CN115796559A

CN115796559A - Adjustable load sorting method and system considering demand response scene

Info

Publication number: CN115796559A
Application number: CN202310053017.2A
Authority: CN
Inventors: 李波; 黄奇峰; 庄重; 吴争; 杨世海; 段梅梅; 孔月萍; 陆婋泉; 苏慧玲; 方凯杰; 黄艺璇; 程含渺
Original assignee: State Grid Jiangsu Electric Power Co ltd Marketing Service Center
Current assignee: State Grid Jiangsu Electric Power Co ltd Marketing Service Center
Priority date: 2023-02-03
Filing date: 2023-02-03
Publication date: 2023-03-14

Abstract

An adjustable load sorting method and system considering a demand response scene is characterized by comprising the following steps: step 1, setting an adjustable potential evaluation element of a user side load according to a demand response scene, and calculating characteristic elements and weights thereof under different demand response scenes through a multi-scene characteristic membership measuring and calculating model; step 2, dividing the user load groups based on constraint conditions to obtain labeled user load groups, and clustering the divided labeled user load groups by adopting a K-medoids clustering method to obtain a plurality of adjustable load resource groups to be sequenced; and 3, further dividing the adjustable load resource group based on the scheduling characteristics to obtain a load group response sequence, and realizing the generation and the regulation of a load response plan in the power system based on the load group response sequence. The invention flexibly selects the characteristic combination coefficient and gives consideration to the direct switching of a plurality of different demand scenes.

Description

Adjustable load sorting method and system considering demand response scene

Technical Field

The invention relates to the field of power systems, in particular to an adjustable load sequencing method and an adjustable load sequencing system considering a demand response scene.

Background

The adjustable load refers to demand side electric equipment, power supply equipment and energy storage equipment which can be started, stopped, adjusted in operation state or adjusted in operation time period according to electricity price, incentive or transaction information. The adjustable load comprises industrial enterprise production load, production auxiliary load, building load, resident electrical appliance load, distributed energy storage, electric vehicles and the like.

The adjustable potential evaluation is carried out on the demand response resources, and the electric power grid operator can be instructed to make scientific electricity price policy, incentive policy and resource scheduling plan, so that the comprehensive optimization configuration of the demand response resources is promoted, and the operation flexibility of the electric power grid is improved; the method can guide the load aggregator to make an accurate demand response resource control strategy, and is beneficial to efficiently exploring the potential of demand response resources; the method can guide the power consumers to reasonably arrange the load response quantity participating in demand response, form a scientific power utilization mode and stimulate the enthusiasm of the power consumers participating in power grid interaction.

At present, the research on demand response potential evaluation at home and abroad mainly comprises two types: one method is to obtain the power consumption characteristics of the user through analysis of a load curve, so as to qualitatively analyze the power consumption characteristics of the demand response potential, for example, the power consumption characteristics of the load are evaluated from a membership dimension, a time dimension and a response dimension, which respectively correspond to analysis of user incentive type user potential and user price type potential. The method can be obtained through clustering analysis of load curves or related indexes, and can be used for obtaining a load electricity utilization mode, electricity utilization regularity, a relation between electricity utilization quantity and electricity price and the like through analysis. The method can analyze the potential of a specific load, such as an air conditioner, a typical building and the like, so as to obtain the accurate reduction potential of the load of the type. For example, the reduction potential of a user is evaluated by estimating the change of the electricity price using the price elasticity coefficient of the user, and this method has an advantage that all users can be described using the price elasticity, and can take all users in the area into account in the potential evaluation, and has a disadvantage that the price elasticity coefficient cannot be obtained as an accurate value.

According to the other method, the demand response potential can be quantitatively calculated by modeling the load, and through quantitative evaluation of the demand response potential and a demand response potential evaluation system based on an analytic hierarchy process, the method is used for solving the distribution problem of each main body reduction amount under the condition of determining the load reduction amount. Thus, this approach can be used to perform a potential analysis on the general load to evaluate the demand response potential of a certain area, but cannot be used to evaluate the total demand response potential of a larger area.

Furthermore, as diversified loads such as distributed power supplies and electric vehicles are widely connected to an electric power system, the difficulty of load prediction and scheduling control is gradually increased. The users with different adjusting potentials are reasonably classified, and the power utilization behaviors of the users are mastered, so that the method has important significance in the aspects of load prediction, demand side management, power utilization pricing and the like. However, in the prior art, there is no suitable method for accurately acquiring the actual condition of the demand response in the partial area of the power system as a whole.

In view of the foregoing, there is a need for an adjustable load sorting method and system considering a demand response scenario.

Disclosure of Invention

In order to solve the defects in the prior art, the invention provides an adjustable load sorting method and an adjustable load sorting system considering a demand response scene.

The invention adopts the following technical scheme.

The invention relates to a first aspect of an adjustable load sequencing method considering a demand response scene, which comprises the following steps: step 1, setting an adjustable potential evaluation element of a user side load according to a demand response scene, and calculating characteristic elements and weights thereof under different demand response scenes through a multi-scene characteristic membership measuring and calculating model; step 2, dividing the user load groups based on constraint conditions to obtain labeled user load groups, and clustering the divided labeled user load groups by adopting a K-medoids clustering method to obtain a plurality of adjustable load resource groups to be sequenced; and 3, further dividing the adjustable load resource group based on the scheduling characteristics to obtain a load group response sequence, and realizing the generation and the regulation of a load response plan in the power system based on the load group response sequence.

Preferably, the demand response scenario includes a seasonal peak shaving scenario, an emergency load control scenario, an economic peak clipping scenario, and a clean energy consumption scenario.

Preferably, the adjustable potential evaluation elements include load response cost, load response speed, load response capacity and load response duration.

Preferably, the multi-scenario characteristic membership degree measurement and calculation model is generated based on characteristic element values of historical load data of the power consumer in the current scenario.

Preferably, the characteristic elements of the historical load data of the power consumer in the current scene are

Wherein, in the step (A),

the sequence number is the current scene sequence number;

respectively taking values of load response cost, load response speed, load response capacity and load response duration in the current scene; and the values of the load response cost, the load response speed, the load response capacity and the load response duration are respectively the values after the average value normalization of the historical load data in the current scene.

Preferably, the weights are obtained based on a normalization.

Preferably, the constraint conditions comprise load equipment operation characteristic constraint, load controllable constraint and user use habit constraint; and performing load group division on all users of the power grid participating in the current scene based on the constraint conditions to obtain a labeled user load group.

Preferably, the characteristic requirements of all users in the current scene are used as input data, and the labeled user load groups are clustered and grouped based on a K-medoids clustering method.

Preferably, a combination coefficient range of the feature elements is generated based on the scheduling features, and users conforming to the current combination coefficient range are extracted from the adjustable load resource group; acquiring a primary characteristic element under the current scene based on the weight; and sequencing the extracted users which accord with the current combination coefficient range based on the value of the primary characteristic element so as to generate a load group response sequence.

Preferably, the demand response is implemented for each user in the load group in sequence according to the load group response sequence.

The second aspect of the invention relates to an adjustable load sequencing system considering a demand response scene, which is realized by adopting the adjustable load sequencing method considering the demand response scene in the first aspect of the invention; the system comprises an evaluation module, a clustering module and a regulation module; the evaluation module is used for setting an adjustable potential evaluation element of the user side load according to the demand response scene and calculating the characteristic elements and weights thereof under different demand response scenes through a multi-scene characteristic membership measuring and calculating model; the clustering module is used for dividing the user load groups based on constraint conditions to obtain labeled user load groups, and clustering the divided labeled user load groups by adopting a K-medoids clustering method to obtain a plurality of adjustable load resource groups to be sequenced; and the regulation and control module is used for further dividing the adjustable load resource group based on the scheduling characteristics so as to obtain a load group response sequence and realizing the generation and regulation and control of a load response plan in the power system based on the load group response sequence.

Compared with the prior art, the adjustable load sorting method and system considering the demand response scene have the advantages that the adjustable potential evaluation requirement is set manually through the demand response scene, the characteristic elements are calculated through the multi-scene characteristic membership measuring and calculating model, and clustering and sorting are performed according to the constraint conditions, so that an accurate load response plan is obtained. The invention can promote the efficient utilization of demand response resources, flexibly select the characteristic combination coefficient and consider the direct switching of a plurality of different demand scenes.

The beneficial effects of the invention also include:

1. the invention gives consideration to different load response requirements such as response capacity, response cost, response time length, response speed and the like, considers the comfort level of users and meets the requirements of different scenes, clusters the user load groups according to the feature membership degree of the demand response scene, and determines the load group response sequence ordering according to the feature combination coefficient selected by the experience of a dispatcher. As different demand response scenes have obvious difference on the response characteristics of the user adjustable load, clustering of load groups is carried out by constructing multi-scene demand response, reasonable classification of users with different adjustment capacities is facilitated, the aggregate response potential of the user load is fully excavated, the adjustable capacity of orderly participation of the user load in demand response is enhanced, and the method has great guiding significance on formulating a control strategy of load participation in power grid active deficit response.

2. The method can support accurate evaluation of the total demand response potential of the power grid in a large area range, can avoid the influence of inaccurate price elasticity coefficient, and replaces price elasticity by comprehensively considering various constraints of users, so that the factors of user response are accurately evaluated, accurate modeling and estimation are carried out on the user response, and the accuracy of the algorithm is ensured.

Drawings

FIG. 1 is a schematic diagram illustrating the steps of an adjustable load sequencing method in consideration of a demand response scenario according to the present invention;

FIG. 2 is a schematic diagram illustrating the calculation of feature elements in an adjustable load ranking method considering a demand response scenario according to the present invention;

FIG. 3 is a schematic diagram of clustering in an adjustable load sorting method considering a demand response scenario according to the present invention;

FIG. 4 is a schematic diagram of a control sequence in the method for adjustable load sequencing in consideration of a demand response scenario according to the present invention;

FIG. 5 is a schematic view of a load joint adjustment effect in an embodiment of the adjustable load sorting method considering a demand response scenario according to the present invention;

FIG. 6 is a schematic diagram illustrating temperature changes of the electric heat pumps in an embodiment of the method for load-adjustable sequencing in view of a demand response scenario of the present invention;

fig. 7 is a schematic diagram illustrating a state of charge change of an electric vehicle according to an embodiment of the method for sorting adjustable loads in consideration of a demand response scenario;

fig. 8 is a schematic diagram illustrating a state of charge change of an energy storage device according to an embodiment of the method for sorting adjustable loads in consideration of a demand response scenario;

FIG. 9 is a schematic diagram illustrating schedulable resource allocation in different time periods according to an embodiment of the method for load-adjustable sequencing considering a demand response scenario of the present invention;

FIG. 10 is a schematic diagram illustrating an allocation of schedulable resource amounts in different time periods according to an embodiment of the method for load-scheduling with consideration of a demand response scenario of the present invention;

fig. 11 is a schematic diagram of response curves of different scenarios in the method for adjustable load sequencing considering a demand response scenario according to the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. The described embodiments of the invention are only some, not all embodiments of the invention. All other embodiments of the invention that are not described in the present application and are obtained by the embodiments of the invention described in the present application without creative efforts should be included in the protection scope of the present application by those of ordinary skill in the art.

Fig. 1 is a schematic diagram illustrating steps of an adjustable load sorting method in consideration of a demand response scenario according to the present invention. As shown in fig. 1, a first aspect of the present invention relates to an adjustable load sorting method considering a demand response scenario, where the method includes steps 1 to 3.

Step 1, setting an adjustable potential evaluation element of a user side load according to a demand response scene, and calculating characteristic elements and weights thereof under different demand response scenes through a multi-scene characteristic membership measuring and calculating model.

It can be understood that, in the first aspect of the present invention, the different demand response scenarios can be pre-constructed according to the actual demand situation in the power grid and the overall regulation and control requirement.

In the invention, four different most commonly used demand response scenes in the power grid are screened out according to actual conditions, the scenes can cause great influence on the demand response of the power grid according to the influence of actual seasons, environment, economy, other human factors and the like, and the influence is not the influence of the power grid on the property of power grid equipment and the self influence of the power grid caused by equipment operation regulation and control.

Specifically, an emergency load control scenario needs to have higher requirements on communication bandwidth and delay of a user side load, and when large-scale load group regulation is performed, if an emergency regulation task is met, the regulation accuracy can be properly ignored, and a faster regulation mode is adopted, so that the load response speed in this scenario is particularly important.

An economic peak clipping scene often appears in summer high temperature or winter severe cold weather, at the moment, the frequent opening of industrial and commercial user loads such as industrial loads, non-industrial air-conditioning loads, household air conditioners, electric water heaters and other electric equipment inevitably causes the appearance of a peak of a power grid load curve, the scene needs an electric power company and users to sign a power supply and utilization contract in advance, the load capacity reduced by the users in peak clipping events is mainly determined, the control mode is widely applied in China, the purpose is to reduce the user loads to reach a certain capacity, and the imbalance of the power grid supply and demand is reduced, so the load response capacity under the scene is particularly important.

The seasonal peak load adjusting scene occurs in urban power loads which change along with the change of seasons and living periods. The electricity load curve has electricity consumption peaks and valleys every month and every day due to the imbalance of electricity consumption caused by the change of seasonal temperature, humidity, day-to-day ratio and the like in one year. The peak clipping and valley filling is a measure for adjusting the power utilization load in seasonal peak shaving, and the power utilization time of various users is reasonably and programmatically arranged and organized according to the power utilization rules of different users so as to reduce the load peak, fill the load valley, reduce the load peak-valley difference of a power grid and lead the power generation and the power utilization to be balanced.

In a scene of consuming clean energy, the regulating capacity of a conventional power supply and the traditional scheduling operation mode of a power grid cannot meet the sending-out requirement of large-scale wind power, the problem of real-time supply and demand balance after large-scale fluctuating new energy power generation is accessed cannot be solved only by the regulating capacity of a power generation side, demand side management needs to be strengthened urgently, and local consumption of the new energy power is realized by utilizing user side load resources such as user side cold accumulation, heat accumulation type load, distributed energy storage and the like. Considering from the power grid side, the response rate and the response capacity of the consumed clean energy to the load of the user side have certain requirements, and considering from the user side, the response cost of the user can also influence the event of consuming the clean energy.

Preferably, the adjustable potential evaluation elements comprise load response cost, load response speed, load response capacity and load response duration.

It can be understood that, for the above four different scenarios, the present invention can extract the basic features of some load data that are most or more concerned by the different scenarios. Therefore, the invention selects the load response cost, the load response speed, the load response capacity and the load response time length from the load data as the basic characteristics of the load, thereby obtaining the classification and the sequencing of the load.

In the present invention, it can be assumed that all of the four demand response scenarios have the first consideration feature elements, and in a specific case, other feature elements are also important components of the policy. In order to quickly respond to the characteristics of different demand response scenes and form a specific user side load adjustable potential sorting scheme, different feature elements and different scenes need to be subjected to feature matching to form different weight schemes, and then different sorting implementation schemes are formed.

Fig. 2 is a schematic diagram of calculating characteristic elements in the adjustable load sorting method considering a demand response scenario. As shown in fig. 2, the present invention can implement the combination of multi-scene feature weights according to the above diagram.

Specifically, the target layer in fig. 2 uses a demand response scene as a partition object, and includes an emergency load control scene, an economic peak clipping scene, a seasonal peak shaving scene, and a clean energy consumption scene. The criterion layer mainly considers four characteristic indexes of response cost, response speed, response capacity and response duration. The weight layer is used for reasonably assigning the weights of four indexes, namely response cost, response speed, response capacity and response duration, in a specific demand response scene, and reflecting the matching condition of the user side resources participating in the demand response scene through reasonable combination of index weight coefficients.

The calculation of the feature elements and the weights thereof can be realized according to the thought.

Wherein, in the step (A),

for the sequence number of the current scene,

It can be understood that, according to the scene, the invention assigns factor weights, and according to the values of the four factors affecting the scene, i.e. response cost, response speed, response capacity, and response duration, for different scenes, an average value is taken as a reference, and then the ratio of a certain factor to the average value is calculated as the magnitude of the factor weights.

First, the present invention needs to calculate an average value of a certain element of response cost in an emergency load control scenario, where the average value may be calculated according to data of all historical load users in the scenario. Subsequently, the average of the response cost elements in all scenarios is calculated. The average of all the scenarios here can then be used to achieve the normalization calculation.

And then, calculating the ratio of the response cost elements of the emergency load control scene relative to all scenes, wherein the ratio can represent the value of the response cost after normalization. In a similar way, the invention can also obtain the ratio of other elements, and in this way, the weight of each characteristic element can be obtained.

Preferably, the weights are obtained based on a normalization approach. Specifically, the magnitude of each feature element ratio is compared, and the element corresponding to the maximum value is used as the primary consideration element of the scene. Or carrying out summation operation according to the magnitude of the normalization value of each characteristic element, and realizing the calculation of the weight.

And 2, dividing the user load groups based on the constraint conditions to obtain labeled user load groups, and clustering the divided labeled user load groups by adopting a K-medoids clustering method to obtain a plurality of adjustable load resource groups to be sequenced.

After the data are obtained, clustering of the load users can be achieved. Specifically, before the clustering operation is performed, labeling of the users, that is, preliminary extraction of the user group, may be implemented according to a constraint condition.

It can be understood that, before implementing a specific clustering operation, the present invention first loads a label to the user load according to the relevant constraint condition of the load in the power grid. In the invention, the constraint condition can be set according to the running characteristic of the equipment, the controllable condition of the load, the use habit of the user and the like. For example, the operational characteristics of certain devices result in their ability to provide responses to demand response scenarios, and such devices may be referred to as elastic or variable loads. While other devices have difficulty providing varying load capabilities when demand response scenarios exist on the grid. According to the content, the invention can provide labels for all the power grid loads in advance, if the load has stronger capacity and higher speed for responding to the demand, one type of screening labels can be provided, and for the loads which are difficult to respond to the demand, other labels can be provided.

Therefore, in different application scenarios, the invention can filter the load users in advance, only find the partial load meeting the label description from the load, and take the partial load as the load needing to realize the demand response in a key way.

It can be understood that the method in the present invention can implement clustering according to the values and corresponding weights of the feature elements obtained in step 1.

Fig. 3 is a schematic diagram of clustering in the adjustable load sorting method considering a demand response scenario according to the present invention. As shown in FIG. 3, in the present invention, a K-medoids clustering method is used to realize clustering calculation. The K-medoids algorithm selects the minimum point of the distance sum of all other points in the current class as the central point, so that the influence of extreme values can be effectively reduced, and the noise robustness is better. And clustering partial data by adopting a K-medoids algorithm, adding a label obtained in the previous stage to a special class, taking all loads meeting the label as training samples, and screening out vectors with long distances from the data vectors in the classes through a clustering center calculation method. When the training sample block is sampled in the load data obtained by clustering, the situation that the base classifier for learning the training sample block has poor classification performance due to the fact that too much remote data is extracted during random sampling can be prevented, and the purpose of optimizing the quality of the training data in the clustering process is achieved. And selecting the Euclidean distance as a clustering similarity index, wherein the local load data clustering process is as follows.

First, a cluster center, a load data set in the present invention, can be initialized

Is composed of n feature vectors represented as

In a

Randomly selecting K vectors as initial class centers

. Then class division is carried out, namely all load characteristic vectors are divided into various class centers according to the Euclidean distance,

to

The distance calculation formula of (c) is:

in the formula, ED represents

And

t is the time sequence load characteristic vector dimension, namely the number of time periods for collecting the load of a certain user in one day,

。

the invention can also update the class center during multiple iterations. Specifically, in each class, the distance sum from each load feature vector to other data vectors in the current class is calculated according to the following formula, and the load data vector with the minimum distance sum is selected as a new class center.

In the formula (I), the compound is shown in the specification,

representing feature vectors

To

The distance of (a) to (b),

to represent

The number of the feature vectors of the class in which the feature vectors belong,

is composed of

The sum of the distances to all vectors of the current class.

It can be understood that the invention can obtain the desired collection of load devices in the current scene by the above method. After further processing in step 3, an actual load response plan can be generated.

And 3, further dividing the adjustable load resource group based on the scheduling characteristics to obtain a load group response sequence, and realizing the generation and the regulation of a load response plan in the power system based on the load group response sequence.

After the adjustable load resource group is acquired, the resource group can be further divided in an artificial mode according to the scheduling characteristics. Specifically, the scheduling characteristics may be selected by the scheduler according to actual situations. For example, different feature combination coefficients are selected.

It can be understood that, in order to realize diversity requirements under different scenes, a strategic feature combination coefficient needs to be performed according to scene features, a response sequence is formed in different clustering groups, and hierarchical ordering of load groups is realized.

Taking an emergency load control scene as an example, assuming that response speed is selected as feature membership, the remaining three indexes are taken as ranking elements, a proportionality coefficient is given to each feature membership element, load response sequence parameters of each load are calculated, and after comparison, comparison is performed in each load group to form a response sequence.

In an embodiment of the present invention, the response sequence may be generated according to the size of the response sequence parameter. The specific calculation formula is as follows:

in the formula, M is a load response sequence parameter, i.e. a comprehensive index of the sequencing reference. Wherein the content of the first and second substances,

~

and k is 1 when the characteristic value type belongs to the target characteristic value, and is 0 otherwise.

、

、

、

Representing the magnitude of various characteristic values of the demand side load i;

、

a maximum value and a minimum value representing response costs in the load group;

、

a maximum value and a minimum value representing a response speed in the load group;

、

a maximum value and a minimum value representing response capacity in the load group;

、

a start time and an end time representing the scene schedule time;

~

representing the weight of different scenes to various types of feature values, wherein

。

Fig. 4 is a schematic diagram of a regulation and control sequence in the adjustable load sorting method considering a demand response scenario. As shown in fig. 4, after the order of the load group response sequence is calculated by using the above formula, the demand responses can be performed in sequence according to the regulation order.

In fact, the dispatcher in the invention can generate a plurality of groups of different load group response sequences according to a plurality of different characteristic combination coefficients, so that the dispatcher can also sort different load groups and realize the sequential dispatching and control of each load group.

In this way, the invention can establish an aggregation model of the elastic load groups by classifying the elastic load resources required by regulation and control of different power systems, and provides an elastic load matching capability evaluation method by considering different resource response characteristics, thereby providing a resource matching strategy for the power grid in operation scenes such as emergency load control, seasonal peak regulation, economic peak clipping, clean energy consumption and the like, and realizing maximum, rapid and flexible regulation of the elastic load.

The method employed in the present invention will be described below using a specific example. The embodiment of the invention adopts a resource pool optimized scheduling technology based on a dynamic planning technology to plan the resource scheduling of the demand side in the resource pool in the same area so as to meet the requirements of different scenes on response speed, response cost, response capacity and response time. And initializing a scene by taking 15min as a period during initialization of the embodiment, and scheduling resources by taking 1min as a time period during load scheduling. The load simulation parameter settings are shown in table 1.

TABLE 1 load simulation parameter Table

As shown in Table 1, the present invention provides basic information for each different type of load, such as the number, the range of the load in different states, the cost of the response, the speed, etc. basic parameters. Therefore, the method can perform simulation analysis in four aspects of the load joint scheduling completion condition in multiple scenes, the state change condition of the load group in the resource standby pool, the adjustable capacity and scheduling resource condition of the resource standby pool and the response requirement condition of the multi-scene elastic load strategy.

Fig. 5 is a schematic view of a load joint adjustment effect in an adjustable load sorting method considering a demand response scenario according to an embodiment of the present invention. As shown in fig. 5, the two curves of the target consumption and the joint consumption are basically fitted, the response error rate is less than 2%, which indicates that the multi-class load joint scheduling can completely meet the scene requirement. The other three curves respectively represent consumption curves of the electric heat pump EP, the electric vehicle EV and the energy storage equipment ES in the dispatching process, and compared with a combined response curve, response errors are increased by about 40%.

As can be seen from the curves in the figure, the absorption curves of the three types of loads are also respectively characterized due to the difference between the time domain characteristics and the functional characteristics of the three types of loads. In a non-emergency scene, the dispatching priority of the electric automobile and the electric heat pump is higher than that of the energy storage device, but the electric heat pump and the electric automobile only have the consumption capability, and the energy storage device simultaneously has the consumption capability and the reduction capability. And the electric automobile can concentrate on exerting force in a certain time period due to the limitation of the residence time.

Fig. 6 is a schematic diagram of temperature changes of the electric heat pump according to an embodiment of the method for load-adjustable sequencing in consideration of a demand response scenario. As shown in fig. 6, in the example, 400 electric heat pumps, 300 electric vehicles and 300 energy storage devices are selected, and in the scheduling process, the state change conditions of various loads are also simulated. The temperature of the electric heat pump is always maintained in a comfortable temperature range of 22-30 degrees in the dispatching process.

Fig. 7 is a schematic diagram of a state of charge change of an electric vehicle according to an embodiment of the adjustable load sorting method considering a demand response scenario. As shown in fig. 7, the electric vehicles all reach the desired state when leaving the field, and since the charging time of some electric vehicles is long, some electric vehicles in this example are in a range of 6:00 to 10:00 a concentrated charging phenomenon occurs.

Fig. 8 is a schematic diagram of a change in the state of charge of the energy storage device according to an embodiment of the adjustable load sorting method in consideration of a demand response scenario. As shown in fig. 8, at 6:00 to 10: the load charge demand is greater than the consumption demand during time period 00, so the energy storage device discharges, at 12: after 00, the charging requirements of other loads except for energy storage are reduced, and the energy storage device is charged, so that the consumption requirements under different scenes are met.

Fig. 9 is a schematic diagram of schedulable resource allocation in different time periods according to an embodiment of the method for load-tunable ordering considering a demand response scenario of the present invention. As shown in fig. 9, the comparison between the schedulable resource capacity (resource allocation amount in fig. 9) reserved in each time segment all day and the actual scheduling capacity (resource demand amount in fig. 9) is implemented by using the dynamic planning technology, and it is known from the figure that there are more resources reserved and allocated for the time segment with a large capacity demand, and the scheduling demand of the scene can be satisfied as much as possible.

Fig. 10 is a schematic diagram illustrating allocation of schedulable resource amounts in different time periods in an adjustable load sorting method considering a demand response scenario according to an embodiment of the present invention. As shown in fig. 10, the invention realizes the reserved quantity and the actual scheduling quantity of the electric heat pumps, the electric vehicles and the energy storage devices in each time period all day, and the resource pool in each time period makes a load joint scheduling combination in each time period according to the scene requirements and various load reservation conditions in the scene.

Fig. 11 is a schematic diagram of response curves of different scenarios in the adjustable load sorting method considering a demand response scenario according to the present invention. As shown in fig. 11, the scene information in each time period of the whole day and the response cost level and the response speed level of the load scheduling in different scenes, wherein the higher the response cost level is, the smaller the response cost is; the higher the response speed level, the faster the response speed.

In the simulation experiment, the primary characteristic element (namely the scene characteristic membership degree) in the emergency scene is specified as the response speed, and the figure shows that the response speed of the load group scheduled in the emergency scene is obviously higher than that of other scenes, so that the scene requirement can be met. The difference between the new energy consumption scene and the peak clipping and valley filling scene is different from the energy source, and the consumption/reduction capacity is matched to the maximum degree on the premise of ensuring the minimum response cost in the aspect of target. The required response capacity is input in the simulation, and the corresponding cost of the scheduling load group in the two scenes is lower than that in the emergency load control scene, so that the scene requirements can be met.

The second aspect of the invention relates to an adjustable load sorting system considering a demand response scene, which is realized by adopting the adjustable load sorting method considering the demand response scene in the first aspect of the invention; the system comprises an evaluation module, a clustering module and a regulation module; the evaluation module is used for setting an adjustable potential evaluation element of the user side load according to the demand response scene and calculating the characteristic elements and weights thereof under different demand response scenes through the multi-scene characteristic membership degree measuring and calculating model; the clustering module is used for dividing the user load groups based on constraint conditions to obtain labeled user load groups, and clustering the divided labeled user load groups by adopting a K-medoids clustering method to obtain a plurality of adjustable load resource groups to be sequenced; and the regulation and control module is used for further dividing the adjustable load resource group based on the scheduling characteristics so as to obtain a load group response sequence and realizing the generation and regulation and control of a load response plan in the power system based on the load group response sequence.

It is understood that the system in the present invention includes a hardware structure and/or a software module for performing each function in the method provided in the embodiments of the present application. Those of skill in the art will readily appreciate that the present application is capable of hardware or a combination of hardware and computer software implementing the various illustrative algorithm steps described in connection with the embodiments disclosed herein. Whether a function is performed as hardware or computer software drives hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the embodiment of the present application, the system may be divided into functional modules according to the above method example, for example, each functional module may be divided corresponding to each function, or two or more functions may be integrated into one processing module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. It should be noted that, in the embodiment of the present application, the division of the module is schematic, and is only one logic function division, and there may be another division manner in actual implementation.

The system may be implemented by one or more server devices. The devices are connected with each other, and each device comprises at least one processor, a bus system and at least one communication interface. The processor may be a Central Processing Unit (CPU), and may be replaced by a Field Programmable Gate Array (FPGA), an Application-specific integrated circuit (ASIC), or other hardware, or the FPGA or other hardware and the CPU may be used together as the processor.

The memory may be, but is not limited to, a read-only memory (ROM) or other type of static storage device that may store static information and instructions, a Random Access Memory (RAM) or other type of dynamic storage device that may store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory may be self-contained and coupled to the processor via a bus. The memory may also be integral to the processor.

The hard disk may be a mechanical disk or a Solid State Drive (SSD), etc. The Interface card may be a Host Bus Adapter (HBA), a Redundant Array of Independent Disks (RID), an Expander card (Expander), or a Network Interface Controller (NIC), which is not limited in this embodiment of the present invention. And an interface card in the hard disk module communicates with the hard disk. The storage node communicates with the interface card of the hard disk module, thereby accessing the hard disk in the hard disk module.

The Interface of the hard disk may be a Serial Attached Small Computer System Interface (SAS), a Serial Advanced Technology Attachment (SATA), a high speed Serial Computer extended bus standard (PCIe), or the like.

In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented using a software program, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The procedures or functions described in accordance with the embodiments of the present application are all or partially generated upon loading and execution of computer program instructions on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL), for short) or wireless (e.g., infrared, wireless, microwave, etc.). Computer-readable storage media can be any available media that can be accessed by a computer or data storage device including one or more available media integrated servers, data centers, and the like. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Disk (SSD)), among others.

The computer program instructions for carrying out operations of the present invention may be assembler instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present invention are implemented by personalizing an electronic circuit, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA), with state information of computer-readable program instructions, which can execute the computer-readable program instructions.

Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims

1. An adjustable load ordering method considering a demand response scenario, the method comprising the steps of:

step 1, setting an adjustable potential evaluation element of a user side load according to a demand response scene, and calculating characteristic elements and weights thereof under different demand response scenes through a multi-scene characteristic membership measuring and calculating model;

step 2, dividing the user load groups based on constraint conditions to obtain labeled user load groups, and clustering the divided labeled user load groups by adopting a K-medoids clustering method to obtain a plurality of adjustable load resource groups to be sequenced;

2. The method of claim 1, wherein the method further comprises:

the demand response scenes include seasonal peak shaving scenes, emergency load control scenes, economic peak clipping scenes and clean energy consumption scenes.

3. The adjustable load sequencing method taking into account a demand response scenario as recited in claim 2, wherein:

the adjustable potential evaluation elements comprise load response cost, load response speed, load response capacity and load response duration.

4. The adjustable load sequencing method taking into account a demand response scenario as recited in claim 3, wherein:

the multi-scene characteristic membership measuring and calculating model is generated based on characteristic element values of historical load data of the power consumer in the current scene.

5. The method of claim 4 for tunable load sequencing taking into account a demand response scenario, wherein:

the characteristic elements of the historical load data of the power consumer under the current scene are

Wherein, in the step (A),

for the sequence number of the current scene,

respectively taking values of load response cost, load response speed, load response capacity and load response duration in the current scene;

and the values of the load response cost, the load response speed, the load response capacity and the load response duration are respectively the values after the average value normalization of the historical load data in the current scene.

6. The method of claim 5, wherein the load ordering is adjustable in consideration of a demand response scenario, and wherein:

the weights are obtained based on the normalization mode.

7. The adjustable load sequencing method taking into account a demand response scenario as recited in claim 6, wherein:

the constraint conditions comprise load equipment operation characteristic constraint, load controllable constraint and user use habit constraint;

and carrying out load group division on all users of the power grid participating in the current scene based on the constraint conditions to obtain a labeled user load group.

8. The adjustable load sequencing method taking into account a demand response scenario as recited in claim 7, wherein:

and taking the characteristic requirements of all users in the current scene as input data, and clustering the labeled user load groups based on a K-medoids clustering method.

9. The adjustable load sequencing method of claim 8, in which:

generating a combination coefficient range of the characteristic elements based on the scheduling characteristics, and extracting users in accordance with the current combination coefficient range from the adjustable load resource group;

acquiring a primary characteristic element under the current scene based on the weight;

and sequencing the extracted users which accord with the current combination coefficient range based on the value of the primary characteristic element so as to generate a load group response sequence.

10. The method of claim 9, wherein the method further comprises:

and sequentially realizing demand response aiming at each user in the load group according to the load group response sequence.

11. An adjustable load sequencing system considering a demand response scenario, characterized by:

the system is implemented by using an adjustable load ordering method considering a demand response scenario as claimed in any one of claims 1 to 10; and the number of the first and second electrodes,

the system comprises an evaluation module, a clustering module and a regulation and control module; wherein the content of the first and second substances,

the evaluation module is used for setting an adjustable potential evaluation element of the user side load according to the demand response scene and calculating the characteristic elements and the weights thereof under different demand response scenes through a multi-scene characteristic membership measuring and calculating model;

the clustering module is used for dividing the user load groups based on constraint conditions to obtain labeled user load groups, and clustering the divided labeled user load groups by adopting a K-medoids clustering method to obtain a plurality of adjustable load resource groups to be sequenced;

and the regulation and control module is used for further dividing the adjustable load resource group based on the scheduling characteristics so as to obtain a load group response sequence, and realizing the generation and regulation and control of a load response plan in the power system based on the load group response sequence.