling strategy is maintained; In the case that there is surplus in the scheduling day and no power shortage in the day before scheduling, the energy storage charging is reduced to reduce the state of charge; The model prediction control method of the intra-day rolling optimization module comprises a prediction model, rolling optimization and feedback correction. The scheduling scheme is linearized based on scenario identification variables Z1 and Z2, wherein represents the day before the scheduling day is both wind curtailment and light curtailment, and the scheduling day is power shortage, represents the day before the scheduling day is power shortage, and the scheduling day is both wind curtailment and light curtailment. 6.The dual-time-scale optimal dispatching system considering the coal storage, supply assurance and new energy consumption according to claim 1, wherein, ​ The prediction model is based on a state space equation, a system state vector includes wind power, photovoltaic power, energy storage charging and discharging power, diesel generator power, load power, abandoned wind and light power, power shortage and electric vehicle scheduling power; a control vector includes energy storage charging and discharging increment, diesel generator output increment and electric vehicle scheduling increment; disturbance input includes wind and light output prediction error and load prediction error; The rolling optimization takes the minimum adjustment amount as the objective function, optimizes the strategy in the control time domain, and updates rolling in the preset time period; The feedback correction corrects the subsequent prediction error by comparing the actual output with the predicted output. 7.The dual-time-scale optimal dispatching system considering the coal storage, supply assurance and new energy consumption according to claim 1, wherein, The system also includes an electric vehicle scheduling access mechanism, wherein the electric vehicle charging is set with 10% power safety redundancy, and the discharging needs to guarantee the subsequent travel demand of the vehicle; The maximum value of the schedulable power is limited to 10% of the theoretical power adjustable range, and the specific constraint is: ； wherein, is the maximum discharging power of the electric vehicle at time t; I and J are the total number of electric vehicles participating in discharging and the total number of vehicles charging at the charging station at time t, respectively, is the state of charge adjustable range of the i-th electric vehicle participating in discharging; is the state of charge adjustable range of the j-th electric vehicle in the charging state; and are the battery capacities of the i-th vehicle and the j-th vehicle, respectively; is the charging and discharging efficiency. 8.The dual-time-scale optimal dispatching system considering the coal storage, supply assurance and new energy consumption according to claim 1, wherein, The system optimizes the energy storage cross-day energy transfer, and constrains the state of charge of the energy storage at the preset time point as the scheduling node.

9. A double-time-scale optimal scheduling method considering coal storage supply and new energy consumption, characterized in that, It includes: Real-time acquisition of wind power output data, photovoltaic output data, electric vehicle charging demand data, energy storage state of charge data and diesel generator operating state data, and output of historical data set and real-time monitoring data to day-ahead pre-scheduling module; Based on the wind and light output prediction value and the load prediction value of the day before scheduling and the scheduling day, a multi-objective optimization model is constructed, and the energy storage charging and discharging plan, the diesel generator start-stop plan and the electric vehicle scheduling instruction are output, realizing the cross-day energy transfer; Based on the model predictive control principle, taking the preset time period as the scheduling step, combining the ultra-short-term source and load prediction data to roll correct the day-ahead scheduling plan, the prediction deviation is smoothed; According to the scheduling instruction, the energy storage system charging and discharging switching, the diesel generator start-stop and output adjustment, the electric vehicle charging power dynamic adjustment and the charging pile operating state are controlled; Through the coordinated operation of day-ahead pre-scheduling and intra-day rolling optimization, source and load balance and low-carbon operation are realized.

10. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the double-time-scale optimization scheduling method considering diesel storage power supply and new energy consumption of claim 9.

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