Virtual power plant energy management and control method and system

Abstract

The invention provides a virtual power plant energy management and control method and system, and belongs to the technical field of energy management and control, and the method comprises the steps: determining a target calculation resource based on the power supply amount of a virtual power plant and the load amount of a user side; determining a target energy scheduling strategy from a plurality of energy scheduling strategies based on the target computing resource, the control precision of the plurality of energy scheduling strategies being different; and performing energy management and control on the virtual power plant based on the target energy scheduling strategy. According to the virtual power plant energy management and control method and system provided by the invention, the efficiency and reliability of virtual power plant energy management and control can be improved.

Description

Technical Field

[0001] The present disclosure belongs to the field of energy management and control technology, and more specifically, to a virtual power plant energy management and control method and system. Background Art

[0002] As the world pays more attention to environmental protection and sustainable development, the traditional energy structure is gradually transforming to clean energy. Virtual power plant is a power coordination management system that aggregates and coordinates multiple distributed energy sources. Distributed energy is widely distributed, small in scale and relatively dispersed, making it difficult to uniformly dispatch and manage like traditional centralized power generation.

[0003] Therefore, an efficient and reliable virtual power plant energy management and control method is needed. Summary of the invention

[0004] The purpose of the present disclosure is to provide a virtual power plant energy management method and system to improve the efficiency and reliability of virtual power plant energy management.

[0005] A first aspect of an embodiment of the present disclosure provides a virtual power plant energy management and control method, including: Determine the target computing resources based on the power supply of the virtual power plant and the load on the user side; Determining a target energy scheduling strategy from a plurality of energy scheduling strategies based on the target computing resource, wherein the plurality of energy scheduling strategies have different control accuracies; Energy management and control of the virtual power plant is performed based on the target energy scheduling strategy.

[0006] A second aspect of the embodiments of the present disclosure provides a virtual power plant energy management and control system, including: A computing resource determination module, used to determine target computing resources based on the power supply of the virtual power plant and the load of the user end; A scheduling strategy determination module, configured to determine a target energy scheduling strategy from a plurality of energy scheduling strategies based on the target computing resource, wherein the plurality of energy scheduling strategies have different control precisions; An energy management and control module is used to perform energy management and control on the virtual power plant based on the target energy scheduling strategy.

[0007] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned virtual power plant energy management and control method when executing the computer program.

[0008] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned virtual power plant energy management and control method are implemented.

[0009] The beneficial effects of the virtual power plant energy management and control method and system provided by the embodiments of the present disclosure are: The present disclosure determines the target computing resources through the power supply of the virtual power plant and the load on the user side, and can dynamically adjust the computing resources according to the actual situation to avoid waste or shortage of resources. Then, the target energy management and control strategy is determined based on the target computing resources, so that the operating cost and energy utilization rate can be reasonably planned. Afterwards, the present disclosure uses the target energy management and control strategy to control the energy of the virtual power plant, which can ensure the stability and reliability of the power supply, effectively guarantee the power demand of the user side, reduce the risk of power shortage, and improve the safety and quality of power grid operation. At the same time, the present disclosure enables the virtual power plant to operate efficiently under different working conditions, enhances its adaptability and flexibility to complex power environments, and promotes the optimal allocation and sustainable development of energy. Therefore, the present disclosure can improve the efficiency and reliability of energy management and control of virtual power plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 A schematic diagram of a flow chart of a virtual power plant energy management and control method provided in an embodiment of the present disclosure; Figure 2 A structural block diagram of a virtual power plant energy management and control system provided in one embodiment of the present disclosure; Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0012] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present disclosure. However, it should be clear to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present disclosure with unnecessary details.

[0013] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0014] Please refer to Figure 1 , Figure 1 A flow chart of a virtual power plant energy management and control method provided in an embodiment of the present disclosure, the method comprising: S101: Determine target computing resources based on the power supply of the virtual power plant and the load on the user side.

[0015] In this embodiment, the virtual power plant includes multiple distributed power sources and energy storage systems, and the multiple distributed power sources include solar photovoltaic power generation, wind power generation, small hydropower and biomass power generation. The power supply of the virtual power plant is the total amount of electric energy generated by the discharge of multiple distributed power sources and energy storage systems at a certain moment.

[0016] The load at the user end is the total amount of electricity consumed by all users served by the virtual power plant at a certain moment. All users include residential users, commercial users, industrial users, and public facility users.

[0017] The target computing resources are to achieve effective control of the energy of the virtual power plant. According to the current power supply of the virtual power plant and the load of the user end, the required computing power related resources are determined. The target computing resources can include computer hardware, software algorithms and data processing tools. The computer hardware can include the CPU performance, memory size, storage capacity, etc. of the server. Alternatively, the target computing resources can also be the selection of the power supply quantity of multiple distributed power sources and energy storage systems in the virtual power plant, the setting of constraints and the clarification of control parameters.

[0018] Specifically, the steps of this embodiment may be as follows: First, the total amount of electricity that the virtual power plant can provide (power supply) and the total amount of electricity actually consumed by the user end (load) are counted and compared; Second, the current target computing resource is determined based on the comparison result of the first step.

[0019] Suppose there is a virtual power plant that integrates distributed power sources from multiple regions to supply power to part of a small city, including residential areas, commercial areas, and small industrial areas.

[0020] During the day, the solar photovoltaic power generation is sufficient, and the power supply of the virtual power plant can reach 8MW. At this time, the load on the user side is about 3MW because most residents go out to work, some commercial stores are not fully open, and some equipment of industrial enterprises is running at low load. In this case, the power supply is greater than the load, and the overall energy management is simple. For example, a medium-configuration server (such as a 4-core CPU, 8GB memory, and a 500GB hard disk) runs a simple load balancing and power generation priority scheduling algorithm to determine that the excess power is stored in the energy storage system first, and appropriately reduce the power generation of some distributed power sources.

[0021] S102: Determine a target energy scheduling strategy from a plurality of energy scheduling strategies based on a target computing resource, wherein the plurality of energy scheduling strategies have different control accuracies.

[0022] In this embodiment, the energy dispatch strategy is used to control how the virtual power plant coordinates and manages multiple distributed power sources and energy storage systems within it, and can achieve different energy management goals. For example, an energy dispatch strategy can stipulate that during the low-power consumption period, the energy storage system is given priority to charge, while the power generation output of the distributed power source is reduced to store the excess power; during the peak power consumption period, the distributed power source is given priority to be fully dispatched, the energy storage system is discharged to make up for the shortfall, and the controllable load is appropriately adjusted to balance supply and demand.

[0023] Control accuracy refers to the degree of detail and accuracy of the energy scheduling strategy in regulating and controlling various energy sources in the virtual power plant.

[0024] If the control accuracy is greater than the preset accuracy, the selected energy scheduling strategy requires fine scheduling, the considered constraints are greater than the first preset number, and the considered control parameters are greater than the second preset number; If the control accuracy is less than the preset accuracy, the selected energy scheduling strategy requires conventional scheduling, the considered constraints are less than the first preset number, and the considered control parameters are less than the second preset number.

[0025] Therefore, after determining the target computing resources suitable for the current operation of the virtual power plant, it is necessary to select an optimal target energy scheduling strategy from multiple available energy scheduling strategies. Each energy scheduling strategy has differences in control accuracy, but all can ensure the stable operation of the virtual power plant and the reasonable allocation of energy.

[0026] Suppose there is a virtual power plant consisting of multiple distributed solar power stations, wind farms, battery energy storage systems, and some controllable loads, providing electricity services to a comprehensive community.

[0027] If the control accuracy required by the target computing resource is greater than the preset accuracy, the energy scheduling strategy selected at this time can be: This embodiment can monitor the power generation of each power generation equipment in each distributed solar power station and wind farm in real time, and accurately adjust the power generation of each equipment according to the weather change prediction model and real-time meteorological data, so that it always operates at the optimal efficiency point. For the battery energy storage system, the charge and discharge status of each battery module can be accurately determined, and the optimal charge and discharge current and voltage can be controlled to extend the battery life and improve the energy storage efficiency. In terms of controllable loads, the cooling capacity of the central air-conditioning system can be adjusted at time intervals of minutes according to the electricity demand and real-time electricity prices in different regions. Different cooling capacities correspond to different power consumptions, and according to the production task priorities of industrial enterprises, the start and stop time and operating power of interruptible production equipment can be accurately controlled to achieve refined scheduling of the energy of the entire virtual power plant, minimize operating costs and improve energy utilization efficiency.

[0028] S103: Perform energy management and control on the virtual power plant based on the target energy scheduling strategy.

[0029] In this embodiment, energy management and control refers to comprehensive monitoring, analysis, scheduling and control of all aspects of energy production, transmission, storage, distribution and consumption of the virtual power plant. The purpose of energy management and control is to ensure that the virtual power plant can achieve optimal energy configuration, reduce operating costs, improve energy utilization, and ensure the safe and stable operation of the power system while meeting the power needs of users.

[0030] For example, this embodiment monitors the power generation of distributed power sources, the charging and discharging status of the energy storage system, and the load changes at the user end in real time, and uses the target energy scheduling strategy to dynamically adjust the above energy resources, including controlling the start and stop of distributed power sources and power output, adjusting the charging and discharging process of the energy storage system, and managing the power consumption of controllable loads, so as to achieve effective management and control of the energy of the entire virtual power plant.

[0031] Suppose there is a virtual power plant that integrates a 3MW solar photovoltaic power station, a 2MW wind farm, a battery energy storage system with a total capacity of 1.5MW / 3MWh, and several controllable loads to supply power to an area including residential areas, small commercial areas and light industrial parks.

[0032] The target energy dispatch strategy determined by the virtual power plant is as follows: During the low electricity consumption period (such as 0:00 am to 6:00 am), if the user-side load is less than 1MW and the state of charge (SOC) of the battery energy storage system is less than 80%, the solar photovoltaic power station and wind farm are controlled to operate at the lowest power to only meet the basic load needs of the user, and the battery energy storage system is charged with appropriate power. The charging power is determined according to the health status and remaining capacity of the battery, but not more than 0.8MW. For example, at 3:00 am, the user load is 0.5MW, the solar photovoltaic power station generates electricity at 0.3MW, and the wind turbine stops generating electricity. At this time, the energy storage system charging power is 0.3MW (0.3-0.5+0.5=0.3, which is not more than 0.8MW due to the policy limit), so as to make full use of the low-price electricity period to reserve electricity and prepare for the subsequent peak electricity consumption.

[0033] From the above, it can be concluded that the present disclosure determines the target computing resources through the power supply of the virtual power plant and the load on the user side, and can dynamically adjust the computing resources according to the actual situation to avoid waste or shortage of resources. Then, the target energy management and control strategy is determined based on the target computing resources, so that the operating cost and energy utilization rate can be reasonably planned. Afterwards, the present disclosure uses the target energy management and control strategy to control the energy of the virtual power plant, which can ensure the stability and reliability of the power supply, effectively guarantee the electricity demand of the user side, reduce the risk of power shortage, and improve the safety and quality of power grid operation. At the same time, the present disclosure enables the virtual power plant to operate efficiently under different working conditions, enhances its adaptability and flexibility to complex power environments, and promotes the optimal allocation and sustainable development of energy. Therefore, the present disclosure can improve the efficiency and reliability of energy management and control of virtual power plants.

[0034] In one embodiment of the present disclosure, determining a target computing resource based on the power supply of a virtual power plant and the load of a user terminal includes: If the power supply is greater than the load, the first computing resource is used as the target computing resource; If the power supply is less than the load, the second computing resource is used as the target computing resource; The first computing resource is smaller than the second computing resource.

[0035] In this embodiment, first, the relationship between the power supply of the virtual power plant and the load on the user side is determined. If the power supply is greater than the load, it means that there is a surplus of electricity at this time, and the supply and demand of electricity are relatively loose. In this case, the first computing resource is selected as the target computing resource, because the complexity and accuracy requirements for energy scheduling are relatively low at this time, and extremely fine regulation is not required. The first computing resource is sufficient to cope with relatively simple tasks, such as reasonably allocating and storing excess electricity in the energy storage system or transmitting electricity according to basic market rules.

[0036] On the contrary, if the power supply is less than the load, it means that the power supply is tight, and more sophisticated and complex energy scheduling is needed to balance the supply and demand relationship, such as accurately controlling the power generation of distributed power sources, reasonably arranging the discharge strategy of the energy storage system, and carefully adjusting various controllable loads to reduce power demand. At this time, a more powerful second computing resource is needed as the target computing resource to meet the high-precision and high-complexity energy scheduling requirements and ensure the stable operation of the power system.

[0037] Suppose there is a virtual power plant that supplies electricity to a complex area that includes residential areas, commercial areas, and small industrial areas.

[0038] During the peak period of electricity consumption in the evening, solar power generation stops, wind power generation drops to 1MW, and the load on the user side increases significantly to 6MW, and the power supply is less than the load. At this time, the virtual power plant uses the second computing resource, a cluster consisting of multiple high-performance servers running complex energy scheduling software. The software first accurately analyzes the remaining power and discharge capacity of the energy storage system. For example, the energy storage system can discharge 2MW, and then tries to further increase the wind power generation capacity (assuming it can be increased to 1.5MW) according to the power generation cost and response characteristics of different distributed power sources. At the same time, it fine-tunes the controllable load, such as reducing the brightness of some non-critical lighting in the commercial area and reducing the power consumption by 0.5MW. Through these precise control measures, the user's electricity demand is met as much as possible, ensuring the stable operation of the power system under tight power supply conditions.

[0039] From the above, it can be concluded that when the power supply is greater than the load, the first computing resource is enabled, which can meet relatively simple energy allocation needs at a lower cost, such as easily arranging the storage or export of excess electricity, avoiding excessive investment in computing resources. When the power supply is less than the load, the powerful second computing resource can cope with complex supply and demand imbalances, through fine scheduling, precise control of distributed power generation, energy storage system discharge and controllable load regulation, to ensure stable power supply, reduce the risk of production and life interruption caused by power shortages, and enhance the reliability and adaptability of virtual power plants under different working conditions.

[0040] In one embodiment of the present disclosure, determining a target energy scheduling strategy from a plurality of energy scheduling strategies based on a target computing resource includes: In response to the target computing resource being the first computing resource, selecting any energy scheduling strategy having a control accuracy less than the first accuracy from a plurality of energy scheduling strategies as the target energy scheduling strategy; In response to the target computing resource being the second computing resource, any energy scheduling strategy having a control accuracy greater than the first accuracy is selected from a plurality of energy scheduling strategies as the target energy scheduling strategy.

[0041] In this embodiment, the first accuracy is an accuracy value pre-set according to actual conditions, which is used as a boundary standard for distinguishing different control accuracy levels and is used to screen appropriate energy scheduling strategies.

[0042] When the target computing resource is determined to be the first computing resource, due to its limited computing power, a strategy with a control accuracy less than the first accuracy is selected from a number of energy scheduling strategies as the target energy scheduling strategy. This type of strategy is relatively simple and direct, with low demand for computing resources. In the relatively loose power supply and demand situation where the power supply is greater than the load, it can complete basic energy allocation tasks at a low computing cost, such as simply storing excess electricity in a fixed ratio in the energy storage system or making some basic arrangements for electricity export.

[0043] If the target computing resource is the second computing resource, because it has powerful computing power, a strategy with a control accuracy greater than the first accuracy can be selected from multiple energy scheduling strategies as the target energy scheduling strategy. This type of high-precision strategy is more complex and requires processing a large amount of data and performing fine calculations. It can make full use of the powerful performance of the second computing resource under the tight supply and demand situation where the power supply is less than the load, and conduct in-depth and precise regulation of distributed power sources, energy storage systems and controllable loads. For example, it can accurately analyze the power generation potential of each distributed power source and generate power in the optimal order and power, accurately control the discharge curve according to the detailed status of the energy storage system, and carefully group and adjust various controllable loads in different time periods, so as to maximize the balance of power supply and demand and ensure the stable operation of the power grid.

[0044] Suppose there is a virtual power plant that includes several solar photovoltaic arrays, wind turbines, battery energy storage systems, and some controllable loads, which supplies power to a community and surrounding small commercial areas.

[0045] When there is sufficient sunshine and wind during the day, the power supply of the virtual power plant is greater than the load on the user side. At this time, it is determined to use the first computing resource, that is, an ordinary server. Select the "basic surplus power allocation strategy" with lower control accuracy from the energy scheduling strategy library as the target energy scheduling strategy. This strategy mainly calculates the difference between the power supply and the load, and then stores 80% of the excess power in the battery energy storage system at a fixed charging power, and the remaining 20% ​​is sold to the power grid based on the current market electricity price. For example, at this time the power supply is 5MW, the load is 3MW, and there is 2MW of excess power, then 1.6MW is stored in the energy storage system, and 0.4MW is determined whether to be exported based on the electricity price. Because the power supply and demand in this case is relatively simple, and no complex regulation is required, ordinary servers and low-precision strategies are sufficient to cope with it.

[0046] From the above, it can be concluded that when using the first computing resource, choosing a low control precision strategy can reduce computing complexity and resource consumption, and when there is an oversupply of electricity, it can quickly and economically process the excess electricity, such as simply allocating it to energy storage or selling it externally, thereby improving operational efficiency. As for the second computing resource with a high control precision strategy, when there is insufficient power supply, it can give full play to its powerful computing power, accurately coordinate distributed power sources, energy storage and controllable loads, deeply optimize energy utilization, ensure reliable power supply, reduce the risk of power outages, improve grid stability and power supply quality, and enable virtual power plants to flexibly adapt to different power supply and demand scenarios, and achieve efficient control and stable allocation of energy.

[0047] In one embodiment of the present disclosure, a control accuracy less than the first accuracy is a first control accuracy, a control accuracy greater than the first accuracy is a second control accuracy, and the first control accuracy is less than the second control accuracy; Energy management and control methods also include: If the control accuracy is a first control accuracy, selecting a first number of control parameters; If the control accuracy is the second control accuracy, selecting a second number of control parameters; The first number is smaller than the second number.

[0048] In this embodiment, the first control accuracy indicates that the energy scheduling control accuracy is relatively low. When regulating the distributed energy of the virtual power plant, relatively rough and broad operations can be performed, and the adjustment steps of various parameters are large and the adjustment frequency is low, and very meticulous control cannot be performed.

[0049] The second control accuracy means that the energy dispatch control accuracy is relatively high, which can finely and accurately regulate the energy of the virtual power plant, can adjust the energy resource status within a very small power change and a very short time interval, and can perform complex coordinated control of multiple parameters at the same time.

[0050] The first number is the number of control parameters involved and required to be considered when using the first control accuracy for energy management and control, which is relatively small. In this control mode, only some parameters that have a greater impact on the overall energy scheduling and are easy to handle are focused on, while some minor parameters or parameters that require a high level of computing resources are ignored.

[0051] The second number is the number of control parameters selected when the second control accuracy is adopted. This number is large, indicating that in the high-precision control mode, the numerous characteristic parameters of various energy resources in the virtual power plant and their interrelationships will be comprehensively and deeply considered to achieve more accurate and optimized energy scheduling, but this also requires more computing resource support.

[0052] When the control accuracy of energy dispatch is determined to be the first control accuracy, only the first number of control parameters are selected to perform energy management and control operations due to its low accuracy and relative simplicity. For example, only several main parameters such as the total power generation of distributed power sources, the overall state of charge of the energy storage system, and the total load on the user side can be considered.

[0053] If the control accuracy is the second control accuracy, the second number of control parameters should be selected because high-precision complex regulation is required. At this time, not only the above main parameters should be considered, but also the detailed characteristic parameters of each power generation unit in the distributed power source, the specific type and operating characteristics of the controllable load, and through the comprehensive analysis and precise calculation of these large number of parameters, a refined energy scheduling strategy can be formulated.

[0054] In this embodiment, the number of control parameters is selected based on the control accuracy, which is the first formula.

[0055] The first formula is:

[0056] in, Indicates the number of control parameters, Indicates the control accuracy, represents a natural constant, approximately equal to 2.71828, are all constants, depending on the specific characteristics of the virtual power plant, the type of energy equipment and its scale. Indicates rounding down.

[0057] Suppose there is a virtual power plant consisting of a 2MW solar photovoltaic power station, a 1MW wind farm, a 1.5MWh battery energy storage system and several controllable loads, which supplies power to a community and surrounding commercial areas.

[0058] When the sunlight is good and the wind is stable during the day, the power supply of the virtual power plant is greater than the load at the user end. At this time, the first control accuracy is used for energy management and control, that is, only the first number of control parameters are selected. For example, only the total power generation of the solar photovoltaic power station, the total power generation of the wind farm, the total load at the user end, and the total charge state of the battery energy storage system are considered. If the solar power generation is 1.5MW, the wind power generation is 0.8MW, the load is 1MW, and the battery energy storage system is charged at 50%, it is simply calculated that the power generation exceeds the load by 1.3MW. According to the set rules, 1MW of it is stored in the battery energy storage system with a fixed charging current, and the remaining 0.3MW is determined whether to be delivered based on the market electricity price. No more complex analysis is performed, such as not considering the differences in power generation efficiency between solar panels and the different wind speed response characteristics of wind turbines.

[0059] From the above, it can be concluded that under the first control accuracy, only the first quantity control parameter is used, the calculation burden is light, the decision-making process is simple, and it is suitable for when the power supply is greater than the load, and it can quickly balance the energy, such as easily allocating surplus power, reducing management costs and resource consumption. The second control accuracy combined with the second quantity parameter has advantages when supply and demand are tight, and can finely control each energy unit and fully tap the potential of distributed energy, such as accurately controlling energy storage charging and discharging, optimizing distributed power output, and reasonably adjusting controllable loads, improving energy utilization efficiency, ensuring stable and reliable power supply, and enhancing the virtual power plant's ability to respond to complex working conditions and system stability.

[0060] In one embodiment of the present disclosure, the virtual power plant energy management and control method further includes: Determining a first ratio based on a ratio of the power supply amount to the load amount; If the first ratio is greater than the preset ratio, multiplying the first ratio and the first quantity to determine the quantity of the first prediction parameter; If the first ratio is less than the preset ratio, multiplying the first ratio and the second number to determine the number of the second prediction parameters; The number of the first prediction parameters is smaller than the number of the second prediction parameters.

[0061] In this embodiment, the first ratio is a value calculated by the ratio of the power supply of the virtual power plant to the load at the user end, reflecting the surplus or shortage of the power supply relative to the load.

[0062] The preset ratio is a pre-set reference ratio that can be dynamically adjusted according to actual conditions and is used to compare with the first ratio to distinguish different electricity supply and demand conditions, and then decide what calculation method to use to determine the number of prediction parameters.

[0063] The number of the first prediction parameters and the number of the second prediction parameters are respectively determined according to the ratio of power supply amount to load amount and related calculation rules.

[0064] First, the ratio of power supply to load is calculated to obtain a first ratio, which can intuitively reflect the current power supply and demand relationship of the virtual power plant.

[0065] Then, the first ratio is compared with the preset ratio. If the first ratio is greater than the preset ratio, it means that the power supply is relatively sufficient relative to the load. At this time, the first ratio and the first quantity are multiplied to determine the quantity of the first prediction parameter.

[0066] Finally, if the first ratio is less than the preset ratio, it indicates that the power supply is not much surplus or even insufficient relative to the load. At this time, the first ratio and the second quantity are multiplied to determine the number of the second prediction parameter. Due to the tight supply and demand of electricity, more sophisticated and comprehensive energy management is required, so decisions should be made based on more prediction parameters.

[0067] From the above, it can be concluded that when the first ratio is greater than the preset ratio, that is, the power supply is sufficient, the first ratio and the first quantity are used to determine a smaller number of first prediction parameters, which can reduce unnecessary calculations and resource investment on the basis of ensuring basic energy optimization, quickly plan surplus power storage, and improve operational efficiency. When the first ratio is less than the preset ratio, that is, the power supply is tight, a larger number of second prediction parameters is obtained by multiplying it with the second quantity, which can comprehensively and deeply analyze various energy elements, finely control distributed power sources, energy storage and loads, enhance the ability of virtual power plants to cope with complex changes in supply and demand, ensure stable power supply, and improve the accuracy and reliability of energy utilization.

[0068] In one embodiment of the present disclosure, the virtual power plant energy management and control method further includes: If the load amount is greater than or equal to the first power consumption, reducing the power of the controllable load by a first adjustment amount; If the load amount is less than the first power consumption, the power of the controllable load is increased by the first adjustment amount.

[0069] In this embodiment, the first power consumption is a preset power threshold value, which is used to determine the load capacity.

[0070] Controllable loads are load devices that can change their power consumption within a certain range according to the operation requirements and control instructions of the power system in the area served by the virtual power plant. For example, some industrial production equipment with adjustable power, smart home appliances, and some electrical equipment in commercial places, such as central air conditioners.

[0071] The first adjustment amount is a fixed power change value used when adjusting the power of the controllable load. In different power supply and demand scenarios, the power of the controllable load is increased or decreased by this fixed value according to established rules.

[0072] When it is monitored that the user-side load in the area served by the virtual power plant reaches or exceeds the set threshold of the first power consumption, it indicates that the power supply is facing tight pressure or needs to be optimized and adjusted to meet other operating goals, such as reducing energy costs, improving energy utilization efficiency, etc. At this time, this embodiment will take power reduction measures for the controllable load and reduce the power consumption of the controllable load according to the value of the first adjustment amount.

[0073] On the contrary, when the load is lower than the first power consumption, it means that the power supply is relatively loose and there is a certain amount of capacity available. At this time, in order to improve the overall utilization efficiency of energy or meet some specific needs, the power of the controllable load will be increased according to the first adjustment amount.

[0074] The formula for the first adjustment amount can be:

[0075] in, represents the first adjustment amount, Indicates the power supply. Indicates the load, represents the total power of the controllable load, represents the first adjustment scale factor, .

[0076] Alternatively, the first adjustment amount may take user priority and electricity price into consideration, and by taking user priority and electricity price factors into consideration, a controllable load power increase amount that balances economics and user demand is determined.

[0077] At this time, the formula of the first adjustment amount can be:

[0078] in, represents the first adjustment amount, Indicates the power supply. Indicates the load, represents the total power of the controllable load, Indicates the user priority weight, Represents different types of users, Indicates the electricity price, represents the second adjustment scale factor, For example, residential users , Commercial Users 、Industrial users .

[0079] From the above, it can be concluded that when the load exceeds the first power consumption, reducing the controllable load power can effectively alleviate the pressure on power supply, avoid overload risks, ensure the stable operation of the power system, and prevent power outages caused by overload from affecting production and life. At the same time, it helps to reasonably allocate limited power resources and improve the rationality of energy utilization. When the load is less than the first power consumption, increasing the power can make full use of excess electricity and improve the overall energy utilization rate. For example, it can make some flexible equipment run at full load, improve production or service efficiency, and also help balance the relationship between power supply and demand and enhance the adaptability of virtual power plants to changes in the power market.

[0080] In one embodiment of the present disclosure, after energy management and control of the virtual power plant is performed based on the target energy scheduling strategy, the method further includes: Evaluate the effectiveness of energy management and control; Update the target energy scheduling strategy based on the evaluation results.

[0081] In this embodiment, the energy management and control effect refers to the results and achievements in multiple aspects after implementing the target energy scheduling strategy to manage and control the virtual power plant, including but not limited to the comprehensive performance in terms of energy utilization efficiency, power supply stability, cost control, and impact on the environment.

[0082] In this embodiment, the target energy scheduling strategy is updated based on the evaluation result, including: Obtaining a first feature and a second feature of the energy management and control effect, the first feature being the energy utilization rate of the virtual power plant, and the second feature being the carbon emissions; If the first characteristic is less than the first value, and the second characteristic is greater than or equal to the second value, the target energy scheduling strategy is deleted; If the first characteristic is less than the first value, and the second characteristic value is less than the second value, the target energy scheduling strategy is first optimized; If the first characteristic is greater than or equal to the first value, and the second characteristic value is greater than or equal to the second value, the target energy scheduling strategy is second optimized; The intensity of the first optimization is smaller than that of the second optimization.

[0083] The first value and the second value can be dynamically adjusted according to actual conditions.

[0084] This embodiment finds out the deficiencies or room for optimization in the current target energy scheduling strategy based on the evaluation conclusion of the energy management and control effect, and makes adjustments and updates.

[0085] From the above, it can be concluded that this embodiment can consider the status of energy utilization efficiency and carbon emissions and clarify the pros and cons of the current strategy by evaluating the effect of energy management and control. By updating the target energy scheduling strategy based on the evaluation results, deficiencies can be corrected in a timely manner, such as optimizing energy storage charging and discharging strategies, improving controllable load regulation methods, etc., so as to continuously improve energy utilization efficiency and reduce carbon emissions. This embodiment can also enhance the reliability of power supply, reduce fluctuations and interruption risks, ensure users' electricity experience, and help virtual power plants better adapt to changes in the energy market and adjustments to the internal energy structure, maintain good operating conditions and competitiveness, and achieve a virtuous cycle and continuous optimization of energy management and control.

[0086] Corresponding to the virtual power plant energy management and control method in the above embodiment, Figure 2This is a block diagram of a virtual power plant energy management and control system provided by an embodiment of the present disclosure. For ease of explanation, only the parts related to the embodiment of the present disclosure are shown. Figure 2 The virtual power plant energy management and control system 20 includes: a computing resource determination module 21, a scheduling strategy determination module 22 and an energy management and control module 23.

[0087] The computing resource determination module 21 is used to determine the target computing resources based on the power supply of the virtual power plant and the load of the user end; A scheduling strategy determination module 22, configured to determine a target energy scheduling strategy from a plurality of energy scheduling strategies based on a target computing resource, wherein the plurality of energy scheduling strategies have different control accuracies; The energy management and control module 23 is used to perform energy management and control on the virtual power plant based on the target energy scheduling strategy.

[0088] In one embodiment of the present disclosure, the computing resource determination module 21 is further configured to: If the power supply is greater than the load, the first computing resource is used as the target computing resource; If the power supply is less than the load, the second computing resource is used as the target computing resource; The first computing resource is smaller than the second computing resource.

[0089] In one embodiment of the present disclosure, the scheduling strategy determination module 22 is further configured to: In response to the target computing resource being the first computing resource, selecting any energy scheduling strategy having a control accuracy less than the first accuracy from a plurality of energy scheduling strategies as the target energy scheduling strategy; In response to the target computing resource being the second computing resource, any energy scheduling strategy having a control accuracy greater than the first accuracy is selected from a plurality of energy scheduling strategies as the target energy scheduling strategy.

[0090] In one embodiment of the present disclosure, a control accuracy less than the first accuracy is a first control accuracy, a control accuracy greater than the first accuracy is a second control accuracy, and the first control accuracy is less than the second control accuracy; The virtual power plant energy management and control system 20 further includes: a control parameter selection module; A control parameter selection module, configured to select a first number of control parameters if the control accuracy is a first control accuracy; If the control accuracy is the second control accuracy, selecting a second number of control parameters; The first number is smaller than the second number.

[0091] In one embodiment of the present disclosure, the virtual power plant energy management and control system 20 further includes: a control parameter adjustment module; A control parameter adjustment module, configured to determine a first ratio based on a ratio of a power supply amount to a load amount; If the first ratio is greater than the preset ratio, multiplying the first ratio and the first quantity to determine the quantity of the first prediction parameter; If the first ratio is less than the preset ratio, multiplying the first ratio and the second number to determine the number of the second prediction parameter; The number of the first prediction parameters is smaller than the number of the second prediction parameters.

[0092] In one embodiment of the present disclosure, the virtual power plant energy management and control system 20 further includes: a controllable load power adjustment module; A controllable load power adjustment module, configured to reduce the power of the controllable load by a first adjustment amount if the load amount is greater than or equal to the first power consumption; If the load amount is less than the first power consumption, the power of the controllable load is increased by the first adjustment amount.

[0093] In one embodiment of the present disclosure, the virtual power plant energy management and control system 20 further includes: an effect evaluation module; Effect evaluation module, used to evaluate the effect of energy management and control; Update the target energy scheduling strategy based on the evaluation results.

[0094] See also Figure 3 , Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 3 The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303 and one or more memories 304. The processors 301, input devices 302, output devices 303 and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of each module / unit in the above-mentioned system embodiments, such as Figure 2 The functions of modules 21 to 23 are shown.

[0095] It should be understood that in the embodiment of the present disclosure, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0096] The input device 302 may include a touch panel, a fingerprint collection sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.

[0097] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0098] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure can execute the implementation methods described in the first and second embodiments of the virtual power plant energy management and control method provided in the embodiments of the present disclosure, and can also execute the implementation methods of the electronic device described in the embodiments of the present disclosure, which will not be repeated here.

[0099] In another embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by the processor, all or part of the processes in the above-mentioned embodiment method are implemented, and the computer program can also be completed by instructing the relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0100] The computer-readable storage medium may be an internal storage unit of the electronic device of any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Furthermore, the computer-readable storage medium may also include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0101] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0102] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0103] In the several embodiments provided in the present application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or it can be an electrical, mechanical or other form of connection.

[0104] The units described as separate components may or may not be physically separated, and the components shown as units 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 units may be selected according to actual needs to achieve the purpose of the embodiments of the present disclosure.

[0105] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or 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 software functional units.

[0106] The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present disclosure, and these modifications or replacements should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A virtual power plant energy management and control method, characterized in that: include: Determine the target computing resources based on the power supply of the virtual power plant and the load on the user side; Determining a target energy scheduling strategy from a plurality of energy scheduling strategies based on the target computing resource, wherein the plurality of energy scheduling strategies have different control accuracies; Energy management and control of the virtual power plant is performed based on the target energy scheduling strategy.

2. The virtual power plant energy management and control method according to claim 1, characterized in that: The determining of the target computing resources based on the power supply of the virtual power plant and the load of the user end includes: If the power supply is greater than the load, taking the first computing resource as the target computing resource; If the power supply is less than the load, taking the second computing resource as the target computing resource; The first computing resource is smaller than the second computing resource.

3. The virtual power plant energy management and control method according to claim 2, characterized in that: The determining a target energy scheduling strategy from a plurality of energy scheduling strategies based on the target computing resource includes: In response to the target computing resource being the first computing resource, selecting any energy scheduling strategy having a control accuracy less than a first accuracy from the multiple energy scheduling strategies as a target energy scheduling strategy; In response to the target computing resource being the second computing resource, any energy scheduling strategy having a control accuracy greater than a first accuracy is selected from the multiple energy scheduling strategies as a target energy scheduling strategy.

4. The virtual power plant energy management and control method according to claim 1, characterized in that: A control accuracy less than the first accuracy is a first control accuracy, a control accuracy greater than the first accuracy is a second control accuracy, and the first control accuracy is less than the second control accuracy; The energy management and control method further includes: If the control accuracy is the first control accuracy, selecting a first number of control parameters; If the control accuracy is the second control accuracy, selecting a second number of control parameters; Wherein, the first number is smaller than the second number.

5. The virtual power plant energy management and control method according to claim 4, characterized in that: Also includes: Determining a first ratio based on a ratio of the power supply amount to the load amount; If the first ratio is greater than a preset ratio, multiplying the first ratio and the first number to determine the number of first prediction parameters; If the first ratio is less than a preset ratio, multiplying the first ratio and the second number to determine the number of second prediction parameters; The number of the first prediction parameters is smaller than the number of the second prediction parameters.

6. The virtual power plant energy management and control method according to claim 1, characterized in that: Also includes: If the load amount is greater than or equal to the first power consumption, reducing the power of the controllable load by a first adjustment amount; If the load amount is less than the first power consumption, the power of the controllable load is increased by a first adjustment amount.

7. The virtual power plant energy management and control method according to claim 1, characterized in that: After the energy management and control of the virtual power plant is performed based on the target energy scheduling strategy, the method further includes: Evaluate the energy management effect; The target energy scheduling strategy is updated based on the evaluation result.

8. A virtual power plant energy management and control system, characterized in that: include: A computing resource determination module, used to determine target computing resources based on the power supply of the virtual power plant and the load of the user end; A scheduling strategy determination module, configured to determine a target energy scheduling strategy from a plurality of energy scheduling strategies based on the target computing resource, wherein the plurality of energy scheduling strategies have different control precisions; An energy management and control module is used to perform energy management and control on the virtual power plant based on the target energy scheduling strategy.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images