Virtual power plant energy storage capacity configuration method and device
By building and optimizing the objective function of energy storage equipment, considering multiple costs and benefits, and generating configuration information, integrating it into the energy management platform of virtual power plants, the problem of low energy storage capacity configuration in virtual power plants is solved, and more efficient and accurate energy storage management is achieved.
Patent Information
- Application Number
- CN202510143162.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-10
- Publication Date
- 2025-03-11
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The energy storage capacity configuration efficiency of virtual power plants is low, and the existing energy management system has delays or inaccuracies in data acquisition, analysis and processing, and control decision-making, resulting in the energy storage capacity configuration being unable to effectively exert its effectiveness.
By constructing the initial objective function of the energy storage equipment and fine-tuning it based on performance indicators, the final objective function is formed, taking into account the purchase, installation, maintenance, operation costs and additional costs in the failure, and weighting different costs and benefits items in the objective function to generate the final energy storage equipment configuration information, and integrate it into the energy management platform of the virtual power plant for charging and discharging control.
It significantly improves the efficiency and accuracy of the energy storage capacity configuration of virtual power plants, ensures that the performance of energy storage equipment in different application scenarios is better, and can better play the regulating role and value in virtual power plants, and improves the overall energy management level and operation efficiency of virtual power plants.
Smart Images
Figure CN119602353A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of power plant configuration, and in particular to a method and device for configuring energy storage capacity of a virtual power plant. Background Art
[0002] In the context of continuous reform and development of the power market, virtual power plants, as a new type of power market entity, need to enhance their market competitiveness by providing high-quality and efficient power services. Virtual power plants involve many distributed energy and energy storage equipment, which require a powerful energy management system to coordinate. At present, some energy management systems have delays or inaccuracies in data collection, analysis and processing, and control decision-making. For example, the energy storage charging and discharging strategy cannot be adjusted in time according to the changes in distributed energy generation, resulting in power waste or insufficient supply, making the energy storage capacity configuration unable to play its due role.
[0003] At the same time, if the configuration of energy storage capacity of virtual power plants only considers a single cost or benefit factor, it is easy to cause resource mismatch. Accurately predicting the output of distributed energy and load demand is the key to reasonably configuring energy storage capacity. However, due to factors such as weather and changing user electricity habits, the current prediction technology has large errors, the load in the power system fluctuates at any time, and new energy power generation is intermittent due to factors such as weather. Traditional fixed constraints cannot adapt to such changes, making it difficult for the configured energy storage capacity to play its best role at different times. Therefore, how to improve the efficiency of virtual power plant energy storage capacity configuration has become an urgent problem to be solved. Summary of the invention
[0004] The present invention provides a method and device for configuring energy storage capacity of a virtual power plant, the main purpose of which is to solve the problem of low efficiency in configuring energy storage capacity of a virtual power plant.
[0005] To achieve the above object, the present invention provides a method for configuring energy storage capacity of a virtual power plant, comprising: Construct the initial objective function of the energy storage device; Refining the initial objective function based on pre-acquired performance indicators to obtain a secondary objective function of the energy storage device, wherein the pre-acquired performance indicators include: a reliability indicator of the energy storage device and an energy storage scenario application indicator; Based on the importance of the performance indicators, the secondary objective functions are weighted to obtain the final objective function of the energy storage device, wherein the final objective function is: ; in, is the total cost of the energy storage device, is the purchase cost of the energy storage device, is the installation cost of the energy storage device, is the maintenance cost of the energy storage device, is the operating cost of the energy storage device, is the additional cost caused by the failure of the energy storage device, is the frequency regulation service income of the energy storage device, is the peak-shaving and valley-filling benefit of the energy storage device, is the backup power service income of the energy storage device, yes The corresponding weight, is the weight corresponding to the frequency modulation service revenue, is the weight corresponding to the peak-shaving and valley-filling benefits, is the weight corresponding to the revenue of backup power service; Constructing initial constraints of the final objective function, wherein the initial constraints include: physical constraints, technical constraints and operational constraints; Dynamically processing the power balance constraint condition in the operation constraint condition, so as to obtain the final constraint condition of the initial constraint condition; Based on the final objective function and the final constraint condition, generating configuration information of the energy storage device using a linear programming algorithm; The configuration information is integrated into an energy management platform of a virtual power plant, and the energy management platform controls the charging and discharging of the energy storage device based on the configuration information.
[0006] Optionally, the initial objective function is: ; in, is the total cost of the energy storage device, is the purchase cost of the energy storage device, is the installation cost of the energy storage device, is the maintenance cost of the energy storage device, is the operating cost of the energy storage device, is the revenue of the energy storage device.
[0007] Optionally, in detail, the operating cost of the energy storage device for: ; in, is the operating cost of the energy storage device, is a time stamp, is the charge and discharge efficiency of the energy storage device, yes The charging and discharging power at the moment, yes The amount of energy transferred at a given moment.
[0008] Optionally, the secondary objective function is: ; in, is the total cost of the energy storage device, is the purchase cost of the energy storage device, is the installation cost of the energy storage device, is the maintenance cost of the energy storage device, is the operating cost of the energy storage device, is the additional cost caused by the failure of the energy storage device, is the frequency regulation service income of the energy storage device, is the peak-shaving and valley-filling benefit of the energy storage device, It is the backup power service income of the energy storage equipment.
[0009] Optionally, the physical constraint condition is: ; in, is the minimum value of the rated power of the energy storage device, yes The charging power of the energy storage device at the moment, yes The discharge power of the energy storage device at the moment, is the maximum value of the rated power of the energy storage device, is the minimum value of the energy transfer amount of the energy storage device, yes The amount of energy transferred by the energy storage device at the time, is the maximum value of the energy transfer amount of the energy storage device, It is a time stamp.
[0010] Optionally, the technical constraint condition is: ; in, yes The amount of energy transferred by the energy storage device at the moment, yes The amount of energy transferred by the energy storage device at the time, is the charging efficiency of the energy storage device, is the discharge efficiency of the energy storage device, is a time stamp, yes The charging power of the energy storage device at the moment, yes The discharge power of the energy storage device at the moment.
[0011] Optionally, the dynamically processing the power balance constraint in the operation constraint to obtain the final constraint of the initial constraint includes: Obtaining the real-time electricity market price and distributed energy supply of the energy storage device; Based on the real-time electricity market price and the distributed energy supply, generating a random variable of the power balance constraint condition; The power balance constraint condition is dynamically processed according to the random variable to obtain a dynamic power balance constraint, thereby obtaining a final constraint condition of the energy storage device, wherein the dynamic power balance constraint is: ; in, yes The grid interaction power at each moment, yes Load power at any time, yes Photovoltaic power generation at all times, yes Wind power generation at any given moment, yes The charging power of the energy storage device at the moment, yes The discharge power of the energy storage device at the moment, is a time stamp, is a random variable representing real-time power fluctuation.
[0012] Optionally, the generating the configuration information of the energy storage device by using a linear programming algorithm based on the final objective function and the final constraint condition includes: Transforming the final objective function based on a predetermined linear programming standard form; Converting the final constraint condition into a constraint condition in the form of an equality and a constraint condition in the form of an inequality; Based on the final objective function after the form transformation and the final constraint conditions after the transformation, the configuration information of the energy storage device is generated using a preset linear programming solver.
[0013] Optionally, the energy management platform controls the charging and discharging of the energy storage device based on the configuration information, including: The energy management platform generates a charge and discharge control strategy for the energy storage device based on the configuration information; Collecting real-time monitoring data of the energy storage device, and adjusting the strategy of the energy storage device according to the real-time monitoring data to obtain an adjustment strategy of the energy storage device; The energy storage device is controlled to be charged and discharged according to the adjustment strategy.
[0014] In order to solve the above problems, the present invention further provides a virtual power plant energy storage capacity configuration device, the device comprising: An initial objective function building module is used to build the initial objective function of the energy storage device; A function refinement processing module, used to refine the initial objective function based on pre-acquired performance indicators to obtain a secondary objective function of the energy storage device, wherein the pre-acquired performance indicators include: a reliability indicator of the energy storage device and an energy storage scenario application indicator; The performance index weighting module is used to perform weighted processing on the secondary objective function based on the index importance of the performance index to obtain the final objective function of the energy storage device, wherein the final objective function is: ; in, is the total cost of the energy storage device, is the purchase cost of the energy storage device, is the installation cost of the energy storage device, is the maintenance cost of the energy storage device, is the operating cost of the energy storage device, is the additional cost caused by the failure of the energy storage device, is the frequency regulation service income of the energy storage device, is the peak-shaving and valley-filling benefit of the energy storage device, is the backup power service income of the energy storage device, yes The corresponding weight, is the weight corresponding to the frequency modulation service revenue, is the weight corresponding to the peak-shaving and valley-filling benefits, is the weight corresponding to the revenue of backup power service; An initial constraint condition building module, used to build initial constraint conditions of the final objective function, wherein the initial constraint conditions include: physical constraint conditions, technical constraint conditions and operational constraint conditions; A constraint condition dynamic processing module, used for dynamically processing the power balance constraint condition in the operation constraint condition, so as to obtain the final constraint condition of the initial constraint condition; A configuration information generating module, configured to generate configuration information of the energy storage device using a linear programming algorithm based on the final objective function and the final constraint condition; The device charge and discharge control module is used to integrate the configuration information into the energy management platform of the virtual power plant, and the energy management platform controls the charge and discharge of the energy storage device based on the configuration information.
[0015] The present invention constructs an initial objective function for the energy storage device and performs refined processing based on performance indicators, comprehensively considering the purchase cost, installation cost, maintenance cost, operating cost and additional cost caused by failures of the energy storage device. In the final objective function, these costs are uniformly quantified and optimized, which is helpful to find the energy storage capacity configuration scheme with the lowest total cost. The pre-acquired performance indicators include the reliability index of the energy storage device. When the initial objective function is refined, this index is fully considered, so that the optimized secondary objective function and the final objective function can better meet the reliability requirements of the energy storage device. By reflecting the reliability factor in the objective function and comprehensively considering the requirements of these energy storage scenario application indicators in the optimization process of the objective function, the final determined energy storage capacity configuration scheme can be more in line with the actual needs of different energy storage scenarios, thereby significantly improving the performance of the energy storage device in various application scenarios and better playing the role of storage. The role of energy storage equipment in virtual power plants, through dynamic processing of power balance constraints, can make the constraints more accurately reflect the actual operating status and power balance requirements of the power system at different times, and ensure that the charging and discharging power of the energy storage equipment at any time can be reasonably matched and balanced with other parts of the power system, thereby effectively improving the virtual power plant's adaptability and regulation capabilities to dynamic changes in the power system, and integrating the generated configuration information into the energy management platform of the virtual power plant. The energy management platform controls the charging and discharging of the energy storage equipment based on the configuration information, realizing the close integration and efficient collaborative operation between the virtual power plant energy storage system and the energy management platform, which not only gives full play to the regulatory role and value of the energy storage equipment in the virtual power plant, but also significantly improves the overall energy management level and operation efficiency of the virtual power plant. Therefore, the present invention proposes a virtual power plant energy storage capacity configuration method and device, which can solve the problem of low efficiency of virtual power plant energy storage capacity configuration. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] Figure 1 A schematic diagram of a flow chart of a method for configuring energy storage capacity of a virtual power plant provided in one embodiment of the present invention; Figure 2 A functional module diagram of a virtual power plant energy storage capacity configuration device provided by an embodiment of the present invention; The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION
[0017] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.
[0018] The embodiment of the present application provides a method for configuring the energy storage capacity of a virtual power plant. The execution subject of the method for configuring the energy storage capacity of a virtual power plant includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided in the embodiment of the present application. In other words, the method for configuring the energy storage capacity of a virtual power plant can be executed by software or hardware installed on a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms.
[0019] Reference Figure 1 FIG. 1 is a flow chart of a method for configuring energy storage capacity of a virtual power plant provided in an embodiment of the present invention. In this embodiment, the method for configuring energy storage capacity of a virtual power plant includes: S1. Construct the initial objective function of the energy storage device.
[0020] In the embodiment of the present invention, a basic economic model is established to express the total cost of energy storage equipment as the sum of purchase cost, installation cost, maintenance cost, and operating cost minus revenue, so as to achieve the goal of minimizing total cost. This is a basic economic model that takes into account the main cost and revenue elements of energy storage equipment throughout its life cycle. This comprehensive consideration of multiple cost and revenue elements provides a comprehensive economic evaluation framework for the configuration of virtual power plant energy storage capacity.
[0021] In detail, assuming that the purchase cost of an energy storage device is Installation cost Maintenance cost Operating cost The profit should be calculated based on the operation situation. Yuan.
[0022] In this embodiment of the present invention, the initial objective function is: ; in, is the total cost of the energy storage device, is the purchase cost of the energy storage device, is the installation cost of the energy storage device, is the maintenance cost of the energy storage device, is the operating cost of the energy storage device, is the revenue of the energy storage device.
[0023] In detail, for operating costs, assume that at a certain time When the charge and discharge efficiency , charge and discharge power Watt, amount of energy transferred Watt-hour, then according to the formula ,at this time
[0024] In detail, the calculation of operating costs takes into account the charging and discharging power, energy transfer amount and charging and discharging efficiency at different moments, reflecting the cumulative evaluation of operating costs in the time dimension.
[0025] For example, for an energy storage device, its charging and discharging conditions are different at different time points, which will affect the operating costs. The operating cost calculation formula can accurately calculate the operating costs by accumulating the cost contributions at different times, providing a basic basis for subsequent cost optimization.
[0026] In detail, the operating cost of the energy storage device for: ; in, is the operating cost of the energy storage device, is a time stamp, is the charge and discharge efficiency of the energy storage device, yes The charging and discharging power at the moment, yes The amount of energy transferred at a given moment.
[0027] In detail, operating costs cover various expenses incurred by energy storage equipment during normal operation, such as maintenance costs, energy loss costs, personnel management costs, etc. Maintenance costs may be related to the complexity of the equipment and the frequency of replacement of wearing parts. For example, lithium-ion batteries require regular inspection and replacement of battery modules, while compressed air energy storage devices may require maintenance of compressors and gas tanks. Taking lithium-ion batteries as an example, the annual maintenance cost may be about 2%-5% of the initial investment cost. The energy loss cost can be calculated based on the charging and discharging efficiency of the energy storage equipment. For frequent charging and discharging, the energy loss cost will increase significantly, which can be calculated using the above operating cost calculation formula.
[0028] In detail, the charge and discharge efficiency reflects the energy conversion efficiency of the energy storage device during the charge and discharge process, and the charge and discharge efficiency of different energy storage technologies varies.
[0029] In general, constructing the initial objective function of the energy storage device is the starting point for subsequent optimization. It comprehensively considers the main economic factors during the life cycle of the energy storage device, lays the foundation for the overall economic evaluation, and provides a basic economic perspective for the energy storage capacity configuration of the virtual power plant.
[0030] S2. Based on the pre-acquired performance indicators, the initial objective function is refined to obtain a secondary objective function of the energy storage device, wherein the pre-acquired performance indicators include: a reliability indicator of the energy storage device and an energy storage scenario application indicator.
[0031] In the embodiment of the present invention, reliability indicators of energy storage equipment and energy storage scenario application indicators are introduced, and the initial objective function is refined. At the same time, by adding additional costs caused by failure of energy storage equipment and the benefits of different services (frequency regulation, peak shaving and valley filling, backup power supply), the cost-effectiveness of energy storage equipment in different application scenarios and reliability requirements is more accurately reflected.
[0032] In detail, in the complex and ever-changing operating environment of virtual power plants, the reliability of energy storage equipment is crucial. Once a device fails, it will not only cause the device to fail to operate normally, but may also have an impact on the stability of the power supply of the entire virtual power plant, thereby generating a series of additional costs. For example, power outages caused by failures may trigger economic compensation on the user side, human and material investment in the repair process, and market revenue opportunities missed during equipment failures. These additional costs caused by energy storage equipment failures are .
[0033] For example, in different virtual power plant operation scenarios, energy storage equipment may participate in frequency regulation services, provide peak shaving and valley filling, or serve as a backup power source. The additional costs and benefits generated will have a significant impact on the economic performance of the entire device. This step incorporates these factors into the objective function, making the evaluation of energy storage capacity configuration more detailed and comprehensive, reflecting a richer cost-benefit structure in actual application scenarios. In an embodiment of the present invention, the secondary objective function is: ; in, is the total cost of the energy storage device, is the purchase cost of the energy storage device, is the installation cost of the energy storage device, is the maintenance cost of the energy storage device, is the operating cost of the energy storage device, is the additional cost caused by the failure of the energy storage device, is the frequency regulation service income of the energy storage device, is the peak-shaving and valley-filling benefit of the energy storage device, It is the backup power service income of the energy storage equipment.
[0034] In the embodiment of the present invention, continuing the above example, assuming that the additional cost caused by equipment failure is RMB, FM service revenue Yuan, peak-shaving and valley-filling benefits Yuan, backup power service revenue , then the secondary objective function is .
[0035] In detail, energy storage equipment can play a variety of functions and create different types of benefits in different application scenarios. Common application scenarios include participating in frequency regulation services, performing peak-shaving and valley-filling tasks, and serving as a backup power source to ensure power supply. Among them, the benefits of frequency regulation services are closely related to the rapid response capability of energy storage equipment. It can quickly adjust its own charging and discharging power according to the slight fluctuations in the grid frequency, maintain the stability of the grid frequency, and thus obtain corresponding rewards; the benefits of peak-shaving and valley-filling are to use the peak-valley electricity price difference in the electricity market, charge during the low electricity price period, discharge during the peak period, realize low buying and high selling, and earn the difference; the benefits of backup power service are that when there is a sudden failure in the grid or the power supply is tight, the energy storage equipment is put into use in time to ensure the continuous power supply of key loads, and then obtain economic returns.
[0036] In general, more practical application scenarios and fault factors are taken into consideration, making the configuration objective function of the energy storage equipment more in line with the actual operating conditions and enriching the data processing and cost optimization of virtual power plant management.
[0037] S3. Based on the importance of the performance indicators, weighted processing is performed on the secondary objective functions to obtain a final objective function of the energy storage device.
[0038] In the embodiment of the present invention, different weights are assigned to different cost and benefit items, reflecting the importance of different factors in the configuration of energy storage equipment. This weighted processing allows the objective function to be flexibly adjusted according to different operational needs and scenarios, improving the adaptability and customizability of the energy storage capacity configuration method.
[0039] In detail, the final objective function is: ; in, is the total cost of the energy storage device, is the purchase cost of the energy storage device, is the installation cost of the energy storage device, is the maintenance cost of the energy storage device, is the operating cost of the energy storage device, is the additional cost caused by the failure of the energy storage device, is the frequency regulation service income of the energy storage device, is the peak-shaving and valley-filling benefit of the energy storage device, is the backup power service income of the energy storage device, yes The corresponding weight, is the weight corresponding to the frequency modulation service revenue, is the weight corresponding to the peak-shaving and valley-filling benefits, is the weight corresponding to the revenue of backup power service.
[0040] For example, for some virtual power plants with high requirements for frequency regulation services, the weight of frequency regulation service revenue can be increased. For scenarios that focus on cost savings, the weight of cost items can be increased. This allows the objective function to be flexibly adjusted to adapt to different operational goals and scenario requirements, improving the flexibility and pertinence of energy storage capacity configuration. This weighted processing is an innovative improvement on the traditional single cost minimization or benefit maximization objectives. By setting weights, the optimization process can be guided in a direction that is more in line with actual needs, achieving customized energy storage capacity configuration.
[0041] In detail, assuming the weights , then the final objective function is: .
[0042] Furthermore, the various costs (purchase, installation, maintenance, operation, failure) and benefits (frequency regulation services, peak load shaving, backup power supply) of energy storage equipment are incorporated into the construction of the objective function. Through gradually refined steps, from the initial objective function, through the secondary objective function, and finally to the final objective function, the economic performance of energy storage equipment is comprehensively evaluated. Compared with the traditional method of only considering part of the cost or benefit, this multi-dimensional consideration method can more accurately reflect the comprehensive economic value of energy storage equipment in different scenarios, and provide a more complete economic model foundation for the optimal configuration of the energy storage capacity of the virtual power plant.
[0043] In detail, weighted processing is introduced to adjust the secondary objective function according to the importance of different performance indicators, so that the objective function has higher flexibility and adaptability. Different cost and benefit factors can be evaluated on their importance according to different virtual power plant operation scenarios and needs, thus achieving customized energy storage capacity configuration optimization.
[0044] S4. Constructing initial constraints of the final objective function, wherein the initial constraints include: physical constraints, technical constraints and operational constraints.
[0045] In the embodiment of the present invention, the physical constraint condition is: ; in, is the minimum value of the rated power of the energy storage device, yes The charging power of the energy storage device at the moment, yes The discharge power of the energy storage device at the moment, is the maximum value of the rated power of the energy storage device, is the minimum value of the energy transfer amount of the energy storage device, yes The amount of energy transferred by the energy storage device at the moment, is the maximum value of the energy transfer amount of the energy storage device, It is a time stamp.
[0046] In detail, the physical constraints specify the upper and lower limits of the rated power and energy transfer of the energy storage device, ensuring that the energy storage device operates within the safety and design range, which is a basic requirement for ensuring the stability of the physical performance of the energy storage device.
[0047] In detail, it is assumed that the rated power range of the energy storage device is Watts to Watts, the energy transfer range is Watt hours Watt, at a certain moment , charging power Watt, discharge power Watt, amount of energy transferred Watt-hours, the physical constraints are met.
[0048] In the embodiment of the present invention, the technical constraints are: ; in, yes The amount of energy transferred by the energy storage device at the moment, yes The amount of energy transferred by the energy storage device at the moment, is the charging efficiency of the energy storage device, is the discharge efficiency of the energy storage device, is a time stamp, yes The charging power of the energy storage device at the moment, yes The discharge power of the energy storage device at the moment.
[0049] In detail, the technical constraints describe the relationship between the energy transfer amounts of energy storage devices at different times, taking into account the charging and discharging efficiency, ensuring the dynamic balance of energy of the energy storage devices at different times and the accuracy of energy conversion, and are quantitative constraints on the technical performance of energy storage devices.
[0050] In detail, according to the application scenarios and needs of the virtual power plant, select appropriate energy storage technologies, such as lithium-ion batteries, lead-acid batteries, flow batteries, compressed air energy storage, pumped storage, etc. Different energy storage technologies have different performance parameters, such as energy density, power density, charge and discharge efficiency, cycle life, cost, etc.
[0051] In general, comprehensive physical and technical constraints provide physical and technical boundaries for the performance and operation of energy storage equipment, ensuring the physical and technical feasibility of the optimization results, which is an important guarantee for achieving optimal energy storage capacity configuration.
[0052] S5. Dynamically process the power balance constraint in the operation constraint to obtain the final constraint of the initial constraint.
[0053] In the embodiment of the present invention, the dynamic processing of the power balance constraint in the operation constraint to obtain the final constraint of the initial constraint includes: Obtaining the real-time electricity market price and distributed energy supply of the energy storage device; Based on the real-time electricity market price and the distributed energy supply, generating a random variable of the power balance constraint condition; The power balance constraint condition is dynamically processed according to the random variable to obtain a dynamic power balance constraint, thereby obtaining a final constraint condition of the energy storage device, wherein the dynamic power balance constraint is: ; in, yes The grid interaction power at each moment, yes Load power at any time, yes Photovoltaic power generation at all times, yes Wind power generation at any given moment, yes The charging power of the energy storage device at the moment, yes The discharge power of the energy storage device at the moment, is a time stamp, is a random variable representing real-time power fluctuation.
[0054] In the embodiment of the present invention, the real-time electricity market price and distributed energy supply are taken into account, and random variables are introduced to dynamically process the power balance constraint conditions. This reflects the interaction and real-time adjustment capabilities of the virtual power plant with the external environment, so that the configuration and operation of the energy storage equipment can adapt to the dynamic changes of the electricity market.
[0055] In an embodiment of the present invention, the power balance constraint condition is: ; in, yes The grid interaction power at each moment, yes Load power at any time, yes Photovoltaic power generation at all times, yes Wind power generation at any given moment, yes The charging power of the energy storage device at the moment, yes The discharge power of the energy storage device at the moment, It is a time stamp.
[0056] In detail, assuming that hour, watt, watt, watt, watt, watt, Watts, a random variable of real-time power fluctuations watt.
[0057] For example, when electricity market prices fluctuate or distributed energy supply is unstable, the power balance of energy storage equipment needs to take these real-time changes into account. Dynamic power balance constraints enable the configuration and operation of energy storage equipment to respond to the external environment in real time, thereby improving the adaptability and flexibility of energy storage equipment in dynamic power installations and better coordinating the power interaction between energy storage equipment and the power grid and distributed energy.
[0058] S6. Based on the final objective function and the final constraint condition, generate configuration information of the energy storage device using a linear programming algorithm.
[0059] In an embodiment of the present invention, the step of generating the configuration information of the energy storage device using a linear programming algorithm based on the final objective function and the final constraint condition includes: Transforming the final objective function based on a predetermined linear programming standard form; Converting the final constraint condition into a constraint condition in the form of an equality and a constraint condition in the form of an inequality; Based on the final objective function after the form transformation and the final constraint conditions after the transformation, the configuration information of the energy storage device is generated using a preset linear programming solver.
[0060] In detail, the final objective function is converted into a linear programming standard form, and the constraints are converted accordingly, and the preset linear programming solver is used to solve the problem to achieve the optimal configuration of the energy storage device. This method of converting complex objective functions and constraints into solvable linear programming problems is an efficient optimization method.
[0061] In detail, it is assumed that the above-mentioned final objective function and constraints are converted into a standard linear programming form and solved using a linear programming solver (such as CPLEX) to obtain the optimal configuration of the energy storage device, such as the rated power, energy capacity and other specific parameters of the energy storage device.
[0062] Specifically, the final objective function needs to be converted into a standard linear programming form. Since the final objective function contains multiple cost and benefit items, each with its corresponding weight, we use a series of mathematical transformations to organize it into the form The standard form of is the coefficient vector of the objective function, which is composed of the weights and correlation coefficients of each cost and benefit item; is a decision variable vector, covering key configuration parameters such as rated power, energy capacity, charge and discharge power of energy storage equipment. At the same time, the final constraints are carefully transformed and broken down into constraints in the form of equations and constraints in the form of inequalities. Equality constraints are mainly used to describe strict equal relationships at the physical and technical levels such as energy conservation and power balance; inequality constraints correspond to boundary conditions such as upper and lower limits of power and energy range.
[0063] S7. Integrate the configuration information into the energy management platform of the virtual power plant, and the energy management platform controls the charging and discharging of the energy storage device based on the configuration information.
[0064] In an embodiment of the present invention, integrating the configuration information into an energy management platform of a virtual power plant includes: The configuration information is transmitted to the energy management platform of the virtual power plant according to a predefined communication interface.
[0065] In the embodiment of the present invention, the optimized configuration information is transmitted to the energy management platform through the communication interface. The platform generates a charge and discharge control strategy based on the configuration information, and adjusts the strategy based on the real-time monitoring data, forming a closed loop from planning optimization to actual operation control. For example, based on the configuration information, the energy management platform can set the charge and discharge power of the energy storage device at different time periods. When the temperature of the energy storage device is too high or the power is too low, the strategy is adjusted according to the real-time monitoring data to ensure the safe and efficient operation of the equipment.
[0066] In an embodiment of the present invention, the energy management platform controls the charging and discharging of the energy storage device based on the configuration information, including: The energy management platform generates a charge and discharge control strategy for the energy storage device based on the configuration information; Collecting real-time monitoring data of the energy storage device, and adjusting the strategy of the energy storage device according to the real-time monitoring data to obtain an adjustment strategy of the energy storage device; The energy storage device is controlled to be charged and discharged according to the adjustment strategy.
[0067] In detail, this approach of closely combining optimization results with actual operations innovatively realizes refined control of energy storage equipment, ensures that the operation of energy storage equipment can be flexibly adjusted according to actual conditions, improves the overall operating efficiency and stability of the virtual power plant, and ensures the effective and reliable operation of energy storage equipment in the entire virtual power plant device.
[0068] The present invention constructs an initial objective function for the energy storage device and performs refined processing based on performance indicators, comprehensively considering the purchase cost, installation cost, maintenance cost, operating cost and additional cost caused by failures of the energy storage device. In the final objective function, these costs are uniformly quantified and optimized, which is helpful to find the energy storage capacity configuration scheme with the lowest total cost. The pre-acquired performance indicators include the reliability index of the energy storage device. When the initial objective function is refined, this index is fully considered, so that the optimized secondary objective function and the final objective function can better meet the reliability requirements of the energy storage device. By reflecting the reliability factor in the objective function and comprehensively considering the requirements of these energy storage scenario application indicators in the optimization process of the objective function, the final determined energy storage capacity configuration scheme can be more in line with the actual needs of different energy storage scenarios, thereby significantly improving the performance of the energy storage device in various application scenarios and better developing In order to give full play to the role of energy storage equipment in the virtual power plant, by dynamically processing the power balance constraint conditions, the constraint conditions can more accurately reflect the actual operating status and power balance requirements of the power system at different times, and ensure that the charging and discharging power of the energy storage equipment at any time can be reasonably matched and balanced with other parts of the power system, thereby effectively improving the virtual power plant's adaptability and regulation capabilities to dynamic changes in the power system, and integrating the generated configuration information into the energy management platform of the virtual power plant. The energy management platform controls the charging and discharging of the energy storage equipment based on the configuration information, thereby realizing the close integration and efficient collaborative operation between the virtual power plant energy storage system and the energy management platform, which not only gives full play to the regulatory role and value of the energy storage equipment in the virtual power plant, but also significantly improves the overall energy management level and operation efficiency of the virtual power plant. Therefore, the present invention proposes a virtual power plant energy storage capacity configuration method, which can solve the problem of low efficiency in energy storage capacity configuration of the virtual power plant.
[0069] like Figure 2 , which is a functional module diagram of a virtual power plant energy storage capacity configuration device provided by an embodiment of the present invention.
[0070] The virtual power plant energy storage capacity configuration device 100 of the present invention can be installed in an electronic device. According to the functions to be implemented, the virtual power plant energy storage capacity configuration device 100 can include an initial objective function construction module 101, a function refinement processing module 102, a performance index weighting module 103, an initial constraint condition construction module 104, a constraint condition dynamic processing module 105, a configuration information generation module 106 and an equipment charging and discharging control module 107. The module of the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.
[0071] In this embodiment, the functions of each module / unit are as follows: The initial objective function construction module 101 is used to construct the initial objective function of the energy storage device; The function refinement processing module 102 is used to refine the initial objective function based on the pre-acquired performance indicators to obtain the secondary objective function of the energy storage device, wherein the pre-acquired performance indicators include: the reliability indicator of the energy storage device and the energy storage scenario application indicator; The performance index weighting module 103 is used to perform weighted processing on the secondary objective function based on the index importance of the performance index to obtain the final objective function of the energy storage device, wherein the final objective function is: ; in, is the total cost of the energy storage device, is the purchase cost of the energy storage device, is the installation cost of the energy storage device, is the maintenance cost of the energy storage device, is the operating cost of the energy storage device, is caused by a failure of the energy storage device Additional costs, is the frequency regulation service income of the energy storage device, is the peak-shaving and valley-filling benefit of the energy storage device, is the backup power service income of the energy storage device, yes The corresponding weight, is the weight corresponding to the frequency modulation service revenue, is the weight corresponding to the peak-shaving and valley-filling benefits, is the weight corresponding to the revenue of backup power service; The initial constraint condition building module 104 is used to build the initial constraint conditions of the final objective function, wherein the initial constraint conditions include: physical constraint conditions, technical constraint conditions and operational constraint conditions; The constraint condition dynamic processing module 105 is used to dynamically process the power balance constraint condition in the operation constraint condition, so as to obtain the final constraint condition of the initial constraint condition; The configuration information generating module 106 is used to generate the configuration information of the energy storage device using a linear programming algorithm based on the final objective function and the final constraint condition; The device charge and discharge control module 107 is used to integrate the configuration information into the energy management platform of the virtual power plant, and the energy management platform performs charge and discharge control on the energy storage device based on the configuration information.
[0072] In the several embodiments provided by the present invention, it should be understood that the disclosed methods and devices can be implemented in other ways. For example, the device embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.
[0073] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0074] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.
[0075] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.
[0076] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology and application device that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.
[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.
Claims
1. A method for configuring energy storage capacity of a virtual power plant, characterized in that: The method comprises: Construct the initial objective function of the energy storage device; Refining the initial objective function based on pre-acquired performance indicators to obtain a secondary objective function of the energy storage device, wherein the pre-acquired performance indicators include: a reliability indicator of the energy storage device and an energy storage scenario application indicator; Based on the importance of the performance indicators, the secondary objective functions are weighted to obtain the final objective function of the energy storage device, wherein the final objective function is: ； in, is the total cost of the energy storage device, is the purchase cost of the energy storage device, is the installation cost of the energy storage device, is the maintenance cost of the energy storage device, is the operating cost of the energy storage device, is the additional cost caused by the failure of the energy storage device, is the frequency regulation service income of the energy storage device, is the peak-shaving and valley-filling benefit of the energy storage device, is the backup power service income of the energy storage device, yes The corresponding weight, is the weight corresponding to the frequency modulation service revenue, is the weight corresponding to the peak-shaving and valley-filling benefits, is the weight corresponding to the revenue of backup power service; Constructing initial constraints of the final objective function, wherein the initial constraints include: physical constraints, technical constraints and operational constraints; Dynamically processing the power balance constraint condition in the operation constraint condition, so as to obtain the final constraint condition of the initial constraint condition; Based on the final objective function and the final constraint condition, generating configuration information of the energy storage device using a linear programming algorithm; The configuration information is integrated into an energy management platform of a virtual power plant, and the energy management platform controls the charging and discharging of the energy storage device based on the configuration information.
2. The method for configuring energy storage capacity of a virtual power plant according to claim 1, characterized in that: The initial objective function is: ； in, is the total cost of the energy storage device, is the purchase cost of the energy storage device, is the installation cost of the energy storage device, is the maintenance cost of the energy storage device, is the operating cost of the energy storage device, is the revenue of the energy storage device.
3. The method for configuring energy storage capacity of a virtual power plant according to claim 2, characterized in that: The operating cost of the energy storage device for: ； in, is the operating cost of the energy storage device, is a time stamp, is the charge and discharge efficiency of the energy storage device, yes The charging and discharging power at the moment, yes The amount of energy transferred at a given moment.
4. The method for configuring energy storage capacity of a virtual power plant according to claim 1, characterized in that: The secondary objective function is: ； in, is the total cost of the energy storage device, is the purchase cost of the energy storage device, is the installation cost of the energy storage device, is the maintenance cost of the energy storage device, is the operating cost of the energy storage device, is the additional cost caused by the failure of the energy storage device, is the frequency regulation service income of the energy storage device, is the peak-shaving and valley-filling benefit of the energy storage device, It is the backup power service income of the energy storage equipment.
5. The method for configuring energy storage capacity of a virtual power plant according to claim 1, characterized in that: The physical constraints are: ； in, is the minimum value of the rated power of the energy storage device, yes The charging power of the energy storage device at the moment, yes The discharge power of the energy storage device at the moment, is the maximum value of the rated power of the energy storage device, is the minimum value of the energy transfer amount of the energy storage device, yes The amount of energy transferred by the energy storage device at the time, is the maximum value of the energy transfer amount of the energy storage device, It is a time stamp.
6. The method for configuring energy storage capacity of a virtual power plant according to claim 1, characterized in that: The technical constraints are: ； in, yes The amount of energy transferred by the energy storage device at the moment, yes The amount of energy transferred by the energy storage device at the moment, is the charging efficiency of the energy storage device, is the discharge efficiency of the energy storage device, is a time stamp, yes The charging power of the energy storage device at the moment, yes The discharge power of the energy storage device at the moment.
7. The method for configuring energy storage capacity of a virtual power plant according to claim 1, characterized in that: The dynamically processing the power balance constraint condition in the operation constraint condition to obtain the final constraint condition of the initial constraint condition includes: Obtaining the real-time electricity market price and distributed energy supply of the energy storage device; Based on the real-time electricity market price and the distributed energy supply, generating a random variable of the power balance constraint condition; The power balance constraint condition is dynamically processed according to the random variable to obtain a dynamic power balance constraint, thereby obtaining a final constraint condition of the energy storage device, wherein the dynamic power balance constraint is: ； in, yes The grid interaction power at each moment, yes Load power at any time, yes Photovoltaic power generation at all times, yes Wind power generation at any given moment, yes The charging power of the energy storage device at the moment, yes The discharge power of the energy storage device at the moment, is a time stamp, is a random variable representing real-time power fluctuation.
8. The method for configuring energy storage capacity of a virtual power plant according to claim 1, characterized in that: The step of generating the configuration information of the energy storage device by using a linear programming algorithm based on the final objective function and the final constraint condition includes: Transforming the final objective function based on a predetermined linear programming standard form; Converting the final constraint condition into a constraint condition in the form of an equality and a constraint condition in the form of an inequality; Based on the final objective function after the form transformation and the final constraint conditions after the transformation, the configuration information of the energy storage device is generated using a preset linear programming solver.
9. The method for configuring energy storage capacity of a virtual power plant according to any one of claims 1 to 8, characterized in that: The energy management platform controls the charging and discharging of the energy storage device based on the configuration information, including: The energy management platform generates a charge and discharge control strategy for the energy storage device based on the configuration information; Collecting real-time monitoring data of the energy storage device, and adjusting the strategy of the energy storage device according to the real-time monitoring data to obtain an adjustment strategy of the energy storage device; The energy storage device is controlled to be charged and discharged according to the adjustment strategy.
10. A virtual power plant energy storage capacity configuration device, characterized in that: The device comprises: An initial objective function building module is used to build the initial objective function of the energy storage device; A function refinement processing module, used to refine the initial objective function based on pre-acquired performance indicators to obtain a secondary objective function of the energy storage device, wherein the pre-acquired performance indicators include: a reliability indicator of the energy storage device and an energy storage scenario application indicator; The performance index weighting module is used to perform weighted processing on the secondary objective function based on the index importance of the performance index to obtain the final objective function of the energy storage device, wherein the final objective function is: ； in, is the total cost of the energy storage device, is the purchase cost of the energy storage device, is the installation cost of the energy storage device, is the maintenance cost of the energy storage device, is the operating cost of the energy storage device, is the additional cost caused by the failure of the energy storage device, is the frequency regulation service income of the energy storage device, is the peak-shaving and valley-filling benefit of the energy storage device, is the backup power service income of the energy storage device, yes The corresponding weight, is the weight corresponding to the frequency modulation service revenue, is the weight corresponding to the peak-shaving and valley-filling benefits, is the weight corresponding to the revenue of backup power service; An initial constraint condition building module, used to build initial constraint conditions of the final objective function, wherein the initial constraint conditions include: physical constraint conditions, technical constraint conditions and operational constraint conditions; A constraint condition dynamic processing module, used for dynamically processing the power balance constraint condition in the operation constraint condition, so as to obtain the final constraint condition of the initial constraint condition; A configuration information generating module, configured to generate configuration information of the energy storage device using a linear programming algorithm based on the final objective function and the final constraint condition; The device charge and discharge control module is used to integrate the configuration information into the energy management platform of the virtual power plant, and the energy management platform controls the charge and discharge of the energy storage device based on the configuration information.