A dynamic electricity price-based energy storage resource cross-period optimization scheduling method and system

Abstract

The application belongs to the technical field of automatic processing of electric power systems, and particularly relates to a dynamic electricity price-based energy storage resource cross-period optimization scheduling method and system, which comprises the following steps: collecting electricity prices and load powers in real time, and synchronously acquiring temperature differences between the inside and outside of an energy storage cabin and current intensities; for any sampling time: calculating electricity price sensitivity coefficients and energy maintenance costs; using the electricity price sensitivity coefficients to weight the difference between real-time electricity prices and a reference electricity price, and determining a scheduling priority index based on the energy maintenance costs under the reference electricity price; when the absolute value of the scheduling priority index is greater than a set scheduling threshold, calculating an execution power according to the difference between the absolute value and the set scheduling threshold, and combining the load power to determine the final value of the execution power. The application effectively avoids power reverse sending and power distribution overload while improving cross-period scheduling economic benefits, and enhances the operation stability of the energy storage system.

Description

Technical Field

[0001] This invention relates to the field of power system automation technology. More specifically, this invention relates to a method and system for cross-time-period optimized scheduling of energy storage resources based on dynamic electricity prices. Background Technology

[0002] In modern power systems, energy storage systems are key resources for regulating the balance of power supply and demand. Through peak shaving and valley filling, they play an important role in improving grid stability and reducing user electricity costs by storing energy during periods of low electricity prices and releasing it during periods of high electricity prices. With the development of smart grids, dynamic pricing mechanisms have been widely introduced. By flexibly adjusting price signals at different times, they guide the rational allocation of energy storage resources. The cross-time scheduling of energy storage systems not only needs to consider the current price difference, but also the physical losses during battery charging and discharging cycles, energy conversion efficiency, and changes in future load demand, in order to maximize the comprehensive benefits over the long term.

[0003] Currently, the traditional energy storage dispatch method is to pre-set the electricity price thresholds for charging and discharging. When the real-time electricity price is lower than the charging threshold, charging is initiated, and when it is higher than the discharging threshold, discharging is executed. This approach is applicable to certain scenarios where electricity price fluctuations are regular and singular. Its core logic lies in using the static price difference space to obtain peak-shaving revenue.

[0004] Because dynamic electricity prices are affected by multiple factors such as the access of new energy sources and sudden load changes, they exhibit nonlinear and high-frequency fluctuations. Traditional energy storage dispatch methods cannot adapt to this complex electricity price distribution, resulting in premature discharge at the second-highest point of electricity price or premature charging at the second-lowest point, causing ineffective use of energy storage resources. At the same time, because the differences in heat loss of energy storage media at different power levels are ignored, the overall energy utilization rate in the actual dispatch process is low, the optimal charging and discharging timing cannot be identified, and the economy and response accuracy of cross-time scheduling of resources are reduced. Summary of the Invention

[0005] To address the technical problems of inflexible energy storage dispatch and unreasonable cross-period resource allocation due to neglect of energy loss under dynamic electricity pricing, this invention provides solutions in the following aspects.

[0006] In a first aspect, the present invention provides a method for cross-time-period optimized scheduling of energy storage resources based on dynamic electricity prices, comprising: real-time acquisition of electricity prices and load power, and simultaneous acquisition of temperature difference and current intensity inside and outside the energy storage chamber; for any sampling time: calculating the relative difference between the real-time electricity price and the average electricity price of multiple adjacent sampling times before the sampling time, and obtaining the electricity price deviation; determining the electricity price sensitivity coefficient based on the instantaneous change rate of the real-time electricity price and the electricity price deviation; calculating the internal resistance heat loss based on the extracted physical parameters of the energy storage system and the current intensity at the sampling time, and determining the energy consumption based on the electrical energy consumed by the temperature difference inside and outside the energy storage chamber. Maintaining costs; obtaining the benchmark electricity price at the sampling time, and using an electricity price sensitivity coefficient to weight the difference between the real-time electricity price at the sampling time and the benchmark electricity price, determining a scheduling priority index based on the weighted value and the energy maintenance cost under the benchmark electricity price; in response to the absolute value of the scheduling priority index being greater than a set scheduling threshold, executing a cross-time period scheduling action, calculating the execution power based on the difference between the absolute value and the set scheduling threshold, and if the absolute value of the execution power exceeds the load power at the sampling time, taking the load power as the final value of the execution power; otherwise, taking the absolute value of the execution power as the final value of the execution power.

[0007] This invention addresses the problems of insufficient flexibility and ineffective resource utilization in traditional scheduling by introducing an adaptive electricity price sensitivity coefficient to capture the nonlinear fluctuations and instantaneous changes in dynamic electricity prices. Furthermore, it overcomes the technical deficiency of inefficiency caused by ignoring energy losses by analyzing internal resistance heat loss and temperature control energy consumption to construct an energy maintenance cost. It further integrates loss costs and profit margins to construct a scheduling priority index and introduces load power to achieve boundary constraints on execution power, significantly improving the economy and distribution security of cross-time period scheduling.

[0008] Preferably, obtaining the electricity price deviation includes: calculating the average real-time electricity price of multiple adjacent sampling times before the sampling time, obtaining the absolute difference between the real-time electricity price at the sampling time and the average value; and using the ratio of the absolute difference to the average value as the electricity price deviation.

[0009] Preferably, the electricity price sensitivity coefficient satisfies the following expression: In the formula, For the first Electricity price sensitivity coefficient at each sampling time; For the first Electricity price deviation at each sampling time; , For the first The sampling time and the first sampling time Real-time electricity price at each sampling point; To take the absolute value; It is a natural exponential function; This is the standard normalization function.

[0010] This invention utilizes the natural exponential function to nonlinearly amplify the instantaneous rate of change of real-time electricity prices, and in conjunction with recent deviation analysis, significantly enhances the system's sensitivity to capturing price trend inflection points and sudden peaks. This enables energy storage dispatch to accurately avoid the risk of ineffective occupancy due to early charging at the second lowest price point or early discharging at the second highest price point, thereby improving the economy and response accuracy of cross-time period resource dispatch.

[0011] Preferably, the physical parameters of the energy storage system refer to the equivalent internal resistance, temperature control efficiency coefficient, and rated total energy.

[0012] Preferably, the energy maintenance cost satisfies the expression: In the formula, For the first Energy maintenance cost at each sampling time; For the first Current intensity at each sampling time; This is the equivalent internal resistance; Temperature control efficiency coefficient; For the first Temperature difference between the inside and outside of the energy storage chamber at each sampling time; This refers to the rated total energy. This represents the sampling time interval.

[0013] This invention calculates the Joule heat loss generated by the current flowing through the battery's internal resistance and introduces a temperature control system to overcome the additional power consumption required to overcome the temperature difference between the inside and outside of the storage chamber. This enables accurate modeling of the physical cost of energy storage operation, providing a realistic bottom-line reference for scheduling decisions. It ensures that the system only starts during periods when the net benefit is sufficient to cover the cost of physical degradation, effectively guaranteeing the maximization of comprehensive benefits over a long period.

[0014] Preferably, the benchmark electricity price at the sampling time is obtained by: retrieving the electricity market price sequence of the power dispatching agency on the day in which the sampling time is located, and extracting the average price in the sequence as the benchmark electricity price.

[0015] Preferably, the scheduling priority index satisfies the expression: In the formula, For the first The scheduling priority index for each sampling moment; For the first Electricity price sensitivity coefficient at each sampling time; For the first Energy maintenance cost at each sampling time; , For the first Real-time electricity price and benchmark electricity price at each sampling time; To avoid zero parameters.

[0016] This invention weights the difference between the real-time electricity price and the benchmark price, and uses the cost of physical losses as a feedback constraint to ensure that the dispatching instructions can be adjusted adaptively. It achieves the optimal balance between economy and energy storage life by pursuing high-efficiency revenue while taking into account the equipment operating status.

[0017] Preferably, the step of performing cross-time period scheduling includes: recording the scheduling priority index of the sampling time as... Set the scheduling threshold as ;like Perform the discharge action; if , and perform the charging action.

[0018] This invention establishes an automated logic for scheduling direction, switching charging and discharging actions in real time according to the positive and negative polarities of the priority index. Through precise docking of positive discharge peak shaving and negative charging valley filling, coupled with a threshold-based standby protection mechanism, invalid fluctuation interference is effectively filtered out, avoiding frequent start-stop of the energy storage system in the low-profit range, thereby extending the life of the energy storage equipment.

[0019] Preferably, the calculation of execution power includes: , For the first Execution power at each sampling time, Based on power, This is the proportionality coefficient. For the first The difference between the absolute value of the scheduling priority index at each sampling time and the set scheduling threshold.

[0020] Secondly, the present invention provides a time-period optimization scheduling system for energy storage resources based on dynamic electricity prices, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned time-period optimization scheduling method for energy storage resources based on dynamic electricity prices is implemented.

[0021] By adopting the above technical solution, a computer program is generated for the above-mentioned method of cross-time-period optimization scheduling of energy storage resources based on dynamic electricity price, and stored in the memory so that it can be loaded and executed by the processor. In this way, a terminal device can be made based on the memory and the processor for convenient use.

[0022] The beneficial effects of this invention are as follows:

[0023] This invention constructs a scheduling mechanism that combines electricity price trend capture with physical energy efficiency hedging. By utilizing a two-dimensional model that includes recent deviation and instantaneous rate of change, combined with the natural exponential function, it significantly enhances the sensitivity of capturing price inflection points and avoids ineffective resource utilization. At the same time, by constructing an energy maintenance cost and introducing rigid constraints on load power, it achieves linear and smooth adjustment of commands. This eliminates the risks of distribution overload and reverse power transmission, while maximizing the efficient allocation of resources across time periods and maximizing economic benefits. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating an energy storage resource cross-time-period optimization scheduling method based on dynamic electricity prices in this invention;

[0025] Figure 2 It schematically illustrates the curves showing the changes in electricity price and load power;

[0026] Figure 3 This is a schematic diagram illustrating the execution of the scheduling logic and the effect of the limiting. Detailed Implementation

[0027] The technical solutions of the embodiments of the present 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 the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0029] This invention discloses a method for cross-time-period optimal scheduling of energy storage resources based on dynamic electricity prices, referring to... Figure 1 This includes steps S1-S5:

[0030] S1. Real-time acquisition of electricity price and load power, and simultaneous acquisition of temperature difference and current intensity inside and outside the energy storage compartment.

[0031] It should be noted that dynamic electricity prices reflect the real-time changes in the supply and demand relationship of the power grid and are the core driving force for energy storage dispatch decisions, while user load demand determines the boundary conditions of dispatch actions. Therefore, collecting multi-dimensional real-time data can provide a basis for subsequent evaluation of energy storage actions, ensuring that dispatch instructions do not exceed physical limits and keep pace with power grid demand.

[0032] Specifically, electricity prices are retrieved in real time through the electricity market trading interface; simultaneously, load power is acquired in real time through smart meters installed at the distribution box bus position; the internal temperature sensor of the energy storage system is used to acquire the internal temperature of the energy storage compartment in real time, and the external temperature sensor of the energy storage system is used to acquire the external ambient temperature of the energy storage compartment in real time, and the absolute difference between the internal temperature of the energy storage compartment and the external ambient temperature of the energy storage compartment is calculated in real time, and the absolute difference is used as the real-time temperature difference between the inside and outside of the energy storage compartment; and the current intensity of the energy storage converter control is acquired in real time; to ensure the synchronization of multi-dimensional data, a fixed sampling interval is set to synchronously collect multi-dimensional data once. In this embodiment of the invention, the fixed sampling interval is 15 minutes, which can be adjusted by the implementers according to the actual scheduling requirements.

[0033] At this point, the basic scheduling dataset has been obtained.

[0034] S2. For any sampling time: calculate the relative difference between the real-time electricity price and the average electricity price of multiple adjacent sampling times before the sampling time, and obtain the electricity price deviation; determine the electricity price sensitivity coefficient based on the instantaneous change rate of the real-time electricity price and the electricity price deviation.

[0035] It should be noted that dynamic electricity prices are not isolated values; their distribution over time shows obvious clustering and trends. If prices are in a rapid upward trend, even if the current price has reached the discharge standard, premature discharge will result in the loss of energy storage resources. Therefore, it is necessary to analyze the degree of deviation of the current price from the center of fluctuation within the historical window and the slope of its change, to assess the strength and urgency of the current price signal, and to provide decision weights for subsequent cross-period adaptive power allocation.

[0036] Specifically, for any given sampling time, the real-time electricity price of multiple adjacent sampling times preceding that sampling time is obtained. These multiple adjacent sampling times are used to define the number of reference prices, with a value range of [4, 24]. In this embodiment of the invention, 12 is used to ensure that the reference price has sufficient statistical representativeness. Implementers can adjust this according to the actual market electricity price fluctuation frequency requirements. Furthermore, in the early stages of system operation, when the number of collected sampling points is less than 12, it is not possible to directly obtain the set multiple sampling times. In this case, the system uses a linear interpolation compensation method to construct a fitted price on the missing time axis, thereby filling in the missing data.

[0037] The calculation method for calculating the electricity price deviation at any sampling time includes: calculating the average real-time electricity price of multiple adjacent sampling times before the sampling time, calculating the absolute difference between the real-time electricity price at the sampling time and the average value, and taking the ratio of the absolute difference to the average value as the electricity price deviation at the sampling time.

[0038] Based on the instantaneous rate of change and price deviation of the real-time electricity price at the sampling time, the electricity price sensitivity coefficient at that sampling time is determined; the electricity price sensitivity coefficient satisfies the expression:

[0039]

[0040] In the formula, For the first Electricity price sensitivity coefficient at each sampling time; For the first Electricity price deviation at each sampling time; , For the first The and the first Real-time electricity price at each sampling point; To take the absolute value; It is a natural exponential function; This is the standard normalization function.

[0041] in, Reflects the first The relative deviation of the real-time electricity price at a sampling moment from the real-time electricity price at several adjacent sampling moments is the largest value. This indicates that the real-time electricity price at that sampling moment has deviated significantly from the recent normal electricity price, which means that the potential arbitrage space or power regulation value of energy storage dispatch at this time is greater. This reflects the instantaneous rate of change of the real-time electricity price at the sampling moment. The larger the value, the more drastic the trend of change in the real-time electricity price at that sampling moment, which means that the probability of a peak price in the future increases significantly. By using a natural exponential function to nonlinearly amplify the instantaneous rate of change of the real-time electricity price, even small price fluctuations can be significantly reflected in the sensitivity coefficient, thereby enhancing the system's sensitivity to capturing price inflection points and sudden peaks. That is, when the real-time electricity price at the sampling moment is at an extremely high level and still continues to grow rapidly, This will significantly increase, indicating that the sampling time is extremely sensitive to price signals; it should be added that, due to the influence of renewable energy consumption, the electricity price is always greater than 0 in the daily electricity market.

[0042] Thus, the electricity price sensitivity coefficient at each sampling time has been obtained.

[0043] S3. Calculate the internal resistance heat loss based on the extracted physical parameters of the energy storage system and the current intensity at the sampling time, and determine the energy maintenance cost by combining the electrical energy consumed by the temperature difference between the inside and outside of the energy storage chamber.

[0044] It should be noted that energy storage systems are not ideal energy converters. During high-power charging and discharging, a large amount of Joule heat is generated due to the internal resistance of the battery. This heat not only results in energy loss but also causes the battery temperature to rise, which in turn increases the power consumption of the air conditioning cooling system. This energy loss increases quadratically with the increase of power. If the additional cost of maintaining system operation and thermal balance is not considered, the calculated dispatch benefits will be inflated. Therefore, it is necessary to combine power level and ambient temperature to assess the energy expenditure required to maintain the energy storage system in a healthy operating state, so as to provide more accurate data for cross-time dispatch.

[0045] Specifically, the physical parameters of the energy storage system are extracted. These physical parameters are obtained from the energy storage battery management system process manual and include: equivalent internal resistance, temperature control efficiency coefficient, and rated total energy.

[0046] For any sampling moment, the internal resistance heat loss is calculated based on the physical parameters of the energy storage system and the current intensity at that sampling moment. Combined with the electrical energy consumed by the temperature difference between the inside and outside of the energy storage chamber, the energy maintenance cost at that sampling moment is determined. The energy maintenance cost satisfies the following expression:

[0047]

[0048] In the formula, For the first Energy maintenance cost at each sampling time; For the first Current intensity at each sampling time; This is the equivalent internal resistance; Temperature control efficiency coefficient; For the first Temperature difference between the inside and outside of the energy storage chamber at each sampling time; This refers to the rated total energy. This represents the sampling time interval.

[0049] in, Reflects the first The direct Joule heat loss generated by the current intensity flowing through the battery's internal resistance at each sampling moment. The larger this value is, the more severe the electrochemical loss inside the battery at that sampling moment, which means the lower the energy conversion efficiency. This reflects the electrical energy consumed by the temperature control system to overcome the temperature difference between the inside and outside of the storage chamber in order to maintain the energy storage battery within its optimal operating temperature range. A larger value indicates a more extreme temperature difference between the inside and outside of the storage chamber at that sampling moment, meaning poorer chamber insulation performance and higher auxiliary energy consumption for maintaining system operation at that sampling moment. Dividing by... This allows the energy maintenance cost to be normalized. The larger the value, the higher the proportion of the physical cost of executing the current scheduling command to the total electricity consumption. In this case, the net benefit of the scheduling behavior will be suppressed.

[0050] Thus, the energy maintenance cost at each sampling moment is obtained.

[0051] S4. Obtain the benchmark electricity price at the sampling time, and use the electricity price sensitivity coefficient to weight the difference between the real-time electricity price at the sampling time and the benchmark electricity price. Determine the scheduling priority index based on the weighted value and the energy maintenance cost under the benchmark electricity price.

[0052] It should be noted that the essence of cross-time scheduling is to find a balance between electricity price revenue and physical losses. If there is only high electricity price sensitivity but the maintenance cost is extremely high, such as in extremely hot environments where ultra-high power discharge is required, the overall net benefit of this action may not be ideal. Therefore, the expected profit margin after trend weighting and the energy maintenance cost of system operation are comprehensively considered to guide the scheduling direction with a priority index. When the index reaches a certain level, the system should automatically adjust the charging and discharging power to achieve the optimal balance between economy and equipment lifespan, and avoid wasting charging and discharging times in low-efficiency ranges.

[0053] Specifically, the benchmark electricity price at any sampling time is obtained by retrieving the electricity market price sequence of the power dispatching agency on the day in which the sampling time is located, and extracting the average of the electricity prices in the sequence as the benchmark electricity price.

[0054] The difference between the electricity price and the benchmark electricity price is weighted using the electricity price sensitivity coefficient at the sampling time. Based on the weighted value and the energy maintenance cost at the benchmark electricity price, a scheduling priority index is determined at that sampling time. The scheduling priority index satisfies the following expression:

[0055]

[0056] In the formula, For the first The scheduling priority index for each sampling moment; For the first Electricity price sensitivity coefficient at each sampling time; For the first Real-time electricity price at each sampling point; For the first The benchmark electricity price at each sampling time; For the first Energy maintenance cost at each sampling time; To avoid zero parameters.

[0057] in, This reflects the consideration of the first The expected profit margin after considering the fluctuation trend of electricity price relative to the benchmark electricity price at each sampling time, when hour, A positive value indicates that the electricity price at that sampling time is worthwhile for generating profits from discharge; the larger the value, the more significant the profit potential at that sampling time. This reflects the costs that must be incurred to obtain the aforementioned benefits, calculated based on electricity prices. The larger this value, the greater the loss caused by physical losses at that sampling point. To prevent small perturbation constants with a denominator of zero, a value of 0.001 is used in this embodiment of the invention; in summary, when When the value is much greater than 0, it indicates that the electricity price sensitivity is high and the loss is relatively low at that sampling time, making this the optimal priority for discharge; when When the value is much less than 0, it indicates that the electricity price at that sampling time is extremely low, representing the golden window for charging.

[0058] At this point, the scheduling priority index for each sampling moment has been obtained.

[0059] S5. In response to the absolute value of the scheduling priority index being greater than the set scheduling threshold, a cross-time period scheduling action is performed. The execution power is calculated based on the difference between the absolute value and the set scheduling threshold, and the final value of the execution power is determined in combination with the load power at the sampling time.

[0060] It should be noted that the positive and negative attributes and magnitude of the scheduling priority index dynamically determine the execution direction and intensity of energy storage resources. Since the scheduling priority index integrates market electricity price sensitivity and system physical loss cost, its value distribution directly reflects the performance of cross-time period resource scheduling at each sampling moment. User-side load demand data is introduced as the physical constraint boundary of power scheduling to ensure that scheduling instructions not only meet economic optimization but also meet the safe operation requirements of the distribution side. By performing interval mapping of the scheduling priority index and combining it with load constraints for post-processing, the system can adaptively operate at saturation during high-value periods and actively avoid actions in low-efficiency or high-risk periods.

[0061] Specifically, the following scheduling strategy is applied to the scheduling priority index at each sampling time:

[0062] Set scheduling threshold ,like Determine the first The sampling time has value for cross-time period scheduling, and the system, based on the first sampling time, has value for cross-time period scheduling. The difference between the absolute value of the scheduling priority index at each sampling time and the set scheduling threshold. Calculate the first Execution power at each sampling time , , Based on power, It is a proportionality coefficient; and when When the discharge action is performed, When performing a charging action, if the absolute value of the executed power exceeds the first... The load power at each sampling time is taken as the final value of the execution power; otherwise, the absolute value of the execution power is taken as the final value of the execution power.

[0063] like This means the first The gains at a single sampling moment are insufficient to cover the physical costs required for energy maintenance, so the system enters standby mode and does not perform cross-period scheduling actions to reduce meaningless battery cycling losses.

[0064] It should be added that the scheduling threshold ranges from [0.5, 0.9], in this embodiment of the invention. A value of 0.75 is used to balance scheduling frequency and single-time revenue quality; the base power is usually taken as 5%-10% of the rated power of the energy storage system. In this embodiment, the base power is taken as 8% of the rated power of the energy storage system. The proportional coefficient is the value normalized by the maximum statistical deviation of the scheduling priority index; the implementer can adjust it according to battery life preference or cost per kilowatt-hour.

[0065] For example, Figure 2 The graph shows the changes in electricity price and load power. The horizontal axis represents the sampling time, and the vertical axis represents the electricity price and power value. Around 3 PM, the real-time electricity price is high, which is conducive to discharging for profit. However, at this time, the user load power is also at a high level. If the energy storage system blindly discharges at its rated power at this moment, although it can make a profit, if the discharge power exceeds the real-time load power, the remaining electricity will be fed back to the public power grid, increasing the operating pressure on the distribution transformer.

[0066] Figure 3 The diagram illustrates the execution and limiting effects of the scheduling logic. The horizontal axis represents sampling time, and the vertical axis represents the scheduling priority index strength and the execution power value. After 3 PM, although the scheduling priority index remains at a high level, the execution power curve does not increase indefinitely with the rise of the index. Instead, it exhibits a limited plateau or fluctuates downwards, ensuring local energy consumption. For example, between 6 AM and 12 PM, the scheduling priority index is between ±0.75, and the execution power curve remains near the 0 mark. This avoids meaningless charge-discharge cycles in low-profit ranges, thereby extending battery life. This adjustment method avoids the power surges caused by traditional on / off control, effectively suppressing frequent start-ups and shutdowns of the energy storage system in scenarios with high-frequency electricity price fluctuations, and ensuring real-time coordination between charging / discharging power and electricity price-sensitive signals.

[0067] This invention also discloses a cross-time-period optimization scheduling system for energy storage resources based on dynamic electricity prices, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the cross-time-period optimization scheduling method for energy storage resources based on dynamic electricity prices according to this invention is implemented.

[0068] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

Claims

1. A method for cross-time-period optimal scheduling of energy storage resources based on dynamic electricity prices, characterized in that, include: Real-time data collection of electricity price and load power, and simultaneous acquisition of temperature difference and current intensity inside and outside the energy storage compartment; For any sampling time: calculate the relative difference between the real-time electricity price and the average electricity price of multiple adjacent sampling times before the sampling time to obtain the electricity price deviation; determine the electricity price sensitivity coefficient based on the instantaneous change rate of the real-time electricity price and the electricity price deviation. The internal resistance heat loss is calculated based on the extracted physical parameters of the energy storage system and the current intensity at the sampling time. Combined with the electrical energy consumed by the temperature difference between the inside and outside of the energy storage chamber, the energy maintenance cost is determined. Obtain the benchmark electricity price at the sampling time, use the electricity price sensitivity coefficient to weight the difference between the real-time electricity price at the sampling time and the benchmark electricity price, and determine the dispatch priority index based on the weighted value and the energy maintenance cost under the benchmark electricity price; In response to the absolute value of the scheduling priority index being greater than the set scheduling threshold, a cross-time period scheduling action is executed. The execution power is calculated based on the difference between the absolute value and the set scheduling threshold. If the absolute value of the execution power exceeds the load power at the sampling time, the load power is taken as the final value of the execution power. Otherwise, the absolute value of the execution power is taken as the final value of the execution power; The electricity price sensitivity coefficient satisfies the following expression: ; For the first Electricity price sensitivity coefficient at each sampling time; For the first Electricity price deviation at each sampling time; , For the first The sampling time and the first sampling time Real-time electricity price at each sampling point; To take the absolute value; It is a natural exponential function; For standard normalized functions; The energy maintenance cost satisfies the expression: ; For the first Energy maintenance cost at each sampling time; For the first Current intensity at each sampling time; This is the equivalent internal resistance; Temperature control efficiency coefficient; For the first Temperature difference between the inside and outside of the energy storage chamber at each sampling time; This refers to the rated total energy. The sampling time interval; The scheduling priority index satisfies the expression: ; For the first The scheduling priority index for each sampling moment; For the first The benchmark electricity price at each sampling time; To avoid zero parameters.

2. The method for cross-time-period optimal scheduling of energy storage resources based on dynamic electricity prices according to claim 1, characterized in that, The acquisition of electricity price deviation includes: Calculate the average real-time electricity price of multiple adjacent sampling times before the sampling time, and obtain the absolute difference between the real-time electricity price at the sampling time and the average value; use the ratio of the absolute difference to the average value as the electricity price deviation.

3. The method for cross-time-period optimal scheduling of energy storage resources based on dynamic electricity prices according to claim 1, characterized in that, The physical parameters of the energy storage system refer to the equivalent internal resistance, temperature control efficiency coefficient, and rated total energy.

4. The method for cross-time-period optimal scheduling of energy storage resources based on dynamic electricity prices according to claim 1, characterized in that, The benchmark electricity price at the sampling time is obtained as follows: Retrieve the electricity market price sequence of the power dispatching agency on the day of sampling, and extract the average price in the sequence as the benchmark price.

5. The method for cross-time-period optimal scheduling of energy storage resources based on dynamic electricity prices according to claim 1, characterized in that, The execution of cross-time period scheduling actions includes: Let the scheduling priority index at the sampling time be . Set the scheduling threshold as ;like Perform the discharge action; if , and perform the charging action.

6. The method for cross-time-period optimal scheduling of energy storage resources based on dynamic electricity prices according to claim 1, characterized in that, The calculation of execution power includes: , For the first Execution power at each sampling time, Based on power, This is the proportionality coefficient. For the first The difference between the absolute value of the scheduling priority index at each sampling time and the set scheduling threshold.

7. A cross-time-period optimized scheduling system for energy storage resources based on dynamic electricity prices, characterized in that, include: The processor and memory, wherein the memory stores computer program instructions, which, when executed by the processor, implement a cross-time-period optimization scheduling method for energy storage resources based on dynamic electricity prices as described in any one of claims 1-6.

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