Virtual power plant load resource allocation method and system based on comprehensive energy storage

Abstract

The invention provides a virtual power plant load resource allocation method and system based on comprehensive energy storage, and relates to the technical field of power systems, comprising the steps of obtaining operation state data, a demand response instruction and power demand data, analyzing operation characteristics of multiple types of energy storage equipment, constructing a demand response optimization target, and quantifying the response capability of an energy storage cluster. A demand response task is decomposed into a continuous adjustment task and a transient balance task which are respectively allocated to energy-type and power-type energy storage devices, a dynamic coupling relationship is established, rolling prediction is performed, and a scheduling priority and a power allocation proportion are dynamically adjusted. The virtual power plant response capability can be improved, the resource configuration efficiency can be optimized, and the system stability can be enhanced.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method and system for allocating load resources in a virtual power plant based on integrated energy storage. Background Technology

[0002] With the large-scale integration of renewable energy and the increasing demand for power system flexibility, virtual power plants (VPS) are gradually gaining attention as a new power system operation mode. VPS aggregate distributed energy resources, controllable loads, and energy storage devices to form dispatchable power resources, participating in electricity market transactions and system service provision. Among these, various types of energy storage devices, as important adjustable resources in VPS, can effectively cope with grid fluctuations and improve system stability. Existing VPS dispatch and control technologies mainly adopt centralized control strategies, treating different types of energy storage devices as independent resources for dispatch, lacking in-depth utilization of the synergistic and complementary characteristics of multiple types of energy storage devices.

[0003] Existing technologies fail to fully consider the differences in characteristics among different types of energy storage devices. For example, energy-type energy storage devices are suitable for long-term power support, while power-type energy storage devices are suitable for instantaneous power regulation, leading to unreasonable allocation of energy storage resources and low system response efficiency. Traditional scheduling methods lack a dynamic evaluation mechanism for the real-time operating status of energy storage devices, making it impossible to accurately quantify the overall response capability of energy storage clusters under different states of charge, and hindering precise resource allocation for complex demand response scenarios. Existing demand response task allocation methods often use statically preset power allocation ratios, failing to establish a dynamic coupling mechanism between different types of energy storage devices. This makes it difficult to adapt to load fluctuations and changes in response demand, limiting the overall response capability of virtual power plants and the utilization efficiency of energy storage resources. Summary of the Invention

[0004] This invention provides a method and system for allocating virtual power plant load resources based on integrated energy storage, which can solve the problems in the prior art.

[0005] A first aspect of this invention provides a method for allocating virtual power plant load resources based on integrated energy storage, comprising:

[0006] Acquire virtual power plant operation status data, demand response commands, and electricity demand data;

[0007] Based on the operational status data, the charging and discharging characteristics and energy conversion efficiency of various types of energy storage devices are analyzed to obtain the comprehensive operational characteristics of the energy storage devices. Demand response optimization targets are then constructed based on the demand response instructions and the electricity demand data.

[0008] Based on the comprehensive operating characteristics of the energy storage equipment, the dispatchable power range and continuous response time of various types of energy storage equipment under the current state of charge are calculated to obtain the quantitative results of the overall response capability of the energy storage cluster.

[0009] Based on the quantitative results of the overall response capability of the energy storage cluster and the demand response optimization objective, the demand response task is decomposed into a continuous adjustment task and a transient balancing task according to the response duration requirement. The continuous adjustment task is assigned to the energy-type energy storage device to form a power reference trajectory, and the transient balancing task is assigned to the power-type energy storage device to form a power deviation correction amount. A dynamic coupling relationship is established between the power reference trajectory and the power deviation correction amount.

[0010] Based on the dynamic coupling relationship, the charging and discharging status of energy storage devices is predicted in a rolling manner within a preset time window. The scheduling priority and power allocation ratio of multiple types of energy storage devices are dynamically adjusted according to the results of the rolling prediction to obtain the load resource allocation scheme of the virtual power plant.

[0011] Based on the aforementioned operational status data, the charging and discharging characteristics and energy conversion efficiency of various types of energy storage devices are analyzed, yielding the following comprehensive operational characteristics of the energy storage devices:

[0012] Historical charge / discharge curve data and real-time state of charge data of various types of energy storage devices are extracted from the operational status data.

[0013] Based on the historical charge and discharge curve data, the power change rate and energy loss characteristics during the charge and discharge process are identified to obtain charge and discharge dynamic characteristic parameters; based on the real-time state of charge data and the charge and discharge dynamic characteristic parameters, the energy conversion efficiency curves of various types of energy storage devices in different charge ranges are calculated.

[0014] The energy conversion efficiency curve is segmented and mapped according to the charge interval to establish a nonlinear correlation between the state of charge and the energy conversion efficiency.

[0015] Based on the power change rate in the charging and discharging dynamic characteristic parameters, the response delay characteristics and regulation accuracy characteristics of various types of energy storage devices to power regulation commands are identified.

[0016] By tracking the power output fluctuation amplitude and stable recovery time before and after the state transition of various types of energy storage devices, switching adaptability features reflecting the state transition adaptability of energy storage devices are extracted.

[0017] Based on the nonlinear correlation, the response delay characteristics, the adjustment accuracy characteristics, and the switching adaptability characteristics, the comprehensive operating characteristics of the energy storage device are constructed.

[0018] The demand response optimization objectives are constructed based on the demand response instructions and the electricity demand data, including:

[0019] The power regulation demand corresponding to each response period is parsed from the demand response command;

[0020] The load forecast curve and load fluctuation characteristic data are extracted from the electricity demand data, and the load gap distribution characteristics and load change trend characteristics in each response period are calculated.

[0021] Based on the nonlinear correlation in the comprehensive operation characteristics of the energy storage equipment, the power regulation demand corresponding to each response period is decomposed into segmented power regulation sub-demands that match different charge ranges, and a power regulation transition range is set between adjacent response periods based on the stable recovery time in the switching adaptability characteristics.

[0022] Based on the response delay characteristics and regulation accuracy characteristics in the comprehensive operation characteristics of the energy storage equipment, a feasible scheduling set of energy storage equipment that meets the requirements of the segmented power regulation sub-system is determined, and the feasible scheduling set is sorted by response time sequence based on the load gap distribution characteristics and the load change trend characteristics.

[0023] Based on the segmented power regulation sub-demands, the power regulation transition intervals, the feasible scheduling set, and the response timing ranking results, a demand response optimization objective is constructed with the dual goals of minimizing response costs and maximizing load tracking accuracy.

[0024] Based on the comprehensive operating characteristics of the energy storage devices, the dispatchable power range and continuous response time of various types of energy storage devices under the current state of charge are calculated, resulting in the quantitative results of the overall response capability of the energy storage cluster, including:

[0025] Query the energy conversion efficiency values ​​of various types of energy storage devices within the corresponding charge range of the current state of charge data;

[0026] Based on the energy conversion efficiency value and the remaining capacity data of multiple types of energy storage devices, the maximum discharge power and maximum recharge power of multiple types of energy storage devices under the current state of charge are calculated to obtain the dispatchable power range of the single device.

[0027] Based on the response delay characteristics and the adjustment accuracy characteristics, the single device schedulable power range is corrected by response time constraints and control accuracy constraints to obtain the corrected single device schedulable power range.

[0028] Based on the switching adaptability characteristics in the comprehensive operation characteristics of the energy storage device, the power output attenuation ratio of various types of energy storage devices during the charging and discharging state transition is identified, and the power output attenuation ratio is used as the dynamic reduction coefficient of the corrected single device schedulable power range to calculate the effective adjustable power range.

[0029] Based on the energy conversion efficiency value, the remaining capacity data, and the effective adjustable power range, calculate the continuous response time of various types of energy storage devices under the condition of maintaining the current power output.

[0030] The effective adjustable power range and the continuous response duration are aggregated at the cluster level to obtain the quantitative result of the overall response capability of the energy storage cluster.

[0031] Based on the quantitative results of the overall response capability of the energy storage cluster and the demand response optimization objective, the demand response task is decomposed into continuous adjustment tasks and transient balancing tasks according to the response duration requirements, including:

[0032] Extract the power regulation duration corresponding to the segmented power regulation sub-demands from the demand response optimization objectives; extract the continuous response duration and effective adjustable power range of multiple types of energy storage devices from the overall response capability quantification results of the energy storage cluster;

[0033] The power regulation duration and the continuous response duration are numerically matched to construct a duration adaptation coefficient matrix that reflects the time sequence fit between task duration requirements and equipment response capabilities. Based on the duration adaptation coefficient matrix, long-term power regulation requirements that require multiple energy storage devices to respond in succession and short-term power regulation requirements that can be completed independently by a single energy storage device are identified.

[0034] The power adjustment transition interval is extracted from the demand response optimization objective. By analyzing the time span of the power adjustment transition interval and the power output stability characteristics in the effective adjustable power range, a spatiotemporal coupling constraint on the equipment power stability interval is established.

[0035] Based on the spatiotemporal coupling constraint, the long-term power regulation requirement is decomposed into the continuous regulation task that requires steady-state power output, and the short-term power regulation requirement is decomposed into the transient balancing task that requires instantaneous power response.

[0036] Assigning the continuous adjustment task to the energy storage device to form a power reference trajectory, assigning the transient balancing task to the power storage device to form a power deviation correction amount, and establishing a dynamic coupling relationship between the power reference trajectory and the power deviation correction amount includes:

[0037] Based on the duration and intensity of power regulation, a sequence of target power output values ​​is planned within the effective adjustable power range of the energy storage device; the sequence of target power output values ​​is then processed continuously in the time dimension to form the power reference trajectory describing the steady-state power output trajectory of the energy storage device.

[0038] Extract the power jump amplitude corresponding to the power adjustment transition interval from the transient balance task, and calculate the power gap value between the power reference trajectory and the power jump amplitude as the power deviation correction amount that the power storage device needs to bear by analyzing the power output slope of the power reference trajectory in the power adjustment transition interval.

[0039] Based on the power output fluctuation range and power output slope of the power reference trajectory, the dynamic adjustment boundary of the power deviation correction amount is determined. By establishing a real-time tracking mechanism for the degree of deviation of the power deviation correction amount from the power reference trajectory, a dynamic coupling relationship reflecting the collaborative output relationship between energy storage devices and power storage devices is constructed. The dynamic coupling relationship constrains the change direction of the power deviation correction amount to be consistent with the deviation direction of the power reference trajectory.

[0040] Based on the dynamic coupling relationship, the charging and discharging status of energy storage devices is predicted in a rolling manner within a preset time window. The scheduling priority and power allocation ratio of various types of energy storage devices are dynamically adjusted according to the results of the rolling prediction, resulting in a load resource allocation scheme for the virtual power plant, including:

[0041] Calculate the power output change trend of the power reference trajectory and the power deviation correction amount within a preset time window;

[0042] Based on the power output change trend of the power reference trajectory and combined with the remaining capacity data of the energy storage device, the predicted value of the state of charge of the energy storage device within the preset time window is calculated, and it is determined whether the energy storage device needs to undergo a charge / discharge state transition. Based on the power output change trend of the power deviation correction amount and combined with the remaining capacity data of the power storage device, the predicted value of the state of charge of the power storage device within the preset time window is calculated, and it is determined whether the power storage device needs to undergo a charge / discharge state transition, thus obtaining the rolling prediction result.

[0043] Identify energy storage devices that need to undergo charge / discharge state transitions from the results of the rolling prediction, and extract the switching adaptability features corresponding to the energy storage devices that need to undergo charge / discharge state transitions from the comprehensive operating characteristics of the energy storage devices.

[0044] Based on the power output attenuation ratio and state switching delay in the switching adaptability characteristics, the power output loss of the energy storage device that needs to undergo charging and discharging state transition is calculated. The power output loss is then inversely proportional to the scheduling priority weight and directly proportional to the power allocation ratio to obtain the load resource allocation scheme of the virtual power plant.

[0045] A second aspect of the present invention provides a virtual power plant load resource allocation system based on integrated energy storage, comprising:

[0046] The first unit is used to acquire the virtual power plant's operating status data, demand response instructions, and electricity demand data;

[0047] The second unit is used to analyze the charging and discharging characteristics and energy conversion efficiency of multiple types of energy storage devices based on the operating status data, obtain the comprehensive operating characteristics of the energy storage devices, and construct demand response optimization targets based on the demand response instructions and the electricity demand data.

[0048] The third unit is used to calculate the dispatchable power range and continuous response time of various types of energy storage devices under the current state of charge based on the comprehensive operating characteristics of the energy storage devices, so as to obtain the quantitative result of the overall response capability of the energy storage cluster.

[0049] The fourth unit is used to decompose the demand response task into a continuous adjustment task and a transient balancing task according to the response duration requirement, based on the quantitative result of the overall response capability of the energy storage cluster and the demand response optimization target. The continuous adjustment task is assigned to the energy storage device to form a power reference trajectory, and the transient balancing task is assigned to the power storage device to form a power deviation correction amount. A dynamic coupling relationship is established between the power reference trajectory and the power deviation correction amount.

[0050] The fifth unit is used to perform rolling prediction of the charging and discharging status of energy storage devices within a preset time window based on the dynamic coupling relationship, and dynamically adjust the scheduling priority and power allocation ratio of multiple types of energy storage devices according to the rolling prediction results to obtain the load resource allocation scheme of the virtual power plant.

[0051] A third aspect of the present invention,

[0052] An electronic device is provided, comprising:

[0053] processor;

[0054] Memory used to store processor-executable instructions;

[0055] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0056] Fourth aspect of the embodiments of the present invention,

[0057] A computer-readable storage medium is provided, having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0058] The beneficial effects of this application are as follows:

[0059] By analyzing the charging and discharging characteristics and energy conversion efficiency of various types of energy storage devices, the comprehensive operating characteristics of these devices are obtained. This allows for a more comprehensive evaluation of the performance characteristics of different energy storage devices, providing a scientific basis for subsequent resource allocation. Based on these comprehensive operating characteristics, the dispatchable power range and continuous response duration of the energy storage cluster are quantitatively calculated, achieving precise quantification of the overall response capability of energy storage resources and avoiding the inaccurate assessment of energy storage capacity in traditional methods. Innovatively, the demand response task is decomposed into continuous adjustment tasks and transient balancing tasks according to response duration requirements, and these are allocated to energy-type and power-type energy storage devices respectively. This fully leverages the technical advantages of different types of energy storage devices and improves the overall system response efficiency. Attached Figure Description

[0060] Figure 1 This is a flowchart illustrating the virtual power plant load resource allocation method based on integrated energy storage, as described in an embodiment of the present invention.

[0061] Figure 2 A schematic diagram illustrating the process of constructing comprehensive operational characteristics for energy storage devices. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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.

[0063] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0064] Figure 1 This is a flowchart illustrating the load resource allocation method for a virtual power plant based on integrated energy storage, as described in an embodiment of the present invention. Figure 1 As shown, the method includes:

[0065] Acquire virtual power plant operation status data, demand response commands, and electricity demand data;

[0066] Based on the operational status data, the charging and discharging characteristics and energy conversion efficiency of various types of energy storage devices are analyzed to obtain the comprehensive operational characteristics of the energy storage devices. Demand response optimization targets are then constructed based on the demand response instructions and the electricity demand data.

[0067] Based on the comprehensive operating characteristics of the energy storage equipment, the dispatchable power range and continuous response time of various types of energy storage equipment under the current state of charge are calculated to obtain the quantitative results of the overall response capability of the energy storage cluster.

[0068] Based on the quantitative results of the overall response capability of the energy storage cluster and the demand response optimization objective, the demand response task is decomposed into a continuous adjustment task and a transient balancing task according to the response duration requirement. The continuous adjustment task is assigned to the energy-type energy storage device to form a power reference trajectory, and the transient balancing task is assigned to the power-type energy storage device to form a power deviation correction amount. A dynamic coupling relationship is established between the power reference trajectory and the power deviation correction amount.

[0069] Based on the dynamic coupling relationship, the charging and discharging status of energy storage devices is predicted in a rolling manner within a preset time window. The scheduling priority and power allocation ratio of multiple types of energy storage devices are dynamically adjusted according to the results of the rolling prediction to obtain the load resource allocation scheme of the virtual power plant.

[0070] In one optional implementation, the charging and discharging characteristics and energy conversion efficiency of various types of energy storage devices are analyzed based on the operational status data to obtain the comprehensive operational characteristics of the energy storage devices, including:

[0071] Historical charge / discharge curve data and real-time state of charge data of various types of energy storage devices are extracted from the operational status data.

[0072] Based on the historical charge and discharge curve data, the power change rate and energy loss characteristics during the charge and discharge process are identified to obtain charge and discharge dynamic characteristic parameters; based on the real-time state of charge data and the charge and discharge dynamic characteristic parameters, the energy conversion efficiency curves of various types of energy storage devices in different charge ranges are calculated.

[0073] The energy conversion efficiency curve is segmented and mapped according to the charge interval to establish a nonlinear correlation between the state of charge and the energy conversion efficiency.

[0074] Based on the power change rate in the charging and discharging dynamic characteristic parameters, the response delay characteristics and regulation accuracy characteristics of various types of energy storage devices to power regulation commands are identified.

[0075] By tracking the power output fluctuation amplitude and stable recovery time before and after the state transition of various types of energy storage devices, switching adaptability features reflecting the state transition adaptability of energy storage devices are extracted.

[0076] Based on the nonlinear correlation, the response delay characteristics, the adjustment accuracy characteristics, and the switching adaptability characteristics, the comprehensive operating characteristics of the energy storage device are constructed.

[0077] like Figure 2 As shown, the method includes:

[0078] Data acquisition terminals are used to obtain time-series data from the energy storage management system, including parameters such as power, current, voltage, state of charge (SOC), and device temperature. For electrochemical energy storage devices such as lithium batteries, lead-acid batteries, and sodium-sulfur batteries, historical charge-discharge curve data are typically recorded as power change trajectories over the past 24 hours to 7 days, with one sampling point per minute. For short-term energy storage devices such as flywheels and supercapacitors, high-frequency sampling at the second level is required to record their rapid response characteristics. Real-time SOC data is acquired in real time through the battery management system (BMS) or energy management system (EMS) integrated into the energy storage device. Missing data points are filled using linear interpolation or cubic spline interpolation methods to ensure data continuity and completeness.

[0079] The power change rate sequence is obtained by calculating the difference in power values ​​between adjacent time points and dividing by the time interval. For electrochemical energy storage devices, the power change rate is typically in the range of 0.1C to 2C; for flywheels or supercapacitors, the power change rate can reach over 10C. Energy loss characteristics are calculated by analyzing the energy balance throughout the charge-discharge cycle, i.e., the ratio of the difference between the total input energy and the total output energy to the input energy. By repeating the above calculations under different SOC ranges and power levels, a dynamic characteristic parameter matrix for charge-discharge is obtained. This matrix includes key parameters such as the upper limit of the power change rate, the power response time constant, and the energy loss coefficient under different operating conditions.

[0080] The energy conversion efficiency curves of various types of energy storage devices in different charge ranges can be calculated using an energy efficiency model. This model typically divides the State of Charge (SOC) range into several intervals from 10% to 20%, such as [0-20%), [20%-40%), [40%-60%), [60%-80%), and [80%-100%). For each interval under different charge and discharge powers, the energy conversion efficiency η can be expressed as a function of charge and discharge power P and SOC: η(P, SOC) = α0 + α1·P + α2·SOC + α3·P 2 + α4·SOC 2+ α5·P·SOC, where α0, α1, α2, α3, α4, and α5 are model fitting coefficients, obtained by least squares fitting of historical charge and discharge data. P is the normalized charge and discharge power, defined as the ratio of actual power to rated power, with a value range of [-1, 1], where negative values ​​indicate charging and positive values ​​indicate discharging; SOC is the state of charge percentage, with a value range of [0, 1]. For lithium battery energy storage devices, the energy conversion efficiency usually reaches its peak at a medium SOC range [0.4, 0.8] and a medium power level [0.3, 0.7], and the function surface exhibits a saddle-shaped distribution characteristic with a central convexity; while when the SOC is close to the boundary value or the power is close to the limit value, the efficiency function shows a significant nonlinear decreasing trend. For lithium battery energy storage devices, they typically exhibit high and stable energy conversion efficiency in the medium SOC range [40%-80%], while efficiency decreases significantly in the low SOC <20% or high SOC >90% range; for mechanical energy storage such as compressed air energy storage, the efficiency curve depends more on the power level than on the SOC state.

[0081] The energy conversion efficiency curve is segmented according to the charge interval. For electrochemical energy storage devices, a quadratic polynomial function is usually used to fit the efficiency curve for each SOC interval, in the form of η(SOC) = a·SOC. 2 + b·SOC+ c, where a, b, and c are fitting coefficients. For boundary regions with drastic efficiency changes, a piecewise linear mapping can be used for a more refined description. This piecewise mapping method accurately captures the efficiency variation characteristics of energy storage devices across the entire SOC range, establishing a precise nonlinear correlation model and providing a theoretical basis for subsequent scheduling decisions.

[0082] This analysis examines the time delay between receiving a regulation command and the actual power output reaching the target value in an energy storage device. The response delay characteristic is typically characterized by the response time constant τ, which is the time required for the power output change to reach 63.2% of the target value. The response time constant of electrochemical energy storage devices is typically on the order of seconds, while flywheels and supercapacitors can reach the order of milliseconds. Regulation accuracy is quantified by the average deviation rate between the actual power output and the target power value, calculated as the ratio of the absolute value of the difference to the target power value. High-precision energy storage devices typically have a regulation accuracy better than ±2%, while some devices, limited by battery chemistry, fluctuate within the range of ±5% to ±10%.

[0083] By tracking the power output fluctuation amplitude and stabilization recovery time before and after the state transition of various types of energy storage devices, switching adaptability characteristics reflecting the adaptability of energy storage devices during state transitions can be extracted. Power output fluctuation amplitude is defined as the ratio of the maximum power deviation after the state transition is triggered to the stable power value, obtained by monitoring the transition process from charging to discharging or discharging to charging. Stabilization recovery time refers to the time required from the issuance of the state transition command to the power output stabilizing again within ±3% of the target value. Switching adaptability characteristics can be characterized by constructing a feature vector containing dimensions such as transition type, initial SOC, target power level, fluctuation amplitude, and recovery time, thereby evaluating the state switching capability of energy storage devices under different operating conditions.

[0084] Employing a multidimensional feature tensor or parametric model, this study constructs comprehensive operational characteristics of energy storage devices based on nonlinear correlations, response delay characteristics, regulation accuracy characteristics, and switching adaptability. These comprehensive characteristics encompass at least four dimensions: energy efficiency E(SOC, P), power response R(τ, P), regulation accuracy A(SOC, P), and state switching adaptability S(SOC, TYPE). Here, E(SOC, P) represents the energy conversion efficiency under different SOC and power levels; R(τ, P) describes the response time characteristics under different power levels; A(SOC, P) represents the regulation accuracy under different SOC and power levels; and S(SOC, TYPE) characterizes the state switching adaptability under different SOCs. This multidimensional feature representation comprehensively reflects the dynamic characteristics of energy storage devices under various operating conditions, providing a scientific basis for subsequent resource allocation and scheduling optimization based on energy storage characteristics.

[0085] In one optional implementation, constructing a demand response optimization objective based on the demand response command and the electricity demand data includes:

[0086] The power regulation demand corresponding to each response period is parsed from the demand response command;

[0087] The load forecast curve and load fluctuation characteristic data are extracted from the electricity demand data, and the load gap distribution characteristics and load change trend characteristics in each response period are calculated.

[0088] Based on the nonlinear correlation in the comprehensive operation characteristics of the energy storage equipment, the power regulation demand corresponding to each response period is decomposed into segmented power regulation sub-demands that match different charge ranges, and a power regulation transition range is set between adjacent response periods based on the stable recovery time in the switching adaptability characteristics.

[0089] Based on the response delay characteristics and regulation accuracy characteristics in the comprehensive operation characteristics of the energy storage equipment, a feasible scheduling set of energy storage equipment that meets the requirements of the segmented power regulation sub-system is determined, and the feasible scheduling set is sorted by response time sequence based on the load gap distribution characteristics and the load change trend characteristics.

[0090] Based on the segmented power regulation sub-demands, the power regulation transition intervals, the feasible scheduling set, and the response timing ranking results, a demand response optimization objective is constructed with the dual goals of minimizing response costs and maximizing load tracking accuracy.

[0091] After receiving demand response commands from the power grid dispatching agency, the virtual power plant control system decodes them using a command parsing module, extracting key parameters such as response start and end times, target power values, and response duration. For each response period, the corresponding power regulation demand is determined, quantified in kilowatts or megawatts, representing the amount of power the grid needs to adjust for the virtual power plant within a specific time period. For example, during peak load periods, power generation needs to be increased or power consumption reduced, while during off-peak load periods, power consumption needs to be increased or power generation reduced.

[0092] The processing of electricity demand data involves the application of load forecasting models. Based on historical electricity consumption data, weather factors, seasonal characteristics, and other multi-dimensional information, load forecast curves for the next 24 hours or longer are generated. Data mining algorithms are used to extract load fluctuation characteristic data, including fluctuation frequency, amplitude, and duration, which reflect the dynamic characteristics of load changes. By comparing and analyzing the forecasted load with actual available resources, the load gap distribution characteristics within each response period are calculated, including gap size, occurrence time, and duration. Simultaneously, trend analysis algorithms are used to extract load change trend characteristics to determine whether the load is in an upward, stable, or downward phase, providing a basis for subsequent energy storage dispatch strategies.

[0093] The nonlinear relationships in the overall operational characteristics of energy storage devices are mainly reflected in the changing patterns of charging and discharging efficiency, response speed, and power output capability under different states of charge. For example, lithium batteries typically have high charging and discharging efficiency under moderate states of charge, while their efficiency decreases significantly when approaching full charge or depletion; flywheel energy storage, on the other hand, exhibits better power response characteristics under high-speed operation. Based on these nonlinear characteristics, the power regulation demand corresponding to each response period is decomposed into segmented power regulation sub-demands matched with different charge ranges. In practical implementation, the state of charge can be divided into multiple ranges, such as low charge range, low-to-medium charge range, medium-to-high charge range, and high charge range, and the most suitable energy storage type and scheduling strategy can be determined for each range.

[0094] To ensure a smooth transition between adjacent response periods, a power regulation transition range is set based on the switching adaptability characteristics of the energy storage device. These characteristics primarily include parameters such as mode transition time, stable recovery time, and switching energy loss. The transition range takes into account the differences in characteristics between various energy storage technologies. For example, mode switching for supercapacitors can be completed in milliseconds, while some chemical batteries require stable recovery times in the seconds or even minutes. By reserving an appropriate transition time between response periods, the impact of frequent switching on the lifespan of the energy storage device can be reduced, while ensuring the continuity and stability of the system response.

[0095] The response delay characteristic of energy storage devices describes the time delay between receiving a dispatch command and the actual change in power output. Different energy storage technologies exhibit significant differences in response delay, ranging from milliseconds to minutes. Regulation accuracy characteristics reflect the degree of deviation between the actual output power of the energy storage device and the target power. Based on the time sensitivity and accuracy requirements of segmented power regulation sub-demands, a combination of energy storage devices that meets the conditions is selected to form a feasible dispatch set. For regulation sub-demands with high time sensitivity, energy storage devices with low response delays are preferred; for regulation sub-demands with high accuracy requirements, energy storage devices with high regulation accuracy are preferred.

[0096] Once the feasible scheduling set is determined, its response timing is ranked based on the characteristics of load gap distribution and load change trends. The ranking rules comprehensively consider factors such as load change rate, gap severity, and trend persistence. Periods with high load change rates, severe gaps, and a continuously expanding trend are assigned higher response priorities; periods with slow load changes, smaller gaps, and stable trends are assigned lower response priorities. This dynamic ranking mechanism prioritizes handling load imbalances that have a significant impact on grid stability.

[0097] By integrating segmented power regulation sub-demands, power regulation transition intervals, feasible scheduling sets, and response timing ranking results, a dual optimization objective is constructed. On the one hand, the optimization objective aims to minimize response costs, including charging and discharging losses, lifetime losses, and operation and maintenance costs of energy storage devices. On the other hand, it aims to maximize load tracking accuracy, ensuring that the actual output power of the virtual power plant is as close as possible to the target power required by the demand response command. This dual-objective design allows the virtual power plant to achieve a balance between economic efficiency and technical feasibility while ensuring response effectiveness. Weighting coefficients are introduced into the optimization objective function to dynamically adjust the relative importance of the two objectives—minimizing costs and maximizing accuracy—based on the current grid state and market conditions, thereby flexibly adapting to demand response requirements in different scenarios.

[0098] In one optional implementation, the dispatchable power range and continuous response duration of multiple types of energy storage devices under the current state of charge are calculated based on the comprehensive operating characteristics of the energy storage devices, resulting in a quantitative result of the overall response capability of the energy storage cluster, including:

[0099] Query the energy conversion efficiency values ​​of various types of energy storage devices within the corresponding charge range of the current state of charge data;

[0100] Based on the energy conversion efficiency value and the remaining capacity data of multiple types of energy storage devices, the maximum discharge power and maximum recharge power of multiple types of energy storage devices under the current state of charge are calculated to obtain the dispatchable power range of the single device.

[0101] Based on the response delay characteristics and the adjustment accuracy characteristics, the single device schedulable power range is corrected by response time constraints and control accuracy constraints to obtain the corrected single device schedulable power range.

[0102] Based on the switching adaptability characteristics in the comprehensive operation characteristics of the energy storage device, the power output attenuation ratio of various types of energy storage devices during the charging and discharging state transition is identified, and the power output attenuation ratio is used as the dynamic reduction coefficient of the corrected single device schedulable power range to calculate the effective adjustable power range.

[0103] Based on the energy conversion efficiency value, the remaining capacity data, and the effective adjustable power range, calculate the continuous response time of various types of energy storage devices under the condition of maintaining the current power output.

[0104] The effective adjustable power range and the continuous response duration are aggregated at the cluster level to obtain the quantitative result of the overall response capability of the energy storage cluster.

[0105] The virtual power plant management system maintains a database of characteristics for various types of energy storage devices, including energy conversion efficiency curves corresponding to different states of charge (SOC) ranges. For lithium-ion batteries, the SOC is typically divided into multiple ranges, each corresponding to a different energy conversion efficiency value. For example, lithium-ion batteries can achieve an energy conversion efficiency of around 95% within the 40%-60% SOC range; however, when the SOC is below 20% or above 80%, the energy conversion efficiency drops to 90% and 85%, respectively. For vanadium redox flow batteries, a stable energy conversion efficiency of 80% is maintained within the 20%-80% SOC range. The management system obtains the current SOC of the energy storage devices through real-time monitoring and queries the corresponding energy conversion efficiency value accordingly.

[0106] After obtaining the energy conversion efficiency value, based on this value and the remaining capacity data of various types of energy storage devices, the maximum discharge power and maximum recharge power of each device under the current state of charge are calculated, forming the dispatchable power range of a single device. The calculation process considers factors such as the rated power, remaining capacity, and current operating status of the energy storage device. Specifically, for the discharge process, the maximum discharge power is limited by the device's rated power and current remaining capacity; for the charging process, the maximum recharge power is limited by the device's rated power and current available capacity. For example, for a lithium-ion battery with a total capacity of 100kWh and a rated power of 20kW, when its SOC is 30%, the remaining capacity is 30kWh, and the theoretical maximum discharge power is 20kW. However, considering the battery protection mechanism under low SOC conditions, the maximum discharge power is limited to 15kW.

[0107] For the calculated dispatchable power range of a single device, constraints are applied and corrected based on the response delay and regulation accuracy characteristics of the energy storage device. The response delay reflects the time lag between receiving a command and the actual change in output power; different types of energy storage devices exhibit different response delays. For example, supercapacitors and flywheel energy storage have response times in the millisecond range, while compressed air energy storage has response times in the minute range. Regulation accuracy reflects the deviation range between the actual output power and the set power of the energy storage device. Corrections are made to the dispatchable power range of a single device based on these two characteristics to ensure that the energy storage device can regulate power according to the expected time and accuracy requirements during demand response.

[0108] The correction process can be achieved through a response time reduction factor and a precision margin factor. The response time reduction factor decreases as the response delay increases. For example, for supercapacitors with a response time of less than 1 second, the response time reduction factor can approach 1; while for some chemical batteries with a response time of more than 30 seconds, the response time reduction factor drops to 0.8. The adjustment precision margin factor reflects the proportion of power margin reserved to ensure adjustment precision. Generally, the higher the adjustment precision, the closer its margin factor is to 1.

[0109] Based on the switching adaptability characteristics of energy storage devices, this study identifies the power output attenuation ratios of various types of energy storage devices during charge / discharge state transitions. By extracting power output fluctuation data from historical state transition events, the study analyzes the stable power value before transition, the lowest power value after transition, and the power value after re-stabilization. The power output attenuation ratio is defined as the ratio of the maximum power drop during the transition to the stable power value before transition. Specifically, for a state transition from charging to discharging, the study records the stable charging power P1 before the transition command is issued, the lowest discharge power P2 during the transition, and the final stable discharge power P3. The power output attenuation ratio is then (P3-P2) / P3. For multiple transition events under different SOC ranges and target power levels, statistical analysis is used to obtain the average power output attenuation ratio of the energy storage device under specific operating conditions. For example, a supercapacitor undergoing a charge / discharge transition at 50% SOC experiences a power output attenuation ratio of approximately 5%; while some lithium-ion batteries can experience attenuation ratios of 15% to 20% under the same conditions.

[0110] The power output attenuation ratio is used as a dynamic reduction factor for the corrected single-device dispatchable power range to calculate the effective adjustable power range. The calculation process is as follows: multiply the upper and lower limits of the corrected single-device dispatchable power range by (1 - power output attenuation ratio) to obtain the effective adjustable power range considering the impact of state transitions. Taking a lithium-ion battery as an example, after correction for response time and accuracy constraints, its dispatchable power range is charging power [-18kW, -2kW] and discharging power [2kW, 18kW]. If the power output attenuation ratio of the device in the current SOC state is 15%, then its effective adjustable power range is charging power [-15.3kW, -1.7kW] and discharging power [1.7kW, 15.3kW]. This effective adjustable power range truly reflects the actual dispatchable capability of the energy storage device after considering the dynamic characteristics of state transitions, providing a more accurate power boundary constraint for subsequent dispatch decisions.

[0111] Based on energy conversion efficiency values, remaining capacity data, and effective adjustable power range, the continuous response time of various types of energy storage devices under the condition of maintaining current power output can be calculated. For the discharge process, the continuous response time can be calculated by dividing the remaining capacity by the effective discharge power and taking into account the energy conversion efficiency; for the charging process, it is calculated by dividing the available capacity by the effective charging power and taking into account the energy conversion efficiency. For example, for an energy storage device with a remaining capacity of 50kWh, an effective discharge power of 10kW, and an energy conversion efficiency of 90%, its continuous discharge response time is approximately 4.5 hours (50kWh / (10kW / 0.9)).

[0112] Cluster-level aggregation calculations were performed on the effective adjustable power range and continuous response time of each energy storage device to obtain quantitative results of the overall response capability of the energy storage cluster. The aggregation calculation process considered the mutual influence and synergistic effects between the energy storage devices, such as energy complementarity during charging and discharging and tiered configuration of response time. Through cluster-level aggregation, quantitative indicators of overall response capability, including total adjustable power range, average response delay, comprehensive regulation accuracy, and cluster continuous response capability, can be obtained, providing a basis for decision-making in subsequent load resource allocation.

[0113] In one optional implementation, based on the quantification results of the overall response capability of the energy storage cluster and the demand response optimization objective, the demand response task is decomposed into a continuous adjustment task and a transient balancing task according to the response duration requirement, including:

[0114] Extract the power regulation duration corresponding to the segmented power regulation sub-demands from the demand response optimization objectives; extract the continuous response duration and effective adjustable power range of multiple types of energy storage devices from the overall response capability quantification results of the energy storage cluster;

[0115] The power regulation duration and the continuous response duration are numerically matched to construct a duration adaptation coefficient matrix that reflects the time sequence fit between task duration requirements and equipment response capabilities. Based on the duration adaptation coefficient matrix, long-term power regulation requirements that require multiple energy storage devices to respond in succession and short-term power regulation requirements that can be completed independently by a single energy storage device are identified.

[0116] The power adjustment transition interval is extracted from the demand response optimization objective. By analyzing the time span of the power adjustment transition interval and the power output stability characteristics in the effective adjustable power range, a spatiotemporal coupling constraint on the equipment power stability interval is established.

[0117] Based on the spatiotemporal coupling constraint, the long-term power regulation requirement is decomposed into the continuous regulation task that requires steady-state power output, and the short-term power regulation requirement is decomposed into the transient balancing task that requires instantaneous power response.

[0118] Based on the quantitative results of the overall response capability of the energy storage cluster and the optimization objectives of demand response, in the process of decomposing the demand response task into continuous adjustment tasks and transient balancing tasks according to the response duration requirements, it is necessary to finely match the power adjustment requirements with the response capability of the energy storage equipment.

[0119] The virtual power plant control system performs time-series analysis on received demand response commands. These commands typically contain power regulation target values ​​for multiple time periods, each with different power regulation depth and duration. The control system employs a sliding time window method, using five minutes as the basic time unit, to segment the power regulation commands within the entire demand response cycle, identifying time intervals where power values ​​remain relatively stable and their corresponding durations. When the difference in power regulation target values ​​between adjacent time windows is less than a preset threshold (e.g., 3% of rated power), these time windows are merged into a single power regulation sub-demand, with the corresponding power regulation duration being the length of the merged time interval.

[0120] The continuous response time and effective adjustable power range of various types of energy storage devices are extracted from the quantitative results of the overall response capability of the energy storage cluster. The control system calculates these parameters based on the type and characteristics of the energy storage devices and their current operating status. For energy-type energy storage devices such as lithium-ion batteries, the continuous response time T_energy can be expressed as the ratio of the current available energy capacity to the rated power, while also considering the battery's state of charge (SOC) limitation. For power-type energy storage devices such as supercapacitors, the continuous response time T_power is mainly limited by their rated power continuous output capability. The effective adjustable power range is the actual dispatchable power interval after considering power conversion efficiency and device power limitations, usually expressed as [P_min, P_max], where P_min is the minimum adjustable power and P_max is the maximum adjustable power.

[0121] The duration of power regulation and the duration of continuous response are numerically matched. For each energy storage device i and each power regulation sub-demand j, a duration matching coefficient C_ij is calculated. The duration matching coefficient reflects the degree of matching between the device's response capability and the task requirement in the time dimension; the closer the value is to 1, the higher the matching degree. The calculation method is the ratio of the device's continuous response duration to the power regulation duration. When the ratio is between 0.8 and 1.2, it indicates that the device's continuous response capability is highly matched with the task requirement; when the ratio is less than 0.8, it indicates that the device cannot independently complete the entire regulation task; when the ratio is greater than 1.2, it indicates that the device's continuous response capability exceeds the task requirement. By calculating the duration matching coefficients between all energy storage devices and all power regulation sub-demands, a duration matching coefficient matrix is ​​formed.

[0122] Based on the duration adaptation coefficient matrix, the system identifies long-term power regulation needs that require multiple energy storage devices to respond sequentially and short-term power regulation needs that can be completed independently by a single energy storage device. The control system performs cluster analysis on the matrix. When the duration adaptation coefficient C_ij of a certain power regulation sub-demand j with all individual energy storage devices is less than 0.8, the sub-demand is identified as a long-term power regulation demand that requires multiple energy storage devices to work in coordination or sequentially. When there is at least one energy storage device i such that C_ij is greater than or equal to 0.8, the sub-demand is identified as a short-term power regulation demand that can be completed independently by a single device.

[0123] The power regulation transition interval refers to the time period during which power regulation sub-demand transitions from one sub-demand to the next, characterized by a rapid change in power value. The transition interval is identified using the power derivative method, which calculates the rate of change of power over time. When the rate of change of power exceeds a preset threshold (e.g., 5% of rated power per minute), this period is marked as the power regulation transition interval.

[0124] By analyzing the time span of the power regulation transition interval and the power output stability characteristics within the effective adjustable power range, a spatiotemporal coupling constraint for the device's power stability interval is established. Power output stability characteristics are an indicator of an energy storage device's ability to maintain stable operation under different power output levels. For lithium-ion batteries, high power output stability is typically observed within a range of 20% to 80% of the rated power; while supercapacitors maintain good power stability even near their rated power. The spatiotemporal coupling constraint describes the mathematical relationship between the energy storage device's power regulation capability at different points in time and its historical operating trajectory, expressed through a state transition matrix. This constraint considers factors such as response delay, power slope limitation, and energy loss during rapid power changes.

[0125] Based on spatiotemporal coupling constraints, long-term power regulation needs are decomposed into continuous regulation tasks requiring steady-state power output, and short-term power regulation needs are decomposed into transient balancing tasks requiring instantaneous power response. Continuous regulation tasks are primarily undertaken by energy-type energy storage devices, characterized by long power regulation duration and low power change rate, suitable for stable output. Transient balancing tasks are primarily undertaken by power-type energy storage devices, characterized by fast response speed and high power density, suitable for handling power fluctuations. The control system determines the power allocation scheme for continuous regulation and transient balancing tasks by solving a multi-objective optimization problem, maximizing overall energy storage resource utilization efficiency and minimizing system operating costs. During actual operation, the control system dynamically adjusts the boundaries of the two types of tasks based on real-time monitoring data to adapt to changes in grid demand and energy storage device status.

[0126] In one optional implementation, assigning the continuous adjustment task to the energy storage device to form a power reference trajectory, assigning the transient balancing task to the power storage device to form a power deviation correction amount, and establishing a dynamic coupling relationship between the power reference trajectory and the power deviation correction amount includes:

[0127] Based on the duration and intensity of power regulation, a sequence of target power output values ​​is planned within the effective adjustable power range of the energy storage device; the sequence of target power output values ​​is then processed continuously in the time dimension to form the power reference trajectory describing the steady-state power output trajectory of the energy storage device.

[0128] Extract the power jump amplitude corresponding to the power adjustment transition interval from the transient balance task, and calculate the power gap value between the power reference trajectory and the power jump amplitude as the power deviation correction amount that the power storage device needs to bear by analyzing the power output slope of the power reference trajectory in the power adjustment transition interval.

[0129] Based on the power output fluctuation range and power output slope of the power reference trajectory, the dynamic adjustment boundary of the power deviation correction amount is determined. By establishing a real-time tracking mechanism for the degree of deviation of the power deviation correction amount from the power reference trajectory, a dynamic coupling relationship reflecting the collaborative output relationship between energy storage devices and power storage devices is constructed. The dynamic coupling relationship constrains the change direction of the power deviation correction amount to be consistent with the deviation direction of the power reference trajectory.

[0130] In the process of load resource allocation, virtual power plants need to rationally assign continuous regulation tasks and transient balancing tasks to different types of energy storage devices to fully utilize their characteristics. Energy-type energy storage devices typically include electrochemical energy storage devices such as lithium-ion batteries, lead-acid batteries, and sodium-sulfur batteries. These devices have high energy density and are suitable for performing long-term continuous regulation tasks. Power-type energy storage devices include supercapacitors, flywheel energy storage, and superconducting magnetic energy storage, which have high power density and fast response capabilities, making them suitable for handling short-term power fluctuations and transient balancing tasks.

[0131] Based on the power regulation duration and intensity, a sequence of target power output values ​​is planned within the effective adjustable power range of the energy storage device. Considering the characteristic limitations of the energy storage device, let the upper limit of the effective adjustable power be P_max_e, the lower limit be P_min_e, and the state of charge be SOC_e. Then, the effective adjustable power range at the current time t is [P_min_e(SOC_e), P_max_e(SOC_e)]. The power regulation duration is denoted as T_duration, and the power regulation intensity is denoted as P_intensity. According to the specific requirements of the demand response task, within the time interval [t, t+T_duration], at a certain time step Δt (e.g., 15 minutes or 30 minutes), the target power output value P_target(t+i·Δt) at each time point is calculated, where i=0, 1, 2, ..., n, and n·Δt=T_duration. Each target value must satisfy P_min_e(SOC_e) ≤ P_target(t+i·Δt) ≤ P_max_e(SOC_e), while ensuring that the state of charge (SOC_e) of the energy storage device remains within a safe range throughout the entire adjustment process to avoid overcharging or over-discharging.

[0132] A piecewise linear interpolation method is used to transform the discrete power output target value sequence P_target(t+i·Δt) into a continuous function P_base(τ), where τ∈[t, t+T_duration]. This continuous transformation smooths the power change process, reduces the mechanical and thermal stresses on energy storage devices, and extends the device's lifespan. The power baseline trajectory P_base(τ) represents the ideal power output curve of the energy storage device under steady-state conditions, serving as a basic reference for load resource allocation in virtual power plants.

[0133] The power jump amplitude corresponding to the power regulation transition interval is extracted from the transient balancing task to identify significant change points in the power output curve. Let the power regulation transition interval be [t_start, t_end], within which the power changes rapidly from P_start to P_end, and the power jump amplitude ΔP_jump is defined as |P_end - P_start|. Since energy storage devices have limited power regulation rates and cannot immediately respond to large power changes, power storage devices are needed to provide auxiliary regulation during the transition phase.

[0134] By analyzing the power output slope of the power reference trajectory in the power regulation transition interval, the power gap value between the power reference trajectory and the power jump amplitude is calculated as the power deviation correction amount that the power-type energy storage device needs to bear. The average power output slope k_avg of the power reference trajectory in the transition interval [t_start, t_end] is defined as (P_base(t_end) - P_base(t_start)) / (t_end - t_start). Considering the maximum power change rate k_max_e of the energy-type energy storage device, if |k_avg| > k_max_e, then a power gap exists. The power gap value ΔP_gap(τ) at time point τ∈[t_start, t_end] is calculated as ΔP_gap(τ) = P_required(τ) - P_base(τ), where P_required(τ) is the actual power output required by the demand response task at time τ. This power gap value is the power deviation correction amount P_correction(τ), representing the auxiliary regulation amount that the power-type energy storage device needs to provide.

[0135] Based on the power output fluctuation range and power output slope of the power reference trajectory, the dynamic adjustment boundary of the power deviation correction is determined. Let the maximum output power of the power-type energy storage device be P_max_p and the minimum output power be P_min_p. The dynamic adjustment boundary of the power deviation correction [P_min_correction(τ), P_max_correction(τ)] satisfies the following conditions: P_min_correction(τ) = max{P_min_p, -k_max_p·(t_end - τ)}, P_max_correction(τ) = min{P_max_p, k_max_p·(t_end - τ)}, where k_max_p is the maximum power change rate of the power-type energy storage device.

[0136] By establishing a real-time tracking mechanism for the deviation of the power deviation correction from the power reference trajectory, a dynamic coupling relationship reflecting the collaborative output relationship between energy-type and power-type energy storage devices is constructed. Let the actual deviation of the power reference trajectory be ΔP_deviation(τ) = P_actual_base(τ) - P_base(τ), where P_actual_base(τ) is the actual output power of the energy-type energy storage device. The dynamic coupling relationship requires that the direction of change of the power deviation correction P_correction(τ) is consistent with the direction of deviation of the power reference trajectory, i.e., satisfying ΔP_deviation(τ)·P_correction(τ) ≥ 0. This dynamic coupling mechanism ensures that the energy-type and power-type energy storage devices can coordinate and cooperate to complete the demand response task. When the actual output of the energy-type energy storage device is lower than the power reference trajectory, the power-type energy storage device provides positive power supplementation; conversely, when the actual output of the energy-type energy storage device is higher than the power reference trajectory, the power-type energy storage device provides negative power absorption.

[0137] In one optional implementation, based on the dynamic coupling relationship, the charging and discharging status of the energy storage device is rolled out within a preset time window. The scheduling priority and power allocation ratio of various types of energy storage devices are dynamically adjusted according to the results of the rolling forecast, resulting in a load resource allocation scheme for the virtual power plant, including:

[0138] Calculate the power output change trend of the power reference trajectory and the power deviation correction amount within a preset time window;

[0139] Based on the power output change trend of the power reference trajectory and combined with the remaining capacity data of the energy storage device, the predicted value of the state of charge of the energy storage device within the preset time window is calculated, and it is determined whether the energy storage device needs to undergo a charge / discharge state transition. Based on the power output change trend of the power deviation correction amount and combined with the remaining capacity data of the power storage device, the predicted value of the state of charge of the power storage device within the preset time window is calculated, and it is determined whether the power storage device needs to undergo a charge / discharge state transition, thus obtaining the rolling prediction result.

[0140] Identify energy storage devices that need to undergo charge / discharge state transitions from the results of the rolling prediction, and extract the switching adaptability features corresponding to the energy storage devices that need to undergo charge / discharge state transitions from the comprehensive operating characteristics of the energy storage devices.

[0141] Based on the power output attenuation ratio and state switching delay in the switching adaptability characteristics, the power output loss of the energy storage device that needs to undergo charging and discharging state transition is calculated. The power output loss is then inversely proportional to the scheduling priority weight and directly proportional to the power allocation ratio to obtain the load resource allocation scheme of the virtual power plant.

[0142] A preset time window is set to a time range of 15 to 60 minutes from the future, with the specific duration determined based on the type of demand response task the virtual power plant participates in and the grid dispatch cycle. The preset time window is divided into multiple consecutive prediction periods, each prediction period serving as a rolling prediction step, typically set to 1 to 5 minutes. For example, a 30-minute preset time window can be divided into six 5-minute prediction periods or 30 1-minute prediction periods. The granularity of the prediction period division needs to be balanced between computational accuracy and real-time performance; overly fine division can improve prediction accuracy but increase computational burden, while overly coarse division may miss critical state transition moments.

[0143] Within each rolling forecast step, the state of charge (SOC) prediction of the energy storage device is performed based on the power output trend of the power baseline trajectory. The power baseline trajectory reflects the expected output power of the energy storage device during continuous regulation tasks, and this trend can be obtained through time-series analysis of historical power data or load forecasting models. Combining the current remaining capacity data of the energy storage device, including parameters such as current SOC, rated capacity, and charge / discharge efficiency, the predicted SOC value at the end of the forecast period is calculated using the energy accumulation method. The calculation process must consider charge / discharge efficiency losses and self-discharge effects. When the predicted SOC reaches or exceeds a preset upper threshold, it is determined that the energy storage device needs to switch from charging to discharging or stop charging; when the predicted SOC is below a preset lower threshold, it is determined that it needs to switch from discharging to charging or stop discharging. For lithium battery energy storage systems, the upper threshold is typically set to 85% to 95%, and the lower threshold is set to 10% to 20%.

[0144] The state of charge (SOC) prediction for power-type energy storage devices is performed based on the power output change trend of the power deviation correction. The power deviation correction reflects the rapid power response requirements of power-type energy storage devices during transient balancing tasks. Power-type energy storage devices, such as supercapacitors and flywheel energy storage, have high power density but relatively low energy density, and their SOC change rate is significantly faster than that of energy-type energy storage devices. Combining the remaining capacity data of the power-type energy storage devices, the predicted SOC value at the end of the prediction period is calculated using the same energy accumulation method. Due to the large instantaneous power fluctuations of power-type energy storage devices, their safe operating range of SOC is typically narrower than that of energy-type energy storage devices. For example, the effective SOC range of supercapacitors is set between 30% and 90% to ensure sufficient power response margin.

[0145] After calculating the state of charge (SOC) for the current prediction period, the predicted SOC at the end of the current prediction period is used as the initial SOC for the next prediction period, and the process continues with the next longer SOC prediction and state transition determination. This rolling update process continues until the entire preset time window is covered. Through this rolling prediction mechanism, the complete SOC trajectory of each energy storage device within the entire preset time window can be obtained, and the specific time when each energy storage device undergoes a state transition within the window can be identified. For example, for a 30-minute window with a 5-minute step rolling prediction, if the SOC of a certain lithium battery energy storage device is predicted to be 88% at the 10th minute, 93% at the 15th minute, and 96% at the 20th minute, exceeding the upper limit threshold of 95%, it is determined that the device needs to undergo a charge / discharge state transition around the 20th minute.

[0146] After identifying energy storage devices that need to undergo charge / discharge state transitions within a preset time window and their predicted transition times from the rolling predicted state of charge trajectory, corresponding switching adaptability features are extracted from the comprehensive operational characteristics of the energy storage devices. These switching adaptability features include two core indicators: power output attenuation ratio and state transition delay. The power output attenuation ratio describes the instantaneous decrease in power output capability of the energy storage device during state transition. Different types of energy storage devices have different attenuation characteristics; lithium batteries experience a power output attenuation of 15% to 30% during charge / discharge state transitions, supercapacitors typically experience less than 10%, while flywheel energy storage experiences an attenuation of only about 5%. The state transition delay represents the time required for the energy storage device to fully enter the new state after the state transition command is issued. The state transition delay for lithium batteries is 5 to 30 seconds, while that for flywheel energy storage is less than 1 second.

[0147] Based on the extracted switching adaptability features, the power output loss of energy storage devices undergoing charge / discharge state transitions at the predicted transition time is calculated. The power output loss is quantified by multiplying the planned output power at the predicted transition time by the power output attenuation ratio, and then by the state transition delay, reflecting the impact of the state transition process on the system's power balance. For example, if a lithium battery energy storage device has a planned output power of 100kW at the predicted transition time, a power output attenuation ratio of 20%, and a state transition delay of 10 seconds, then its power output loss is 100kW × 20% × 10 seconds equals 200kW·s. An inverse relationship is established between the power output loss and the scheduling priority weight; energy storage devices with larger power output losses are assigned lower scheduling priority weights, and devices with smaller losses are prioritized during resource scheduling to reduce the negative impact of state transitions on system stability. Simultaneously, a direct relationship is established between the power output loss and the power allocation ratio; for energy storage devices with larger power output losses, a larger power reserve ratio is allocated or their actual scheduling power is reduced before and after the state transition, ensuring that the system's power balance is maintained even during the state transition. By using this method based on rolling forecasting and dynamic adjustment, a virtual power plant load resource allocation scheme can be formed that can adapt to changes in the operating status of energy storage equipment, thereby improving the reliability and economy of demand response execution.

[0148] This invention relates to a virtual power plant load resource allocation system based on integrated energy storage, the system comprising:

[0149] The first unit is used to acquire the virtual power plant's operating status data, demand response instructions, and electricity demand data;

[0150] The second unit is used to analyze the charging and discharging characteristics and energy conversion efficiency of multiple types of energy storage devices based on the operating status data, obtain the comprehensive operating characteristics of the energy storage devices, and construct demand response optimization targets based on the demand response instructions and the electricity demand data.

[0151] The third unit is used to calculate the dispatchable power range and continuous response time of various types of energy storage devices under the current state of charge based on the comprehensive operating characteristics of the energy storage devices, so as to obtain the quantitative result of the overall response capability of the energy storage cluster.

[0152] The fourth unit is used to decompose the demand response task into a continuous adjustment task and a transient balancing task according to the response duration requirement, based on the quantitative result of the overall response capability of the energy storage cluster and the demand response optimization target. The continuous adjustment task is assigned to the energy storage device to form a power reference trajectory, and the transient balancing task is assigned to the power storage device to form a power deviation correction amount. A dynamic coupling relationship is established between the power reference trajectory and the power deviation correction amount.

[0153] The fifth unit is used to perform rolling prediction of the charging and discharging status of energy storage devices within a preset time window based on the dynamic coupling relationship, and dynamically adjust the scheduling priority and power allocation ratio of multiple types of energy storage devices according to the rolling prediction results to obtain the load resource allocation scheme of the virtual power plant.

[0154] A third aspect of the present invention provides an electronic device, comprising:

[0155] processor;

[0156] Memory used to store processor-executable instructions;

[0157] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0158] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0159] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0160] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A virtual power plant load resource allocation method based on integrated energy storage, characterized in that, include: Acquire virtual power plant operation status data, demand response commands, and electricity demand data; Based on the operational status data, the charging and discharging characteristics and energy conversion efficiency of various types of energy storage devices are analyzed to obtain the comprehensive operational characteristics of the energy storage devices. Demand response optimization targets are then constructed based on the demand response instructions and the electricity demand data. Based on the comprehensive operating characteristics of the energy storage equipment, the dispatchable power range and continuous response time of various types of energy storage equipment under the current state of charge are calculated to obtain the quantitative results of the overall response capability of the energy storage cluster. Based on the quantitative results of the overall response capability of the energy storage cluster and the demand response optimization objective, the demand response task is decomposed into a continuous adjustment task and a transient balancing task according to the response duration requirement. The continuous adjustment task is assigned to the energy-type energy storage device to form a power reference trajectory, and the transient balancing task is assigned to the power-type energy storage device to form a power deviation correction amount. A dynamic coupling relationship is established between the power reference trajectory and the power deviation correction amount. Based on the dynamic coupling relationship, the charging and discharging status of energy storage devices is predicted in a rolling manner within a preset time window. The scheduling priority and power allocation ratio of multiple types of energy storage devices are dynamically adjusted according to the results of the rolling prediction to obtain the load resource allocation scheme of the virtual power plant.

2. The method according to claim 1, characterized in that, Based on the aforementioned operational status data, the charging and discharging characteristics and energy conversion efficiency of various types of energy storage devices are analyzed, yielding the following comprehensive operational characteristics of the energy storage devices: Historical charge / discharge curve data and real-time state of charge data of various types of energy storage devices are extracted from the operational status data. Based on the historical charge and discharge curve data, the power change rate and energy loss characteristics during the charge and discharge process are identified to obtain charge and discharge dynamic characteristic parameters; based on the real-time state of charge data and the charge and discharge dynamic characteristic parameters, the energy conversion efficiency curves of various types of energy storage devices in different charge ranges are calculated. The energy conversion efficiency curve is segmented and mapped according to the charge interval to establish a nonlinear correlation between the state of charge and the energy conversion efficiency. Based on the power change rate in the charging and discharging dynamic characteristic parameters, the response delay characteristics and regulation accuracy characteristics of various types of energy storage devices to power regulation commands are identified. By tracking the power output fluctuation amplitude and stable recovery time before and after the state transition of various types of energy storage devices, switching adaptability features reflecting the state transition adaptability of energy storage devices are extracted. Based on the nonlinear correlation, the response delay characteristics, the adjustment accuracy characteristics, and the switching adaptability characteristics, the comprehensive operating characteristics of the energy storage device are constructed.

3. The method according to claim 2, characterized in that, The demand response optimization objectives are constructed based on the demand response instructions and the electricity demand data, including: The power regulation demand corresponding to each response period is parsed from the demand response command; The load forecast curve and load fluctuation characteristic data are extracted from the electricity demand data, and the load gap distribution characteristics and load change trend characteristics in each response period are calculated. Based on the nonlinear correlation in the comprehensive operation characteristics of the energy storage equipment, the power regulation demand corresponding to each response period is decomposed into segmented power regulation sub-demands that match different charge ranges, and a power regulation transition range is set between adjacent response periods based on the stable recovery time in the switching adaptability characteristics. Based on the response delay characteristics and regulation accuracy characteristics in the comprehensive operation characteristics of the energy storage equipment, a feasible scheduling set of energy storage equipment that meets the requirements of the segmented power regulation sub-system is determined, and the feasible scheduling set is sorted by response time sequence based on the load gap distribution characteristics and the load change trend characteristics. Based on the segmented power regulation sub-demands, the power regulation transition intervals, the feasible scheduling set, and the response timing ranking results, a demand response optimization objective is constructed with the dual goals of minimizing response costs and maximizing load tracking accuracy.

4. The method according to claim 2, characterized in that, Based on the comprehensive operating characteristics of the energy storage devices, the dispatchable power range and continuous response time of various types of energy storage devices under the current state of charge are calculated, resulting in the quantitative results of the overall response capability of the energy storage cluster, including: Query the energy conversion efficiency values ​​of various types of energy storage devices within the corresponding charge range of the current state of charge data; Based on the energy conversion efficiency value and the remaining capacity data of various types of energy storage devices, the maximum discharge power and maximum recharge power of various types of energy storage devices under the current state of charge are calculated to obtain the dispatchable power range of a single device. Based on the response delay characteristics and the adjustment accuracy characteristics, the single device schedulable power range is corrected by response time constraints and control accuracy constraints to obtain the corrected single device schedulable power range. Based on the switching adaptability characteristics in the comprehensive operation characteristics of the energy storage device, the power output attenuation ratio of various types of energy storage devices during the charging and discharging state transition is identified, and the power output attenuation ratio is used as the dynamic reduction coefficient of the corrected single device schedulable power range to calculate the effective adjustable power range. Based on the energy conversion efficiency value, the remaining capacity data, and the effective adjustable power range, calculate the continuous response time of various types of energy storage devices under the condition of maintaining the current power output. The effective adjustable power range and the continuous response duration are aggregated at the cluster level to obtain the quantitative result of the overall response capability of the energy storage cluster.

5. The method according to claim 3, characterized in that, Based on the quantitative results of the overall response capability of the energy storage cluster and the demand response optimization objective, the demand response task is decomposed into continuous adjustment tasks and transient balancing tasks according to the response duration requirements, including: Extract the power regulation duration corresponding to the segmented power regulation sub-demands from the demand response optimization objectives; extract the continuous response duration and effective adjustable power range of multiple types of energy storage devices from the overall response capability quantification results of the energy storage cluster; The power regulation duration and the continuous response duration are numerically matched to construct a duration adaptation coefficient matrix that reflects the time sequence fit between task duration requirements and equipment response capabilities. Based on the duration adaptation coefficient matrix, long-term power regulation requirements that require multiple energy storage devices to respond in succession and short-term power regulation requirements that can be completed independently by a single energy storage device are identified. The power adjustment transition interval is extracted from the demand response optimization objective. By analyzing the time span of the power adjustment transition interval and the power output stability characteristics in the effective adjustable power range, a spatiotemporal coupling constraint on the equipment power stability interval is established. Based on the spatiotemporal coupling constraint, the long-term power regulation requirement is decomposed into the continuous regulation task that requires steady-state power output, and the short-term power regulation requirement is decomposed into the transient balancing task that requires instantaneous power response.

6. The method according to claim 5, characterized in that, Assigning the continuous adjustment task to the energy storage device to form a power reference trajectory, assigning the transient balancing task to the power storage device to form a power deviation correction amount, and establishing a dynamic coupling relationship between the power reference trajectory and the power deviation correction amount includes: Based on the duration and intensity of power regulation, a sequence of target power output values ​​is planned within the effective adjustable power range of the energy storage device; the sequence of target power output values ​​is then processed continuously in the time dimension to form the power reference trajectory describing the steady-state power output trajectory of the energy storage device. Extract the power jump amplitude corresponding to the power adjustment transition interval from the transient balance task, and calculate the power gap value between the power reference trajectory and the power jump amplitude as the power deviation correction amount that the power storage device needs to bear by analyzing the power output slope of the power reference trajectory in the power adjustment transition interval. Based on the power output fluctuation range and power output slope of the power reference trajectory, the dynamic adjustment boundary of the power deviation correction amount is determined. By establishing a real-time tracking mechanism for the degree of deviation of the power deviation correction amount from the power reference trajectory, a dynamic coupling relationship reflecting the collaborative output relationship between energy storage devices and power storage devices is constructed. The dynamic coupling relationship constrains the change direction of the power deviation correction amount to be consistent with the deviation direction of the power reference trajectory.

7. The method according to claim 1, characterized in that, Based on the dynamic coupling relationship, the charging and discharging status of energy storage devices is predicted in a rolling manner within a preset time window. The scheduling priority and power allocation ratio of various types of energy storage devices are dynamically adjusted according to the results of the rolling prediction, resulting in a load resource allocation scheme for the virtual power plant, including: Calculate the power output change trend of the power reference trajectory and the power deviation correction amount within a preset time window; Based on the power output change trend of the power reference trajectory and combined with the remaining capacity data of the energy storage device, the predicted value of the state of charge of the energy storage device within the preset time window is calculated, and it is determined whether the energy storage device needs to undergo a charge / discharge state transition. Based on the power output change trend of the power deviation correction amount and combined with the remaining capacity data of the power storage device, the predicted value of the state of charge of the power storage device within the preset time window is calculated, and it is determined whether the power storage device needs to undergo a charge / discharge state transition, thus obtaining the rolling prediction result. Identify energy storage devices that need to undergo charge / discharge state transitions from the results of the rolling predictions, and extract the corresponding switching adaptability features of the energy storage devices that need to undergo charge / discharge state transitions from the comprehensive operating characteristics of the energy storage devices. Based on the power output attenuation ratio and state switching delay in the switching adaptability characteristics, the power output loss of the energy storage device that needs to undergo charging and discharging state transition is calculated. The power output loss is then inversely proportional to the scheduling priority weight and directly proportional to the power allocation ratio to obtain the load resource allocation scheme of the virtual power plant.

8. A virtual power plant load resource allocation system based on integrated energy storage, used to implement the method as described in any one of claims 1-7, characterized in that, include: The first unit is used to acquire the virtual power plant's operating status data, demand response instructions, and electricity demand data; The second unit is used to analyze the charging and discharging characteristics and energy conversion efficiency of multiple types of energy storage devices based on the operating status data, obtain the comprehensive operating characteristics of the energy storage devices, and construct demand response optimization targets based on the demand response instructions and the electricity demand data. The third unit is used to calculate the dispatchable power range and continuous response time of various types of energy storage devices under the current state of charge based on the comprehensive operating characteristics of the energy storage devices, so as to obtain the quantitative result of the overall response capability of the energy storage cluster. The fourth unit is used to decompose the demand response task into a continuous adjustment task and a transient balancing task according to the response duration requirement, based on the quantitative result of the overall response capability of the energy storage cluster and the demand response optimization target. The continuous adjustment task is assigned to the energy storage device to form a power reference trajectory, and the transient balancing task is assigned to the power storage device to form a power deviation correction amount. A dynamic coupling relationship is established between the power reference trajectory and the power deviation correction amount. The fifth unit is used to perform rolling prediction of the charging and discharging status of energy storage devices within a preset time window based on the dynamic coupling relationship, and dynamically adjust the scheduling priority and power allocation ratio of multiple types of energy storage devices according to the rolling prediction results to obtain the load resource allocation scheme of the virtual power plant.

9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 7.

Images