CN114336594A - An energy aggregation customer monitoring and demand response scheduling system and method - Google Patents

Abstract

The invention proposes an energy aggregation customer monitoring and demand response scheduling system and method, including: acquiring basic file information, power access point information, load resource data and collaborative interaction response data of small energy aggregators; processing small energy aggregators form a dynamic demand response plan; monitor and warn of abnormal changes in the basic file information, power access point information, load resource data and collaborative interactive response data of small energy aggregators, and dynamically adjust demand response scheduling according to the change information plan. The present invention can satisfy the load control device of a large number of small energy aggregators and simultaneously access the power grid demand response system by executing the results of the dynamic programming algorithm, thereby increasing the proportion of the demand response load in the power grid load, thereby improving the dispatching efficiency of the demand response system.

Description

An energy aggregation customer monitoring and demand response scheduling system and method

technical field

The invention belongs to the technical field of electric power dispatching, and in particular relates to an energy aggregation customer monitoring and demand response dispatching system and method.

Background technique

The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

In the existing demand response dispatching system, the demand response dispatching for large power sources of 35kV and above and high power users of 10kV and above can basically be realized, but there is a lack of power supply below 35kV, power users below 10kV, distributed power, and microgrid storage. The effective monitoring of energy equipment can not meet the needs of a large number of small energy aggregators such as clean distributed power sources, micro-grid energy storage and other small energy aggregators. The proportion of the whole network load is getting smaller and smaller.

Large power sources of 35kV and above, namely large thermal power plants, large hydropower stations and other large power generation facilities, are characterized by a small number, concentrated distribution, stable or predictable power generation load, and are basically not affected by the external environment, or are significantly affected by the external environment. Periodic; high-power power users of 10kV and above are power users with large scale, complex production processes or 7*24-hour operation of production lines, which are characterized by stable or predictable power consumption loads, and are basically not affected by the external environment, or affected by external Environmental impacts are clearly cyclical.

Power sources below 35kV are small hydropower facilities, pumped storage, photovoltaic power stations and other facilities, which are characterized by small numbers, unstable or unpredictable power generation loads, and are greatly affected by the external environment; distributed power sources are connected to the voltage grid Distributed photovoltaic, biomass power generation and other small and micro power generation facilities are characterized by a large number, wide distribution, unstable or unpredictable power generation load, and are greatly affected by the external environment; Equipment such as energy storage stations and V2G charging piles in the voltage grid are characterized by the fact that the equipment assumes the role of power supply or electricity consumption at different times and spaces in the power grid. It is not an energy conversion equipment, and the changes are highly unpredictable; power users below 10kV That is to say, non-resident users connected to the low-voltage power grid are characterized by a large number, wide distribution, rapid changes, unstable or unpredictable electricity load, and are greatly affected by the external environment. Because power sources below 35kV, power users below 10kV, distributed power sources, and microgrid energy storage equipment have the characteristics of unstable or unpredictable load changes and are greatly affected by the external environment, compared with large power sources of 35kV and above, 10kV and above High-power power users show new characteristics of load data collection volume, huge processing volume, and difficult monitoring.

SUMMARY OF THE INVENTION

In order to overcome the above-mentioned deficiencies of the prior art, the present invention provides an energy aggregation customer monitoring and demand response scheduling system, which classifies resource customers participating in demand response according to different scheduling objects, thereby realizing energy data aggregation scheduling for similar customers. .

To achieve the above object, one or more embodiments of the present invention provide the following technical solutions:

In the first aspect, an energy aggregation customer monitoring and demand response scheduling method is disclosed, including:

Obtain basic file information, power access point information, load resource data and collaborative interaction response data of small energy aggregators;

Process data from small energy aggregators to form dynamic demand response plans;

Monitor and warn of abnormal changes in the basic file information, power access point information, load resource data and collaborative interaction response data of small energy aggregators, and dynamically adjust the demand response scheduling plan according to the change information.

A further technical solution to dynamically adjust the demand response scheduling plan is as follows:

The dynamic programming algorithm is used to calculate the load data of a large number of small energy aggregators, and the calculation results are formed into executable instructions to achieve dynamic balance.

For a further technical solution, the demand response of small energy aggregators is divided into several interconnected stages according to time periods. In each stage, a demand response decision needs to be made. After the demand response decision of one stage is determined, it will affect the demand response of the next stage. decision-making, thereby determining the course of activities of a process;

The demand response decisions at each stage constitute a decision sequence, which is called a demand response strategy;

Each stage has several demand response decisions to choose from, so there are many demand response strategies to choose from, corresponding to the effect of a strategy used to determine the load balance of the power grid, and this effect can be determined by quantity;

Different demand response strategies have different effects of power grid load balancing. The multi-stage demand response decision-making problem is to select an optimal demand response strategy among those demand response strategies that can be selected, which is the dynamic balance of the dispatch plan.

A further technical solution also includes storing the demand response scheduling plan, and executing the demand response scheduling plan that has been stored on the storage medium in the event of a large-scale communication interruption in the area.

A further technical solution to obtain the basic file information of small energy aggregators, including customer number, customer name, contact information, electricity address, electricity type, customer type, contract capacity, user classification, city, and power supply unit information;

The information of the power access point includes the voltage level, the name of the station area, the name of the line, and the name of the substation;

Load resource data includes load curve, voltage curve, current curve, and electricity data;

The collaborative interaction response data includes load thresholds available for demand response scheduling, and demand response plan execution data.

A further technical solution, the processing of data information of small energy aggregators, includes:

Data conversion and data calculation;

The data is converted into the code of the customer type, power supply unit, and electricity category classification of the small energy aggregator into the corresponding name, so that the implementation of the demand response plan can be visualized in the form of the converted name in the visual display. ;

The data calculation is based on the characteristics and operation mode of the aggregated load-side resources, and the adjustment method, adjustment capacity and performance of the load-side resources are fitted and calculated. Statistical analysis of the temporal and spatial characteristic extremes of distributed resources based on operating modes and ramping characteristics, graphically analyzes the characteristic extremes of each resource load to form a multi-scenario multi-objective aggregation scheme, aggregates to form physical characteristics of different adjustment scenarios, and forms an executable demand response scheduling plan .

A further technical solution also includes obtaining the monitoring needs of different small energy aggregators to execute the demand response dispatch plan, and visually displaying the execution status of the demand response dispatch plans of the small energy aggregators obtained according to the operational monitoring needs.

In the second aspect, an energy aggregation customer monitoring and demand response scheduling system is disclosed, including:

The resource user management module obtains the basic file information, power access point information, load resource data and collaborative interaction response data of small energy aggregators;

The load aggregation management module is used to process the data information of small energy aggregators to form a dynamic demand response plan;

The platform resource operation monitoring module is used to monitor and warn of abnormal changes in the basic file information, power access point information, load resource data and collaborative interaction response data of small energy aggregators, and dynamically adjust the demand response scheduling plan according to the change information.

One or more of the above technical solutions have the following beneficial effects:

The invention accesses the resource user management module through the load data collection and access resource user management module of the power supply below 35kV, the power user below 10kV, the distributed power source, and the microgrid energy storage equipment that apply for becoming a small energy aggregator, and the load aggregation management module forms the entire network through dynamic programming algorithm. Or the optimal solution of regional power grid demand response, increasing the proportion of dispatchable load in the whole network load.

The present invention can satisfy the load control device of a large number of small energy aggregators and simultaneously access the power grid demand response system by executing the results of the dynamic programming algorithm, thereby increasing the proportion of the demand response load in the power grid load, thereby improving the dispatching efficiency of the demand response system.

By storing the demand response scheduling plan, the present invention can execute the demand response scheduling plan that has been stored on the storage medium even in the case of large-scale communication interruption in the area, and can greatly improve the reliability of the demand response system.

Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will become apparent from the description which follows, or may be learned by practice of the invention.

Description of drawings

The accompanying drawings forming a part of the present invention are used to provide further understanding of the present invention, and the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, and do not constitute an improper limitation of the present invention.

Fig. 1 is a data flow process diagram of the disclosed system according to the embodiment;

Fig. 2 is the response situation diagram of the distributed power source disclosed in the embodiment;

FIG. 3 is a response situation diagram of energy storage resources disclosed in the embodiment;

FIG. 4 is a response situation diagram of the charging facility disclosed in the embodiment;

FIG. 5 is a diagram of a client-side electrical response situation disclosed in the embodiment.

Detailed ways

It should be noted that the following detailed description is exemplary and intended to provide further explanation of the invention. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

It should be noted that the terminology used herein is for the purpose of describing specific embodiments only, and is not intended to limit the exemplary embodiments according to the present invention.

Embodiments of the invention and features of the embodiments may be combined with each other without conflict.

Example 1

This embodiment discloses an energy aggregation customer monitoring and demand response scheduling method, including:

It is used to obtain basic file information, power access point information, load resource data and collaborative interactive response data of small energy aggregators, and issue demand response scheduling plans to small energy aggregators;

Used to process data information from small energy aggregators to form dynamic demand response plans;

It is used to monitor and warn of abnormal changes in the basic file information, power access point information, load resource data and collaborative interactive response data of small energy aggregators, and dynamically adjust the demand response scheduling plan according to the change information.

The dynamic adjustment of the above scheduling plan is to use the dynamic programming algorithm to calculate the load data of a large number of small energy aggregators, and form the calculation results into executable instructions to achieve dynamic balance.

The dynamic programming algorithm means that each decision depends on the current state, and immediately causes the transition of the state, and each decision sequence is generated in the changing state.

The problem to be solved is decomposed into several sub-problems, and the sub-stages are solved in sequence. The solution of the former sub-problem provides useful information for the solution of the latter sub-problem. When solving any sub-problem, various possible local solutions are listed, those local solutions that are likely to be optimal are retained by decision-making, and other local solutions are discarded. Solve each sub-problem in turn, and the last sub-problem is the solution to the initial problem.

The dynamic programming algorithm is usually used to solve problems with certain optimal properties. In this embodiment, the demand response of small energy aggregators can be divided into several interconnected stages according to time periods, and demand response needs to be made in each stage. Decision, after the demand response decision of one stage is determined, it often affects the demand response decision of the next stage, thus completely determining the activity route of a process; the demand response decision of each stage constitutes a decision sequence, which is called a demand response strategy . Each stage has several demand response decisions to choose from, so there are many demand response strategies to choose from. Corresponding to a strategy, the effect of grid load balancing can be determined, and this effect can be determined by quantity. Different demand response strategies have different effects of power grid load balancing. The multi-stage demand response decision-making problem is to select an optimal demand response strategy among those demand response strategies that can be selected, which is the dynamic balance of the dispatch plan.

In another feasible fact example, it also includes storing the demand response scheduling plan, and executing the demand response scheduling plan that has been stored on the storage medium in the case of large-scale communication interruption in the area.

More specifically, obtain the basic file information of small energy aggregators, including customer number, customer name, contact information, electricity address, electricity type, customer type, contract capacity, user classification, city, and power supply unit information;

The information of the power access point includes the voltage level, the name of the station area, the name of the line, and the name of the substation;

Load resource data includes load curve, voltage curve, current curve, and electricity data;

The collaborative interaction response data includes load thresholds available for demand response scheduling, and demand response plan execution data.

In one embodiment, processing data information from small energy aggregators includes:

Data conversion and data calculation;

The data is converted into the code of the customer type, power supply unit, and electricity category classification of the small energy aggregator into the corresponding name, so that the implementation of the demand response plan can be visualized in the form of the converted name in the visual display. ;

The data calculation is based on the characteristics and operation mode of the aggregated load-side resources, and the adjustment method, adjustment capacity and performance of the load-side resources are fitted and calculated. Statistical analysis of the temporal and spatial characteristic extremes of distributed resources based on operating modes and ramping characteristics, graphically analyzes the characteristic extremes of each resource load to form a multi-scenario multi-objective aggregation scheme, aggregates to form physical characteristics of different adjustment scenarios, and forms an executable demand response scheduling plan .

In addition, it also includes obtaining the monitoring requirements of different small energy aggregators to execute the demand response dispatch plan, and visualizing the implementation of the demand response dispatch plan of the small energy aggregators obtained according to the operation monitoring requirements.

Embodiment 2

The purpose of this embodiment is to provide a computing device, including a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of the above method when the processor executes the program.

Embodiment 3

The purpose of this embodiment is to provide a computer-readable storage medium.

A computer-readable storage medium having a computer program stored thereon, the program executing the steps of the above method when executed by a processor.

Embodiment 4

The purpose of this embodiment is to provide an energy aggregation customer monitoring and demand response scheduling system disclosed in this embodiment, including:

The resource user management module is used to obtain basic file information, power access point information, load resource data and collaborative interaction response data of small energy aggregators, and issue demand response scheduling plans to small energy aggregators.

The load aggregation management module is used to process the data information of small energy aggregators, form a dynamic demand response plan, and transmit it to the resource user management module.

The platform resource operation monitoring module is used to monitor and warn of abnormal changes in the basic file information, power access point information, load resource data and collaborative interaction response data of small energy aggregators, and transmit the change information to the load aggregation management module. For dynamic adjustment of demand response schedules.

The above resource user management module is used to obtain the basic file information of small energy aggregators, including customer number, customer name, contact information, electricity address, electricity type, customer type, contract capacity, user classification, city, and power supply unit. Information; power access point information includes voltage level, name of station area to which it belongs, name of line to which it belongs, and name of substation to which it belongs; load resource data includes load curve, voltage curve, current curve, and power data; collaborative interaction response data includes available demand response scheduling load threshold, demand response plan execution data.

The above load aggregation management module is used to process the data information from the small energy aggregators of the resource user management module, form a dynamic demand response plan, and transmit it to the resource user management module.

The above-mentioned platform resource operation monitoring module is used to input the scheduling requirements of the demand response, convert them into computer instructions, and transmit them to the load aggregation management module.

The following is a detailed description of an energy aggregation customer monitoring and demand response scheduling system disclosed in this embodiment.

The data flow process of the energy aggregation customer monitoring and demand response scheduling system is shown in Figure 1. The data is obtained through the resource user management module, and the obtained data is sent to the load aggregation management module for data processing. The load aggregation management module will The results of data processing are sent to the resource user management module for execution. The platform resource operation monitoring module visualizes the execution process and results, and sends execution exceptions to the load aggregation management module for dynamic adjustment of the demand response scheduling plan.

The resource user management module includes a data acquisition module, a data storage module and a communication module.

The data acquisition module is used to obtain customer monitoring and demand response scheduling information for energy aggregation from the data source. The monitoring and demand response scheduling information for energy aggregation customers mainly includes basic file information of small energy aggregators, power access point information, and load resources. Data and collaborative interaction response data.

The data storage module is used to receive and store the energy aggregation customer monitoring and demand response scheduling information accessed from the marketing business system and the electricity consumption information collection system. The energy aggregation customer monitoring and demand response scheduling information includes:

Basic information of customer files: customer number, customer name, contact information, electricity address, electricity type, customer type, contract capacity, user classification, locality and city, power supply unit, etc.

The power access point information includes the voltage level, the name of the station area to which it belongs, the name of the line to which it belongs, and the name of the substation to which it belongs.

Load resource data includes load curve, voltage curve, current curve, and electricity data.

The collaborative interaction response data includes load thresholds available for demand response scheduling, and demand response plan execution data.

The communication module is used to transmit the demand response plan to the data source.

The load aggregation management module calls the corresponding data information in the data storage module according to the scheduling requirements of the platform resource operation monitoring module, processes the data in the data storage module, and transmits the processing results to the resource user management module and the platform resource operation module. monitoring module.

Among them, the processing process involved in the load aggregation management module includes: data conversion, data aggregation, data calculation and data storage.

The data is converted into the code of the customer type, power supply unit, and electricity category classification of the small energy aggregator into the corresponding name, so that the implementation of the demand response plan can be visualized in the form of the converted name in the visual display. , Power supply units include three levels of prefecture-level power supply companies, county-level power supply companies and township-level power supply stations.

The data calculation provides the characteristics and operation mode of the aggregated load-side resources according to the resource user management module, and fits and calculates the adjustment method, adjustment capacity and performance of the load-side resources. Statistical analysis of the spatial and temporal characteristic extremes of distributed resources, power characteristics, operating modes, and ramping characteristics, graphically analyzing the load characteristic extremes of each resource to form a multi-scene and multi-objective aggregation scheme, which aggregates the physical characteristics of different adjustment scenarios to form executable Demand response scheduling plan.

In addition, when the demand response scheduling plan is executed, the data calculation is also performed to correct the demand response scheduling plan according to the abnormality found by the platform resource operation monitoring module.

Data storage is to store the results of data calculation for the visual display of the platform resource operation monitoring module.

The platform resource operation monitoring module is used to obtain the monitoring requirements of different small energy aggregators to execute the demand response dispatch plan, and visualize the execution of the demand response dispatch plan of the small energy aggregators obtained by the load aggregation management module according to the operation monitoring requirements.

The monitoring requirements obtained by the customer control and power outage monitoring and dispatching module can be: distributed power supply response, energy storage resource response, charging facility response, customer-side electrical response, etc.

Among them, the distributed power response monitoring mainly monitors: the distributed power response, and the corresponding distributed power response is shown in Figure 2.

The response monitoring of energy storage resources mainly monitors: the response of energy storage resources, and the corresponding response of energy storage resources is shown in Figure 3.

The response monitoring of charging facilities mainly monitors: the response of charging facilities, and the corresponding response of charging facilities is shown in Figure 4.

The electrical response monitoring on the client side mainly monitors: the electrical response on the client side, and the corresponding electrical response on the client side is shown in Figure 5.

The response of small energy aggregators is divided according to equipment functions. Distributed power sources are load-side equipment, customer-side electrical and charging facilities are customer-side equipment, and energy storage resources and charging facilities (V2G) can be connected between load-side equipment and customer-side equipment. Switch between devices. In different time periods, different types of aggregators respond differently.

An energy aggregator customer monitoring and demand response scheduling system disclosed in this embodiment can form an aggregation optimization algorithm according to the characteristics and operation modes of load-side resources of different types of small energy aggregators. It provides technical support for small energy aggregators to connect to the grid, so as to improve the efficiency of demand response in a targeted manner.

Through the visual display of the response situation, business personnel can carry out intelligent demand-side response scheduling, which will improve the scheduling ability of business personnel, change the original "source-following-load" status quo, reduce the repetitive work of business personnel, and reduce labor costs.

The solution of the present invention can respond to the scheduling requirements and the load characteristics of the aggregator according to the demand, and provides flexible adjustment resources that can be used for the regulation of the power grid.

The steps involved in the apparatuses of the second, third, and fourth embodiments above correspond to the method embodiment 1, and the specific implementation can refer to the relevant description part of the embodiment 1. The term "computer-readable storage medium" should be understood to include a single medium or multiple media including one or more sets of instructions; it should also be understood to include any medium capable of storing, encoding or carrying for use by a processor The executed instruction set causes the processor to perform any of the methods of the present invention.

Those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general-purpose computer device, or alternatively, they can be implemented by a program code executable by the computing device, so that they can be stored in a storage device. The device is executed by a computing device, or they are separately fabricated into individual integrated circuit modules, or multiple modules or steps in them are fabricated into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

Although the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, they do not limit the scope of protection of the present invention. Those skilled in the art should understand that on the basis of the technical solutions of the present invention, those skilled in the art do not need to pay creative efforts. Various modifications or deformations that can be made are still within the protection scope of the present invention.

Claims (10)

1. An energy aggregation customer monitoring and demand response scheduling method is characterized by comprising the following steps:

acquiring basic archive information, power access point information, load resource data and cooperative interaction response data of the small energy aggregator;

processing data information of the small energy aggregators to form a dynamic demand response plan;

monitoring and early warning the abnormal change of the basic archive information, the power supply access point information, the load resource data and the cooperative interaction response data of the small energy aggregator, and dynamically adjusting a demand response scheduling plan according to the change information.

2. The energy aggregation customer monitoring and demand response scheduling method of claim 1, wherein the dynamically adjusting the demand response scheduling plan specifically comprises:

and calculating the load data of a large number of small energy aggregators by using a dynamic programming algorithm, and forming an executable instruction by using a calculation result to realize dynamic balance.

3. The energy aggregator customer monitoring and demand response scheduling method of claim 1, wherein the small energy aggregator demand response is divided into a plurality of interrelated phases by time period, a demand response decision is made at each phase, and after a demand response decision at one phase is determined, the demand response decision at the next phase is influenced to determine the activity route of a process;

the demand response decisions of each stage form a decision sequence, which is called a demand response strategy;

each stage has a plurality of demand response decisions to choose from, so that a plurality of demand response strategies are selected, and the effect of determining the load balance of the power grid corresponding to one strategy can be determined by quantity;

the multi-stage demand response decision problem is that an optimal demand response strategy is selected from selectable demand response strategies, namely the dynamic balance of a dispatching plan.

4. The energy aggregation customer monitoring and demand response scheduling method of claim 1, further comprising storing a demand response scheduling plan, and executing the demand response scheduling plan stored on the storage medium in case of a large-scale communication outage in the area.

5. The energy aggregation customer monitoring and demand response scheduling method of claim 1, wherein the basic archive information of the small energy aggregator is acquired and includes customer numbers, customer names, contact ways, electricity utilization addresses, electricity utilization categories, customer categories, contract capacities, user classifications, local cities to which the user belongs, and information of power supply units;

the power access point information comprises a voltage grade, a name of a station area to which the power access point belongs, a name of a line to which the power access point belongs and a name of a transformer substation to which the power access point belongs;

the load resource data comprises a load curve, a voltage curve, a current curve and electric quantity data;

the cooperative interaction response data comprises a load threshold value for demand response scheduling and demand response plan execution data.

6. The energy aggregator customer monitoring and demand response scheduling method of claim 1, wherein the processing the data messages of the small energy aggregators comprises:

data conversion and data calculation;

the data are converted into codes for classifying the customer types, the power supply units and the electricity utilization categories of the small energy aggregators, and the codes are converted into corresponding names, so that the execution condition of the demand response plan can be visually displayed in the form of the converted names when the data are visually displayed;

the data calculation is used for fitting and calculating the adjusting mode, the adjusting capacity and the performance of the load side resources according to the characteristics and the running mode of the aggregated load side resources, the time-space characteristic extreme values of the distributed resources are counted from the controllable capacity, the power characteristics, the running mode and the climbing characteristics through the peak-load-adjusting accumulated mileage and the contribution rate of the controllable load units, the resource load characteristic extreme values are analyzed graphically to form a multi-scene multi-target aggregation scheme, the physical characteristics of different adjusting scenes are aggregated to form an executable demand response scheduling plan.

7. The energy aggregation customer monitoring and demand response scheduling method of claim 1, further comprising obtaining monitoring demands of different small energy aggregators for executing the demand response scheduling plans, and visually displaying the execution conditions of the small energy aggregators demand response scheduling plans obtained according to the operation monitoring demands.

8. An energy aggregation customer monitoring and demand response scheduling system is characterized by comprising:

the resource user management module is used for acquiring basic file information, power access point information, load resource data and cooperative interaction response data of the small energy aggregator;

the load aggregation management module is used for processing data information of the small energy aggregator to form a dynamic demand response plan;

and the platform resource operation monitoring module is used for monitoring and early warning abnormal changes of basic archive information, power supply access point information, load resource data and cooperative interaction response data of the small energy aggregator, and dynamically adjusting a demand response scheduling plan according to the change information.

9. A computing device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of the method of any one of claims 1 to 7.

10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, is adapted to carry out the steps of the method according to any one of the preceding claims 1 to 7.

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