A method for aggregation and adjustable capacity evaluation of massive distributed adjustable resources

Abstract

The present invention discloses a method for aggregating and evaluating adjustable capacity of massive distributed adjustable resources. The present invention clusters distributed resources to obtain a small number of cluster groups, constructs a scheduling model for the cluster groups, regards the scheduling instructions issued by the system side as uncertain variables, constructs and solves a two-stage robust optimization model, and can effectively evaluate the adjustable capacity of the distributed resource cluster groups, thereby achieving full utilization of various types of distributed resources, helping distributed resources to become a supplement to the regulating resources of the power system, thereby improving the regulating capacity of the power system, reducing the peak-shaving and frequency-regulating pressure of the system, and playing a positive role in peak-shaving and valley-filling and energy conservation and environmental protection.

Description

Technical Field

[0001] The invention relates to a method for aggregating massive distributed adjustable resources and evaluating adjustable capacity, and belongs to the field of user-side optimization dispatching of power systems. Background Art

[0002] As global environmental pollution and energy crisis become increasingly serious, the development and utilization of clean, sustainable and renewable energy has become an important development trend in the energy industry. The large-scale deployment of renewable energy such as wind and light has increased the uncertainty of power supply in the power grid, and the peak and frequency regulation pressure of the system has become increasingly greater. At present, the power system relies heavily on centralized power sources with relatively weak regulation capabilities, namely thermal power. With the increase in the penetration rate of intermittent distributed power sources, the traditional regulation mode that relies solely on centralized power sources will not be able to meet the requirements of high-quality real-time control of future energy and power systems. Therefore, it is necessary to guide user-side distributed resources, such as electric vehicles, distributed energy storage, air conditioners and other flexible resources with rapid regulation capabilities, to participate in the work of load-side regulation. The premise for achieving this work is to evaluate the adjustable capacity of the distributed resource group (that is, to obtain the adjustable capacity range of the distributed resource group), but there is currently no corresponding method. Summary of the invention

[0003] The present invention provides a method for aggregating massive distributed adjustable resources and evaluating adjustable capacity, which solves the problems disclosed in the background technology.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0005] A method for aggregating and evaluating adjustable capacity of massive distributed adjustable resources, comprising:

[0006] Cluster the underlying monomers of distributed resources according to similar scheduling parameters;

[0007] According to the scheduling model of the underlying monomer of distributed resources, a scheduling model of distributed resource clusters is constructed;

[0008] The dispatching instructions issued by the distribution network are regarded as uncertain variables, and a two-stage robust optimization model is constructed based on the dispatching model of distributed resource clusters.

[0009] A two-stage robust optimization model is solved to evaluate the adjustable capacity of distributed resource clusters.

[0010] Cluster the underlying monomers of distributed resources according to similar scheduling parameters, including:

[0011] According to similar scheduling parameters, the K-means clustering method is used to cluster the underlying monomers of distributed resources.

[0012] The scheduling model of a distributed resource cluster is the sum of all underlying monomer scheduling models in the distributed resource cluster.

[0013] The scheduling model of distributed resource clusters is described as an equivalent energy storage scheduling model.

[0014] The two-stage robust optimization model takes the benchmark power consumption and adjustable capacity range of distributed resource clusters as decision variables, takes the lowest electricity cost as the goal, and takes the distributed adjustable resource clusters fully responding to scheduling instructions under extreme scenarios of uncertainty variables as the constraint.

[0015] Solve a two-stage robust optimization model to evaluate the adjustable capacity of distributed resource clusters, including:

[0016] S1) solving the main problem of the two-stage robust optimization model, obtaining the benchmark power consumption and adjustable capacity interval of the distributed resource cluster group, and sending the benchmark power consumption and adjustable capacity interval of the distributed resource cluster group to the sub-problem of the two-stage robust optimization model;

[0017] S2) Solve the sub-problems of the two-stage robust optimization model to obtain extreme scenarios of uncertain variables. If the preset convergence conditions are met, obtain the adjustable capacity interval of the distributed resource cluster group. Otherwise, upload the extreme scenarios of uncertain variables to the main problem of the two-stage robust optimization model and go to S1).

[0018] The preset convergence condition is that the response deviation of the distributed resource cluster group to the dispatching instructions issued by the distribution network in extreme scenarios is 0.

[0019] Get the adjustable capacity range of the distributed resource cluster, including:

[0020] The adjustable capacity interval obtained from the last main problem is used as the adjustable capacity interval of the required distributed resource cluster group.

[0021] The beneficial effects achieved by the present invention are as follows: the present invention clusters distributed resources, obtains a small number of cluster groups, constructs a scheduling model for the cluster groups, regards the scheduling instructions issued by the system side as uncertain variables, constructs and solves a two-stage robust optimization model, and can effectively evaluate the adjustable capacity of the distributed resource cluster groups, thereby achieving full utilization of various types of distributed resources, and can help distributed resources become a supplement to the regulating resources of the power system, thereby improving the regulation capacity of the power system, reducing the peak-shaving and frequency-regulating pressure of the system, and will play a positive role in peak shaving and valley filling and energy conservation and environmental protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a flow chart of the method of the present invention;

[0023] Figure 2 Flowchart for clustering;

[0024] Figure 3 Flowchart for solving a two-stage robust optimization model. DETAILED DESCRIPTION

[0025] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0026] like Figure 1 As shown, a method for aggregating and evaluating adjustable capacity of massive distributed adjustable resources includes:

[0027] Step 1: Cluster the underlying monomers of distributed resources according to similar scheduling parameters;

[0028] Step 2: construct a scheduling model for distributed resource clusters based on the scheduling model of the underlying monomer of the distributed resource;

[0029] Step 3: Consider the dispatching instructions issued by the distribution network as uncertain variables, and construct a two-stage robust optimization model based on the dispatching model of the distributed resource clustering group;

[0030] Step 4: Solve the two-stage robust optimization model to evaluate the adjustable capacity of the distributed resource clusters.

[0031] The above method clusters distributed resources, obtains a small number of cluster groups, constructs a scheduling model for the cluster groups, regards the scheduling instructions issued by the system side as uncertain variables, constructs and solves a two-stage robust optimization model, and can effectively evaluate the adjustable capacity of distributed resource cluster groups, thereby achieving full utilization of various types of distributed resources, and helping distributed resources to become a supplement to the regulation resources of the power system, thereby improving the regulation capacity of the power system, reducing the peak and frequency regulation pressure of the system, and will play a positive role in peak shaving and valley filling and energy conservation and environmental protection.

[0032] Since there are a large number of distributed resources in the network, such as electric vehicles, distributed energy storage, and air conditioners, in order to reduce the difficulty of regulation, the distributed resources can be clustered according to similar scheduling parameters (for example, for air conditioners, room thermal resistance and room thermal capacity can be selected as scheduling parameters), and the K-means clustering method can be used to obtain a small number of distributed resource clusters.

[0033] The clustering process can be as follows Figure 2 As shown:

[0034] 1) Establish an evaluation matrix for each distributed resource scheduling parameter, and initialize K cluster centers based on the evaluation matrix;

[0035] 2) Calculate the Euclidean distance from each distributed resource to the K cluster centers, and divide each distributed resource into the cluster group corresponding to the cluster center closest to it;

[0036] 3) Calculate the center point of the cluster group in 2) and update the center point as the new cluster center, and repeat 2); when the cluster center no longer changes, end the calculation and form K cluster groups under each distributed resource.

[0037] In the cluster group, each underlying monomer of the distributed resource can build a corresponding scheduling model. Then, based on the scheduling model of the underlying monomer of the distributed resource, the scheduling model of the distributed resource cluster group can be constructed, which can be specifically:

[0038] 11) Construct a scheduling model for the underlying monomers of each distributed resource in the distributed resource cluster;

[0039] 12) Add the scheduling models of the underlying monomers of the distributed resources to obtain the scheduling model of the distributed resource cluster group;

[0040] The scheduling model of distributed resource clusters is described as an equivalent energy storage scheduling model, that is, a linear constraint on the coupling of virtual state of charge and power consumption.

[0041] In order to evaluate the adjustable capacity of distributed resource clusters, the dispatch instructions issued by the distribution network side can be regarded as uncertain variables, and they can be merged with the uncertainty variables contained in the distributed resources themselves into the total uncertainty variables. The benchmark power consumption and adjustable capacity range of the distributed resource clusters are taken as decision variables, the lowest electricity cost is taken as the goal, and the distributed adjustable resource clusters fully respond to the dispatch instructions under extreme scenarios of uncertainty variables is taken as the constraint condition. The adjustable capacity evaluation can be described as a two-stage robust optimization model constructed according to the distributed resource cluster scheduling model.

[0042] The two-stage robust optimization model mainly includes the main problem and sub-problems. Through iterative solution, the adjustable capacity interval of the distributed resource cluster group can be obtained to realize the adjustable capacity evaluation. Specifically, the column constraint generation technology can be used to solve the two-stage robust optimization model, such as Figure 3 As shown:

[0043] S1) solving the main problem of the two-stage robust optimization model, obtaining the benchmark power consumption and adjustable capacity interval of the distributed resource cluster group, and sending the benchmark power consumption and adjustable capacity interval of the distributed resource cluster group to the sub-problem of the two-stage robust optimization model;

[0044] S2) Solve the sub-problems of the two-stage robust optimization model to obtain extreme scenarios of uncertain variables. If the preset convergence conditions are met, obtain the adjustable capacity interval of the distributed resource cluster group, that is, use the adjustable capacity interval obtained by the last main problem as the required adjustable capacity interval of the distributed resource cluster group. Otherwise, upload the extreme scenarios of the uncertain variables to the main problem of the two-stage robust optimization model, and go to S1); wherein the preset convergence conditions are: the response deviation of the distributed resource cluster group to the dispatching instructions issued by the distribution network under extreme scenarios is 0.

[0045] The following takes air conditioning as an example to explain the above method in detail. The adjustable capacity of the air conditioning 4 hours after the current moment with a resolution of 15 minutes is evaluated. The specific process is as follows:

[0046] A1) Aggregate air conditioners with similar scheduling parameters.

[0047] First, the air conditioners are classified according to whether there are people in each hour. Since the adjustable capacity for the next 4 hours is evaluated, a total of 2 4 = 16 classes; secondly, the K-means clustering method is used to perform the second step aggregation on each class, the process is as follows:

[0048] A11) The scheduling parameters of air conditioners are mainly room thermal resistance R and room heat capacity C, so the evaluation matrix is ​​D = [D1, D2, ..., D N ], where D1, D2, …, D N are the scheduling parameters of N air conditioners, and N is the total number of air conditioners in each class. Assuming that there are K=5 clusters in each class, K air conditioners are randomly selected as the initial cluster centers in each class;

[0049] A12) calculating the Euclidean distance between each air conditioner and the K cluster centers, and dividing each air conditioner into the cluster group corresponding to the cluster center closest to it;

[0050] A13) Calculate the center point of the cluster in A12), that is, The obtained center point is updated as the new cluster center, and A12) is repeated. When the cluster center no longer changes, the calculation is terminated to form K cluster groups under each distributed resource; where E k is the center point of the kth cluster, N k is the number of air conditioners contained in the kth cluster, D i is the scheduling parameter of the i-th air conditioner.

[0051] A2) Construct a scheduling model for a single air conditioner in summer:

[0052]

[0053]

[0054]

[0055] Among them, e is a natural number, R is the room thermal resistance, C is the room heat capacity, T t out is the outdoor temperature at time t, is the room temperature at time t+1, T t H is the room temperature at time t, and are the required indoor temperature and upper bound respectively, Ω is the time period when there are people in the room, P t is the regulating power of the air conditioner at time t, The upper limit of the air conditioner's adjustable power.

[0056] A3) Establish a scheduling model for each air conditioning cluster.

[0057] Transforming the i-th air conditioning scheduling model, we can get:

[0058]

[0059] The newly added subscript i represents the i-th air conditioner. For each air conditioner cluster, the room thermal resistance R, room heat capacity C, and outdoor temperature T t out Therefore, these parameters have no subscript i.

[0060] Add up the dispatch models of all air conditioners in the cluster and we get:

[0061]

[0062] The virtual state of charge of the air conditioner is defined as:

[0063]

[0064] Then the scheduling model of the cluster group can be transformed into:

[0065] S t+1 =αS t +x t +c t

[0066] Among them, parameter α = e -1 / (R·C) ,

[0067] Virtual state of charge S t And power consumption x t The upper and lower limits of are:

[0068]

[0069]

[0070] in, P i,t The upper limit of P i,t The lower limit of P i,t is the adjustment power of the i-th air conditioner at time t.

[0071] A4) The scheduling instruction Δx t is regarded as an uncertain variable. The uncertain variable of the air conditioner itself is the outdoor temperature, corresponding to c t , then the total uncertain variable is [Δx t ,c t ], the two-stage robust optimization model is:

[0072]

[0073]

[0074] in, is the benchmark power consumption of the air conditioning cluster, r t u 、r t d are the upward and downward adjustable capacities provided to the air conditioning clusters, respectively, t is the time-of-use electricity price, λ t For the backup price, is an auxiliary variable, is the actual response of the air conditioning cluster, t is the uncertainty set,

[0075] A5) Solve the above model and evaluate the adjustable capacity of the air conditioner cluster.

[0076] The specific steps can be as follows:

[0077] A51) Optimize the main problem of the first stage to obtain the optimal benchmark power consumption and adjustable capacity range that meet the extreme scenarios of all known uncertain variables;

[0078]

[0079]

[0080] Where, subscript k represents the kth extreme scenario, is a 0-1 binary constant, which is obtained from the second stage sub-problem and marks the uncertainty variable Δx in the kth extreme scenario t,k and c t,kThe value of (i.e., upper bound, lower bound, or expected value), the other variables with k subscripts are the decision variables under the kth extreme scenario, Θ is the set of extreme scenarios, is the uncertainty variable c t Expected value;

[0081] After the optimization is completed, the benchmark power consumption and adjustable capacity range are sent to the second stage sub-problem;

[0082] A52) optimizing the second stage sub-problem to obtain an extreme scenario of uncertain variables, which makes the response deviation of the distributed resource cluster group to the power command issued by the distribution network maximum;

[0083]

[0084]

[0085] In the formula, r t u,* ,r t d,* , is the upward and downward adjustable capacity and benchmark load of the distributed resource cluster, which is obtained by optimizing the main problem in the first stage. It is a 0-1 binary variable;

[0086] First, the inner min problem is dualized into a max problem, then the above problem is transformed into a max problem as a whole, and then solved by mixed integer programming;

[0087] After the optimization is completed, the extreme values ​​of the uncertainty variables will be marked. The binary variable values ​​are incorporated into the set of extreme scenarios Θ in the main problem;

[0088] A53) If the convergence condition is met, that is, the sub-problem objective value is equal to 0, the optimization is completed, and the reference power consumption and adjustable capacity interval obtained from the last main problem are the optimal solutions; otherwise, repeat A51) and A52).

[0089] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0090] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0091] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0092] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0093] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A method for aggregating and evaluating adjustable capacity of massive distributed adjustable resources, characterized in that: include: According to similar scheduling parameters, the underlying monomers of distributed resources are clustered; among them, the underlying monomers of distributed resources are air conditioners; According to the scheduling model of the underlying monomer of distributed resources, a scheduling model of distributed resource clusters is constructed; The dispatching instructions issued by the distribution network are regarded as uncertain variables. According to the dispatching model of distributed resource clusters, a two-stage robust optimization model is constructed. The two-stage robust optimization model is: In the formula, is the benchmark power consumption of the air conditioner cluster, are the upward and downward adjustable capacities provided to the air conditioning clusters, respectively, t is the time-of-use electricity price, λ t For the backup price, is an auxiliary variable, t is the uncertainty set, Δx t is the scheduling instruction, S t and S t+1 are the virtual state of charge of the air conditioner at time t and t+1 respectively, and the parameter α=e -1 / (R·C) , R is the room thermal resistance, C is the room heat capacity, T t out is the outdoor temperature at time t, N k is the number of air conditioners contained in the kth cluster, P i,t is the adjustment power of the i-th air conditioner at time t, c t The lower and upper limits of x t The lower and upper limits of S t The lower and upper limits of Solve a two-stage robust optimization model to evaluate the adjustable capacity of distributed resource clusters; Among them, solving a two-stage robust optimization model to evaluate the adjustable capacity of distributed resource clusters includes: A51) Optimize the main problem of the first stage to obtain the optimal benchmark power consumption and adjustable capacity range that meet the extreme scenarios of all known uncertain variables; Where, subscript k represents the kth extreme scenario, is a 0-1 binary constant obtained from the second-stage subproblem, Θ is a set of extreme scenarios, c t Expected value; After the optimization is completed, the benchmark power consumption and adjustable capacity range are sent to the second stage sub-problem; A52) optimizing the second stage sub-problem to obtain an extreme scenario of uncertain variables, which makes the response deviation of the distributed resource cluster group to the power command issued by the distribution network maximum; In the formula, is the upward and downward adjustable capacity and benchmark load of the distributed resource cluster, which is obtained by optimizing the main problem in the first stage. It is a 0-1 binary variable; First, the inner min problem is dualized into a max problem, then the above problem is transformed into a max problem as a whole, and then solved by mixed integer programming. After the optimization is completed, the extreme value of the uncertainty variable is marked The binary variable values ​​are incorporated into the set of extreme scenarios Θ in the main problem; A53) If the sub-problem objective value is equal to 0, the optimization is completed, and the reference power consumption and adjustable capacity interval obtained from the last main problem are the optimal solutions; otherwise, repeat A51) and A52).

2. A method for aggregating and evaluating adjustable capacity of massive distributed adjustable resources according to claim 1, characterized in that: Cluster the underlying monomers of distributed resources according to similar scheduling parameters, including: According to similar scheduling parameters, the K-means clustering method is used to cluster the underlying monomers of distributed resources.

3. The method for aggregating and evaluating adjustable capacity of massive distributed adjustable resources according to claim 1, characterized in that: The scheduling model of distributed resource clusters is described as an equivalent energy storage scheduling model.

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