Multi-scenario two-stage demand response resource optimization scheduling method, device and equipment

Abstract

The application relates to a multi-scene two-stage demand response resource optimization scheduling method, device and equipment, wherein the method comprises the following steps: extracting six key response indexes of each resource based on the model of all device resources, generating at least one resource group after aggregation, generating a multi-scene response output curve of the resource group, obtaining a typical response scene and a key index of each resource group through scene clustering, deciding a response period and a response amount of all demand response resources according to a demand response index distributed by a power grid side, minimizing self operation cost, simultaneously satisfying the demand response amount index of the power grid side and self operation constraints, and generating an optimization scheduling strategy. Therefore, the shortcomings of an existing industrial load aggregator demand response resource optimization scheduling method are solved, 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 significantly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of demand response optimization operation, and particularly relates to a multi-scenario two-stage demand response resource optimization scheduling method, device and equipment. BACKGROUND

[0002] Demand response is an important regulating means of a power system, which refers to guiding and encouraging power users to actively change power consumption behaviors through market price signals such as time-of-use electricity price or incentive mechanisms such as financial subsidies, so as to promote power supply and demand balance and guarantee stable operation of a power grid. Demand response has become an important means for a new generation of energy systems to cope with generation uncertainty and load demand fluctuation and promote high proportion of renewable energy consumption, and can bring significant benefits to a power grid.

[0003] Demand response mainly includes two types, one is price-based demand response, and the other is incentive-based demand response. The price-based demand response refers to the response behavior of power users adjusting their power consumption modes according to the change of power price signals, mainly including time-of-use electricity price, real-time electricity price, peak electricity price and the like. The incentive-based demand response refers to a series of incentive policies formulated by a demand response implementation agency to encourage users to reduce power load in the case of reduced reliability of a power system or increased price of power consumption, mainly including direct load control, interruptible load, demand-side bidding, emergency power demand response, capacity / auxiliary service plan and the like.

[0004] Industrial load is an important part of power load, and accounts for a large proportion of total social electricity consumption. Industrial users have advantages such as large response capacity and high technical performance compared with commercial and residential users, and are the most important demand response resources in the power system. From the perspective of industrial load aggregators, how to optimize the scheduling of response resources is of great practical significance. The existing demand response resource optimization scheduling techniques and methods for industrial load aggregators mainly have the following problems: 1) With the development of demand response, the data scale of response resources is becoming larger and larger, and large-scale industrial load aggregators need to manage and make decisions on large-scale equipment resources. Therefore, industrial load aggregators need to finely model and reasonably group the demand response resources which are large in number, scattered in existence and different in characteristics. 2) When industrial load aggregators optimize the scheduling of demand response resources, the day-ahead and day-ahead stages need to be considered. The day-ahead stage needs to optimize the calling plan of the resource group according to the power grid demand response plan, and the day-ahead stage needs to optimize the response output of the resource group according to the real-time operation and boundary conditions. 3) When the demand response equipment resources are called, it is not necessarily guaranteed to respond successfully 100%. Due to the uncertainty of the response state of each resource in the resource group, the response capacity curve of the resource group will no longer be a certain curve, but a random variable curve satisfying a certain probability distribution. Industrial load aggregators need to consider the random distribution characteristics of the response capacity of each resource group to make the actual response capacity under the calling result meet the requirements of the power grid side.

[0005] Therefore, it is necessary to establish a more systematic and comprehensive demand response resource optimization scheduling method, device and equipment to realize the optimal operation control of industrial load aggregators participating in the power grid demand response and improve the operation efficiency of industrial load aggregators. SUMMARY

[0006] The present application provides a multi-scene two-stage demand response resource optimization scheduling method, device and equipment to solve the shortcomings of the existing demand response resource optimization scheduling method of industrial load aggregators, realize the resource optimization scheduling control of industrial load aggregators participating in the power grid demand response, and bring significant efficiency improvement space.

[0007] The first aspect embodiment of the present application provides a multi-scene two-stage demand response resource optimization scheduling method, comprising the following steps:

[0008] Extract 6 key response indicators of each resource based on the model of all equipment resources, and generate at least one resource group after aggregation;

[0009] Generate a multi-scene of the resource group response output curve of the at least one resource group, and obtain the typical response scene and key indicators of each resource group through scene clustering; and

[0010] Based on the typical response scene and key indicators of each resource group, according to the demand response indicators allocated by the power grid side, the response period and response amount of all demand response resources are decided to minimize the operation cost while meeting the demand response amount indicators of the power grid side and the operation constraints of the self, and an optimized scheduling strategy is generated.

[0011] According to one embodiment of the present application, the six key response indicators include response capacity, response rate, recovery rate, maximum response duration, response reliability and response cost.

[0012] According to one embodiment of the present application, the typical response scene and key indicators of each resource group obtained through scene clustering include:

[0013] The resource group response output random variable is constructed;

[0014] Based on the resource group response output random variable, scene generation is performed through random sampling simulation, demand response output scenes are generated for each resource group, and the resource group demand response output scenes are reduced to the typical response scenes that meet the preset conditions.

[0015] According to one embodiment of the present application, the construction of the resource group response output random variable includes:

[0016] The probability distribution of any resource response state random variable is obtained according to the response reliability indicators of the equipment resources;

[0017] The response capacity output curve random variable of any resource is obtained based on the probability distribution;

[0018] The response capacity output curve random variable of any resource group is obtained based on the response capacity output curve random variable of any resource.

[0019] According to one embodiment of the present application, based on the typical response scene and key indicators of each resource group, according to the demand response indicators allocated by the power grid side, the response period and response amount of all demand response resources are decided to minimize the operation cost while meeting the demand response amount indicators of the power grid side and the operation constraints of the self, and an optimized scheduling strategy is generated, which includes:

[0020] According to the demand response bid amount obtained in the day-ahead, the resource group calling plan for the next day is decided, and a first-stage optimization model is generated;

[0021] A plurality of demand response scenes are constructed for the response uncertainty of each resource group, and corresponding operation constraint conditions are set for each typical scene, the response output curve of each resource group is decided, and a second-stage optimization model is generated;

[0022] The first-stage optimization model and the second-stage optimization model are solved together to obtain a multi-scenario two-stage demand response resource scheduling optimization decision model, and the multi-scenario two-stage demand response resource scheduling optimization decision model is solved to obtain the optimized calling result of each resource group of the industrial load aggregator and the response output curve of each resource group under each typical scenario.

[0023] According to the multi-scenario two-stage demand response resource optimization scheduling method, 6 key response indicators of each resource are extracted based on the model of all device resources, and at least one resource group is generated after aggregation, and the response output curve of the resource group is generated in multiple scenarios, the typical response scenario and key indicators of each resource group are obtained through scenario clustering, and the response period and response amount of all demand response resources are determined according to the demand response indicators allocated by the power grid side, so as to minimize the operation cost, meet the demand response amount indicators and the operation constraints of the power grid side, and generate an optimized scheduling strategy. Therefore, the deficiencies of the existing demand response resource optimization scheduling method of the industrial load aggregator are solved, the uncertain scenarios during the response of the resource group are 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 significantly improved.

[0024] The second aspect embodiment of the present application provides a multi-scenario two-stage demand response resource optimization scheduling device, comprising:

[0025] The extraction module is configured to extract 6 key response indicators of each resource based on the model of all device resources, and generate at least one resource group after aggregation.

[0026] The generation module is configured to generate the response output curve of the resource group in multiple scenarios, and obtain the typical response scenario and key indicators of each resource group through scenario clustering.

[0027] The optimization module is configured to determine the response period and response amount of all demand response resources according to the demand response indicators allocated by the power grid side based on the typical response scenario and key indicators of each resource group, so as to minimize the operation cost, meet the demand response amount indicators and the operation constraints of the power grid side, and generate an optimized scheduling strategy.

[0028] According to an embodiment of the present application, the 6 key response indicators include response capacity, response rate, recovery rate, maximum response duration, response reliability and response cost.

[0029] According to an embodiment of the present application, the generation module is specifically configured to:

[0030] Construct a resource group response output random variable.

[0031] Based on the resource group response output random variable, a scenario generation is performed through random sampling simulation, a demand response output scenario is generated for each resource group, and the resource group demand response output scenario is cut to the typical response scenario satisfying the preset condition.

[0032] According to one embodiment of the present application, the generation module is further configured to:

[0033] Obtain a probability distribution of any resource response state random variable according to the response reliability index of the device resource;

[0034] Obtain the response capacity output curve random variable of any resource based on the probability distribution;

[0035] Obtain the response capacity output curve random variable of any resource group based on the response capacity output curve random variable of any resource.

[0036] According to one embodiment of the present application, the optimization module is specifically configured to:

[0037] Generate a first-stage optimization model according to the demand response bid quantity obtained in the day-ahead;

[0038] Construct a plurality of demand response scenarios for the response uncertainty of each resource group, set corresponding operation constraint conditions for each typical scenario, decide the response output curve of each resource group, and generate a second-stage optimization model;

[0039] Combine the first-stage optimization model and the second-stage optimization model to obtain a multi-scenario two-stage demand response resource scheduling optimization decision model, and solve the multi-scenario two-stage demand response resource scheduling optimization decision model to obtain the optimization calling result of each resource group of the industrial load aggregator and the response output curve of each resource group under each typical scenario.

[0040] The multi-scenario two-stage demand response resource optimization scheduling device according to the embodiment of the present application extracts 6 key response indexes of each resource based on the model of all device resources, aggregates to generate at least one resource group, and generates a multi-scenario for the response output curve of the resource group. Through scenario clustering, the typical response scenario and key indicators of each resource group are obtained. According to the demand response index allocated by the power grid side, the response period and response quantity of all demand response resources are decided to minimize the own operation cost, while satisfying the demand response quantity index of the power grid side and the own operation constraint, and an optimization scheduling strategy is generated. Therefore, the deficiencies of the existing industrial load aggregator demand response resource optimization scheduling method are solved, the uncertain scenarios of the resource group response time are considered, the resource optimization scheduling control of the industrial load aggregator participating in the power grid demand response is realized, and the operation benefit of the industrial load aggregator is significantly improved.

[0041] The third aspect of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the multi-scenario two-stage demand response resource optimization scheduling method according to the above embodiments. The fourth aspect of the present application provides a computer readable storage medium having a computer program stored thereon, which is executed by a processor to implement the multi-scenario two-stage demand response resource optimization scheduling method according to the above embodiments.

[0042] Thus, the deficiencies of the demand response resource optimization scheduling method in the related art are made up, a multi-scenario two-stage demand response resource optimization scheduling method for industrial load aggregators is established, the modeling and grouping of large-scale demand response resources are fully considered, the optimization of different stages of industrial load aggregators participating in demand response is coordinated, and the uncertainty scenarios of resource groups in response are considered. Based on the multi-scenario two-stage demand response resource optimization scheduling method of the present application, the industrial load aggregators can take into account the economy and uncertainty, and plan and optimize the day-ahead resource calling and the intra-day operation response scenarios.

[0043] Additional aspects and advantages of the present application will be made apparent by the following description and the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0044] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description, taken in conjunction with the accompanying drawings, in which:

[0045] Figure 1 A flowchart of a multi-scenario two-stage demand response resource optimization scheduling method according to an embodiment of the present application is provided;

[0046] Figure 2 A flowchart of a multi-scenario two-stage demand response resource optimization scheduling method according to an embodiment of the present application is provided;

[0047] Figure 3 A schematic diagram of resource group 1 internal resource combination configuration according to an embodiment of the present application is provided;

[0048] Figure 4 A schematic diagram of resource group 2 internal resource combination configuration according to an embodiment of the present application is provided;

[0049] Figure 5 A schematic diagram of a typical response capacity curve of resource group 1 according to an embodiment of the present application is provided;

[0050] Figure 6A typical response capacity curve diagram of resource group 2 provided according to an embodiment of the present application;

[0051] Figure 7 A scenario 1 resource group response output result diagram provided according to an embodiment of the present application;

[0052] Figure 8 A scenario 2 resource group response output result diagram provided according to an embodiment of the present application;

[0053] Figure 9 A scenario 3 resource group response output result diagram provided according to an embodiment of the present application;

[0054] Figure 10 A scenario 4 resource group response output result diagram provided according to an embodiment of the present application;

[0055] Figure 11 A scenario 5 resource group response output result diagram provided according to an embodiment of the present application;

[0056] Figure 12 An example diagram of a multi-scenario two-stage demand response resource optimization scheduling device according to an embodiment of the present application;

[0057] Figure 13 A structure diagram of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0058] Embodiments of the present application are described in detail below with reference to the accompanying drawings. Examples of the embodiments are shown in the drawings, wherein the same or similar notations are used to denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by reference to the drawings are exemplary and are intended to explain the present application, and cannot be understood as limiting the present application.

[0059] A multi-scenario two-stage demand response resource optimization scheduling method, device and equipment of embodiments of the present application are described below with reference to the accompanying drawings. In view of the problems of the existing industrial load aggregator demand response resource optimization scheduling method mentioned in the background art, the present application provides a multi-scenario two-stage demand response resource optimization scheduling method. In this method, based on the model of all device resources, 6 key response indicators of each resource are extracted and aggregated to generate at least one resource group, and the resource group response output curve is generated in multiple scenarios. Through scenario clustering, the typical response scenario and key indicators of each resource group are obtained. According to the demand response indicators allocated by the power grid side, the response period and response amount of all demand response resources are decided to minimize the operating cost, while meeting the demand response amount indicators of the power grid side and the operating constraints, and an optimized scheduling strategy is generated. Thus, the deficiencies of the existing industrial load aggregator demand response resource optimization scheduling method are solved, the uncertain scenarios of resource group response are considered, the resource optimization scheduling control of industrial load aggregator participating in the demand response of the power grid is realized, and the operating efficiency of the industrial load aggregator is significantly improved.

[0060] Specifically, Figure 1 A flowchart of a multi-scenario two-stage demand response resource optimization scheduling method provided by embodiments of the present application.

[0061] In this embodiment, the multi-scenario two-stage demand response resource optimization scheduling method mainly includes three parts, as shown in Figure 2 Demand response resource modeling and grouping, resource group demand response multi-scenario generation, and multi-scenario two-stage demand response resource group scheduling optimization decision, which are described in detail below according to specific embodiments.

[0062] As shown in Figure 1 The multi-scenario two-stage demand response resource optimization scheduling method includes the following steps:

[0063] In step S101, based on the model of all device resources, 6 key response indicators of each resource are extracted and aggregated to generate at least one resource group.

[0064] In some embodiments, the 6 key response indicators include response capacity, response rate, recovery rate, maximum response duration, response reliability, and response cost.

[0065] Specifically, the industrial load aggregator collects, statistics and analyzes the technical parameters and historical operation data of all device resources managed by it, and finely models the load resources to extract the following key indicators of each resource:

[0066] (1) Response capacity Q: The maximum load adjustment amount that the device resource can provide for participating in demand response.

[0067] (2) Response rate R U : The maximum load adjustment amount that the device resource can complete per unit time when the demand response is called.

[0068] (3) Recovery rate R D : The maximum recoverable load amount of the device resource per unit time after completing the demand response.

[0069] (4) Maximum response duration T M : The maximum time that the device resource can continue to respond without affecting the production process after the demand response is called.

[0070] (5) Response reliability Y: Due to the existence of safety, economy or other factors in the industrial production process, and the abnormal operating conditions of the device itself, the device resource may not be able to guarantee 100% successful response when the demand response is called. Therefore, the probability of successful response is defined as the response reliability.

[0071] (6) Response cost C: The operating cost of the device resource participating in the demand response to achieve unit capacity load adjustment.

[0072] After the industrial load aggregator completes the above modeling, according to the technical and economic characteristics of each resource, all resources are grouped and aggregated to obtain a certain number of resource groups that meet certain technical conditions. The resource grouping and aggregation process can be based on historical operation experience and actual situation, or can be optimized by using existing research load aggregation technology and related methods in its application.

[0073] In step S102, the resource group response output curve of at least one resource group is generated in multiple scenarios, and the typical response scenario and key indicators of each resource group are obtained through scenario clustering.

[0074] Further, in some embodiments, the typical response scenario and key indicators of each resource group are obtained through scenario clustering, including: constructing a resource group response output random variable; based on the resource group response output random variable, generating scenarios through random sampling simulation, generating demand response output scenarios for each resource group, and reducing the resource group demand response output scenarios to typical response scenarios that meet the preset conditions.

[0075] Further, in some embodiments, the resource group response output random variable is constructed, including: obtaining the probability distribution of any resource response state random variable according to the response reliability indicators of the device resource; obtaining the response capacity output curve random variable of any resource based on the probability distribution; obtaining the response capacity output curve random variable of any resource group based on the response capacity output curve random variable of any resource.

[0076] Specifically, the industrial load aggregator generates multiple scenarios of the response output curve of each resource group based on the resource model and the aggregated resource groups established in the above steps, and obtains the typical response scenarios and key indicators of each resource group through scenario clustering, including the following steps:

[0077] (1) Construct the resource group response output random variable.

[0078] First, according to the resource response reliability index in the above steps, the probability distribution of the response state random variable of any resource i is obtained as:

[0079]

[0080] wherein, is the response state random variable of resource i, 1 represents successful response, and 0 represents unsuccessful response, satisfies 0-1 distribution; Y i is the response reliability of resource i; Ω I is the set of resources responded by the industrial load aggregator.

[0081] Secondly, the response capacity output curve random variable of resource i is obtained as:

[0082]

[0083] wherein, P i is the response capacity of resource i; S i,t represents the response state of resource i, if resource i is responding at time t, then S i,t = 1, otherwise S i,t = 0; Ω T is the set of response periods.

[0084] On this basis, the response capacity output curve random variable of any resource group g is obtained as:

[0085]

[0086] wherein, G g,i represents the grouping state of resource i, if resource i belongs to resource group g, then G g,i = 1, otherwise G g,i = 0; Ω G represents the set of resource groups.

[0087] (2) Multiple scenario generation and clustering.

[0088] ​​According to formula (3), a large number of demand response output scenes are generated for each resource group by random sampling simulation to represent the demand response output uncertainty of each resource group, and a large number of demand response output scenes of the resource groups are reduced to limited typical response scenes by a clustering algorithm in the prior art, so as to reasonably control the calculation scale of the next step and prevent the optimization model from being too large to be solved.

[0089] In step S103, based on the typical response scenes and the key indicators of each resource group, the response time period and the response amount of all demand response resources are determined according to the demand response indicators allocated by the power grid side, so as to minimize the operation cost of the industrial load aggregator while meeting the demand response amount indicators of the power grid side and the operation constraints of the industrial load aggregator, and an optimized scheduling strategy is generated.

[0090] Further, in some embodiments, based on the typical response scenes and the key indicators of each resource group, the response time period and the response amount of all demand response resources are determined according to the demand response indicators allocated by the power grid side, so as to minimize the operation cost of the industrial load aggregator while meeting the demand response amount indicators of the power grid side and the operation constraints of the industrial load aggregator, and an optimized scheduling strategy is generated, including: determining the resource group calling plan of the next day according to the demand response bid amount obtained in advance, to generate a first-stage optimization model; constructing a plurality of demand response scenes for the response uncertainty of each resource group, and setting a corresponding operation constraint condition for each typical scene, to determine the response output curve of each resource group, to generate a second-stage optimization model; combining the first-stage optimization model and the second-stage optimization model to obtain a multi-scenario two-stage demand response resource scheduling optimization decision model, and solving the multi-scenario two-stage demand response resource scheduling optimization decision model to obtain the optimized calling result of each resource group of the industrial load aggregator and the response output curve of each resource group under each typical scene.

[0091] Specifically, the embodiments of the present application establish a multi-scenario two-stage demand response resource scheduling optimization decision model, in which the industrial load aggregator determines the response time period and the response amount of all demand response resources inside the industrial load aggregator according to the demand response indicators allocated by the power grid side, so as to minimize the operation cost of the industrial load aggregator while meeting the demand response amount indicators of the power grid side and the operation constraints of the industrial load aggregator. The overall process includes two stages: in the first stage, the day-ahead calling decision of the resource group is made, and the resource group calling plan of the next day is determined according to the demand response bid amount obtained in advance; in the second stage, a large number of demand response scenes are constructed for the response uncertainty of each resource group, and a set of operation constraint conditions is set for each typical scene to determine the response output curve of each resource group; finally, the two-stage optimization model is combined to realize the two-stage modeling and integrated optimization of the resource group calling optimization and the response output optimization of each resource group. Specifically, the following steps are included:

[0092] (1) Determine the decision variables of the optimization model.

[0093] where the decision variables of the first stage model are:

[0094] 0-1 variable, the current invocation state of resource group g, if denotes that resource group g is in invocation state at time t, otherwise

[0095] 0-1 variable, the start invocation indicator variable of resource group g, if denotes that resource group g starts to be invoked at time t, otherwise

[0096] continuous variable, the day-ahead planned response output of resource group g.

[0097] The decision variables of the second stage model are:

[0098] continuous variable, the intra-day response output of resource group g in scenario s.

[0099] (2) Determine the decision variables of the optimization model.

[0100] The objective function of the first stage is the resource group invocation cost, response quantity cost, and response quantity cost under each scenario in the second stage, expressed as follows:

[0101]

[0102] where: is the single invocation cost of resource group g excluding the response capacity cost; is the unit response output cost of resource group g; Ω S is the set of typical response scenarios; π s is the probability of scenario s.

[0103] (3) Determine the constraints of the first stage optimization model.

[0104] 1) Grid-side allocation response quantity constraint:

[0105] The invocation plan of the resource group needs to meet the demand response bid quantity allocated by the grid side to the industrial load aggregator i.e., the expression is:

[0106]

[0107] 2) Resource group response output range constraint:

[0108]

[0109] where, The maximum response capacity of the resource group g set in step (1) when grouping resources.

[0110] 3) Resource group call frequency constraint:

[0111] Any resource group g can be called at most once in the entire scheduling period, that is, the expression is:

[0112]

[0113] 4) Resource group start call indication variable and current call state variable association constraint:

[0114] The start call indication variable of any resource group g The current call state There is an association constraint as follows:

[0115]

[0116] 5) Resource group call duration constraint:

[0117] The call duration of any resource group g cannot exceed the response time length of the resource group set in step (1) when grouping resources That is, the expression is:

[0118]

[0119] 6) Resource group response output ramp constraint:

[0120]

[0121] Where, The response rate and recovery rate of the resource group g formed in step (1) when grouping resources, respectively.

[0122] (4) Determine the constraint conditions of the second stage optimization model.

[0123] 1) The grid side allocates response amount constraint under each scenario:

[0124]

[0125] 2) Resource group response output range constraint under each scenario;

[0126]

[0127] Where, The response capacity curve of resource group g under typical response scenario s.

[0128] 3) Resource group response output ramp constraint under each scenario:

[0129]

[0130] By the above decision variables The objective function formula (4) and the constraint condition expression (5)-expression (12) constitute the multi-scenario two-stage demand response resource scheduling optimization decision model proposed in the embodiment of the application.

[0131] Therefore, by solving the optimization model, the embodiment of the application can obtain the optimization calling result of each resource group of the industrial load aggregator and the response output curve of each resource group under each typical scenario. On the one hand, the reasonable resource group selection result can be decided in the day-ahead stage, and the calculation scale of the industrial load aggregator in the intra-day operation is reduced, and on the other hand, the uncertainty scenario of the resource response in the intra-day stage is considered, so that the optimization result is more reliable. In the actual intra-day operation, the industrial load aggregator calls the resource group according to the response period and response output of each resource group in the optimization result, so as to realize the minimization of the operation cost on the premise of meeting the response amount index of the power grid side and the self operation constraint.

[0132] In order to facilitate those skilled in the art to further understand the multi-scenario two-stage demand response resource optimization scheduling method of the embodiment of the application, the following will be further described in detail in combination with specific embodiments.

[0133] Specifically, taking the load aggregator A in a certain industrial park as an example, the multi-scenario two-stage demand response resource optimization scheduling method for the industrial load aggregator proposed in the embodiment of the application is described, and the effect realized by the application is verified.

[0134] Further, the load resources managed by the industrial load aggregator A which can participate in demand response include more than 900 device resources in multiple industries such as steel, cement, textile, metal products, etc. It declares to participate in the demand response of the power grid side at 11:00-13:00 the next day in the day-ahead, wherein 15MW is bid for in 11:30-12:00 the next day, 46MW is bid for in 12:00-12:30 the next day, and 37MW is bid for in 12:30-13:00 the next day.

[0135] Industrial load aggregator A, following the first part of the multi-scenario, two-stage demand response resource optimization and scheduling method proposed in this application, models each demand response resource. Based on equipment technical parameters and historical operating data, it extracts indicators such as response capacity, response rate, recovery rate, maximum response duration, response reliability, and response cost. Specifically, the response reliability Y = 0.92 for equipment resources in the steel and metal products industry, and Y = 0.96 for equipment resources in other industries. Based on this, industrial load aggregator A, according to the technical and economic characteristics of each resource and considering the demand response bidding situation, configures all equipment resources into 10 groups during the day-ahead period, with each group having a response duration of... Response capacity for 1 hour The capacity is 10MW. Taking resource group 1 and resource group 2 as examples, their internal resource combination configurations are as follows: Figure 3 and Figure 4 As shown.

[0136] Industrial load aggregator A, following the second part of the multi-scenario two-stage demand response resource optimization scheduling method proposed in the embodiments of this application, randomly samples the response status of 10 resource groups to obtain a large number of response capacity curve scenarios. For each resource group, the k-means algorithm is used to select 10 typical scenarios. Taking resource group 1 and resource group 2 as examples, their typical response capacity curves are as follows: Figure 4 and Figure 5 As shown.

[0137] Industrial load aggregator A, following the third part of the multi-scenario two-stage demand response resource optimization scheduling method proposed in the embodiments of this application, establishes a multi-scenario two-stage demand response resource scheduling optimization decision model. Among them, the probability π of each typical response scenario... s Set to 0.1. Response rate of each resource group. and recovery rate All are 3MW / min. Cost per resource group for a single call. Both are 500 yuan, unit response cost As shown in Table 1:

[0138] Table 1

[0139] Resource Group Unit Response Cost (Yuan / MWh) Resource Group Unit Response Cost (Yuan / MWh) Resource Group 1 308 Resource Group 6 339 Resource Group 2 272 Resource Group 7 331 Resource Group 3 349 Resource Group 8 315 Resource Group 4 286 Resource Group 9 291 Resource Group 5 303 Resource Group 10 321

[0140] Industrial load aggregator A solved the optimization model and obtained the resource group allocation results as shown in Table 2:

[0141] Table 2

[0142]

[0143]

[0144] The response output results of each resource group in scenarios 1-5 in the decision results of the second stage in 10 typical scenarios are as shown in Table 2. Figure 7- Figure 11 As can be seen from Table 2, because the unit response costs of resource groups 2, 4, 5 and 9 are low, these four resource groups are preferentially called in each typical scenario, and the actual response power is the maximum response capacity of the resource group.

[0145] For example, the effect of the embodiment of the application is analyzed in the period of 12:45-13:00. In some scenarios such as scenarios 3 and 4, because the cumulative typical response output of resource groups 1, 2, 4 and 9 is low, the response output of resource group 10 with relatively high cost is increased to compensate for the power shortage caused by the poor response success rate of the resources in resource groups 1, 2, 4 and 9. In scenarios 1, 2 and 5, the response of resource groups 1, 2, 4 and 9 with low cost is good, so resource group 8 with relatively high cost does not need to provide a response. Similar results are obtained in the period of 12:00-12:45. Thus, the multi-scenario two-stage demand response resource scheduling optimization method proposed in the embodiment of the application can well consider the uncertain response scenarios of resource response, and make the overall response cost the lowest under the premise of meeting the constraints of each scenario.

[0146] Further, as a comparison, the method proposed in the embodiment of the application is compared with the traditional demand response resource scheduling optimization method (which does not consider the uncertainty of response). The resource calling results calculated by the latter are shown in Table 3:

[0147] Table 3

[0148] Resource Group Invoking Period Resource Group Invoking Period Resource Group 1 11:30-12:30 Resource Group 6 Not Invoked Resource Group 2 12:00-13:00 Resource Group 7 Not Invoked Resource Group 3 Not Invoked Resource Group 8 12:30-13:00 Resource Group 4 12:00-13:00 Resource Group 9 12:00-13:00 Resource Group 5 11:30-12:30 Resource Group 10 Not Invoked

[0149] As can be seen, the number of resource groups called by the traditional optimization method is one less than the result calculated by the embodiment of the application (resource group 10 is not called), but because the uncertainty of the response of each resource group is not considered, it will not be able to meet the requirements in the daily operation if some internal resources respond poorly. The method proposed in the embodiment of the application can take into account the economy and uncertainty, and optimize the day-ahead resource calling and the daily operation response scenario, which shows the good practical significance and application prospect of the application.

[0150] According to the multi-scene two-stage demand response resource optimization scheduling method, six key response indexes of each resource are extracted based on the model of all device resources, at least one resource group is generated after aggregation, and the resource group response output curve is generated in multiple scenes, the typical response scene and key indexes of each resource group are obtained through scene clustering, the response period and response amount of all demand response resources are decided according to the demand response index allocated by the power grid side, the operation cost is minimized, the demand response amount index of the power grid side and the operation constraint are satisfied, and the optimization scheduling strategy is generated. Therefore, the deficiency of the existing industrial load aggregator demand response resource optimization scheduling method is solved, the uncertain scene of the 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 significantly improved.

[0151] Secondly, the multi-scene two-stage demand response resource optimization scheduling device according to the embodiment of the application is described with reference to the accompanying drawings.

[0152] Figure 12 The block schematic diagram of the multi-scene two-stage demand response resource optimization scheduling device according to the embodiment of the application is shown in the figure.

[0153] As shown in the figure, the multi-scene two-stage demand response resource optimization scheduling device 10 comprises an extraction module 100, a generation module 200 and an optimization module 300. Figure 12 The extraction module 100 is configured to extract six key response indexes of each resource based on the model of all device resources, and generate at least one resource group after aggregation.

[0154] The generation module 200 is configured to generate the resource group response output curve of the at least one resource group in multiple scenes, and obtain the typical response scene and key indexes of each resource group through scene clustering.

[0155] The optimization module 300 is configured to decide the response period and response amount of all demand response resources according to the demand response index allocated by the power grid side based on the typical response scene and key indexes of each resource group, minimize the operation cost, satisfy the demand response amount index of the power grid side and the operation constraint, and generate the optimization scheduling strategy.

[0156] Further, in some embodiments, the six key response indexes include response capacity, response rate, recovery rate, maximum response duration, response reliability and response cost.

[0157] Further, in some embodiments, the generation module 200 is specifically configured to:

[0158] construct a resource group response output random variable;

[0159] obtain the typical response scene and key indexes of each resource group through scene clustering; and

[0160] Based on the resource group response output random variable, a scenario is generated by random sampling simulation, a demand response output scenario is generated for each resource group, and the resource group demand response output scenario is cut to a typical response scenario that meets the preset condition.

[0161] Further, in some embodiments, the generating module 200 is also used for:

[0162] obtaining a probability distribution of any resource response state random variable according to the response credibility index of the device resource;

[0163] obtaining a response capacity output curve random variable of any resource based on the probability distribution;

[0164] obtaining a response capacity output curve random variable of any resource group based on the response capacity output curve random variable of any resource.

[0165] Further, in some embodiments, the optimization module 300 is specifically used for:

[0166] deciding the resource group calling plan of the next day according to the demand response bid quantity obtained in the day-ahead, generating a first-stage optimization model;

[0167] constructing multiple demand response scenarios for the response uncertainty of each resource group, setting corresponding operation constraint conditions for each typical scenario, deciding the response output curve of each resource group, and generating a second-stage optimization model;

[0168] combining the first-stage optimization model and the second-stage optimization model to obtain a multi-scenario two-stage demand response resource scheduling optimization decision model, and solving the multi-scenario two-stage demand response resource scheduling optimization decision model to obtain the optimization calling result of each resource group of the industrial load aggregator and the response output curve of each resource group under each typical scenario.

[0169] The multi-scenario two-stage demand response resource optimization scheduling device according to the embodiments of the present application extracts 6 key response indexes of each resource based on the models of all device resources, aggregates to generate at least one resource group, generates multiple scenarios for the response output curve of the resource group, obtains the typical response scenario and key indexes of each resource group through scenario clustering, decides the response period and response quantity of all demand response resources according to the demand response index allocated by the power grid side, minimizes the own operation cost, meets the demand response quantity index of the power grid side and the own operation constraint, and generates an optimization scheduling strategy. Thus, the deficiencies of the existing industrial load aggregator demand response resource optimization scheduling method are solved, the uncertain scenarios of the resource group response are 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 significantly improved.

[0170] Figure 13 A structural schematic diagram of an electronic device is provided for an embodiment of the present application. The electronic device can include:

[0171] The memory 1301, the processor 1302, and the computer program stored in the memory 1301 and executable on the processor 1302.

[0172] The processor 1302 implements the multi-scenario two-stage demand response resource optimization scheduling method provided in the above embodiments when executing the program.

[0173] Further, the electronic device further includes:

[0174] The communication interface 1303 is configured to communicate between the memory 1301 and the processor 1302.

[0175] The memory 1301 is configured to store the computer program executable on the processor 1302.

[0176] The memory 1301 can include a high-speed RAM memory, and can also include a non-volatile memory, for example, at least one disk memory.

[0177] If the memory 1301, the processor 1302, and the communication interface 1303 are independently implemented, the communication interface 1303, the memory 1301, and the processor 1302 can be connected to each other through a bus and complete communication between each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 13 In the figure, only one thick line is used to represent, but it does not mean that there is only one bus or one type of bus.

[0178] Optionally, in a specific implementation, if the memory 1301, the processor 1302, and the communication interface 1303 are integrated on a chip, the memory 1301, the processor 1302, and the communication interface 1303 can complete communication between each other through an internal interface.

[0179] The processor 1302 can be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or a plurality of integrated circuits configured to implement one or more embodiments of the present application.

[0180] The embodiment further provides a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the multi-scenario two-stage demand response resource optimization scheduling method as above.

[0181] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or N embodiments or examples. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the present specification and the features of the different embodiments or examples without contradiction.

[0182] In addition, the terms "first", "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "N" is at least two, for example, two, three, etc., unless otherwise explicitly specified.

[0183] Any process or method descriptions in flow charts or otherwise described herein can be understood as representing code modules, segments, or portions of code which include one or more executable instructions for implementing the specified logic functions (or steps) and which can be stored in one or more memories associated with the processor 1302, and the preferred embodiments of the present application include additional executable instructions for implementing the functions specified in the process or method descriptions, which can be carried out substantially concurrently with or in a different order than the steps described in the flow charts, and the like, as will be understood by those skilled in the art.

[0184] The logic and / or steps represented in the flowcharts and / or described herein, for example, can be considered as a sequence of instructions to implement logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device, such as a computer-based system, processor- based system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For purposes of this specification, a "computer-readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can be a computer- readable storage medium or a computer-readable signal medium. The computer- readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include the following: an electrical connection having one or more wires (electrical connections), a portable computer diskette (a 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, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium can even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and stored in a computer memory.

[0185] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, the N steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As such, if implemented in hardware, and in another embodiment, any of the following technologies, known in the art, or their combinations can be used: discrete logic circuitry having logic gates for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and the like.

[0186] Those skilled in the art can understand that all or part of the steps carried out by the above-mentioned embodiment methods can be completed by programs instructing related hardware, and the programs can be stored in a computer-readable storage medium. When the programs are executed, one or a combination of the steps of the method embodiments is included.

[0187] In addition, each of the functional units in the various embodiments of the present application can be integrated in one processing module, or each of the units can be physically present separately, or two or more units can be integrated in one module. The integrated module can be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0188] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A multi-scenario two-stage demand response resource optimization scheduling method, characterized in that, The method comprises the following steps: extracting 6 key response indicators of each resource based on a model of all device resources, and generating at least one resource group after aggregation; generating multiple scenes for a resource group response output curve of the at least one resource group, and obtaining a typical response scene and key indicators of each resource group through scene clustering; and based on the typical response scene and key indicators of each resource group, according to a demand response indicator allocated by a power grid side, deciding a response period and a response amount of all demand response resources to minimize a self operation cost while meeting the demand response amount indicator of the power grid side and a self operation constraint, and generating an optimized scheduling strategy; deciding a resource group calling plan of the next day according to a demand response bid amount obtained in advance, and generating a first-stage optimization model; constructing multiple demand response scenes for response uncertainty of the each resource group, and setting corresponding operation constraint conditions for each typical scene, deciding a response output curve of the each resource group, and generating a second-stage optimization model; 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 optimized calling result of each resource group of the industrial load aggregator and a response output curve of each resource group under each typical scene.

2. The method of claim 1, wherein, The 6 key response indicators include a response capacity, a response rate, a recovery rate, a maximum response duration, a response reliability and a response cost.

3. The method of claim 2, wherein, The typical response scene and key indicators of each resource group obtained through scene clustering comprise: constructing a resource group response output random variable; generating a demand response output scene for the each resource group through random sampling simulation scene generation based on the resource group response output random variable, and reducing the resource group demand response output scene to the typical response scene meeting a preset condition.

4. The method of claim 3, wherein, The construction of the resource group response output random variable comprises: obtaining a probability distribution of a response state random variable of any resource according to a response reliability indicator of the device resource; obtaining a response capacity output curve random variable of the any resource based on the probability distribution; obtaining a response capacity output curve random variable of any resource group based on the response capacity output curve random variable of the any resource.

5. A multi-scenario two-stage demand response resource optimal scheduling apparatus, characterized in that, The method comprises the following steps: a extracting module configured to extract 6 key response indicators of each resource based on a model of all device resources, and generate at least one resource group after aggregation; a generating module configured to generate multiple scenes for a resource group response output curve of the at least one resource group, and obtain a typical response scene and key indicators of each resource group through scene clustering; and an optimization module configured to, based on the typical response scene and key indicators of each resource group, according to a demand response indicator allocated by a power grid side, decide a response period and a response amount of all demand response resources to minimize a self operation cost while meeting the demand response amount indicator of the power grid side and a self operation constraint, and generate an optimized scheduling strategy; the optimization module is specifically configured to: According to the demand response benchmark obtained in the day, the resource group calling plan of the next day is decided, and a first-stage optimization model is generated; A plurality of demand response scenarios are constructed according to the response uncertainty of each resource group, and each typical scenario is set with corresponding operation constraint conditions, the response output curve of each resource group is decided, and a second-stage optimization model is generated; The first-stage optimization model and the second-stage optimization model are combined to obtain a multi-scenario two-stage demand response resource scheduling optimization decision model, and the multi-scenario two-stage demand response resource scheduling optimization decision model is solved to obtain the optimized calling result of each resource group of the industrial load aggregator and the response output curve of each resource group under each typical scenario.

6. The apparatus of claim 5, wherein, The six key response indicators include response capacity, response rate, recovery rate, maximum response duration, response reliability and response cost.

7. The apparatus of claim 6, wherein, The generation module is specifically configured to: Construct a resource group response output random variable; Based on the resource group response output random variable, scenario generation is performed through random sampling simulation, demand response output scenarios are generated for each resource group, and the resource group demand response output scenarios are reduced to the typical response scenarios that meet the preset conditions.

8. The apparatus of claim 7, wherein, The generation module is further configured to: According to the response reliability indicator of the equipment resource, obtain the probability distribution of any resource response state random variable; Based on the probability distribution, obtain the response capacity output curve random variable of any resource; Based on the response capacity output curve random variable of any resource, obtain the response capacity output curve random variable of any resource group.

9. An electronic device, comprising: It comprises: A memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the multi-scenario two-stage demand response resource optimization scheduling method according to any one of claims 1-4.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the multi-scenario two-stage demand response resource optimization scheduling method according to any one of claims 1-4.

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