CN117791624A - Electric quantity management method, device, equipment and medium based on demand side task - Google Patents
Electric quantity management method, device, equipment and medium based on demand side task Download PDFInfo
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Abstract
The embodiment of the invention provides an electric quantity management method, device, equipment and medium based on a demand side task, which relate to a circuit device or system for power supply or distribution, wherein the method comprises the following steps: obtaining a virtual machine set of physical power generation equipment to be distributed; reporting the electric quantity related data to a demand side response platform through a data transmission channel corresponding to each virtual machine set according to a preset time interval, and receiving the issued electric quantity management task data generated according to the electric quantity related data; distributing each task in the electric quantity management task data to the virtual machine set with the highest resource utilization rate one by one according to the available resource condition, task priority and task load demand of each virtual machine set; and carrying out electric quantity management on each to-be-distributed physical power generation device in the virtual machine set distributed to obtain each task according to the task electric quantity information of each task. According to the scheme, the electric quantity is scheduled and managed through the demand side response platform, so that the efficiency of electric quantity management is improved.
Description
Technical Field
The present invention relates to the technical field of circuit devices or systems for power supply or distribution, and in particular, to a method, a device, an apparatus, and a medium for managing electric power based on a task on a demand side.
Background
Demand side response is a strategy to respond to power system demands by adjusting load behavior. In terms of charge and discharge, the demand side response can be achieved by issuing a task to the participant. According to the technology, a task is issued to a participant (such as a virtual machine set) through a demand side response platform, the task is instructed to perform charging or discharging operation in a specific time period, the issuing task comprises information such as charging and discharging starting time, charging and discharging ending time, target power or energy range and the like so as to ensure that the participant performs charging and discharging operation according to requirements, and meanwhile, the demand side response platform can monitor charging and discharging behaviors of the participant in real time and adjust and optimize the charging and discharging behaviors according to actual conditions.
However, this solution has the following problems: first, charge and discharge control of the demand side response depends on the partnership of the participants. If the participants (such as the virtual machine set) are not matched or cannot execute the tasks on time, the task execution failure or poor effect can be caused; secondly, issuing tasks and acquiring real-time data of participants need to be carried out through a communication network, and the problems of data transmission and communication delay exist. This may affect the real-time and accuracy of the task; finally, there may be differences in charge and discharge capabilities and performance of different participants (e.g., virtual machine sets). Some devices may have higher flexibility and response speed, while others may be limited by technical or hardware conditions, resulting in limited response capability.
Disclosure of Invention
In view of the above, the embodiment of the invention provides a power management method based on a demand side task, so as to solve the technical problems of low power management efficiency and poor effect in the prior art. The method comprises the following steps:
obtaining a virtual machine set of physical power generation equipment to be distributed;
reporting the electric quantity related data of each virtual machine set to a demand side response platform through a data transmission channel corresponding to each virtual machine set according to a preset time interval, and receiving electric quantity management task data generated according to the electric quantity related data and issued by the demand side response platform, wherein each task in the electric quantity management task data comprises task priority, task load demand and task electric quantity information;
according to the available resource condition, task priority and task load demand of each virtual machine set, each task in the electric quantity management task data is distributed to the virtual machine set with the highest resource utilization rate in all the virtual machine sets one by one;
and carrying out electric quantity management on each to-be-distributed physical power generation device in the virtual machine set distributed to obtain each task according to the task electric quantity information of each task.
The embodiment of the invention also provides an electric quantity management device based on the demand side task, so as to solve the technical problems of low electric quantity management efficiency and poor effect in the prior art. The device comprises:
the virtual machine set acquisition module is used for acquiring a virtual machine set of the physical equipment to be allocated;
the system comprises a task receiving data module, a demand side response platform and a task management data processing module, wherein the task receiving data module is used for reporting the electric quantity related data of each virtual machine set to the demand side response platform through a data transmission channel corresponding to each virtual machine set according to a preset time interval, and receiving electric quantity management task data generated according to the electric quantity related data and issued by the demand side response platform, wherein each task in the electric quantity management task data comprises task priority, task load requirements and task electric quantity information;
the task allocation module is used for allocating each task in the electric quantity management task data to the virtual machine set with the highest resource utilization rate in all the virtual machine sets one by one according to the available resource condition, the task priority and the task load demand of each virtual machine set;
and the electric quantity management module is used for carrying out electric quantity management on each to-be-distributed physical power generation device in the virtual machine set distributed to obtain the task according to the task electric quantity information of each task.
The embodiment of the invention also provides computer equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes any electric quantity management method based on the demand side task when executing the computer program, so as to solve the technical problems of low electric quantity management efficiency and poor effect in the prior art.
The embodiment of the invention also provides a computer readable storage medium which stores a computer program for executing any electric quantity management method based on the demand side task, so as to solve the technical problems of low electric quantity management efficiency and poor effect in the prior art.
Compared with the prior art, the beneficial effects that above-mentioned at least one technical scheme that this description embodiment adopted can reach include at least:
the virtual machine set can timely acquire current electric quantity data and provide accurate electric quantity information, and the demand side response platform can schedule and manage the real-time electric quantity; and distributing the tasks to the virtual machine set with the highest resource utilization rate, and realizing efficient issuing and executing of the tasks.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a power management method based on a demand side task according to an embodiment of the present invention;
FIG. 2 is a block diagram of a demand side response platform, virtual power plant, virtual machine set, EMU, ESCCU, and power plant provided by an embodiment of the present invention;
FIG. 3 is a block diagram of a computer device according to an embodiment of the present invention;
fig. 4 is a block diagram of a power management device based on a demand side task according to an embodiment of the present invention.
Detailed Description
Embodiments of the present application are described in detail below with reference to the accompanying drawings.
Other advantages and effects of the present application will become apparent to those skilled in the art from the present disclosure, when the following description of the embodiments is taken in conjunction with the accompanying drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. The present application may be embodied or carried out in other specific embodiments, and the details of the present application may be modified or changed from various points of view and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
In an embodiment of the present invention, there is provided a power management method based on a demand side task, as shown in fig. 1, including:
step S101: obtaining a virtual machine set of physical power generation equipment to be distributed;
step S102: reporting the electric quantity related data of each virtual machine set to a demand side response platform through a data transmission channel corresponding to each virtual machine set according to a preset time interval, and receiving electric quantity management task data generated according to the electric quantity related data and issued by the demand side response platform, wherein each task in the electric quantity management task data comprises task priority, task load demand and task electric quantity information;
step S103: according to the available resource condition of each virtual machine set, the task priority in the electric quantity management task data and the task load demand, distributing each task in the electric quantity management task data to the virtual machine set with the highest resource utilization rate in all the virtual machine sets one by one;
step S104: and carrying out electric quantity management on each to-be-distributed physical power generation device in the virtual machine set distributed to obtain each task according to the task electric quantity information of each task.
Specifically, in step S103, according to the task priority and the task load demand condition of each power management task, each round distributes the task load demand to the virtual machine set with the highest resource utilization rate of the round and meeting the task load demand from high to low according to the task priority until no virtual machine set or task load demand that can be distributed is cleared.
In the specific implementation, in order to avoid that the physical power generation equipment is not matched with or cannot execute tasks on time, the task execution fails or the effect is poor, and before the task distribution is realized, the virtual machine set with proper charge and discharge time is firstly screened out through the following steps:
before each task in the electric quantity management task data is distributed to the virtual machine set, the virtual machine set which is matched with the charge and discharge start time and the charge and discharge end time in the task electric quantity information is used as the distributable virtual machine set.
Specifically, if the operating time of the virtual machine set 1 is 10:00 to 21:00; the working time of the virtual machine set 2 is 10:00 to 22:00; the operating time of the virtual machine set 3 is 11:00 to 21:00.
the charge/discharge start time in task 1 is 11:00, the charge and discharge end time is 20:00; the charge/discharge start time in task 2 is 09:00, charge-discharge end time 21:00; the charge and discharge start time in task 3 is 10:00, charge-discharge end time is 23:00; the charge/discharge start time in task 4 is 11:00, charge-discharge end time 21:00.
the charge and discharge start and end time of the task 1 is respectively matched with the virtual machine set 1, the virtual machine set 2 and the virtual machine set 3, and only the working time interval of the virtual machine set 3 is more than or equal to the working interval of the task 1, so that the virtual machine set 3 is an allocatable virtual machine set of the task 1;
the charge and discharge start time of the task 2 is respectively matched with the virtual machine set 1, the virtual machine set 2 and the virtual machine set 3, and the charge and discharge start time of the task 2 is earlier than the working time of the virtual machine sets 1, 2 and 3, so that the task 2 has no distributable virtual machine set;
the charge and discharge start and end time of the task 3 are respectively matched with the virtual machine set 1, the virtual machine set 2 and the virtual machine set 3, and the charge and discharge end time of the task 3 is later than the working time of the virtual machine sets 1, 2 and 3, so that the task 3 has no distributable virtual machine set;
the charge and discharge start and end time of the task 4 are respectively matched with the virtual machine set 1, the virtual machine set 2 and the virtual machine set 3, and the working time intervals of the virtual machine sets 1, 2 and 3 are all greater than or equal to the working interval of the task 4, so that the virtual machine sets 1, 2 and 3 are all allocatable virtual machine sets of the task 4.
In the implementation, in order to form a virtual machine set by using the physical power generation equipment with consistent running time, physical position and electric quantity attribute, the virtual machine set is realized by the following steps:
acquiring the running time of all the physical power generation equipment to be distributed, the physical position of the physical power generation equipment to be distributed and the electric quantity attribute of the physical power generation equipment to be distributed, wherein the electric quantity attribute comprises the power generation capacity and the load demand; and forming a virtual machine set by the to-be-allocated physical power generation equipment with consistent running time, consistent physical position and consistent electric quantity attribute, wherein the virtual machine set comprises grouping information of different grouping items, and the grouping information comprises grouping identifiers and grouping available resources.
Specifically, the physical power generation equipment is comprehensively divided by combining the power generation capacity, the load demand, the geographic position and the network topology. For example, physical power generation devices with similar power generation capacity and load requirements are placed in the same virtual machine set, and different virtual machine sets are distributed in different regions or network subnets according to geographic location. To meet the operational requirements and optimization objectives of the virtual power plant (the platform on which the power management method of embodiments of the present invention operates).
As shown in FIG. 2, to correspond to a plurality of third party demand side response platforms, the virtual power plant may be a plurality of virtual power plants and the demand side response platforms are in one-to-one correspondence. Each virtual power plant corresponds to one ESCCU (integrated energy storage centralized control unit) for controlling charging and discharging. For example, assuming that there are a plurality of physical power generating devices in reality, and the physical power generating devices are located in city a, city B, and city C, respectively, the physical power generating devices located in city a are divided into virtual machine set 1, the physical power generating devices located in city B are divided into virtual machine set 2, and the physical power generating devices located in city C are divided into one virtual machine set 3, then each of the virtual power plant 1 and the virtual power plant 2 can assign tasks to the virtual machine set 1, the virtual machine set 2, and the virtual machine set 3. When tasks are distributed, the demand side response platform firstly transmits a task list to the corresponding virtual power plant. Finally, the load demand is converted into the charge or discharge of the physical power plant by means of an ESCCU (integrated energy storage centralized control unit). In the specific implementation, in order to improve the resource utilization rate and enable each virtual machine set to be distributed to the most suitable electric quantity management task data, each task in the electric quantity management task data is distributed to the virtual machine set with the highest resource utilization rate in all the virtual machine sets one by one according to the available resource condition, task priority and task load demand of each virtual machine set through the following steps:
sequencing all tasks in the electric quantity management task data according to the order of the task priority from high to low; according to the sorting, each task is sequentially regarded as a current task, all virtual units are traversed aiming at the current task, the virtual unit which meets the task load requirement of the current task and has the highest resource utilization rate is determined, the current task is distributed to the determined virtual units until all the tasks are distributed completely, wherein each virtual unit can execute one or more tasks, and the resource utilization rate is the utilization rate of available resources of the virtual units relative to the task load requirement.
In the specific implementation, in order to find the virtual machine set with the highest resource utilization rate in all the virtual machine sets, traversing all the virtual machine sets aiming at the current task, determining the virtual machine set which meets the task load requirement of the current task and has the highest resource utilization rate, and distributing the current task to the determined virtual machine sets:
defining the task load demand of the virtual machine set with the available resource larger than or equal to the current task as meeting the task load demand, and forming a first group by each virtual machine set meeting the task load demand; traversing all the virtual machine groups in the first group, and respectively calculating the ratio of the available resources of each virtual machine group to the task load demand of the current task to obtain a plurality of ratios; and among the ratios, determining the virtual machine set corresponding to the ratio closest to 1 as the virtual machine set with the highest resource utilization rate, and distributing the current task to the determined virtual machine set.
Specifically, in one embodiment, for the case of a small number of virtual machine sets, an optimal solution matching (i.e., the matching method with the highest resource utilization) is adopted in each step. Firstly, according to whether the working time of a virtual machine set is matched with the charge and discharge start time and the end time of a task, taking all the matched virtual machine sets as a virtual machine set list; and then, selecting the task with the highest resource utilization rate to be matched with the virtual machine set, and circularly and sequentially distributing the tasks to the virtual machine set until all the tasks are distributed.
Assume that there are 3 virtual machine groups (A, B, C), while the demand side response platform has 5 task lists (T1, T2, T3, T4, T5) to be allocated. Each task has a different load demand and priority, while each virtual machine group has a different available resource. When tasks are distributed, the demand side response platform firstly transmits a task list to the corresponding virtual power plant, and the virtual power plant transmits the task list to the virtual machine set. Finally, the virtual power plant converts the load demand into the charge or discharge of the physical power plant through an ESCCU (integrated energy storage centralized control unit).
The task list is as follows:
t1, load demand 10 and priority 3;
t2, load demand 8 and priority 2;
t3, load demand 6 and priority 4;
t4, load demand 12 and priority 1;
and T5, load demand 9 and priority 5.
The list of virtual machine sets is as follows:
a, an available resource 15;
the available resource 10;
and C, available resources 20.
And a first step, initializing a task.
And sequencing the task list according to the ascending order of the priority of the tasks (the ascending order of the priority is set by a demand side response platform and is required to be distributed according to the priority), and setting the initial resource state of the virtual machine set and the distributed tasks to be empty.
The ordered task list is: t4, T2, T1, T3, T5.
And secondly, distributing tasks.
For the first task T4, traversing the list of virtual machines, selecting the virtual machine set that meets the load demand and has the highest resource utilization (the ratio of the available resources of the virtual machine set to the task load demand is closest to 1). In this embodiment, virtual machine set a is the virtual machine set with the highest resource utilization, and thus task T4 is assigned to virtual machine set a. After the task T4 is distributed, a difference between the available resource data of the virtual machine set a and the task load demand of the task T4 needs to be calculated, and the difference is updated to the available resource of the virtual machine set a.
Therefore, the virtual machine group list after task allocation is:
the available resource 3, the assigned task T4;
the available resource 10;
and C, available resources 20.
For the next task T2, traversing the virtual machine set list, and selecting the virtual machine set which meets the load demand and has the highest resource utilization rate (the ratio of the available resources of the virtual machine set to the task load demand is closest to 1). Wherein, the virtual machine group B is the virtual machine group with the highest resource utilization rate, so the task T2 is allocated to the virtual machine group B, and the available resource state and the allocated task list are updated.
List of virtual machine groups after allocation:
the available resource 3, the assigned task T4;
the available resource 2 is allocated with a task T2;
and C, available resources 20.
For the following tasks T1, T3 and T5, the allocation is performed in the same steps.
Final virtual machine set list:
the available resource 3, the assigned task T4;
the available resource 2 is allocated with a task T2;
and C, the available resources 4 are allocated with tasks T1 and T3. Wherein, T5 is unassigned because no virtual machine set capable of satisfying the matching condition is found, notifying the demand side response platform of the task T5 unassigned, and invalidates the T5 task.
And thirdly, outputting the result.
And after the task allocation is completed, outputting a task list and resource utilization conditions of each virtual machine set.
The virtual machine set A is provided with a task T4;
the virtual machine set B is allocated with a task T2;
virtual machine group C, assigned tasks T1, T3.
Specifically, in one embodiment, for the case of a large number of virtual machine sets, each step adopts a matching method with the least matching times to save operation time. Firstly, according to whether the working time of the virtual machine set is matched with the charge and discharge start time and the end time of a task, sequencing all matched virtual machine sets in descending order according to available resources of the virtual machine set; then, sorting the tasks according to the ascending order of priority and the descending order of load demand; next, tasks are assigned to the physical power plant in turn until all tasks are assigned.
Specifically, under the condition that the working time of the virtual machine set and the available resources of the virtual machine set are met, the steps when the demand side response platform distributes tasks for one virtual machine set are as follows:
assume that there are 3 virtual machine groups (A, B, C), while the demand side response platform has 5 task lists (T1, T2, T3, T4, T5) to be allocated. Each task has a different load demand and priority, while each virtual machine group has a different available resource. When tasks are distributed, the demand side response platform firstly transmits a task list to the corresponding virtual power plant, and the virtual power plant transmits the task list to the virtual machine set. Finally, the load demand is converted into the charge or discharge of the physical power plant by means of an ESCCU (integrated energy storage centralized control unit).
The task list is as follows:
t1, load demand 15 and priority 1;
t2, load demand 8 and priority 2;
t3, load demand 6 and priority 4;
t4, load demand 12 and priority 1;
and T5, load demand 9 and priority 5.
The list of virtual machine sets is as follows:
a, an available resource 15;
the available resource 10;
and C, available resources 20.
And a first step, initializing a task.
The method comprises the steps of sorting task lists according to a priority ascending order of tasks (the priority ascending order is set by a demand side response platform and is required to be distributed according to the priority) and a load demand descending order (tasks with large load demands are distributed first), sorting virtual machine sets according to an available resource descending order, and setting initial resource states of the virtual machine sets and the distributed tasks to be empty.
The ordered task list is: t1, T4, T2, T3, T5.
The ordered virtual machine set list is: c, A, B.
And secondly, distributing tasks.
For the first task T1, a virtual machine set is selected that meets the load demand and that is the first to be matched. In this embodiment, the virtual machine set C is the virtual machine set that the first available resource satisfies the load demand after the available resources are ordered, and thus the task T1 is allocated to the virtual machine set C. After the task T1 is distributed, a difference value between the available resource data of the virtual machine set C and the task load demand of the task T1 needs to be calculated, and the difference value is updated to the available resource of the virtual machine set C.
Therefore, the virtual machine group list after task allocation is:
a, an available resource 15;
the available resource 10;
and C, the available resource 5 is allocated with a task T1.
For the next task T4, the virtual machine set that meets the load demand and that is the first to be matched is selected. Wherein, the virtual machine set A is a virtual machine set which is selected to meet the load requirement and is matched with the first virtual machine set, so that the task T4 is allocated to the virtual machine set A, and the available resource state and the allocated task list are updated.
List of virtual machine groups after allocation:
the available resource 3, the assigned task T4;
the available resource 10;
and C, the available resource 5 is allocated with a task T1.
For the following tasks T2, T3 and T5, the allocation is performed in the same steps.
Final virtual machine set list:
the available resource 3, the assigned task T4;
the available resource 2 is allocated with a task T2;
and C, the available resource 5 is allocated with a task T1. Wherein, T3 and T5 are unassigned because no virtual machine set capable of satisfying the matching condition can be found, the demand side response platform is notified that tasks T3 and T5 are unassigned, and T3 and T5 tasks are invalidated.
And thirdly, outputting the result.
And after the task allocation is completed, outputting a task list and resource utilization conditions of each virtual machine set.
The virtual machine set A is provided with a task T4;
the virtual machine set B is allocated with a task T2;
virtual machine group C, assigned task T1.
In the specific implementation, in order to execute the distributed tasks, the virtual machine set can be charged and discharged according to the requirements, and the electric quantity management is carried out on each to-be-distributed physical power generation device in the virtual machine set distributed to obtain the tasks according to the task electric quantity information of each task through the following steps:
transmitting task electric quantity information to each energy management unit in the virtual machine set distributed with the task, wherein the task electric quantity information comprises charging start time, charging end time, discharging start time, discharging end time and target power; and controlling the physical power generation equipment to be distributed to charge and/or discharge according to the charging start time, the charging end time, the discharging start time, the discharging end time and the target power until the physical power generation equipment to be distributed reaches the target power.
In the implementation process, in order to monitor the operation data through the visualized equipment, the operation data of all the physical power generation equipment to be distributed are obtained in real time through the data transmission channels corresponding to the virtual machine sets, and the operation data are transmitted to the external display equipment.
In the implementation, in order to reduce the amount of data transmitted and reduce the occurrence probability of communication delay, the power-related data is transmitted in json format.
Specifically, the virtual power plant platform and the third party demand side response platform are in butt joint in an interface mode, and data transmission adopts json format.
Specifically, the electricity management method based on the demand side task can be operated on a virtual power plant platform.
Specifically, in some embodiments, the workflow of the third party demand side response platform, virtual power plant, and virtual machine set is as follows:
the virtual machine set and the virtual power plant report the electric quantity related data, and a plurality of virtual machine sets are called a virtual power plant. And if the preset time interval is 5 minutes, reporting current electric quantity related data to a demand side response platform by the virtual machine set and the virtual power plant every 5 minutes. The reported electric quantity related data comprise information such as a time stamp, a unit/power plant identifier, real-time power, power plant state, rated power (namely available resources) and the like;
configuring (or adding) data transmission channels, wherein each data transmission channel corresponds to different demand side response platforms, a third party demand side response platform issues tasks through the data transmission channels, when a virtual machine unit belongs to a plurality of demand side response platforms, after a certain demand side corresponding platform distributes tasks, the current virtual machine unit is locked, when the virtual machine unit executes the tasks and updates available resources, the locking is released, and other demand side corresponding platforms can distribute the tasks according to the available resources;
and issuing a task, and making a charging and discharging task by the demand side response platform according to the reported electric quantity related data and the prediction result. The task electricity quantity information in the electricity quantity management task data comprises information such as charge and discharge starting and ending time, target power and the like. The demand side response platform issues a task to the virtual power plant and instructs the virtual power plant to perform charge and discharge operations;
the corresponding platform on the demand side distributes tasks through the virtual power plant, and after the virtual power plant (the virtual power plant comprises a plurality of virtual machine sets) receives the tasks, the tasks are distributed to the virtual machine sets according to the number of the connected virtual machine sets. The capacity and the characteristics of the virtual machine set are considered when the tasks are distributed, so that the fair distribution of the tasks in the whole virtual power plant is ensured;
and the virtual machine set task is issued, and each virtual machine set receives the distributed task and issues the task to the connected EMU. The EMU (energy management unit) performs corresponding charging and discharging operations according to task requirements, controls the charging and discharging behaviors inside the virtual machine set, is a core control unit of the energy storage system, and can control charging and discharging, monitor the battery state, collect data, report data and the like;
and the demand side response platform acquires reporting data (the reporting data comprises charging and discharging behaviors of the physical power generation equipment to be distributed) from the EMU (energy management unit) in real time, and the demand side response platform communicates with the virtual machine set, the virtual power plant and the EMU (energy management unit) to acquire the real-time data and provide feedback.
Through the business flow, the virtual machine set and the virtual power plant are used as key components of the response of the demand side, so that management of electric quantity reporting, task issuing and charge and discharge control can be effectively realized, and the flexibility and schedulability of the electric power system are improved.
In this embodiment, a computer device is provided, as shown in fig. 3, including a memory 301, a processor 302, and a computer program stored in the memory and capable of running on the processor, where the processor implements any of the above-mentioned power management methods based on the demand side task when executing the computer program.
In particular, the computer device may be a computer terminal, a server or similar computing means.
In the present embodiment, a computer-readable storage medium storing a computer program for executing the electricity management method based on any of the demand side tasks described above is provided.
In particular, computer-readable storage media, including both permanent and non-permanent, removable and non-removable media, may be used to implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer-readable storage media include, but are not limited to, phase-change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable storage media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
Based on the same inventive concept, the embodiment of the invention also provides an electric quantity management device based on the task on the demand side, as described in the following embodiment. Because the principle of the electricity management device based on the demand side task to solve the problem is similar to that of the electricity management method based on the demand side task, the implementation of the electricity management device based on the demand side task can be referred to the implementation of the electricity management method based on the demand side task, and the repetition is omitted. As used below, the term "unit" or "module" may be a combination of software and/or hardware that implements the intended function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
Fig. 4 is a block diagram of a power management apparatus according to an embodiment of the present invention, as shown in fig. 4, including: the configuration of the virtual machine group acquisition module 401, the task data reception module 402, the task allocation module 403, and the power management module 404 is described below.
A virtual machine set obtaining module 401, configured to obtain a virtual machine set of a physical device to be allocated;
the task receiving data module 402 is configured to report, according to a preset time interval, power related data of each virtual machine set to a demand side response platform through a data transmission channel corresponding to each virtual machine set, and receive power management task data generated according to the power related data and issued by the demand side response platform, where each task in the power management task data includes task priority, task load requirement and task power information;
the task allocation module 403 is configured to allocate each task in the power management task data to a virtual machine set with the highest resource utilization rate in all virtual machine sets one by one according to the available resource condition, task priority and task load requirement of each virtual machine set;
and the electric quantity management module 404 is used for carrying out electric quantity management on each to-be-allocated physical power generation device in the virtual machine set allocated with each task according to the task electric quantity information of each task.
In one embodiment, the task allocation module includes:
the sequencing unit is used for sequencing each task in the electric quantity management task data according to the sequence of the task priority from high to low;
the task allocation unit is used for sequentially regarding each task as a current task according to the sequence, traversing all the virtual units aiming at the current task, determining the virtual unit which meets the task load requirement of the current task and has the highest resource utilization rate, and allocating the current task to the determined virtual unit until all the tasks are allocated completely, wherein each virtual unit can execute one or more tasks, and the resource utilization rate is the utilization rate of the available resources of the virtual unit relative to the task load requirement.
In one embodiment, a task allocation unit is configured to define a task load requirement of a current task that is greater than or equal to an available resource of a virtual machine set as meeting the task load requirement, and form a first group of each virtual machine set that meets the task load requirement; traversing all the virtual machine groups in the first group, and respectively calculating the ratio of the available resources of each virtual machine group to the task load demand of the current task to obtain a plurality of ratios; and among the ratios, determining the virtual machine set corresponding to the ratio closest to 1 as the virtual machine set with the highest resource utilization rate, and distributing the current task to the determined virtual machine set.
In one embodiment, the apparatus further comprises:
and the charge-discharge time matching module is used for taking the virtual machine set matched with the charge-discharge start time and the charge-discharge end time in the task electric quantity information as an allocable virtual machine set before each task in the electric quantity management task data is allocated to the virtual machine set.
In one embodiment, a power management module includes:
the task electric quantity distribution unit is used for sending task electric quantity information to each energy management unit in the virtual machine set distributed to obtain the task, wherein the task electric quantity information comprises charging start time, charging end time, discharging start time, discharging end time and target power;
and the charge-discharge control unit is used for controlling the physical power generation equipment to be distributed to charge and/or discharge according to the charge start time, the charge end time, the discharge start time, the discharge end time and the target power until the physical power generation equipment to be distributed reaches the target power.
In one embodiment, the apparatus further comprises:
and the virtual machine set building module is used for building the virtual machine set according to the fixed rule.
In one embodiment, constructing a virtual machine set module includes:
the power attribute obtaining unit is used for obtaining the running time of all the physical power generation equipment to be distributed, the physical position of the physical power generation equipment to be distributed and the power attribute of the physical power generation equipment to be distributed, wherein the power attribute comprises power generation capacity and load demand;
and constructing a virtual machine set unit, wherein the virtual machine set unit is used for forming a virtual machine set by the to-be-allocated physical power generation equipment with consistent running time, consistent physical position and consistent electric quantity attribute, and the virtual machine set comprises grouping information of different grouping items, and the grouping information comprises grouping identifiers and grouping available resources.
In one embodiment, the apparatus further comprises:
the operation data display module is used for acquiring the operation data of all the physical power generation devices to be distributed in real time through the data transmission channels corresponding to the virtual machine sets and transmitting the operation data to the external display device.
In one embodiment, the apparatus further comprises:
and the data transmission module is used for transmitting the data of the electric quantity related data by adopting a json format.
The embodiment of the invention realizes the following technical effects:
the virtual machine set can timely acquire current electric quantity data and provide accurate electric quantity information, and the demand side response platform can schedule and manage the real-time electric quantity; after the virtual machine set receives the task from the demand side response platform, the task is distributed to the virtual machine set with the highest resource utilization rate through a greedy algorithm, the task can be efficiently distributed according to a set rule, the efficient issuing and execution of the task are realized, the greedy algorithm is relatively simple compared with other optimization algorithms, the realization is relatively easy, resource distribution decision can be carried out through simple rules and strategies, the complexity of algorithm design and realization is reduced, meanwhile, although the greedy algorithm cannot guarantee to obtain a global optimal solution, in many cases, the greedy algorithm can obtain an approximate optimal solution, the greedy algorithm can select the optimal solution in the current state each time, thus the overall result is likely to be close to the optimal solution, and compared with other more complex algorithms, the greedy algorithm consumes relatively less resources; the data transmission is carried out on the electric quantity related data by adopting the json format, the data quantity is relatively small, and the data transmission can be effectively carried out and the communication delay is avoided; before the task is distributed, the virtual machine set which is matched with the charge and discharge time in the task electric quantity information is used as the distributable virtual machine set, so that the problem that the task execution failure or poor effect is caused by the fact that the virtual machine set is not matched or cannot execute the task on time is avoided.
It will be apparent to those skilled in the art that the modules or steps of the embodiments of the invention described above may be implemented in a general purpose computing device, they may be concentrated on a single computing device, or distributed across a network of computing devices, they may alternatively be implemented in program code executable by computing devices, so that they may be stored in a storage device for execution by computing devices, and in some cases, the steps shown or described may be performed in a different order than what is shown or described, or they may be separately fabricated into individual integrated circuit modules, or a plurality of modules or steps in them may be fabricated into a single integrated circuit module. Thus, embodiments of the invention are not limited to any specific combination of hardware and software.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, and various modifications and variations can be made to the embodiments of the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The electric quantity management method based on the demand side task is characterized by comprising the following steps of:
obtaining a virtual machine set of physical power generation equipment to be distributed;
reporting the electric quantity related data of each virtual machine set to a demand side response platform through a data transmission channel corresponding to each virtual machine set according to a preset time interval, and receiving electric quantity management task data generated according to the electric quantity related data and issued by the demand side response platform, wherein each task in the electric quantity management task data comprises task priority, task load requirements and task electric quantity information;
according to the available resource condition, the task priority and the task load demand of each virtual machine set, each task in the electric quantity management task data is distributed to the virtual machine set with the highest resource utilization rate in all the virtual machine sets one by one;
and carrying out electric quantity management on each to-be-distributed physical power generation device in the virtual machine set distributed to obtain the task according to the task electric quantity information of each task.
2. The power management method based on demand side tasks according to claim 1, wherein allocating each task in the power management task data to a virtual machine group with highest resource utilization rate among all the virtual machine groups one by one according to available resource conditions of each virtual machine group, the task priority and the task load demand comprises:
sequencing the tasks in the electric quantity management task data according to the order of the task priorities from high to low;
according to the sorting, each task is sequentially regarded as a current task, all the virtual machine sets are traversed aiming at the current task, the virtual machine set which meets the task load requirement of the current task and has the highest resource utilization rate is determined, the current task is distributed to the determined virtual machine sets until all the tasks are distributed, wherein each virtual machine set can execute one or more tasks, and the resource utilization rate is the utilization rate of available resources of the virtual machine sets relative to the task load requirement.
3. The power management method based on a demand side task according to claim 2, wherein traversing all the virtual machine sets for the current task, determining a virtual machine set that satisfies the task load demand of the current task and has a highest resource utilization, and assigning the current task to the determined virtual machine set, comprises:
defining the task load demands of the current task, which are larger than or equal to the available resources of the virtual machine sets, as task load demands, and forming a first group by each virtual machine set meeting the task load demands;
traversing all the virtual machine sets in the first group, and respectively calculating the ratio of the available resources of each virtual machine set to the task load demand of the current task to obtain a plurality of ratios;
and determining the virtual machine set corresponding to the ratio closest to 1 as the virtual machine set with the highest resource utilization rate in the ratios, and distributing the current task to the determined virtual machine set.
4. The power management method based on a demand side task according to claim 1, wherein performing power management on each of the to-be-allocated physical power generation devices in the virtual machine group allocated to the task according to the task power information of each task comprises:
the task electric quantity information is sent to each energy management unit in the virtual machine set distributed with the task, wherein the task electric quantity information comprises charging start time, charging end time, discharging start time, discharging end time and target power;
and controlling the physical power generation equipment to be distributed to charge and/or discharge according to the charging start time, the charging end time, the discharging start time, the discharging end time and the target power until the physical power generation equipment to be distributed reaches the target power.
5. The demand side task-based power management method according to any one of claims 1 to 4, further comprising:
and before each task in the electric quantity management task data is distributed to the virtual machine set, the virtual machine set which is matched and consistent with the charge and discharge start time and the charge and discharge end time in the task electric quantity information is used as an assignable virtual machine set.
6. The demand side task-based power management method according to any one of claims 1 to 4, further comprising:
acquiring the running time of all the physical power generation equipment to be distributed, the physical position of the physical power generation equipment to be distributed and the electric quantity attribute of the physical power generation equipment to be distributed, wherein the electric quantity attribute comprises power generation capacity and load demand;
and forming a virtual machine set by the to-be-allocated physical power generation equipment with consistent running time, consistent physical position and consistent electric quantity attribute, wherein the virtual machine set comprises grouping information of different grouping items, and the grouping information comprises grouping identifiers and grouping available resources.
7. The demand side task-based power management method according to any one of claims 1 to 4, further comprising:
acquiring operation data of all the physical power generation devices to be distributed in real time through a data transmission channel corresponding to the virtual machine set, and transmitting the operation data to an external display device;
and the electric quantity related data is transmitted by adopting json format.
8. An electricity management apparatus based on a demand side task, comprising:
the virtual machine set acquisition module is used for acquiring a virtual machine set of the physical equipment to be allocated;
the system comprises a task data receiving module, a demand side response platform and a task data receiving module, wherein the task data receiving module is used for reporting the electric quantity related data of each virtual machine set to the demand side response platform through a data transmission channel corresponding to each virtual machine set according to a preset time interval, and receiving electric quantity management task data generated according to the electric quantity related data and issued by the demand side response platform, and each task in the electric quantity management task data comprises task priority, task load requirements and task electric quantity information;
the task allocation module is used for allocating each task in the electric quantity management task data to the virtual machine set with the highest resource utilization rate in all the virtual machine sets one by one according to the available resource condition, the task priority and the task load demand of each virtual machine set;
and the electric quantity management module is used for carrying out electric quantity management on each to-be-distributed physical power generation device in the virtual machine set distributed to obtain the task according to the task electric quantity information of each task.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the demand side task based power management method of any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program that performs the demand-side task-based power management method of any one of claims 1 to 7.
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