Independent energy storage multi-time scale transaction optimization method based on electric power spot market

Abstract

The invention belongs to the technical field of electric power markets, and particularly relates to an independent energy storage multi-time scale transaction optimization method based on an electric power spot market, which comprises the following steps of: firstly, acquiring a first power scheduling instruction sequence of an independent energy storage system under a day-ahead scale and a second power scheduling instruction sequence when an intra-day rolling optimization period is started; and performing time alignment on the two power scheduling instruction sequences in the overlapping interval, detecting whether a power instruction difference value at the time boundary exceeds a preset hopping threshold value, if so, setting virtual buffer time periods before and after the time boundary to construct a power smooth transition curve, and controlling the independent energy storage system to execute power transition. And an energy deviation value caused by power smoothing in the virtual buffer period is used as a cost correction term and is fed back to the next round of intra-day rolling optimization target so as to adjust a subsequently generated power scheduling instruction sequence, and the strategy coherence and the execution robustness of the independent energy storage participating in the multi-time scale market are improved.

Description

Technical Field

[0001] This invention belongs to the field of electricity market technology, specifically a method for optimizing multi-timescale trading of independent energy storage based on the electricity spot market. Background Technology

[0002] Within the framework of the electricity spot market, various market participants need to formulate trading strategies at different time scales based on price signals and load forecasts to maximize economic benefits. As a flexible resource with rapid response and two-way regulation capabilities, independent energy storage is no longer limited to a single dispatch command in its market participation. Instead, it needs to make coordinated decisions at multiple time scales, such as day-ahead, intraday, and real-time, to cope with the dual challenges of market price fluctuations and system balance requirements.

[0003] Currently, there are two main types of technical solutions in the industry to handle multi-timescale transaction optimization of independent energy storage: one is single-scale independent optimization, which means making independent decisions at each time scale, with the later scale completely covering or replacing the plan of the previous scale.

[0004] Another type is multi-scale static connection, which uses fixed rules such as linear interpolation to decompose coarse-scale instructions into fine-scale ones.

[0005] The existing technical solutions described above have the following drawbacks in practical applications: 1. Although existing technologies have introduced a rolling optimization mechanism to avoid the serious disconnect between day-ahead planning and real-time execution, drastic power command jumps are prone to occur at time boundaries between the old and new command sequences. Existing methods lack effective detection and suppression strategies, and such jumps can easily exceed the physical regulation capabilities of energy storage devices, leading to command failures or even damage to device safety and lifespan.

[0006] 2. The existing transition rules are statically preset and cannot be adaptively adjusted according to the real-time status of the energy storage system and market dynamics. This results in the transition process being either too conservative and losing benefits, or too aggressive and risky.

[0007] 3. The instruction smoothing operation performed to mitigate power fluctuations inevitably results in a deviation between the actual executed power and the market-bid power, leading to additional economic costs or revenue losses. Existing methods generally ignore the economic value of this deviation and fail to feed it back into subsequent optimizations, creating a contradiction between the theoretically economical overall strategy and the subsequent losses. Summary of the Invention

[0008] To overcome the shortcomings in the background art, embodiments of the present invention provide an optimization method for independent energy storage multi-timescale trading based on the electricity spot market, which can effectively solve the problems involved in the background art.

[0009] The objective of this invention can be achieved through the following technical solution: a multi-timescale trading optimization method for independent energy storage based on the electricity spot market, comprising: obtaining a first power dispatch instruction sequence of the independent energy storage system at the day-ahead scale, and a second power dispatch instruction sequence at the start of the intraday rolling optimization cycle, wherein the second power dispatch instruction sequence and the first power dispatch instruction sequence have an overlapping period in time.

[0010] The first power scheduling instruction sequence and the second power scheduling instruction sequence are time-aligned within the overlapping interval, and the power instruction difference between the two at the time boundary is detected to see if it exceeds a preset transition threshold.

[0011] If the preset transition threshold is exceeded, a virtual buffer period is set before and after the time boundary, and a power smooth transition curve is constructed within the virtual buffer period to control the independent energy storage system to perform power transition.

[0012] The energy deviation caused by power smoothing during the virtual buffer period is used as a cost correction item and fed back into the next round of intraday rolling optimization targets to adjust the subsequently generated power scheduling instruction sequence.

[0013] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) By setting an adaptive jump detection threshold and constructing a virtual buffer period, the present invention can automatically identify and smoothly handle power command mutations that may exceed the physical capacity of the equipment when multiple time scale commands are connected. This helps to avoid problems such as equipment overload and accelerated battery degradation caused by sudden changes in planned commands, and improves the safety and reliability of independent energy storage systems in actual execution.

[0014] (2) The connection strategy of the present invention is not static or empirical, but can dynamically adjust the jump judgment threshold, buffer period length and smooth transition curve according to the real-time state of charge and maximum power capacity of the independent energy storage system. This adaptive mechanism ensures that the command transition process is always within the safe and feasible domain of the equipment, and can make optimized adjustments when the system state changes, taking into account both safety constraints and economic requirements.

[0015] (3) This invention quantifies the energy deviation caused by smooth transition into an economic cost correction term and feeds it back into the subsequent rolling optimization objective function. It can perceive the real economic impact brought about by instruction connection, and thus actively tend to choose a smoother strategy with smaller execution deviation when formulating subsequent plans, which helps to maximize the economic efficiency of the whole cycle transaction. Attached Figure Description

[0016] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the implementation steps of the method of the present invention.

[0018] Figure 2 This is a flowchart illustrating the dynamic threshold setting process of the present invention.

[0019] Figure 3 This is a flowchart illustrating the construction of the power smooth transition curve of the present invention. Detailed Implementation

[0020] 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 embodiments of the present invention, and not all embodiments. 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.

[0021] Reference Figure 1 As shown, the present invention provides a multi-timescale trading optimization method for independent energy storage based on the electricity spot market, including: S1. obtaining a first power dispatch instruction sequence of the independent energy storage system at the day-ahead scale, and a second power dispatch instruction sequence at the start of the intraday rolling optimization cycle, wherein the second power dispatch instruction sequence and the first power dispatch instruction sequence have an overlapping interval in time.

[0022] Based on the multi-timescale trading mechanism of the electricity spot market, the output plans formulated by independent energy storage systems during the day-ahead market phase need to be rolled over and executed during intraday and real-time phases based on updated market information and system status. Therefore, at the start of each intraday rolling optimization cycle, the newly generated intraday-scale plan will inevitably coexist with the day-ahead-scale plan that has not yet been completed in the future, thus forming a temporal overlap. This overlap specifically refers to the continuous time period from the start of the current intraday rolling optimization cycle to a future point covered by the day-ahead plan. This period is characterized by the coexistence of the day-ahead plan not yet completed while the new intraday plan has already taken effect, and it is also the area where conflicts may occur between different scale instructions, requiring coordination.

[0023] Specifically, the first power dispatch instruction sequence corresponding to this independent energy storage system is parsed and read from the day-ahead market clearing results file published by the power dispatching agency or power trading platform. Simultaneously, the second power dispatch instruction sequence most recently generated at the start of the current cycle is directly retrieved from the output of the local intraday rolling optimization platform. This acquisition process is automatically completed through a predefined data interface.

[0024] S2. Time-align the first power scheduling instruction sequence and the second power scheduling instruction sequence within the overlapping interval, and detect whether the power instruction difference between the two at the time boundary exceeds a preset transition threshold.

[0025] Since the first power scheduling instruction sequence originates from the day-ahead market, its time granularity is typically hourly, representing the average power commitment for each full hour. The second power scheduling instruction sequence, however, is generated through intraday rolling optimization, with a finer time granularity, such as 15-minute intervals.

[0026] To compare the power setpoints of the two at the same physical moment, they must be mapped to the same time base. The time boundary specifically refers to the starting moment of the overlapping interval, that is, the instant at which the current intraday rolling optimization cycle begins. This point is the connection point between the execution of the previous day's plan and the effectiveness of the new intraday plan, and it is also the location with the highest risk of discontinuous power commands.

[0027] The time alignment process is as follows: determine the time range of the overlapping interval, and establish a unified time axis covering the overlapping interval based on the time granularity of the second power scheduling instruction sequence.

[0028] For each time node on the unified time axis, it is assigned to the specific hourly time period corresponding to the first power scheduling instruction sequence, and the original power setting value corresponding to the hourly time period is directly determined as the first aligned power value corresponding to the time node.

[0029] The original power setting value corresponding to each time granularity in the second power scheduling instruction sequence is directly assigned to the corresponding time node with the same timestamp on the same time axis, as the second aligned power value.

[0030] This process yields two power sequences that are strictly aligned on a unified time axis and can be directly compared point by point.

[0031] Based on time alignment, in order to quantitatively evaluate the smoothness of instruction connection, it is necessary to detect whether the power instruction difference at the time boundary exceeds the preset jump threshold. The specific implementation process is as follows: determine the last instruction value of the first power scheduling instruction sequence at the start time of the intraday rolling optimization cycle, and the initial instruction value of the second power scheduling instruction sequence at the same start time.

[0032] Calculate the absolute difference between the final instruction value and the initial instruction value.

[0033] The absolute difference is compared with a preset transition threshold. If the absolute difference is greater than the preset transition threshold, it is determined that there is a power command transition, and the time point of the power command transition and the corresponding command value are recorded synchronously.

[0034] Reference Figure 2 As shown, the preset jump threshold is dynamically set according to the physical characteristic parameters of the independent energy storage system. The implementation process is as follows: obtain the maximum allowable power change rate and the battery cycle life degradation sensitive range of the independent energy storage system. The maximum allowable power change rate is an inherent technical parameter obtained from the inverter technical specifications or equipment nameplate parameters of the independent energy storage system. The battery cycle life degradation sensitive range is a state of charge range predefined and stored by the battery management system of the independent energy storage system based on the cycle life test data or electrochemical model provided by the battery manufacturer.

[0035] Multiply the maximum allowable power change rate by the current clearing cycle length of the electricity spot market to obtain the maximum allowable power step within a unit control cycle. Multiply this step by a preset safety margin coefficient to obtain the basic jump threshold.

[0036] It should be noted that the preset safety margin coefficient is used to reserve a certain buffer space before the theoretical limit to prevent the equipment from exceeding the safety boundary due to errors or instantaneous overload. Specifically, it is determined based on the historical operating statistics of the independent energy storage system, and the determination process is as follows: Operating data from multiple complete trading days in the past are extracted from the historical operating database of the independent energy storage system. For each trading day, the following data are obtained for each market clearing cycle: the planned power command value received at the start of the cycle, and the average actual output power value measured and recorded by sensors during the cycle.

[0037] For each transaction clearing period in the historical operational statistics set, the absolute value of the difference between the planned power instruction value and the actual output power value is calculated. This absolute value is then divided by the absolute value of the corresponding planned power instruction value to obtain an instruction execution deviation rate. This deviation rate quantifies the degree of deviation of the historical execution effect from the instruction. Furthermore, when the planned instruction value is zero, a very small positive number is used instead of the denominator to avoid division by zero errors.

[0038] Statistical analysis is performed on the instruction execution deviation rate calculated from all historical transaction clearing cycles. A preset high percentile is calculated as a statistical characteristic value, such as the 95th percentile. This characteristic value represents the upper limit of the accuracy that the system's actual execution capability can achieve relative to the theoretical requirements of the instruction in most cases, and can be customized by technical personnel.

[0039] Establish a mapping relationship between statistical characteristic values ​​and safety margin coefficients. This mapping relationship is constructed as an inverse correlation, meaning that the larger the statistical characteristic value, the greater the historical execution deviation and the higher the system uncertainty, and therefore the larger the required safety margin should be. Consequently, the safety margin coefficient obtained by mapping should be smaller.

[0040] As an example mapping function, the safety margin coefficient can be represented as the difference between 1 and the statistical characteristic value. To ensure the validity of the coefficient, a lower limit and an upper limit must be set. If the calculated value is lower than the lower limit, the lower limit is used; if it is higher than the upper limit, the upper limit is used. This ultimately yields the preset safety margin coefficient used for calculating the current threshold.

[0041] To protect battery life, the base transition threshold needs to be further adjusted based on real-time status. It is determined whether the current state of charge (SOC) of the independent energy storage system is within the battery cycle life degradation sensitive range. If so, the Euclidean distance between the current SOC and the midpoint of the battery cycle life degradation sensitive range is calculated. A reduction factor is determined based on a predefined negative correlation between distance and reduction factor. This reduction factor is then multiplied by the base transition threshold to obtain the final dynamic transition threshold.

[0042] The predefined negative correlation mapping relationship between distance and reduction factor can be exemplarily referred to in the following process: the Euclidean distance between the current state of charge and the midpoint of the battery cycle life degradation sensitive interval is calculated by dividing it by half the span of the battery cycle life degradation sensitive interval to obtain the normalized distance.

[0043] Substitute the normalized distance into the standard negative exponential function to output the reduction coefficient. Compare this reduction coefficient with the predefined reduction coefficient standard range. If the output value is lower than the lower limit, take the lower limit; if it is higher than the upper limit, take the upper limit to obtain the final reduction coefficient.

[0044] It should be noted that the standard negative exponential function is merely an evolution algorithm for negative mapping relationships provided in this embodiment. Under the premise of achieving the purpose of negative mapping relationships, implementers may also adopt other feasible algorithms in the prior art as other implementation methods, and this application does not limit them.

[0045] If the current state of charge is not within the battery cycle life degradation sensitive range, the basic jump threshold is directly used as the final dynamic jump threshold. This dynamic setting process is automatically executed at the start of each daily rolling optimization cycle to ensure that the threshold always matches the current safe operating boundary of the system.

[0046] S3. If the preset transition threshold is exceeded, a virtual buffer period is set before and after the time boundary, and a power smooth transition curve is constructed within the virtual buffer period to control the independent energy storage system to perform power transition.

[0047] When the detected power command difference exceeds the dynamic jump threshold, it indicates a significant power step at the transition point between the day-ahead plan and the new intraday plan. To ensure the safe operation of power electronic equipment such as energy storage converters, and to avoid overcurrent protection or electrical stress damage caused by sudden and drastic power changes, while also smoothing the actual output curve of the energy storage system to meet grid dispatch requirements, a transition period needs to be introduced at the command switching point. This transition period is not the actual market clearing cycle, hence it is called a virtual buffer period, and its function is to provide a time window for smooth power changes.

[0048] The virtual buffer period setting process includes: First, determining the basic time required for the transition based on the physical limits of the equipment. Then, dividing the absolute value of the power command difference by the maximum permissible power change rate of the independent energy storage system yields the minimum transition time required to complete the power command jump.

[0049] However, simply meeting the theoretical minimum time may not be sufficient to address certain real-time constraints. Therefore, it is necessary to further obtain the real-time state of the system at the transition initiation point, including the real-time state of charge and the real-time maximum charge / discharge power limit under the current operating conditions.

[0050] Based on the sign of the power command difference and the real-time state of charge, it is determined whether there is a limitation in power output capability before and after the power transition. The specific determination process is as follows: extract the recommended long-term operating window of state of charge corresponding to the relatively slow rate of battery capacity decay from the battery technical specifications or cycle life test report provided by the battery manufacturer of the independent energy storage system.

[0051] From the historical operation database of the independent energy storage system, the actual operating state of charge (SOC) data of the battery over a period of time is extracted. A statistical distribution histogram of SOC is plotted and its cumulative distribution function is calculated. Based on the preset coverage probability target of the SOC range operated by the reference battery for most of the time, the SOC that makes the cumulative distribution function value equal to half of the coverage probability target is identified as the lower quantile. The SOC that makes the cumulative distribution function value equal to the coverage probability target itself is identified as the upper quantile. The actual operating experience window of SOC is defined based on the upper and lower quantiles.

[0052] A comprehensive safe operating window is obtained by intersecting the recommended long-term operating window for the state of charge with the actual operating experience window. The upper limit of this comprehensive window is used as the high threshold of the first state of charge, and its lower limit is used as the low threshold of the first state of charge.

[0053] When the power command difference is positive, it indicates that the command transition direction is to increase output power. When it is negative, it indicates that the command transition direction is to decrease output power. Combined with the real-time state of charge, the restricted state logic is judged: if the current state of charge is greater than the first high threshold of the state of charge and the power command difference is positive, or if the current state of charge is less than the first low threshold of the state of charge and is negative, then the initial side regulation tension is determined to be high; otherwise, the initial side regulation tension is determined to be low.

[0054] If the current state of charge is greater than the first high threshold of the state of charge and the power command difference is negative, or if the current state of charge is less than the second low threshold of the state of charge and the power command difference is positive, then the target-side regulation tension is determined to be high; otherwise, the target-side regulation tension is determined to be low.

[0055] When the initial or target side is found to have high adjustment tension, it indicates that there is a limitation in power output capability before and after power transition.

[0056] Based on the adjustment tension judgment results of the starting side and the target side, the time lengths of the forward buffer sub-period and the backward buffer sub-period are dynamically allocated on the basis of the minimum theoretical transition time to form the virtual buffer period. If there is a situation where the power output capability is limited, a longer buffer sub-period is allocated to the limited side.

[0057] Specifically, if the tension levels on both the starting and target sides are low, then the forward and backward buffer sub-periods are allocated equal durations, specifically half the total duration of each virtual buffer sub-period, and this allocation is the base value for the forward and backward buffer sub-periods.

[0058] If the initial side has high regulatory tension and the target side has low regulatory tension, the backward buffer sub-period maintains the base value, and the forward buffer sub-period is allocated more than half of the total duration of the virtual buffer sub-period on top of the base value.

[0059] If the target-side regulation tension is high while the initiating-side regulation tension is low, the forward buffer sub-period maintains the base value, and the backward buffer sub-period is allocated more than half of the total duration of the virtual buffer sub-period on top of the base value.

[0060] By setting an adaptive jump detection threshold and constructing a virtual buffer period, this invention can automatically identify and smoothly handle power command mutations that may exceed the physical capacity of the equipment when commands are connected across multiple time scales. This helps to avoid problems such as equipment overload and accelerated battery degradation caused by sudden changes in planned commands, thereby improving the safety and reliability of independent energy storage systems in actual operation.

[0061] The virtual buffer period defines the transition time window, but the trajectory of power change within this window requires the construction of a smooth power transition curve. The goal of this curve is to complete the power change within a given time while minimizing the mechanical and electrical impact on the equipment and strictly adhering to all real-time operating constraints. Based on this, refer to... Figure 3 As shown, the process of constructing the power smooth transition curve includes: calculating the ratio of the absolute value of the power command difference to the total length of the virtual buffer period, which is defined as the required average power change rate.

[0062] The required average power change rate is compared with the maximum allowable power change rate, and the function type of the transition curve is selected based on the comparison result: if the required average power change rate is less than or equal to the first proportional threshold of the maximum allowable power change rate, it indicates that the transition demand is gentle and has little impact on equipment stress. In this case, a linear ramp function with efficient calculation and direct response is selected as the constructor. Its implementation is as follows: using the initial power value and the target power value as endpoints, linear interpolation is performed within the virtual buffer period to generate a preliminary power smoothing transition curve.

[0063] If the change rate exceeds the first proportional threshold of the maximum allowable power change rate, it indicates an urgent need for transition, and directly using a linear ramp may lead to excessive instantaneous stress on the equipment. In this case, a nonlinear function is selected to provide a smoother transition at the endpoints. Specifically, a third-order or higher polynomial function is used as the constructor. Its implementation and constraint method is as follows: calculate the normalized exceedance of the required average power change rate beyond the first proportional threshold.

[0064] Define the order of the polynomial function and construct a polynomial expression with time as the variable.

[0065] The initial power value, the target power value, and the start and end times of the virtual buffer period are used as mandatory boundary conditions. A derivative constraint is applied based on the normalized exceedance amplitude: when the exceedance amplitude is less than half of the first proportional threshold, the first derivative of the polynomial function at the start and end points is additionally constrained to be zero. When the exceedance amplitude is greater than or equal to half of the first proportional threshold, both its first and second derivatives at the start and end points are additionally constrained to be zero.

[0066] The initial instruction value, the final instruction value, and the start and end times of the virtual buffer period are used as boundary conditions and substituted into the selected constructor to solve for the initial power smooth transition curve.

[0067] It should be noted that the process of obtaining the first proportional threshold is as follows: within the typical operating range of the independent energy storage system converter, the power change rate is used as the control command to drive the converter to perform a power step response test.

[0068] The average switching loss rate of the converter power module and the temperature rise rate of key heat dissipation points are collected and recorded synchronously during each test, forming a loss rate sequence and a temperature rise rate sequence that correspond one-to-one with the power change rate command. The key heat dissipation points refer to the junction temperature monitoring points of power devices or the heat sink substrate temperature monitoring points that are most sensitive to temperature or most prone to failure due to overheating.

[0069] For the loss rate sequence and the temperature rise rate sequence, calculate the change between adjacent data points. Identify the data points in each sequence where the change first exceeds a predetermined multiple of its previous average value, and record them as the loss inflection point and the thermal stress inflection point, respectively, and record the corresponding experimental values ​​of the power change rate.

[0070] The experimental values ​​of the power change rate corresponding to the loss inflection point and the thermal stress inflection point are divided by the maximum allowable power change rate to obtain two candidate ratio values.

[0071] Select the smaller of the two candidate ratio values ​​as the basic criterion ratio. Multiply this basic criterion ratio by a preset engineering margin coefficient less than 1, and the result is the final ratio criterion used for subsequent comparisons.

[0072] Exemplary implementation process: Testing revealed that the loss inflection point was 70% of the maximum allowable value, and the thermal stress inflection point was 65% of the maximum allowable value. The smaller 65% was selected as the basic criterion ratio, and then multiplied by a preset engineering margin coefficient of 0.85, resulting in a final comparison ratio of 55.25%. In subsequent steps, when the required average power change rate is less than or equal to 55.25% of the maximum allowable power change rate, a linear ramp function is selected. When this ratio is exceeded, a nonlinear function is selected.

[0073] The generated preliminary power smoothing transition curve must undergo feasibility verification to ensure its physical executability. Specifically, this involves obtaining the real-time maximum charge / discharge power limit of the independent energy storage system during the virtual buffer period.

[0074] Verify that all power values ​​on the initial power smoothing transition curve do not exceed the real-time maximum charge / discharge power limit.

[0075] If the verification fails, then on the preliminary power smoothing transition curve, identify all points where the power value exceeds the real-time maximum charge and discharge power limit, and calculate the maximum absolute amplitude of the value exceeding the limit.

[0076] Divide the power over-limit amplitude by the maximum allowable power change rate of the independent energy storage system to obtain the minimum additional transition time required based on the current severity of the over-limit, and use it as the adjustment amount for the total length of the virtual buffer period in this iteration.

[0077] The current total length of the virtual buffer period is increased by the adjustment amount. Based on the new total length, the time lengths of the forward and backward buffer sub-periods are redistributed, and the process of constructing the power smooth transition curve is returned to generate a new preliminary curve and perform a feasibility check until the check passes. However, if the total length of the virtual buffer period is extended to exceed a preset maximum allowable buffer length and the check still fails, the iteration must be terminated and a safety fault tolerance mechanism is triggered. This mechanism requires abandoning the single smooth transition and decomposing the original transition into multiple smaller sub-transitions within the power limit, which are then completed step by step in subsequent control cycles.

[0078] The connection strategy of this invention is not static or empirical, but can dynamically adjust the jump judgment threshold, buffer period length and smooth transition curve according to the real-time state of charge and maximum power capacity of the independent energy storage system. This adaptive mechanism ensures that the command transition process is always within the safe and feasible domain of the device, and can make optimized adjustments when the system state changes, taking into account both safety constraints and economic requirements.

[0079] S4. The energy deviation caused by power smoothing during the virtual buffer period is used as a cost correction item and fed back into the next round of intraday rolling optimization targets to adjust the subsequently generated power scheduling instruction sequence.

[0080] Based on the rules of the electricity spot market, which settles accounts based on actual delivered electricity, the market revenue of an independent energy storage system is directly determined by its physically executed electricity curve. The power smoothing operation during the virtual buffer period causes a deviation between the actual charging and discharging energy of the system and the energy planned in the intraday rolling optimization. If the economic value change corresponding to this deviation is not taken into account, the intraday rolling optimization process will continue to generate power command sequences with pure market revenue as the objective in subsequent cycles, ignoring the risk of high-cost smoothing operations that may be triggered during physical execution. This will lead to a cycle of planned changes – forced smoothing – revenue loss. Therefore, the energy deviation caused by smoothing needs to be converted into a cost correction term and fed back as input data to the objective function solution process of the next round of rolling optimization. By internalizing this cost, the subsequent power dispatch command sequences obtained can spontaneously balance the power changes that may bring high smoothing costs while pursuing market revenue, ultimately achieving a balance between operational safety and economy.

[0081] The process of using the energy deviation caused by power smoothing during the virtual buffer period as a cost correction item is as follows: First, within the virtual buffer period, using the unified time axis as a reference, calculate the difference between the power value of the power smoothing transition curve and the corresponding power value of the second power scheduling instruction sequence at each time node. Then, numerically integrate this power difference sequence with the time interval of the unified time axis as the step size to obtain the total energy deviation in kilowatt-hours.

[0082] Secondly, the total energy deviation is converted into economic cost. Since the nodal electricity price in the spot market varies over time, the real-time electricity price sequence corresponding to the virtual buffer period needs to be obtained. The weighted value of the total energy deviation and the real-time electricity price sequence is then calculated.

[0083] One specific implementation is as follows: If the electricity price remains constant during the virtual buffer period, the cost correction term is the product of the total energy deviation and the constant electricity price. If the electricity price changes, it is necessary to calculate the energy deviation component in each different electricity price sub-period based on the power difference curve, and then multiply each energy deviation component by the corresponding sub-period electricity price and sum them up to obtain the cost correction term.

[0084] The feedback to the next round of intraday rolling optimization is implemented as follows: when solving the objective function in the next round of intraday rolling optimization, the cost correction term is included as a penalty term in the objective function.

[0085] The sign of the penalty term is set as follows: when the energy deviation leads to a decrease in expected revenue, the penalty term is negative; when the energy deviation leads to an increase in expected cost, the penalty term is positive.

[0086] By optimizing the objective function that includes the penalty term, a new second power scheduling instruction sequence is generated. The power instruction jump tendency of the new second power scheduling instruction sequence at the time boundary is suppressed by the penalty term.

[0087] It should be noted that the cost adjustment item is calculated based on historical or current economic deviations that have occurred or been planned during virtual buffer periods. Feeding this back to subsequent optimization cycles aims to influence the newly generated power scheduling instruction sequences in later cycles, making them smoother at future time boundaries, forming a continuous improvement cycle of assessment-compensation-prevention, and does not constitute a retroactive adjustment to already executed instructions.

[0088] The method further includes a learning optimization step: after each virtual buffering and power smoothing operation, the feature data of this process is recorded as a sample. The recorded data includes at least: the power command difference when the transition is triggered, the dynamic transition threshold used, the total length of the virtual buffer period and its allocation ratio to the preceding and following sub-periods, and the absolute value of the finally calculated cost correction term.

[0089] After accumulating a certain number of historical samples, the ratio of power command difference to dynamic jump threshold is used as the independent variable to reflect the relative severity of the jump, and the cost correction term is used as the dependent variable. Linear regression or nonlinear fitting methods are used to establish a predictive model from independent variable to dependent variable.

[0090] Each time a new intraday rolling optimization is initiated and the dynamic jump threshold is initially calculated, the typical power command difference that may occur is estimated based on the current market forecast and system state. This difference is then substituted into the prediction model to obtain a predicted smoothing cost. If this predicted smoothing cost exceeds the preset tolerance cost, it is determined that the current threshold setting may be too lenient, allowing high-cost jumps to occur. In this case, the calculated base jump threshold is slightly lowered proportionally, pre-tightening the initial value of the dynamic jump threshold to be used for detection in this cycle. Through this feedforward adjustment based on historical experience, the probability of triggering high-cost smoothing events is proactively reduced, enabling the system to learn online and continuously improve.

[0091] In this embodiment of the invention, the energy deviation caused by the smooth transition is quantified as an economic cost correction term and fed back to the subsequent rolling optimization objective function. This allows the system to perceive the real economic impact of instruction connection and thus proactively choose a smoother strategy with smaller execution deviation when formulating subsequent plans, which helps to maximize the economic efficiency of the entire transaction cycle.

[0092] It should be further noted that the instruction smoothing method provided by this invention is not limited to the specific time scales of day-ahead and intraday. The method can be cascaded and applied to the optimized instruction smoothing process between any two adjacent time scales, from coarse to fine, such as between day-ahead-intraday, intraday-real-time, and real-time-second-level control levels.

[0093] In practical implementation, a uniformly structured instruction coordination unit can be deployed between the output interfaces of each pair of adjacent optimization modules. Each unit operates independently, performing instruction alignment, transition detection, and virtual buffer smoothing operations for its respective level. When a lower-level module representing a finer-grained time sequence generates a new instruction sequence, this unit aligns it with the instruction sequences already issued by the upper-level module with a coarser-grained time sequence within the overlapping time period, and detects the power instruction difference at all connecting time points.

[0094] For each detected power transition point exceeding the corresponding level's dynamic transition threshold, the unit independently constructs a local virtual buffer period and generates a local power smoothing transition curve. Subsequently, all generated local smoothing curves are concatenated with the original instruction segments that did not undergo transitions in chronological order to form a globally continuous corrected power instruction sequence suitable for execution between the pairs of levels.

[0095] The revised instruction sequence will serve as the input benchmark for the next finer-scale optimization module, or as a reference for feeding back execution constraints to the next coarser-scale module. Through this hierarchical deployment and connection across multiple levels, this invention can systematically ensure the continuity and executability of the entire instruction chain from long-term planning to instantaneous control, thereby improving the overall robustness of complex time-scale architectures.

[0096] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. An optimization method for independent energy storage trading across multiple time scales based on the electricity spot market, characterized in that: include: Obtain the first power dispatch command sequence of the independent energy storage system on the day-ahead scale, and the second power dispatch command sequence when the intraday rolling optimization cycle starts, wherein the second power dispatch command sequence and the first power dispatch command sequence have an overlapping period in time; The first power scheduling instruction sequence and the second power scheduling instruction sequence are time-aligned within the overlapping interval, and the power instruction difference between the two at the time boundary is detected to exceed a preset jump threshold. If the preset jump threshold is exceeded, a virtual buffer period is set before and after the time boundary, and a power smooth transition curve is constructed within the virtual buffer period to control the independent energy storage system to perform power transition. The energy deviation caused by power smoothing during the virtual buffer period is used as a cost correction item and fed back into the next round of intraday rolling optimization targets to adjust the subsequently generated power scheduling instruction sequence.

2. The method for optimizing independent energy storage multi-timescale trading based on the electricity spot market according to claim 1, characterized in that, The time alignment implementation process is as follows: The time range of the overlapping interval is determined, and a unified time axis covering the overlapping interval is established based on the time granularity of the second power scheduling instruction sequence. For each time node on the unified time axis, it is assigned to the specific hourly time period corresponding to the first power scheduling instruction sequence, and the original power setting value corresponding to the hourly time period is directly determined as the first aligned power value corresponding to the time node. The original power setting value corresponding to each time granularity in the second power scheduling instruction sequence is directly assigned to the corresponding time node with the same timestamp on the same time axis, as the second aligned power value.

3. The method for optimizing independent energy storage multi-timescale trading based on the electricity spot market according to claim 1, characterized in that, The process for detecting whether the power command difference between the two at the time boundary exceeds the preset transition threshold is as follows: Determine the last instruction value of the first power scheduling instruction sequence at the start of the intraday rolling optimization cycle, and the initial instruction value of the second power scheduling instruction sequence at the same start time; Calculate the absolute difference between the final instruction value and the initial instruction value; The absolute difference is compared with a preset transition threshold. If the absolute difference is greater than the preset transition threshold, it is determined that there is a power command transition, and the time point of the power command transition and the corresponding command value are recorded synchronously.

4. The method for optimizing independent energy storage multi-timescale trading based on the electricity spot market according to claim 3, characterized in that, The preset switching threshold is dynamically set based on the physical characteristic parameters of the independent energy storage system, and the implementation process is as follows: Obtain the maximum permissible power change rate and the battery cycle life degradation sensitive range of the independent energy storage system; Multiply the maximum allowable power change rate by the current clearing cycle length of the electricity spot market to obtain the maximum allowable power step within a unit control cycle, and then map the result to the basic jump threshold using a preset safety margin coefficient. Determine whether the current state of charge of the independent energy storage system is within the battery cycle life degradation sensitive range. If so, calculate the distance between the current state of charge and the midpoint of the battery cycle life degradation sensitive range, and use this distance to plan a reduction factor to correct the basic jump threshold, thus obtaining the final dynamic jump threshold. Otherwise, the basic transition threshold is directly used as the final dynamic transition threshold; This process is repeated at the start of each intraday rolling optimization cycle to update the dynamic jump threshold.

5. The method for optimizing independent energy storage multi-timescale trading based on the electricity spot market according to claim 4, characterized in that, The virtual buffer period setting process includes: Calculate the minimum transition time required to complete the power command transition based on the maximum permissible power change rate of the independent energy storage system; Obtain the real-time state of charge and maximum charge / discharge power limit of the independent energy storage system before the time boundary; Based on the sign direction of the power command difference and the real-time state of charge, it is determined whether the system has limited power output capability before and after the power transition; Based on the judgment results, the time lengths of the forward buffer sub-period and the backward buffer sub-period are dynamically allocated on the basis of the minimum theoretical transition time to form the virtual buffer period. If there is a situation where the power output capability is limited, a longer buffer sub-period is allocated to the limited side.

6. The method for optimizing multi-timescale trading of independent energy storage based on the electricity spot market according to claim 5, characterized in that, The process of constructing the power smooth transition curve includes: The ratio of the absolute value of the power command difference to the total length of the virtual buffer period is defined as the required average power change rate. Compare the required average power change rate with the maximum allowable power change rate, and select the function type of the transition curve based on the comparison result: If the required average power change rate is less than or equal to the first proportional threshold of the maximum allowable power change rate, then a linear ramp function is selected as the constructor. If the value exceeds the first proportional threshold of the maximum allowable power change rate, a nonlinear function is selected as the constructor, and the first or second derivative of the function is constrained to zero at the start and end points of the virtual buffer period based on the magnitude of the required average power change rate exceeding the first proportional threshold. The initial instruction value, the final instruction value, and the start and end times of the virtual buffer period are used as boundary conditions and substituted into the selected constructor to solve for the initial power smooth transition curve.

7. The method for optimizing independent energy storage multi-timescale trading based on the electricity spot market according to claim 6, characterized in that, The process of constructing the power smoothing transition curve also includes a feasibility verification of the preliminary power smoothing transition curve: Obtain the real-time maximum charge and discharge power limit of the independent energy storage system during the virtual buffer period; Verify that all power values ​​on the preliminary power smoothing transition curve do not exceed the real-time maximum charge / discharge power limit. If the verification fails, the total length of the virtual buffer period is dynamically adjusted according to the preset rules, and the curve is reconstructed until the verification passes, generating the final power smooth transition curve.

8. The method for optimizing multi-timescale trading of independent energy storage based on the electricity spot market according to claim 1, characterized in that, The energy deviation caused by power smoothing during the virtual buffer period is used as a cost correction item, and the implementation process is as follows: During the virtual buffer period, the energy deviation is calculated by time-granular integration based on the power difference between the constructed power smooth transition curve and the original second power scheduling command sequence. The energy deviation is converted into an equivalent economic cost value by weighted calculation based on the current spot electricity market price signal, and this value is used as a cost correction item.

9. The method for optimizing independent energy storage multi-timescale trading based on the electricity spot market according to claim 1, characterized in that, The feedback is incorporated into the next round of intraday rolling optimization targets, and the implementation process is as follows: When solving its objective function in the next round of intraday rolling optimization, the cost adjustment term will be included as a penalty term in the objective function; The sign of the penalty term is set as follows: when the energy deviation leads to a decrease in expected revenue, the penalty term is negative; when the energy deviation leads to an increase in expected cost, the penalty term is positive. A new second power scheduling instruction sequence is generated by optimizing the objective function that includes the penalty term. The power instruction jump tendency of the new second power scheduling instruction sequence at the time boundary is suppressed by the penalty term.

10. The method for optimizing independent energy storage multi-timescale trading based on the electricity spot market according to claim 1, characterized in that, The method also includes a learning optimization step: Record the setting parameters, power smoothing transition curves, and corresponding cost correction items for each virtual buffer period; Based on historical data, establish the correlation between the preset jump threshold, the power command difference, and the size of the cost correction item; At the start of each intraday rolling optimization, the initial value of the preset jump threshold for the current period is pre-adjusted using the aforementioned correlation to reduce the probability of triggering the virtual buffer and generating cost correction items in the future.

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