Virtual power plant optimal scheduling method, device and equipment based on energy storage estimation

Abstract

The invention discloses a virtual power plant optimal scheduling method, device and equipment based on energy storage estimation, and relates to the technical field of virtual power plant optimal scheduling. According to the method, the real-time charge state of each energy storage module is estimated based on a charge and discharge mathematical model and a discharge curve of a lithium ion battery; according to the method, the accurate SOC estimation value is dynamically converted into the available electricity quantity, and the available electricity quantity is used as the key constraint of the optimal scheduling model of the virtual power plant, so that the scheduling instruction output by the optimal scheduling model is ensured to be accurately matched with the actual capability of the energy storage module, the problems of instruction misalignment, battery overcharge / overdischarge and the like caused by inaccurate SOC estimation are avoided, and the power generation efficiency is improved. And the response accuracy of the virtual power plant to the power grid dispatching instruction and the reliability of system operation are obviously improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of virtual power plant optimization scheduling, and particularly relates to a virtual power plant optimization scheduling method, device and equipment based on energy storage estimation. BACKGROUND

[0002] As a key technology for integrating distributed energy and improving the flexibility and reliability of power grids, a virtual power plant (VPP) aggregates dispersed distributed power sources, energy storage systems and controllable loads into a coordinated whole through advanced communication and control technologies, and participates in power market and grid dispatching. In a typical park-level integrated energy system, there are usually multiple subjects such as system energy operators, distributed photovoltaic users and electric vehicle charging agents. In order to coordinate the interests of all parties, the traditional virtual power plant generally adopts an optimization model based on Stackelberg master-slave game, in which the energy operator acts as the leader to formulate energy prices, and the photovoltaic users and electric vehicle agents act as followers to adjust their power consumption and power selling strategies accordingly, and through multiple rounds of iterative game, a Nash equilibrium is sought to maximize the economic benefits of the system as a whole and each participating subject.

[0003] However, this traditional mode faces severe challenges in practical application, and the core problem lies in the serious lack of dynamic perception and accurate estimation of the state of charge (SOC) of aggregated energy storage resources, especially electric vehicle batteries. Existing technologies usually regard SOC as an idealized and accurately known parameter, or rely on simplified prediction models based on historical statistics and Monte Carlo simulation. This processing method ignores the complex influence of actual driving behavior, battery degradation, environmental temperature and other factors on SOC, resulting in a significant deviation between the estimated value and the true value of SOC. Inaccurate SOC estimation makes the charge and discharge instructions issued by the virtual power plant central controller exceed the actual instantaneous capacity range of the battery unit, and there is a risk of operation. SUMMARY

[0004] The technical problem to be solved by the present application is the operation risk problem caused by inaccurate SOC estimation in the traditional virtual power plant. The present application provides a virtual power plant optimization scheduling method, device and equipment based on energy storage estimation, which solves the above problems.

[0005] The present application is implemented by the following technical solutions:

[0006] In a first aspect, the present application provides a virtual power plant optimization scheduling method based on energy storage estimation, comprising:

[0007] constructing a charge-discharge mathematical model of the lithium ion battery; the charge-discharge mathematical model is used to describe a dynamic relationship between a battery output voltage and a charge-discharge current, a battery capacity and a state of charge of the lithium ion battery in a charge-discharge process;

[0008] obtaining a current remaining battery capacity of each lithium ion battery based on the charge-discharge mathematical model and a discharge curve of each lithium ion battery in the virtual power plant; the discharge curve is used to indicate a dynamic relationship between a capacity and a voltage of the lithium ion battery when discharging in a current working environment;

[0009] obtaining a real-time state of charge estimation value of each energy storage module based on the current remaining battery capacity and a standard capacity of all lithium ion batteries in each energy storage module in the virtual power plant;

[0010] obtaining a saleable electricity quantity of the virtual power plant based on the real-time state of charge estimation value of each energy storage module;

[0011] constructing and solving an optimal scheduling model of the virtual power plant to output an optimal scheduling strategy; the optimal scheduling model takes the saleable electricity quantity as a key constraint condition; and the optimal scheduling strategy is used to indicate a charge-discharge behavior of each energy storage module.

[0012] Optionally, the charge-discharge mathematical model includes a charge mathematical model and a discharge mathematical model.

[0013] The charge mathematical model is as follows:

[0014] ;

[0015] The discharge mathematical model is as follows:

[0016] ;

[0017] wherein, is a battery output voltage; is a constant voltage of the battery; is a polarization resistance of the battery; is a current remaining battery capacity; is a charge-discharge ampere-hour capacity of the battery; is an exponential region voltage value of a battery discharge curve; is an inverse of a time constant of the exponential region; and R is a battery internal resistance; is a battery current; is a sampling current.

[0018] Optionally, the obtaining of the current remaining battery capacity of each lithium ion battery based on the charge-discharge mathematical model and the discharge curve of each lithium ion battery in the virtual power plant includes

[0019] extracting full-charge voltage, capacity and voltage at exponential turning point, capacity and voltage at logarithmic turning point from the discharge curve of each lithium-ion battery;

[0020] solving the preset equation group of key parameters based on the full-charge voltage, the capacity and voltage at exponential turning point, the capacity and voltage at logarithmic turning point, the charging and discharging mathematical model, to obtain the current residual battery capacity of each lithium-ion battery; the key parameters include battery constant voltage, battery polarization resistance, voltage value of battery discharge curve exponential region, and reciprocal of time constant of curve exponential region.

[0021] Optionally, the preset equation group of key parameters is as follows:

[0022] ;

[0023] ;

[0024] ;

[0025] ;

[0026] wherein A is the voltage value of battery discharge curve exponential region; B is the reciprocal of time constant of curve exponential region; K is the battery polarization resistance; is the battery constant voltage; Q is the current residual battery capacity; R is the battery internal resistance; is the battery current; is the capacity at exponential turning point; is the voltage at exponential turning point; is the capacity at logarithmic turning point; is the voltage at logarithmic turning point; is the full-charge voltage.

[0027] Optionally, the energy storage modules include photovoltaic energy storage modules and automobile battery energy storage modules; the obtainable saleable electricity quantity of the virtual power plant based on the real-time state-of-charge estimation values of the energy storage modules includes:

[0028] aggregating the product of the real-time state-of-charge estimation value and the rated energy storage capacity of all photovoltaic energy storage modules, and taking the obtained sum as the user saleable electricity quantity of the photovoltaic energy storage user group;

[0029] aggregating the product of the real-time state-of-charge estimation value and the rated energy storage capacity of all automobile battery energy storage modules, and taking the obtained sum as the agent saleable electricity quantity of the electric vehicle agent group.

[0030] Optionally, the constraint conditions of the optimization scheduling model include:

[0031] The actual total amount of electricity sold by the photovoltaic energy storage user group does not exceed the amount of electricity that can be sold by the user;

[0032] The actual total amount of electricity sold by the electric vehicle agent group does not exceed the amount of electricity that can be sold by the agent.

[0033] Optionally, the real-time state of charge estimation value of each energy storage module is obtained based on the current residual battery capacity and the standard capacity of all lithium ion batteries in each energy storage module in the virtual power plant, including:

[0034] The sum of the current residual battery capacities of all lithium ion batteries in each energy storage module in the virtual power plant is calculated;

[0035] The sum of the standard capacities of all lithium ion batteries in each energy storage module in the virtual power plant is calculated;

[0036] The ratio of the sum of the current residual battery capacities to the sum of the standard capacities is taken as the real-time state of charge estimation value of each energy storage module.

[0037] Optionally, after the optimization scheduling model of the virtual power plant is constructed and solved to output the optimal scheduling strategy, the method further includes:

[0038] The actual amount of electricity sold by each energy storage module in the process of executing the optimal scheduling strategy is obtained;

[0039] The parameters of the charging and discharging mathematical model are corrected and updated according to the difference between the actual amount of electricity sold and the current residual amount of electricity of each energy storage module.

[0040] In a second aspect, the present application provides a virtual power plant optimization scheduling device based on energy storage estimation, including:

[0041] A model construction module is configured to construct a charging and discharging mathematical model of lithium ion batteries; the charging and discharging mathematical model is used to describe the dynamic relationship between the battery output voltage and the charging and discharging current, the battery capacity and the state of charge of lithium ion batteries in the charging and discharging process;

[0042] An estimation module is configured to obtain the current residual battery capacity of each lithium ion battery based on the charging and discharging mathematical model and the discharge curve of each lithium ion battery in the virtual power plant; and obtain the real-time state of charge estimation value of each energy storage module based on the current residual battery capacity and the standard capacity of all lithium ion batteries in each energy storage module in the virtual power plant; the discharge curve is used to indicate the dynamic relationship between the capacity and the voltage of lithium ion batteries when discharging in the current working environment;

[0043] A conversion module is configured to obtain the amount of electricity that can be sold by the virtual power plant based on the real-time state of charge estimation value of each energy storage module;

[0044] An optimization module is configured to build and solve an optimization scheduling model of the virtual power plant, and output an optimal scheduling strategy; the optimization scheduling model takes the amount of sellable electricity as a key constraint condition; and the optimal scheduling strategy is used to indicate the charging and discharging behaviors of each energy storage module.

[0045] In a third aspect, the present application provides a computer device, which comprises a memory and a processor, the memory stores a computer program, and the processor executes the computer program to implement the energy storage estimation-based virtual power plant optimization scheduling method according to any one of the first aspect.

[0046] Compared with the prior art, the present application has the following advantages and beneficial effects:

[0047] The present application provides an energy storage estimation-based virtual power plant optimization scheduling method, which estimates the real-time state of charge of each energy storage module based on a charging and discharging mathematical model and a discharging curve of a lithium ion battery, dynamically converts the accurate SOC estimation value into an amount of sellable electricity, takes the amount of sellable electricity as a key constraint of an optimization scheduling model of a virtual power plant, ensures that the scheduling instruction output by the optimization scheduling model is accurately matched with the actual capacity of the energy storage module, avoids the problems of inaccurate instruction and overcharging / overdischarging of the battery caused by inaccurate SOC estimation, and significantly improves the response accuracy of the virtual power plant to the grid scheduling instruction and the reliability of system operation. In addition, through accurate SOC management and the amount of sellable electricity constraint, the charging and discharging behaviors of the energy storage module are indirectly optimized, the operation behaviors that damage the battery health are effectively avoided, the service life of the energy storage device is prolonged, and the degradation rate and replacement cost of the energy storage module are reduced from the perspective of the whole life cycle. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the example embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be considered as a limitation to the scope. For those skilled in the art, other related drawings can also be obtained without creative labor. In the drawings:

[0049] Figure 1 A traditional virtual power plant master-slave game diagram provided for the embodiments of the present application;

[0050] Figure 2 A flowchart of the energy storage estimation-based virtual power plant optimization scheduling method provided for the embodiments of the present application;

[0051] Figure 3 A charging and discharging mathematical model of a lithium ion battery provided for the embodiments of the present application;

[0052] Figure 4A discharge curve of a lithium ion battery provided for an embodiment of the present application;

[0053] Figure 5 A light-weight energy storage estimation algorithm block diagram provided for an embodiment of the present application;

[0054] Figure 6 A schematic diagram of each energy storage module of a virtual power plant provided for an embodiment of the present application;

[0055] Figure 7 A virtual power plant energy storage sellable power calculation algorithm block diagram provided for an embodiment of the present application;

[0056] Figure 8 A virtual power plant master-slave game diagram considering energy storage estimation provided for an embodiment of the present application;

[0057] Figure 9 A structure schematic diagram of a virtual power plant optimal scheduling device based on energy storage estimation provided for an embodiment of the present application. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical scheme and advantages of the present application clearer, further detailed description will be made to the present application combined with embodiments and drawings, the illustrative embodiments of the present application and the description thereof are only used to explain the present application, and do not limit the present application.

[0059] Please refer to Figure 1 A traditional virtual power plant master-slave game diagram provided for the present application, the traditional virtual power plant usually adopts a Stackelberg master-slave game model. The system energy operator as the leading party first formulates a sellable power price strategy; and the photovoltaic user and the charging agent as the following party then adjust their power consumption and sellable power strategies according to the price signal. Each party takes the maximization of their own interests as the target, forming a multi-layer decision interaction mechanism. The traditional virtual power plant optimal scheduling model usually regards the SOC of the electric vehicle as an accurately known quantity, makes prediction and scheduling based on a simplified statistical model, and ignores the inherent uncertainty and time-varying nature of the SOC estimation, which may lead to the following problems in actual application:

[0060] (1) Estimation error problem: the traditional method generates the electric vehicle charging demand through Monte Carlo simulation based on historical statistical data and unordered charging assumption, and fails to fully consider the influence of actual driving behavior, battery attenuation factors and environmental temperature on the SOC, resulting in significant deviation between the estimated value and the actual value.

[0061] (2) Control instruction misalignment: due to the inaccurate SOC estimation, the optimal scheduling model may issue charging and discharging instructions that exceed the actual capacity of the device, resulting in that the electric vehicle cannot fully respond to the scheduling instructions, and affecting the accuracy of the virtual power plant in regulating the power grid.

[0062] (3) Increased operation risk: inaccurate SOC estimation can cause overcharge or overdischarge of the battery, posing safety hazards, while frequent correction operations increase the communication and calculation burden of the system, reducing the stability and reliability of system operation.

[0063] (4) Distorted optimization results: global optimization (such as market bidding, multi-resource coordination) based on distorted SOC data can cause the results to deviate from the actual optimal state, resulting in damage to the overall economic benefits of the virtual power plant and the inability to achieve the expected peak shaving, market arbitrage, and other goals. In scenarios that require high real-time performance, such as frequency regulation and backup services, the adaptability of this simplified model is obviously insufficient.

[0064] In view of this, the embodiments of the present application provide a virtual power plant optimization scheduling method based on energy storage estimation. Please refer to Figure 2 , a flowchart of the virtual power plant optimization scheduling method based on energy storage estimation provided by the embodiments of the present application is provided below Figure 1 to introduce the virtual power plant optimization scheduling method based on energy storage estimation.

[0065] S1, construct a charging and discharging mathematical model of a lithium ion battery.

[0066] The embodiments of the present application provide a charging and discharging mathematical model of a lithium ion battery, as shown in Figure 3 . The charging and discharging mathematical model is an improved open-circuit voltage-SOC mathematical model applicable to all lithium ion batteries, used to describe the dynamic relationship between the battery output voltage and the charging and discharging current, battery capacity, and state of charge during the charging and discharging process of the lithium ion battery.

[0067] The charging and discharging mathematical model includes a charging mathematical model and a discharging mathematical model; the charging mathematical model is as follows:

[0068] ;

[0069] The discharging mathematical model is as follows:

[0070] ;

[0071] wherein, is the battery output voltage; is the battery constant voltage; K is the battery polarization resistance; is the current remaining battery capacity; is the battery charging and discharging capacity; A is the battery discharging curve exponential region voltage value; is the inverse of the curve exponential region time constant; R is the battery internal resistance, i.e., the battery equivalent series resistance; is the battery current; is the sampling current.

[0072] S2, based on the charge-discharge mathematical model and the discharge curve of each lithium ion battery in the virtual power plant, obtaining the current remaining battery capacity of each lithium ion battery.

[0073] Please refer to Figure 4 , a discharge curve of a lithium ion battery provided by the embodiment of the present application. The discharge curve is used to indicate the dynamic relationship between the capacity and the voltage of the lithium ion battery when discharging in the current working environment. is the horizontal coordinate of the exponential turning point, i.e. the capacity; is the vertical coordinate of the exponential turning point, i.e. the voltage; is the horizontal coordinate of the logarithmic turning point, i.e. the capacity; is the vertical coordinate of the logarithmic turning point, i.e. the voltage; is the full-charge voltage.

[0074] In a possible embodiment, the full-charge voltage, the capacity and voltage of the exponential turning point, and the capacity and voltage of the logarithmic turning point are extracted from the discharge curve of each lithium ion battery; based on the full-charge voltage, the capacity and voltage of the exponential turning point, and the capacity and voltage of the logarithmic turning point, the preset equation set of key parameters and the charge-discharge mathematical model are solved to obtain the current remaining battery capacity of each lithium ion battery; the key parameters include the battery constant voltage, the battery polarization resistance, the voltage value of the exponential region of the battery discharge curve, and the inverse of the time constant of the exponential region of the curve.

[0075] The preset equation set of the key parameters is as follows:

[0076] ;

[0077] ;

[0078] ;

[0079] ;

[0080] wherein A is the voltage value of the exponential region of the battery discharge curve; B is the inverse of the time constant of the exponential region of the curve; K is the battery polarization resistance; is the battery constant voltage; Q is the current remaining battery capacity; R is the battery internal resistance; is the battery current; is the capacity of the exponential turning point; is the voltage of the exponential turning point; is the capacity of the logarithmic turning point; is the voltage of the logarithmic turning point; is the full-charge voltage.

[0081] The embodiment of the present application provides a lightweight energy storage estimation algorithm, and the algorithm block diagram is as shown in Figure 5 .

[0082] Step I: Extract two key feature points from the actual discharge curve of the target lithium-ion battery: the exponential region turning point (coordinates are (t1, I1)) and the logarithmic region turning point (coordinates are (t2, I2)). , ). ).

[0083] Step II: Substitute these key feature points into a preset equation set to solve.

[0084] Step II: Substitute the solution of the preset equation set into the charge-discharge mathematical model to solve and obtain the current remaining capacity Q of the target lithium-ion battery.

[0085] In the embodiments of the present application, by constructing an improved open-circuit voltage-SOC model and identifying key parameters based on the actual discharge curve, the problems of large cumulative error of traditional ampere-hour integral method and complex calculation of Kalman filter method are effectively overcome. The method can realize fast, online and high-precision estimation of the SOC of a large number of distributed energy storage units, and provides a reliable data basis for the optimization decision of the virtual power plant. Compared with the traditional method, the calculation amount of the estimation process of the present application is significantly reduced, the response speed is faster, and the real-time requirement of the virtual power plant can be better met.

[0086] S3, based on the current remaining battery capacity and the standard capacity of all lithium-ion batteries in each energy storage module in the virtual power plant, obtaining the real-time state of charge estimation value of each energy storage module.

[0087] In the specific implementation process, the sum of the current remaining battery capacities of all lithium-ion batteries in each energy storage module in the virtual power plant is calculated; the sum of the standard capacities of all lithium-ion batteries in each energy storage module in the virtual power plant is calculated; the ratio of the sum of the current remaining battery capacities to the sum of the standard capacities is taken as the real-time state of charge estimation value of each energy storage module. The specific calculation formula is as follows:

[0088] ;

[0089] wherein, is the real-time state of charge estimation value of a single energy storage module; Q is the current remaining battery capacity of a single lithium-ion battery; is the standard capacity of a single lithium-ion battery, that is, the capacity of the logarithmic turning point in the discharge curve; m is the number of lithium-ion batteries contained in a single energy storage module.

[0090] ​In this embodiment, the remaining capacity (microstate) of a single battery cell, accurately calculated based on the improved open-circuit voltage-SOC model, is scaled up to the overall state of charge (macrostate) of the energy storage module using a scientific aggregation formula. This process ensures that the module-level state of charge estimate originates from the accurate measurement and calculation of each lithium-ion battery, fundamentally avoiding the errors caused by overall estimation in traditional methods.

[0091] S4. Based on the real-time state of charge estimation of each energy storage module, obtain the amount of electricity available for sale in the virtual power plant.

[0092] Please refer to Figure 6 This is a schematic diagram of the energy storage module of the virtual power plant provided in this application embodiment. The virtual power plant includes multiple energy storage modules (divided into photovoltaic energy storage modules and automotive battery energy storage modules), each of which consists of multiple lithium-ion batteries. The photovoltaic energy storage module refers to a lithium-ion battery energy storage system that is constructed in conjunction with a distributed photovoltaic power generation system and fixedly installed on the user side (such as in a park, factory, or residence). The automotive battery energy storage module refers to the power battery pack onboard to an electric vehicle, specifically referring to electric vehicle batteries that are connected to the virtual power plant via charging piles or V2G (vehicle-to-grid) facilities and can be aggregated and dispatched.

[0093] The specific steps of S4 include:

[0094] S4.1. The sum of the real-time state of charge estimates of all photovoltaic energy storage modules and their rated energy storage capacity is used as the amount of electricity that can be sold by the photovoltaic energy storage user group.

[0095] ;

[0096] in, The amount of electricity available for sale by users of photovoltaic energy storage; This represents the total number of all photovoltaic energy storage modules. Let be the real-time state of charge estimate of the s-th photovoltaic energy storage module. It is the rated energy storage capacity of the s-th photovoltaic energy storage module.

[0097] S4.2. Aggregate the real-time state of charge estimates of all automotive battery energy storage modules and their rated energy storage capacity, and use the sum as the amount of electricity that can be sold by the electric vehicle agent group.

[0098] ;

[0099] in, The amount of electricity that can be sold by the electric vehicle dealership group; This indicates the total number of all automotive battery energy storage modules; Let be the real-time state of charge estimate of the t-th automotive battery energy storage module. It is the rated energy storage capacity of the t-th automotive battery energy storage module.

[0100] In this embodiment, the lightweight SOC estimation result is precisely quantified into the amount of electricity available for sale by photovoltaic energy storage users and electric vehicle agents. This provides key constraints for optimizing the scheduling model and provides accurate and reliable data input for subsequent optimization of the scheduling model, fundamentally improving the economy and security of virtual power plant market behavior.

[0101] S5. Construct and solve the optimal scheduling model of the virtual power plant, and output the optimal scheduling strategy.

[0102] In the specific implementation process, an optimized scheduling model for a virtual power plant is constructed. The output of the optimized scheduling model is the optimal scheduling strategy, which is used to instruct the charging and discharging behavior of each energy storage module to guide the actual operation of the virtual power plant. The optimized scheduling model takes the amount of electricity available for sale as the key constraint, but its constraints are not limited to the amount of electricity available for sale. They may also include at least one of the following: grid safety operation constraints, equipment physical characteristic constraints, and market rule constraints.

[0103] In one possible embodiment, the constraints of the optimized scheduling model include: the actual total electricity sales of the photovoltaic energy storage user group does not exceed the amount of electricity available for sale by the user; and the actual total electricity sales of the electric vehicle agent group does not exceed the amount of electricity available for sale by the agent.

[0104] The specific constraints are expressed by the following inequality:

[0105] ;

[0106] ;

[0107] in, This refers to the amount of electricity that can be sold by users of photovoltaic energy storage, which is the maximum discharge capacity of the photovoltaic energy storage module determined based on real-time state of charge estimation. This refers to the actual electricity sales volume of the photovoltaic energy storage user group, which is the planned electricity sales volume of the photovoltaic energy storage module in the optimized scheduling model. The amount of electricity that can be sold by the electric vehicle dealership group is the maximum discharge capacity of the vehicle battery energy storage module determined based on real-time state of charge estimation. This refers to the actual electricity sales volume of the electric vehicle agent group, which is the planned electricity sales volume of the vehicle battery energy storage module determined in the optimized scheduling model.

[0108] In this embodiment, the amount of electricity available for sale is introduced as a hard constraint into the optimization scheduling model. This constraint sets a clear safety boundary for the operation of the energy storage system, ensuring that the optimization process is always carried out within the safe operating range of the energy storage device. This effectively prevents battery damage or system failure caused by over-discharge, ensures the complete executability of scheduling instructions, significantly reduces system operation risks, and enhances the reliability and credibility of virtual power plants participating in grid regulation.

[0109] The objective function of the optimized scheduling model can be flexibly set according to actual operational needs, including but not limited to maximizing the total economic benefits of the system, minimizing the curtailment rate, and minimizing the total operating cost.

[0110] In one possible implementation, the objective function is set to maximize the total economic benefits of the virtual power plant, as expressed mathematically below:

[0111] ;

[0112] in, Represents the maximization function; For the user benefit of the s-th photovoltaic energy storage module; This represents the total number of all photovoltaic energy storage modules in the virtual power plant. Let the user benefit be for the t-th automotive battery energy storage module; This represents the total number of all automotive battery energy storage modules in the virtual power plant.

[0113] ;

[0114] in, For the user benefit of the s-th photovoltaic energy storage module; The revenue from electricity sales to the user of the s-th photovoltaic energy storage module; Let s be the user's external energy purchase cost for the s-th photovoltaic energy storage module; Let be the operating cost of the energy conversion equipment for the s-th photovoltaic energy storage module.

[0115] ;

[0116] The revenue from electricity sales to the user of the s-th photovoltaic energy storage module; Let be the electricity price of the s-th photovoltaic energy storage module; Let be the actual electricity sales volume of the s-th photovoltaic energy storage module.

[0117] ;

[0118] The operating cost of the s-th photovoltaic energy storage module is determined by the depreciation factor; Let be the purchase cost of the s-th photovoltaic energy storage module; Let be the number of cycles for the s-th photovoltaic energy storage module; Let be the maximum total energy storage of the s-th photovoltaic energy storage module.

[0119] ;

[0120] in, Let the user benefit be for the t-th automotive battery energy storage module; The revenue generated from electricity sales to the user of the t-th automotive battery energy storage module; Let be the user's external energy purchase cost for the t-th automotive battery energy storage module; Let be the operating cost of the energy conversion equipment for the t-th automotive battery energy storage module.

[0121] ;

[0122] The revenue generated from electricity sales to the user of the t-th automotive battery energy storage module; Let be the electricity price for the t-th automotive battery energy storage module; Let t be the actual electricity sold by the t-th automotive battery energy storage module.

[0123] ;

[0124] The operating cost of the t-th automotive battery energy storage module is determined by the depreciation factor; Let be the purchase cost of the t-th automotive battery energy storage module; Let t be the number of cycles for the t-th automotive battery energy storage module; Let t be the maximum total energy storage of the t-th automotive battery energy storage module.

[0125] In this embodiment, the optimized scheduling model takes maximizing the overall interests of both photovoltaic users and electric vehicle dealers as its objective function. By seeking to maximize overall benefits, the scheduling strategy generated by the model can naturally guide photovoltaic users and electric vehicle dealers to achieve the optimal operating state of the system as a whole while pursuing their own interests. This mechanism effectively solves the problem of interest coordination in distributed resource aggregation, fully explores the synergistic value of resources, and thus significantly improves the overall economic benefits of virtual power plants participating in market transactions.

[0126] In one possible embodiment, after constructing and solving the optimal scheduling model of the virtual power plant and outputting the optimal scheduling strategy, the method further includes: obtaining the actual electricity sales of each energy storage module during the execution of the optimal scheduling strategy; and correcting and updating the parameters of the charging and discharging mathematical model based on the difference between the actual electricity sales and the current remaining electricity.

[0127] In the specific implementation process, during the actual electricity sales process in the electricity market, the voltage and current data of each energy storage module during the actual electricity sales period are converted into the actual electricity sales volume, and the actual electricity sales volume of each energy storage module and the current remaining electricity of each energy storage module are calculated. The difference between Q and m is calculated, where Q is the current remaining capacity of a single lithium-ion battery estimated based on the charge-discharge mathematical model, and m is the number of lithium-ion batteries contained in a single energy storage module. When this difference exceeds a preset threshold, key parameters in the charge-discharge mathematical model (including battery internal resistance R, polarization resistance K, capacity Q, etc.) are iteratively optimized using the least squares method or gradient descent method to update the model parameters, thereby updating the system's upper limit of available electricity for the current and next time periods. Furthermore, this dynamic update result is fed back to the decision-making level of energy operators to guide the grid-connected scale of photovoltaic power generation and the charge-discharge strategy of electric vehicle energy storage in real time, thus forming a closed-loop optimization process of "state perception - market decision-making - revenue feedback - capacity update", ultimately achieving a synergistic improvement in the overall operational economy and scheduling accuracy of the virtual power plant.

[0128] Please refer to Figure 7 This is a flowchart of the algorithm for estimating the saleable electricity of virtual power plant energy storage provided in this application embodiment. A lightweight energy storage estimation algorithm is used to estimate the state of charge (SOC) of photovoltaic energy storage modules and electric vehicle charging modules in real time. Based on the SOC estimate and the rated capacity of each energy storage unit, the saleable electricity quantity for photovoltaic users and the agent saleable electricity quantity for electric vehicle agents are dynamically calculated and updated. The energy operator, as the leading party, comprehensively considers its electricity purchase costs and losses, as well as the revenue from electricity sales from photovoltaic and electric vehicles, to construct the objective function. During optimization, the model strictly adheres to the saleable electricity constraint.

[0129] In summary, this application provides a virtual power plant optimization scheduling method based on energy storage estimation. Please refer to [link / reference]. Figure 8 The virtual power plant master-slave game diagram considering energy storage estimation provided in this application embodiment can ensure system stability, improve scheduling accuracy, and enhance economic efficiency. It has the following beneficial effects:

[0130] 1. Significantly improves the accuracy and real-time performance of SOC estimation.

[0131] By constructing a charging and discharging mathematical model suitable for lithium-ion batteries and combining iterative voltage-SOC calculation, the shortcomings of the traditional ampere-hour integration method (poor accuracy) and Kalman filtering method (long time consumption) are overcome, and a fast and accurate estimation of the SOC of the energy storage unit is achieved in a virtual power plant operating environment.

[0132] 2. Enhance the scheduling accuracy and operational stability of virtual power plants.

[0133] By acquiring the precise SOC of the energy storage module in real time, the optimized scheduling model can accurately grasp its adjustable capacity and power limit, thereby generating charging and discharging commands that match the actual capacity. This avoids problems such as scheduling inaccuracy and battery overcharging / over-discharging caused by inaccurate SOC estimation, and improves the reliability and safety of system operation.

[0134] 3. Balancing short-term economic efficiency with long-term battery health.

[0135] Indirect control of battery degradation (SOH) is introduced into the optimization objectives. By optimizing the charging and discharging strategy through precise SOC management, the battery life is extended, thereby improving the overall economic benefits of the virtual power plant from the perspective of the entire life cycle.

[0136] 4. Establish a closed-loop optimization mechanism to enhance the system's adaptive capability:

[0137] Through a closed-loop process of "state perception - market decision-making - revenue feedback - capability update", the system realizes continuous correction of energy storage status and dynamic adjustment of optimization strategies, thereby enhancing the adaptability and robustness of the virtual power plant in complex operating environments.

[0138] Based on the same inventive concept, please refer to Figure 9 This application also provides a virtual power plant optimization scheduling device based on energy storage estimation, the device comprising:

[0139] The model building module is used to build a mathematical model for charging and discharging lithium-ion batteries. The mathematical model for charging and discharging describes the dynamic relationship between the battery output voltage, charging and discharging current, battery capacity, and state of charge during the charging and discharging process of lithium-ion batteries.

[0140] The estimation module is used to obtain the current remaining battery capacity of each lithium-ion battery based on the charge-discharge mathematical model and the discharge curve of each lithium-ion battery in the virtual power plant; based on the current remaining battery capacity and standard capacity of all lithium-ion batteries in each energy storage module in the virtual power plant, it obtains the real-time state of charge estimate of each energy storage module; the discharge curve is used to indicate the dynamic relationship between capacity and voltage when the lithium-ion battery is discharged in the current working environment.

[0141] The conversion module is used to obtain the amount of electricity available for sale from the virtual power plant based on the real-time state of charge estimation of each energy storage module.

[0142] The optimization module is used to construct and solve the optimal scheduling model of the virtual power plant and output the optimal scheduling strategy. The optimal scheduling model takes the amount of electricity available for sale as the key constraint. The optimal scheduling strategy is used to indicate the charging and discharging behavior of each energy storage module.

[0143] Optionally, the charging and discharging mathematical model includes a charging mathematical model and a discharging mathematical model;

[0144] The mathematical model for charging is as follows:

[0145] ;

[0146] The mathematical model for discharge is as follows:

[0147] ;

[0148] in, This refers to the battery output voltage. The battery voltage is constant. Battery polarization resistor; This represents the current remaining battery capacity. The ampere-hour capacity for battery charging and discharging; This refers to the voltage value in the exponential region of the battery discharge curve. R is the reciprocal of the time constant in the exponential region of the curve; R is the internal resistance of the battery. This refers to the battery current. This is the sampling current.

[0149] Optionally, the estimation module is specifically used for:

[0150] From the discharge curves of various lithium-ion batteries, extract the full-charge voltage, capacity and voltage at the exponential inflection point, and capacity and voltage at the logarithmic inflection point;

[0151] Based on the full-charge voltage, capacity and voltage at the exponential inflection point, and capacity and voltage at the logarithmic inflection point, the current remaining battery capacity of each lithium-ion battery is obtained by solving a set of pre-set equations for the combined charge-discharge mathematical model and key parameters. The key parameters include the battery constant voltage, battery polarization resistance, voltage value in the exponential region of the battery discharge curve, and the reciprocal of the time constant in the exponential region of the curve.

[0152] Optional, the preset equation set for the key parameters is as follows:

[0153] ;

[0154] ;

[0155] ;

[0156] ;

[0157] Where A is the voltage value in the exponential region of the battery discharge curve; B is the reciprocal of the time constant in the exponential region of the curve; and K is the battery polarization resistance. R is the constant voltage of the battery; Q is the current remaining battery capacity; R is the internal resistance of the battery. This refers to the battery current. The capacity at the inflection point of the index; The voltage at the exponential inflection point; The capacity of the logarithmic inflection point; The logarithmic inflection point voltage; This is the full charge voltage.

[0158] Optionally, the estimation module is specifically used for:

[0159] The product of the real-time state of charge estimates of all photovoltaic energy storage modules and their rated energy storage capacity is aggregated, and the sum is taken as the amount of electricity that can be sold by the photovoltaic energy storage user group.

[0160] The product of the real-time state of charge estimates of all automotive battery energy storage modules and their rated energy storage capacity is aggregated, and the sum is used as the amount of electricity that can be sold by the electric vehicle agent group.

[0161] Optionally, the constraints for optimizing the scheduling model include:

[0162] The actual total electricity sold by photovoltaic energy storage users shall not exceed the amount of electricity available for sale by the users.

[0163] The actual total electricity sold by the electric vehicle dealership group does not exceed the amount of electricity that the dealership can sell.

[0164] Optionally, the conversion module is specifically used for

[0165] Calculate the sum of the current remaining battery capacity of all lithium-ion batteries in each energy storage module of the virtual power plant;

[0166] Calculate the sum of the standard capacities of all lithium-ion batteries in each energy storage module of the virtual power plant;

[0167] The ratio of the sum of the current remaining battery capacity to the sum of the standard capacity is used as the real-time state of charge estimate for each energy storage module.

[0168] Optionally, the device also includes an update module, which is used to obtain the actual electricity sales of each energy storage module during the execution of the optimal scheduling strategy after constructing and solving the optimal scheduling model of the virtual power plant and outputting the optimal scheduling strategy.

[0169] The parameters of the charging and discharging mathematical model are corrected and updated based on the difference between the actual electricity sold and the current remaining electricity of each energy storage module.

[0170] It should be noted that each module in the virtual power plant optimization scheduling device based on energy storage estimation in this embodiment corresponds one-to-one with each step in the virtual power plant optimization scheduling method based on energy storage estimation in the aforementioned embodiment. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned virtual power plant optimization scheduling method based on energy storage estimation, and will not be repeated here.

[0171] Based on the same inventive concept, this application also provides a computer device, which includes a processor, a memory, and a computer program stored in the memory. The computer program is executed by the processor to implement the aforementioned virtual power plant optimization scheduling method based on energy storage estimation.

[0172] Based on the same inventive concept, this application also provides a computer storage medium storing a computer program, which is executed by a processor to implement the aforementioned virtual power plant optimization scheduling method based on energy storage estimation.

[0173] In some embodiments, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or it may be a device including one or any combination of the above-mentioned memories. The computer may be a variety of computing devices, including smart terminals and servers.

[0174] In some embodiments, executable instructions may take the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0175] As an example, executable instructions may, but do not necessarily, correspond to files in the file system. They may be stored as part of a file that holds other programs or data, for example, in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple co-located files (e.g., a file that stores one or more modules, subroutines, or code sections).

[0176] As an example, executable instructions can be deployed to execute on a single computing device, or on multiple computing devices located in one location, or on multiple computing devices distributed across multiple locations and interconnected via a communication network.

[0177] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0178] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0179] The above specific embodiments further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A virtual power plant optimal scheduling method based on energy storage estimation, characterized in that, include: Construct a mathematical model for the charging and discharging of lithium-ion batteries; The charge-discharge mathematical model is used to describe the dynamic relationship between the battery output voltage, charge-discharge current, battery capacity, and state of charge during the charge-discharge process of a lithium-ion battery. Based on the charge-discharge mathematical model and the discharge curves of each lithium-ion battery in the virtual power plant, the current remaining battery capacity of each lithium-ion battery is obtained; the discharge curve is used to indicate the dynamic relationship between capacity and voltage when the lithium-ion battery is discharged in the current working environment. Based on the current remaining battery capacity and standard capacity of all lithium-ion batteries in each energy storage module of the virtual power plant, the real-time state of charge estimate of each energy storage module is obtained. Based on the real-time state of charge estimation of each energy storage module, the amount of electricity available for sale in the virtual power plant is obtained. An optimal scheduling model for the virtual power plant is constructed and solved, and the optimal scheduling strategy is output. The optimal scheduling model uses the amount of electricity available for sale as a key constraint. The optimal scheduling strategy is used to indicate the charging and discharging behavior of each energy storage module.

2. The virtual power plant optimization scheduling method based on energy storage estimation according to claim 1, characterized in that, The charging and discharging mathematical model includes a charging mathematical model and a discharging mathematical model; The charging mathematical model is as follows: ； The mathematical model for discharge is as follows: ； in, This refers to the battery output voltage. The battery voltage is constant. Battery polarization resistor; This represents the current remaining battery capacity. The ampere-hour capacity for battery charging and discharging; This refers to the voltage value in the exponential region of the battery discharge curve. R is the reciprocal of the time constant in the exponential region of the curve; R is the internal resistance of the battery. This refers to the battery current. This is the sampling current.

3. The virtual power plant optimization scheduling method based on energy storage estimation according to claim 2, characterized in that, The process of obtaining the current remaining battery capacity of each lithium-ion battery based on the charge-discharge mathematical model and the discharge curves of each lithium-ion battery in the virtual power plant includes: From the discharge curves of various lithium-ion batteries, extract the full-charge voltage, capacity and voltage at the exponential inflection point, and capacity and voltage at the logarithmic inflection point; Based on the full-charge voltage, the capacity and voltage at the exponential inflection point, and the capacity and voltage at the logarithmic inflection point, the current remaining battery capacity of each lithium-ion battery is obtained by solving the preset equations of the charge-discharge mathematical model and key parameters. The key parameters include the battery constant voltage, battery polarization resistance, voltage value in the exponential region of the battery discharge curve, and the reciprocal of the time constant in the exponential region of the curve.

4. The virtual power plant optimization scheduling method based on energy storage estimation according to claim 3, characterized in that, The preset equations for the key parameters are as follows: ； ； ； ； Where A is the voltage value in the exponential region of the battery discharge curve; B is the reciprocal of the time constant in the exponential region of the curve; and K is the battery polarization resistance. R is the constant voltage of the battery; Q is the current remaining battery capacity; R is the internal resistance of the battery. This refers to the battery current. The capacity at the inflection point of the index; The voltage at the exponential inflection point; The capacity of the logarithmic inflection point; The logarithmic inflection point voltage; This is the full charge voltage.

5. The virtual power plant optimization scheduling method based on energy storage estimation according to claim 1, characterized in that, The energy storage modules include photovoltaic energy storage modules and automotive battery energy storage modules; obtaining the amount of electricity available for sale from the virtual power plant based on the real-time state of charge estimation values ​​of each energy storage module includes: The product of the real-time state of charge estimates of all photovoltaic energy storage modules and their rated energy storage capacity is aggregated, and the sum is taken as the amount of electricity that can be sold by the photovoltaic energy storage user group. The product of the real-time state of charge estimates of all automotive battery energy storage modules and their rated energy storage capacity is aggregated, and the sum is used as the amount of electricity that can be sold by the electric vehicle agent group.

6. The virtual power plant optimization scheduling method based on energy storage estimation according to claim 5, characterized in that, The constraints of the optimized scheduling model include: The actual total electricity sales of the photovoltaic energy storage user group shall not exceed the electricity available for sale by the user. The actual total electricity sales of the electric vehicle agent group does not exceed the amount of electricity that the agents can sell.

7. The virtual power plant optimization scheduling method based on energy storage estimation according to claim 1, characterized in that, The process of obtaining a real-time state-of-charge estimate for each energy storage module based on the current remaining battery capacity and standard capacity of all lithium-ion batteries in each energy storage module of the virtual power plant includes: Calculate the sum of the current remaining battery capacity of all lithium-ion batteries in each energy storage module of the virtual power plant; Calculate the sum of the standard capacities of all lithium-ion batteries in each energy storage module of the virtual power plant; The ratio of the sum of the current remaining battery capacities to the sum of the standard capacities is used as the real-time state of charge estimate for each energy storage module.

8. The virtual power plant optimization scheduling method based on energy storage estimation according to claim 1, characterized in that, After constructing and solving the optimal scheduling model of the virtual power plant and outputting the optimal scheduling strategy, the method further includes: Obtain the actual electricity sales of each energy storage module during the execution of the optimal scheduling strategy; The parameters of the charging and discharging mathematical model are corrected and updated based on the difference between the actual electricity sold and the current remaining electricity of each energy storage module.

9. A virtual power plant optimization scheduling device based on energy storage estimation, characterized in that, include: The model building module is used to build mathematical models for the charging and discharging of lithium-ion batteries; The charge-discharge mathematical model is used to describe the dynamic relationship between the battery output voltage, charge-discharge current, battery capacity, and state of charge during the charge-discharge process of a lithium-ion battery. The estimation module is used to obtain the current remaining battery capacity of each lithium-ion battery based on the charge-discharge mathematical model and the discharge curve of each lithium-ion battery in the virtual power plant; and to obtain the real-time state of charge estimate of each energy storage module based on the current remaining battery capacity and standard capacity of all lithium-ion batteries in each energy storage module in the virtual power plant; the discharge curve is used to indicate the dynamic relationship between capacity and voltage when the lithium-ion battery is discharged in the current working environment. The conversion module is used to obtain the amount of electricity available for sale from the virtual power plant based on the real-time state of charge estimation values ​​of each energy storage module. An optimization module is used to construct and solve the optimal scheduling model of the virtual power plant and output the optimal scheduling strategy; the optimal scheduling model takes the amount of electricity available for sale as the key constraint; the optimal scheduling strategy is used to indicate the charging and discharging behavior of each energy storage module.

10. A computer device, characterized in that, The computer device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement a virtual power plant optimization scheduling method based on energy storage estimation as described in any one of claims 1-8.

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