CN114580919A - Multi-scene two-stage demand response resource optimal scheduling method, device and equipment - Google Patents
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Abstract
The application relates to a multi-scenario two-stage demand response resource optimization scheduling method, device and equipment, wherein the method comprises the following steps: extracting 6 key response indexes of each resource based on a model of all equipment resources, aggregating the key response indexes to generate at least one resource group, performing multi-scene generation on a resource group response output curve, obtaining a typical response scene and key indexes of each resource group through scene clustering, deciding response time periods and response quantities of all demand response resources according to demand response indexes distributed by a power grid side to minimize self-operation cost, meeting the demand response quantity indexes and self-operation constraints of the power grid side, and generating an optimized scheduling strategy. Therefore, the defects of the existing method for optimizing and scheduling the demand response resources of the industrial load aggregators are overcome, the resource optimization scheduling control of the participation of the industrial load aggregators in the demand response of the power grid is realized, and the operation benefits of the industrial load aggregators are remarkably improved.
Description
Technical Field
The application relates to the technical field of demand response optimization operation, in particular to a multi-scene two-stage demand response resource optimization scheduling method, device and equipment.
Background
The demand response is an important regulation means of the power system, and means that power users are guided and encouraged to actively change power utilization behaviors through market price signals such as time-of-use electricity price or incentive mechanisms such as fund subsidy, so that power supply and demand balance is promoted, and stable operation of a power grid is guaranteed. The demand response becomes an important means for the new generation energy system to deal with the power generation uncertainty and the load demand fluctuation and promote the consumption of high proportion of renewable energy, and can bring remarkable benefits for the power grid.
Demand responses fall largely into two categories, one being price-based demand responses and the other being incentive-based demand responses. The price-based demand response refers to a response behavior that the power consumer adjusts the power consumption mode according to the change of the power price signal, and mainly includes time-of-use power price, real-time power price, peak power price and the like. Incentive-based demand response refers to a series of incentive policies that the demand response enforcement agency sets for encouraging users to curtail power load in situations where the reliability of the power system is reduced or the price of the power is raised, mainly including direct load control, interruptible load, demand-side bidding, emergency power demand response, capacity/auxiliary service plans, and the like.
The industrial load is an important component of the power load and occupies a large proportion of the power consumption of the whole society. Compared with the loads of commercial and residential users, industrial users have the advantages of large response capacity, high technical performance and the like, and are the most important demand response resources in the power system. From the perspective of an industrial load aggregator, how to optimally schedule response resources of the industrial load aggregator has very important practical significance. The existing demand response resource optimization scheduling technology and method for industrial load aggregators mainly have the following problems: 1) with the development of demand response, the data scale of response resources is larger and larger, and a large-scale industrial load aggregator needs to manage and decide large-scale mass equipment resources, so that the industrial load aggregator needs to perform fine modeling and reasonable grouping on demand response resources with large quantity, dispersed existence and different characteristics. 2) When an industrial load aggregator optimizes and schedules demand response resources, day-ahead and in-day phases need to be coordinated and considered, wherein the day-ahead phase needs to optimize a calling plan of a resource group according to a power grid demand response plan, and the in-day phase needs to optimize response output of the resource group according to a real-time running condition and a boundary condition. 3) When the demand response equipment resource is called, 100% successful response cannot be guaranteed, and due to uncertainty of response states of all resources in the resource group, a response capacity curve of the resource group is not a determined curve any more, but a random variable curve meeting certain probability distribution. The industrial load aggregator needs to consider the response capacity random distribution characteristic of each resource group, so that the actual response capacity under the calling result meets the requirement of the power grid side.
Therefore, a more systematic and comprehensive demand response resource optimization scheduling method, device and equipment need to be established to realize the participation of the industrial load aggregators in the optimization operation control of the power grid demand response and improve the operation benefits of the industrial load aggregators.
Disclosure of Invention
The application provides a multi-scene two-stage demand response resource optimization scheduling method, device and equipment, which are used for solving the defects of the existing demand response resource optimization scheduling method of an industrial load aggregator, realizing the resource optimization scheduling control of the participation of the industrial load aggregator in power grid demand response and bringing remarkable benefit promotion space.
An embodiment of a first aspect of the present application provides a multi-scenario two-stage demand response resource optimization scheduling method, including the following steps:
extracting 6 key response indexes of each resource based on the models of all equipment resources, and generating at least one resource group after aggregation;
performing multi-scene generation on the resource group response output curve of the at least one resource group, and obtaining a typical response scene and a key index of each resource group through scene clustering; and
and based on the typical response scene and the key indexes of each resource group, according to the demand response indexes distributed by the power grid side, deciding the response time periods and the response quantities of all demand response resources so as to minimize the self-operation cost, and simultaneously meeting the demand response quantity indexes and the self-operation constraints of the power grid side to generate an optimized scheduling strategy.
According to one embodiment of the application, the 6 key response indicators include response capacity, response rate, recovery rate, maximum response duration, response confidence and response cost.
According to an embodiment of the present application, the obtaining of the typical response scenario and the key index of each resource group through scenario clustering includes:
constructing a resource group response output random variable;
and generating scenes through random sampling simulation based on the resource group response output random variable, generating a demand response output scene for each resource group, and reducing the resource group demand response output scene into the typical response scene meeting preset conditions.
According to an embodiment of the application, the constructing a resource group response output random variable comprises:
obtaining the probability distribution of any resource response state random variable according to the response reliability index of the equipment resource;
obtaining a response capacity output curve random variable of any resource based on the probability distribution;
and obtaining the response capacity output curve random variable of any resource group based on the response capacity output curve random variable of any resource group.
According to an embodiment of the present application, the determining, based on the typical response scenario and the key index of each resource group, response periods and response amounts of all demand response resources according to the demand response indexes allocated by the power grid side to minimize self-operation cost, and simultaneously satisfying the demand response amount indexes and self-operation constraints of the power grid side, to generate an optimized scheduling policy includes:
according to a scalar quantity in the demand response obtained in the day, deciding a resource group calling plan of the next day, and generating a first-stage optimization model;
constructing a plurality of demand response scenes for the response uncertainty of each resource group, setting corresponding operation constraint conditions for each typical scene, deciding a response output curve of each resource group, and generating a second-stage optimization model;
and combining the first-stage optimization model and the second-stage optimization model to obtain a multi-scene two-stage demand response resource scheduling optimization decision model, and solving the multi-scene two-stage demand response resource scheduling optimization decision model to obtain an optimization calling result of each resource group of the industrial load aggregator and a response output curve of each resource group in each typical scene.
According to the multi-scenario two-stage demand response resource optimization scheduling method, 6 key response indexes of each resource are extracted based on a model of all equipment resources, at least one resource group is generated after aggregation, multi-scenario generation is carried out on a resource group response output curve, a typical response scenario and key indexes of each resource group are obtained through scenario clustering, response time intervals and response quantities of all demand response resources are decided according to demand response indexes distributed by the power grid side, the self-operation cost is minimized, the demand response quantity indexes and self-operation constraints of the power grid side are met, and an optimization scheduling strategy is generated. Therefore, the defects of the existing method for optimizing and scheduling the demand response resources of the industrial load aggregator are overcome, the uncertain scene during resource group response is considered, the resource optimization scheduling control of the industrial load aggregator participating in the demand response of the power grid is realized, and the operation benefit of the industrial load aggregator is remarkably improved.
An embodiment of a second aspect of the present application provides a multi-scenario two-stage demand response resource optimization scheduling apparatus, including:
the extraction module is used for extracting 6 key response indexes of each resource based on the models of all equipment resources and generating at least one resource group after aggregation;
the generating module is used for generating multiple scenes for the resource group response output curve of the at least one resource group and obtaining a typical response scene and a key index of each resource group through scene clustering; and
and the optimization module is used for deciding the response time period and the response quantity of all the demand response resources according to the demand response indexes distributed by the power grid side based on the typical response scene and the key indexes of each resource group so as to minimize the self-operation cost, and simultaneously, the demand response quantity indexes and the self-operation constraint of the power grid side are met to generate an optimized scheduling strategy.
According to one embodiment of the present application, the 6 key response indicators include response capacity, response rate, recovery rate, maximum response duration, response confidence, and response cost.
According to an embodiment of the present application, the generating module is specifically configured to:
constructing a resource group response output random variable;
and generating scenes through random sampling simulation based on the resource group response output random variable, generating a demand response output scene for each resource group, and reducing the resource group demand response output scene into the typical response scene meeting preset conditions.
According to an embodiment of the present application, the generating module is further configured to:
obtaining the probability distribution of random variables of any resource response state according to the response reliability index of the equipment resource;
obtaining a response capacity-to-output curve random variable of any resource based on the probability distribution;
and obtaining the response capacity output curve random variable of any resource group based on the response capacity output curve random variable of any resource group.
According to an embodiment of the present application, the optimization module is specifically configured to:
according to a scalar quantity in the demand response obtained in the day, deciding a resource group calling plan of the next day, and generating a first-stage optimization model;
constructing a plurality of demand response scenes for the response uncertainty of each resource group, setting corresponding operation constraint conditions for each typical scene, deciding a response output curve of each resource group, and generating a second-stage optimization model;
and combining the first-stage optimization model and the second-stage optimization model to obtain a multi-scene two-stage demand response resource scheduling optimization decision model, and solving the multi-scene two-stage demand response resource scheduling optimization decision model to obtain an optimization calling result of each resource group of the industrial load aggregator and a response output curve of each resource group in each typical scene.
According to the multi-scenario two-stage demand response resource optimization scheduling device provided by the embodiment of the application, 6 key response indexes of each resource are extracted based on a model of all equipment resources, at least one resource group is generated after aggregation, multi-scenario generation is carried out on a resource group response output curve, a typical response scenario and key indexes of each resource group are obtained through scenario clustering, response time periods and response quantities of all demand response resources are decided according to demand response indexes distributed by a power grid side, so that the self-running cost is minimized, the demand response quantity indexes and self-running constraints of the power grid side are met, and an optimization scheduling strategy is generated. Therefore, the defects of the existing method for optimizing and scheduling the demand response resources of the industrial load aggregator are overcome, the uncertain scene during resource group response is considered, the resource optimization scheduling control of the industrial load aggregator participating in the demand response of the power grid is realized, and the operation benefit of the industrial load aggregator is remarkably improved.
An embodiment of a third aspect of the present application provides an electronic device, including: the system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the multi-scenario two-phase demand response resource optimization scheduling method according to the embodiment. In a fourth aspect, the embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, where the program is executed by a processor, so as to implement the multi-scenario two-stage demand response resource optimization scheduling method as described in the foregoing embodiments.
Therefore, the method makes up the defects of the demand response resource optimization scheduling method in the related technology, establishes a set of multi-scene two-stage demand response resource optimization scheduling method facing the industrial load aggregator, fully considers modeling and grouping of large-scale demand response resources, coordinates optimization of different stages before and in the day when the industrial load aggregator participates in demand response, and simultaneously considers the uncertain scene when resource groups respond. Based on the multi-scene two-stage demand response resource optimization scheduling method, an industrial load aggregator can give consideration to economy and uncertainty, and overall optimization is conducted on the day-ahead resource calling and day-interior operation response scenes.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
The above and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a flowchart of a multi-scenario two-stage demand response resource optimization scheduling method according to an embodiment of the present application;
FIG. 2 is a schematic view of a flow framework of a multi-scenario two-stage demand response resource optimization scheduling method according to an embodiment of the present application;
fig. 3 is a schematic diagram illustrating a configuration situation of resource combinations in a resource group 1 according to an embodiment of the present application;
fig. 4 is a schematic diagram illustrating a configuration of resource combinations in a resource group 2 according to an embodiment of the present application;
FIG. 5 is a graph illustrating an exemplary response capacity curve for a resource group 1 according to an embodiment of the present application;
FIG. 6 is a graph illustrating an exemplary response capacity curve for a resource group 2 according to an embodiment of the present application;
fig. 7 is a schematic diagram of a response contribution result of a resource group 1 according to an embodiment of the present application;
fig. 8 is a schematic diagram of a response contribution result of a scenario 2 resource group according to an embodiment of the present application;
fig. 9 is a schematic diagram of a response contribution result of a scenario 3 resource group according to an embodiment of the present application;
FIG. 10 is a schematic diagram illustrating a response contribution result of a scenario 4 resource group according to an embodiment of the present application;
fig. 11 is a schematic diagram of a response contribution result of a scenario 5 resource group according to an embodiment of the present application;
FIG. 12 is a diagram of an exemplary multi-scenario two-stage demand response resource optimization scheduling apparatus according to an embodiment of the present application;
fig. 13 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to the embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.
The following describes a multi-scenario two-stage demand response resource optimization scheduling method, device and equipment according to an embodiment of the present application with reference to the drawings. In order to solve the problem of the deficiency of the existing demand response resource optimization scheduling method for the industrial load aggregators in the background art, the application provides a multi-scenario two-stage demand response resource optimization scheduling method, in the method, 6 key response indexes of each resource are extracted based on models of all equipment resources and aggregated to generate at least one resource group, multi-scenario generation is carried out on a response output curve of the resource group, a typical response scenario and key indexes of each resource group are obtained through scenario clustering, response time periods and response quantities of all demand response resources are decided according to demand response indexes distributed on the power grid side, so that the self-operation cost is minimized, the demand response quantity indexes and self-operation constraints on the power grid side are met, and an optimization scheduling strategy is generated. Therefore, the defects of the existing method for optimizing and scheduling the demand response resources of the industrial load aggregators are overcome, the uncertain scene of resource group response is considered, the resource optimization scheduling control of participation of the industrial load aggregators in the demand response of the power grid is realized, and the operation benefits of the industrial load aggregators are remarkably improved.
Specifically, fig. 1 is a schematic flowchart of a multi-scenario two-stage demand response resource optimization scheduling method according to an embodiment of the present application.
In this embodiment, the multi-scenario two-stage demand response resource optimization scheduling method mainly includes three parts, as shown in fig. 2, which are respectively modeling and grouping demand response resources, resource group demand response multi-scenario generation, and multi-scenario two-stage demand response resource group scheduling optimization decision, and is described in detail below according to a specific embodiment.
As shown in fig. 1, the multi-scenario two-stage demand response resource optimization scheduling method includes the following steps:
in step S101, 6 key response indicators of each resource are extracted based on the models of all the device resources, and at least one resource group is generated after aggregation.
Wherein, in some embodiments, the 6 key response indicators include response capacity, response rate, recovery rate, maximum response duration, response confidence, and response cost.
Specifically, the industrial load aggregator collects, counts and analyzes technical parameters and historical operating data of all equipment resources managed by the industrial load aggregator, performs fine modeling on the load resources, and extracts the following key indexes of the resources:
(1) response capacity Q: and providing the maximum load adjustment amount for the equipment resource participation demand response.
(2) Response rate RU: the maximum load adjustment that the device resource can achieve per unit time when the demand response is invoked.
(3) Recovery rate RD: is the maximum recoverable load capacity of the device resource per unit time after completion of the demand response.
(4) Maximum response duration TM: the maximum time for the device resource to continuously respond without influencing the production flow after the demand response is called.
(5) Response reliability Y: due to the existence of safety, economy or other factors in the industrial production process and the possible abnormal operation condition of the equipment, the equipment resource cannot necessarily guarantee 100% successful response when the demand response is called, so the successful response probability is defined as the response reliability.
(6) Response cost C: and the running cost of unit capacity load adjustment is realized for the participation of equipment resources in demand response.
After the industrial load aggregator completes the modeling, all resources are grouped and aggregated according to the technical and economic characteristics of each resource, and a certain number of resource groups meeting certain technical conditions are obtained. The resource grouping and aggregation process can be carried out according to historical operation experience and actual conditions, and can also be optimized by adopting the existing research load aggregation technology and related methods in application thereof.
In step S102, multi-scenario generation is performed on the resource group response output curve of at least one resource group, and a typical response scenario and a key index of each resource group are obtained through scenario clustering.
Further, in some embodiments, the typical response scenario and the key index of each resource group are obtained through scenario clustering, which includes: constructing a resource group response output random variable; and based on the resource group response output random variable, carrying out scene generation through random sampling simulation, generating a demand response output scene for each resource group, and reducing the resource group demand response output scene into a typical response scene meeting preset conditions.
Further, in some embodiments, constructing the resource group response contribution random variable comprises: obtaining the probability distribution of random variables of any resource response state according to the response reliability index of the equipment resource; obtaining a random variable of a response capacity output curve of any resource based on probability distribution; and obtaining the random variable of the response capacity output curve of any resource group based on the random variable of the response capacity output curve of any resource group.
Specifically, the industrial load aggregator performs multi-scenario generation on the resource group response output curve according to the resource model established in the above steps and the aggregated resource groups due to uncertainty of response of each resource, and obtains a typical response scenario and a key index of each resource group through scenario clustering, including the following steps:
(1) and constructing a resource group response output random variable.
Firstly, according to each resource response reliability index in the above steps, obtaining the probability distribution of any resource i response state random variable as follows:
wherein the content of the first and second substances,
a random variable that is the response status of resource i, with 1 indicating a successful response, 0 indicating an unsuccessful response,
satisfy the 0-1 distribution; y isiResponse confidence for resource i; omegaIThe aggregators respond to the collection of resources for the industrial load.
Secondly, obtaining the random variable of the response capacity output curve of the resource i
Comprises the following steps:
wherein, PiResponse capacity for resource i; si,tIndicating the response state of the resource i, and if the resource i is responding at the time t, S i,t1, otherwise Si,t＝0；ΩTIs a set of response periods.
On the basis, the random variable of the response capacity output curve of any resource group g is obtained
Comprises the following steps:
wherein G isg,iRepresenting resourcesi, if resource i belongs to resource group G, G g,i1, otherwise Gg,i＝0；ΩGRepresenting a collection of resource groups.
(2) Multi-scene generation and clustering.
According to the formula (3), scene generation is carried out through random sampling simulation, a large number of demand response output scenes are generated for each resource group to represent uncertainty of demand response output of each resource group, and then the large number of resource group demand response output scenes are reduced into limited typical response scenes through a clustering algorithm in the existing research, so that the calculation scale of the next step is reasonably controlled, and the situation that an optimization model is too large and cannot be solved is prevented.
In step S103, based on the typical response scenario and the key index of each resource group, according to the demand response index allocated by the power grid side, the response time periods and the response amounts of all demand response resources are decided, so as to minimize the self-operation cost, and simultaneously satisfy the demand response amount index and the self-operation constraint of the power grid side, and generate an optimized scheduling policy.
Further, in some embodiments, based on a typical response scenario and a key index of each resource group, according to a demand response index allocated on the grid side, a response period and a response amount of all demand response resources are decided to minimize a self-operation cost, and simultaneously meet a grid side demand response amount index and a self-operation constraint, an optimized scheduling policy is generated, including: according to a scalar quantity in the demand response obtained in the day, deciding a resource group calling plan of the next day, and generating a first-stage optimization model; constructing a plurality of demand response scenes according to the response uncertainty of each resource group, setting corresponding operation constraint conditions for each typical scene, deciding a response output curve of each resource group, and generating a second-stage optimization model; and combining the first-stage optimization model and the second-stage optimization model to obtain a multi-scene two-stage demand response resource scheduling optimization decision model, and solving the multi-scene two-stage demand response resource scheduling optimization decision model to obtain an optimization calling result of each resource group of the industrial load aggregator and a response output curve of each resource group in each typical scene.
Specifically, in the model, an industrial load aggregator decides response time periods and response quantities of all internal demand response resources according to demand response indexes allocated by a power grid side so as to minimize self-running cost and simultaneously meet the demand response quantity indexes and self-running constraints of the power grid side. The overall process comprises two stages: the first stage is to make a day-ahead calling decision of the resource group, and make a next-day resource group calling plan according to a scalar in a demand response obtained day-ahead; in the second stage, a large number of demand response scenes are constructed according to the response uncertainty of each resource group, a set of operation constraint conditions is set for each typical scene, and a response output curve of each resource group is decided; and finally, combining the two-stage optimization models to realize the two-stage modeling and integrated optimization of resource group calling optimization and each resource group response output optimization. The method specifically comprises the following steps:
(1) and determining decision variables of the optimization model.
The decision variables of the first-stage model are as follows:
The decision variables of the second stage model are:
(2) And (5) constructing decision variables of the optimization model.
Taking the resource group calling cost and the response cost in the first stage and the response cost in each scene in the second stage as target functions, and expressing the following expressions:
wherein:
a single call cost for resource group g other than the response capacity cost;
the unit response output cost of the resource group g; omegaSA typical response scene set; pisIs the probability of scene s.
(3) Constraints of the first-stage optimization model are determined.
1) And (3) power grid side distribution response quantity constraint:
the calling plan of the resource group needs to satisfy the scalar quantity in the demand response distributed to the industrial load aggregators by the power grid side
Namely, the expression is:
2) the resource group responds to the force scope constraint:
wherein the content of the first and second substances,
the maximum response capacity of the resource group g set when the resource grouping is performed in step (1).
3) The resource group is constrained by the number of times it is invoked:
any resource group g can only be called once at most in the whole scheduling period, namely the expression is:
4) the resource group initial calling indication variable and the current calling state variable are associated and constrained:
starting call indication variable of any resource group g
With the current call state
The following associative constraints exist:
5) resource group invocation duration constraint:
the calling duration of any resource group g cannot exceed the response time length of the resource group set when the resources are grouped in step (1)
Namely, the expression is:
6) the resource group responds to the output climbing constraint:
wherein the content of the first and second substances,
the response rate and the recovery rate of the resource group g formed when the resources are grouped in the step (1) are respectively.
(4) And determining the constraint conditions of the second-stage optimization model.
1) And (3) power grid side distribution response quantity constraint under each scene:
2) resource group response output range constraint under each scene;
wherein the content of the first and second substances,
is a response capacity curve for a resource group g under a typical response scenario s.
3) Resource group response output climbing restraint under each scene:
by making the above decisionVariables of
The objective function expression (4) and the constraint condition expression (5) -expression (12) form a multi-scenario two-stage demand response resource scheduling optimization decision model provided by the embodiment of the application.
Therefore, by solving the optimization model, the optimization calling result of each resource group of the industrial load aggregator and the response output curve of each resource group in each typical scene can be obtained. The result can be used for deciding a reasonable resource group selection result in the previous stage, reducing the calculation scale of the industrial load aggregator during the day operation, and considering the uncertainty scene of resource response in the day stage, so that the optimization result is more reliable. When the system runs in an actual day, the industrial load aggregator calls the resource groups according to the response time period and the response output of each resource group in the optimization result, so that the minimum running cost of the system can be realized on the premise of meeting the response quantity index of the power grid side and the running constraint of the system.
To facilitate those skilled in the art to further understand the multi-scenario two-phase demand response resource optimization scheduling method according to the embodiment of the present application, the following description is made in detail with reference to specific embodiments.
Specifically, a load aggregator a in a certain industrial park is taken as an example to explain the multi-scenario two-stage demand response resource optimization scheduling method for the industrial load aggregator, which is provided by the embodiment of the application, and verify the effect achieved by the invention.
Further, the load resources managed by the industrial load aggregator a, which can participate in demand response, include 900 equipment resources in a plurality of industries such as steel, cement, textile, metal products, and the like. The method reports the demand response of the power grid side on the next day of 11:00-13:00 before the day, wherein 15MW is bid for the next day of 11:30-12:00, 46MW is bid for the next day of 12:00-12:30, and 37MW is bid for the next day of 12:30-13: 00.
The industrial load aggregator a performs optimization scheduling on each demand response resource according to the first part of the multi-scenario two-stage demand response resource optimization scheduling method provided by the embodiment of the applicationAnd (4) modeling, namely extracting indexes such as response capacity, response rate, recovery rate, maximum response duration, response reliability, response cost and the like according to the technical parameters of the equipment and historical operation data. Wherein, the response reliability Y of the equipment resource in the steel and metal product industry is 0.92, and the response reliability Y of the equipment resource in other industries is 0.96. On the basis, the industrial load aggregator A configures all equipment resources into 10 groups in the day-ahead period according to the technical and economic characteristics of each resource and the combination of demand response bid-winning conditions, wherein the response time of each group is long
1 hour, response capacity
Is 10 MW. Taking resource group 1 and resource group 2 as an example, the internal resource combination configuration is shown in fig. 3 and 4.
According to the second part of the multi-scenario two-stage demand response resource optimization scheduling method provided by the embodiment of the application, the industrial load aggregator A randomly samples the response conditions of 10 resource groups to obtain a large number of response capacity curve scenarios, and each resource group selects 10 typical scenarios by adopting a kmeans algorithm. Taking resource group 1 and resource group 2 as an example, typical response capacity curves are shown in fig. 4 and 5.
The industrial load aggregator a establishes a multi-scenario two-stage demand response resource scheduling optimization decision model according to the third part of the multi-scenario two-stage demand response resource optimization scheduling method provided by the embodiment of the application. Wherein each typical response scenario probability issSet to 0.1. Response rate of each resource group
And recovery rate
All are 3 MW/min. Single call cost per resource group
Are all 500 yuan, unit response cost
As shown in table 1:
TABLE 1
| Resource group | Cost per response (Yuan/MWh) | Resource group | Cost per response (Yuan/MWh) |
| |
308 | |
339 |
| |
272 | |
331 |
| |
349 | |
315 |
| |
286 | |
291 |
| |
303 | |
321 |
The industrial load aggregator a solves the optimization model, and the obtained calling result of each resource group is shown in table 2:
TABLE 2
In the decision results in the second stage 10 typical scenarios, the response output results of each resource group in the selected scenarios 1 to 5 are shown in fig. 7 to 11, and it can be seen from this that, because the unit response cost of the resource groups 2, 4, 5, and 9 is low, all of these 4 resource groups in each typical scenario are called with priority, and the actual response power is the maximum response capacity of the resource group.
For example, the time period of 12:45-13:00 is taken as an example to analyze the effect realized by the embodiment of the application: in some scenarios, such as scenarios 3 and 4, since the accumulated typical response output of the resource groups 1, 2, 4 and 9 is low, the resource group 10 with relatively high cost responds to the increase of the output to cope with the power shortage caused by the poor success rate of resource response in the resource groups 1, 2, 4 and 9. Whereas in scenarios 1, 2, 5 the lower cost resource groups 1, 2, 4, 9 respond better and therefore the relatively higher cost resource group 8 does not need to provide a response. Similar results were obtained during the 12:00-12:45 periods. Therefore, by the multi-scene two-stage demand response resource scheduling optimization method provided by the embodiment of the application, the uncertain response scenes of resource response can be well considered, and the overall response cost is the lowest on the premise of meeting the relevant constraints of each scene.
Further, for comparison, the method provided by the embodiment of the present application is compared with a traditional demand response resource scheduling optimization method (without considering response uncertainty), and the optimization calculation result of the latter is shown in table 3:
TABLE 3
| Resource group | Call period | Resource group | Call |
| Resource group | |||
| 1 | 11:30-12:30 | |
Not calling |
| |
12:00-13:00 | |
Not calling |
| |
Not call | |
12:30-13:00 |
| |
12:00-13:00 | |
12:00-13:00 |
| |
11:30-12:30 | |
Not call |
Therefore, the number of resource group calls calculated by the traditional optimization method is 1 less than that of the calculation result of the embodiment of the application (the resource group 10 is not called), but because the uncertainty of response of each resource group is not considered, the requirement in daily operation cannot be met if the response condition of part of internal resources is poor in the daily operation process. The method provided by the embodiment of the application can give consideration to both economy and uncertainty, comprehensively optimizes the day-ahead resource calling and day-interior operation response scene, and shows good practical significance and application prospect of the method.
According to the multi-scenario two-stage demand response resource optimization scheduling method, 6 key response indexes of each resource are extracted based on models of all equipment resources and aggregated to generate at least one resource group, multi-scenario generation is performed on a resource group response output curve, a typical response scenario and key indexes of each resource group are obtained through scenario clustering, response time periods and response quantities of all demand response resources are decided according to demand response indexes distributed by the power grid side, the self-operation cost is minimized, the demand response quantity indexes and self-operation constraints of the power grid side are met, and an optimization scheduling strategy is generated. Therefore, the defects of the existing method for optimizing and scheduling the demand response resources of the industrial load aggregators are overcome, the uncertain scene of resource group response is considered, the resource optimization scheduling control of participation of the industrial load aggregators in the demand response of the power grid is realized, and the operation benefits of the industrial load aggregators are remarkably improved.
Next, a multi-scenario two-phase demand response resource optimization scheduling apparatus proposed according to an embodiment of the present application is described with reference to the drawings.
Fig. 12 is a block diagram illustrating a multi-scenario two-phase demand response resource optimization scheduling apparatus according to an embodiment of the present application.
As shown in fig. 12, the multi-scenario two-stage demand response resource optimization scheduling device 10 includes: an extraction module 100, a generation module 200 and an optimization module 300.
The extraction module 100 is configured to extract 6 key response indicators of each resource based on models of all device resources, and generate at least one resource group after aggregation;
the generation module 200 is configured to perform multi-scenario generation on the resource group response output curve of at least one resource group, and obtain a typical response scenario and a key index of each resource group through scenario clustering; and
the optimization module 300 is configured to decide response time periods and response amounts of all demand response resources according to the demand response indexes allocated by the power grid side based on the typical response scenario and the key indexes of each resource group, so as to minimize the self-operation cost, and simultaneously satisfy the demand response amount indexes and the self-operation constraints of the power grid side, and generate an optimized scheduling policy.
Further, in some embodiments, the 6 key response indicators include response capacity, response rate, recovery rate, maximum response duration, response confidence, and response cost.
Further, in some embodiments, the generating module 200 is specifically configured to:
constructing a resource group response output random variable;
and generating scenes by random sampling simulation based on the resource group response output random variables, generating a demand response output scene for each resource group, and reducing the resource group demand response output scene into a typical response scene meeting preset conditions.
Further, in some embodiments, the generating module 200 is further configured to:
obtaining the probability distribution of random variables of any resource response state according to the response reliability index of the equipment resource;
obtaining a random variable of a response capacity output curve of any resource based on probability distribution;
and obtaining the random variable of the response capacity output curve of any resource group based on the random variable of the response capacity output curve of any resource group.
Further, in some embodiments, the optimization module 300 is specifically configured to:
according to a scalar quantity in the demand response obtained in the day, deciding a resource group calling plan of the next day, and generating a first-stage optimization model;
constructing a plurality of demand response scenes according to the response uncertainty of each resource group, setting corresponding operation constraint conditions for each typical scene, deciding a response output curve of each resource group, and generating a second-stage optimization model;
and combining the first-stage optimization model and the second-stage optimization model to obtain a multi-scene two-stage demand response resource scheduling optimization decision model, and solving the multi-scene two-stage demand response resource scheduling optimization decision model to obtain an optimization calling result of each resource group of the industrial load aggregator and a response output curve of each resource group in each typical scene.
According to the multi-scenario two-stage demand response resource optimization scheduling device provided by the embodiment of the application, 6 key response indexes of each resource are extracted based on a model of all equipment resources and aggregated to generate at least one resource group, a resource group response output curve is subjected to multi-scenario generation, a typical response scenario and key indexes of each resource group are obtained through scenario clustering, response time periods and response quantities of all demand response resources are decided according to demand response indexes distributed by a power grid side, so that the self-running cost is minimized, the demand response quantity indexes and self-running constraints of the power grid side are met, and an optimization scheduling strategy is generated. Therefore, the defects of the existing method for optimizing and scheduling the demand response resources of the industrial load aggregator are overcome, the uncertain scene during resource group response is considered, the resource optimization scheduling control of the industrial load aggregator participating in the demand response of the power grid is realized, and the operation benefit of the industrial load aggregator is remarkably improved.
Fig. 13 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device may include:
The processor 1302 implements the multi-scenario two-stage demand response resource optimization scheduling method provided in the above embodiments when executing a program.
Further, the electronic device further includes:
a communication interface 1303 for communication between the memory 1301 and the processor 1302.
If the memory 1301, the processor 1302, and the communication interface 1303 are implemented independently, the communication interface 1303, the memory 1301, and the processor 1302 may be connected to each other through a bus and perform communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 13, but this is not intended to represent only one bus or type of bus.
Optionally, in a specific implementation, if the memory 1301, the processor 1302, and the communication interface 1303 are integrated on one chip, the memory 1301, the processor 1302, and the communication interface 1303 may complete communication therebetween through an internal interface.
The processor 1302 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement embodiments of the present Application.
The present embodiment also provides a computer-readable storage medium, on which a computer program is stored, where the program is executed by a processor to implement the multi-scenario two-stage demand response resource optimization scheduling method as above.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or to implicitly indicate the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "N" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more N executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or N wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Further, the computer readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware that is related to instructions of a program, and the program may be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer-readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (12)
1. A multi-scenario two-stage demand response resource optimization scheduling method is characterized by comprising the following steps:
extracting 6 key response indexes of each resource based on the models of all equipment resources, and generating at least one resource group after aggregation;
performing multi-scene generation on the resource group response output curve of the at least one resource group, and obtaining a typical response scene and a key index of each resource group through scene clustering; and
and based on the typical response scene and the key indexes of each resource group, according to the demand response indexes distributed by the power grid side, deciding the response time periods and the response quantities of all demand response resources so as to minimize the self-operation cost, and simultaneously meeting the demand response quantity indexes and the self-operation constraints of the power grid side to generate an optimized scheduling strategy.
2. The method of claim 1, wherein the 6 key response indicators comprise response capacity, response rate, recovery rate, maximum response duration, response confidence, and response cost.
3. The method of claim 2, wherein the obtaining of the typical response scenario and the key index of each resource group through scenario clustering comprises:
constructing a resource group response output random variable;
and generating scenes through random sampling simulation based on the resource group response output random variable, generating a demand response output scene for each resource group, and reducing the resource group demand response output scene into the typical response scene meeting preset conditions.
4. The method of claim 3, wherein the constructing a resource group response contribution random variable comprises:
obtaining the probability distribution of random variables of any resource response state according to the response reliability index of the equipment resource;
obtaining a response capacity output curve random variable of any resource based on the probability distribution;
and obtaining the response capacity output curve random variable of any resource group based on the response capacity output curve random variable of any resource group.
5. The method according to any one of claims 1 to 4, wherein the step of deciding response time periods and response quantities of all demand response resources according to the demand response indexes allocated by the power grid side based on the typical response scenarios and the key indexes of each resource group so as to minimize self-operation cost and meet the demand response quantity indexes and self-operation constraints of the power grid side to generate an optimized scheduling strategy comprises the following steps:
according to a scalar quantity in the demand response obtained in the day, deciding a resource group calling plan of the next day, and generating a first-stage optimization model;
constructing a plurality of demand response scenes for the response uncertainty of each resource group, setting corresponding operation constraint conditions for each typical scene, deciding a response output curve of each resource group, and generating a second-stage optimization model;
and combining the first-stage optimization model and the second-stage optimization model to obtain a multi-scene two-stage demand response resource scheduling optimization decision model, and solving the multi-scene two-stage demand response resource scheduling optimization decision model to obtain an optimization calling result of each resource group of the industrial load aggregator and a response output curve of each resource group in each typical scene.
6. A multi-scenario two-stage demand response resource optimization scheduling device is characterized by comprising:
the extraction module is used for extracting 6 key response indexes of each resource based on the models of all equipment resources and generating at least one resource group after aggregation;
the generating module is used for generating multiple scenes for the resource group response output curve of the at least one resource group and obtaining a typical response scene and a key index of each resource group through scene clustering; and
and the optimization module is used for deciding the response time period and the response quantity of all the demand response resources according to the demand response indexes distributed by the power grid side based on the typical response scene and the key indexes of each resource group so as to minimize the self-operation cost, and simultaneously, the demand response quantity indexes and the self-operation constraint of the power grid side are met to generate an optimized scheduling strategy.
7. The apparatus of claim 6, wherein the 6 key response indicators comprise response capacity, response rate, recovery rate, maximum response duration, response confidence, and response cost.
8. The apparatus of claim 7, wherein the generating module is specifically configured to:
constructing a resource group response output random variable;
and generating scenes through random sampling simulation based on the resource group response output random variable, generating a demand response output scene for each resource group, and reducing the resource group demand response output scene into the typical response scene meeting preset conditions.
9. The apparatus of claim 8, wherein the generating module is further configured to:
obtaining the probability distribution of random variables of any resource response state according to the response reliability index of the equipment resource;
obtaining a response capacity output curve random variable of any resource based on the probability distribution;
and obtaining the response capacity output curve random variable of any resource group based on the response capacity output curve random variable of any resource group.
10. The apparatus according to any one of claims 6 to 9, wherein the optimization module is specifically configured to:
according to a scalar quantity in the demand response obtained in the day, deciding a resource group calling plan of the next day, and generating a first-stage optimization model;
constructing a plurality of demand response scenes for the response uncertainty of each resource group, setting corresponding operation constraint conditions for each typical scene, deciding a response output curve of each resource group, and generating a second-stage optimization model;
and combining the first-stage optimization model and the second-stage optimization model to obtain a multi-scene two-stage demand response resource scheduling optimization decision model, and solving the multi-scene two-stage demand response resource scheduling optimization decision model to obtain an optimization calling result of each resource group of the industrial load aggregator and a response output curve of each resource group in each typical scene.
11. An electronic device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the multi-scenario two-phase demand response resource optimization scheduling method of any one of claims 1-5.
12. A computer-readable storage medium having stored thereon a computer program for execution by a processor for implementing a multi-scenario two-phase demand response resource optimization scheduling method according to any one of claims 1-5.
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