An air conditioner load demand response regulation method and device, a terminal device, and a storage medium

Abstract

The application discloses a kind of air conditioner load demand response regulation method, device, terminal equipment and storage medium, by analyzing the historical air conditioner load data of various air conditioner loads, the load curve of various air conditioner loads, adjustable capacity, safety margin and reference price are determined, and constraint set is constructed, then air conditioner load demand response model with the minimum power consumption of air conditioner load user as target is constructed, and the load regulation model with the minimum power system regulation cost as target is constructed, finally, master-slave game method is used to solve the air conditioner load demand response model and the load regulation model, so that the target demand response scheme under each period generated can balance the load regulation cost of power system and the power consumption cost of air conditioner user under each period, therefore, the enthusiasm of user participating in demand response regulation is effectively improved, and the stability and safety of power system operation are further improved.

Description

Technical Field

[0001] The present invention relates to the field of demand-side response technology, and in particular to a demand-side response control method, apparatus, terminal device, and storage medium for air-conditioning load. Background Art

[0002] As a crucial component of the power system, air conditioning loads are increasingly accounting for a larger proportion of the load as economic development improves. Therefore, effective control measures can be used to achieve rapid grid-side dispatch while still meeting user comfort requirements. Proper control of air conditioning loads not only alleviates the imbalance between power supply and demand during peak hours and improves the peak-to-valley difference in the load curve, but also reduces dispatch costs compared to traditional peak-shaving methods.

[0003] As electricity market trading mechanisms mature, air conditioning loads can participate in demand response regulation and control, participating in power resource allocation through the power market trading mechanism. This demand response regulation has great regulatory potential for the safe and stable operation of the power system, effectively achieving peak load shaving and valley filling. Therefore, the electricity price for air conditioning load regulation varies at different times of the day. For example, during peak hours (such as summer afternoons), air conditioning regulation prices may be relatively high to encourage users to reduce air conditioning use. During off-peak hours, prices may be relatively low, which affects the normal electricity consumption behavior of air conditioning users, potentially reducing their electricity comfort and productivity. To encourage air conditioning loads to participate in demand response regulation, the power system dispatch center also provides subsidies or incentives. For example, users are subsidized at an agreed-upon demand response price based on their load response volume.

[0004] Therefore, this process will affect the load regulation cost of the power system and the electricity cost of air-conditioning users. In the process of air-conditioning load participating in demand response regulation, how to balance the load regulation cost of the power system and the electricity cost of air-conditioning users has become an urgent problem that needs to be solved. Summary of the Invention

[0005] Embodiments of the present invention provide a method, apparatus, terminal device, and storage medium for demand response regulation of air conditioning loads, which can effectively increase users' enthusiasm for participating in demand response regulation, thereby improving the stability and safety of power system operation.

[0006] An embodiment of the present invention provides a method for demand response control of air conditioning load, comprising:

[0007] Obtain historical air conditioning load data for various types of air conditioning loads;

[0008] Determining, based on the historical air conditioning load data, load curves, adjustable capacities, safety margins, and reference electricity prices for various types of air conditioning loads, and constructing a constraint set based on the load curves, adjustable capacities, safety margins, and reference electricity prices for various types of air conditioning loads;

[0009] Construct an air conditioning load demand response model that aims to minimize the electricity cost of air conditioning load users, and a load regulation model that aims to minimize the power system regulation cost;

[0010] Under the constraint set, the master-slave game method is used to solve the air-conditioning load demand response model and the load regulation model to generate a target demand response plan for the power system in each time period; wherein, the target demand response plan is the load response amount, load regulation electricity price, and demand response electricity price of each type of air-conditioning load in the corresponding time period.

[0011] Furthermore, the historical air-conditioning load data includes historical load data, historical regulation capacity, historical safety margin, and historical reference electricity price of each air-conditioning load in each historical period.

[0012] Determining the load curve, adjustable capacity, safety margin, and reference electricity price of various air conditioning loads based on the historical air conditioning load data includes:

[0013] Calculating the load similarity of each of the historical air conditioning load data in each type of air conditioning load according to the historical air conditioning load data;

[0014] According to the load similarity, a clustering matrix of various air-conditioning loads is constructed;

[0015] According to the clustering matrix, the historical air-conditioning load data is hierarchically divided to form a plurality of clustering hierarchies under various types of air-conditioning loads;

[0016] The historical load data, historical regulating capacity, historical safety margin, and historical reference electricity price of several historical air-conditioning load data in each clustering level are integrated to determine the load curve, adjustable capacity, safety margin, and reference electricity price of each type of air-conditioning load.

[0017] Furthermore, the historical air-conditioning load data is hierarchically divided according to the clustering matrix to form several clustering hierarchies under various types of air-conditioning loads, including:

[0018] Traversing each type of air-conditioning load, and randomly extracting a historical air-conditioning load data from the currently traversed target air-conditioning load as an initial cluster center, and repeatedly performing a hierarchical division operation on a plurality of historical air-conditioning load data according to the clustering matrix and the initial cluster center, until the plurality of historical air-conditioning load data under the target air-conditioning load are classified into corresponding cluster levels;

[0019] After the traversal is completed, several clustering levels are formed under various air conditioning loads;

[0020] The hierarchical division operation includes:

[0021] Obtaining a cluster center; wherein, initially, the cluster center is the initial cluster center;

[0022] According to the clustering matrix, the cluster centers are sorted by similarity with other historical air-conditioning load data to generate a similarity sequence, and a preset number of first similarities are obtained from the similarity sequence from largest to smallest;

[0023] Acquire first historical air-conditioning load data corresponding to the first similarity, and calculate the neighborhood density to be evaluated between the cluster center and the first historical air-conditioning load data;

[0024] When it is determined that the density of the neighborhood to be evaluated is not less than a preset density threshold, a clustering hierarchy is generated, and the cluster center and the first historical air-conditioning load data are classified under the clustering hierarchy;

[0025] After determining that the neighborhood density to be evaluated is less than a preset density threshold, or generating a clustering hierarchy, determining whether there is unclassified historical air-conditioning load data under the target air-conditioning load;

[0026] If so, determining a second similarity from the similarity sequence, and using the second historical air-conditioning load data corresponding to the second similarity as the cluster center required for the next hierarchical division operation; wherein the second similarity is the maximum similarity in the similarity sequence except for a number of first similarities;

[0027] If not, several clustering levels of the target air-conditioning load are output.

[0028] Furthermore, the various types of air conditioning loads include commercial air conditioning loads, industrial air conditioning loads, and residential air conditioning loads; the air conditioning load demand response model is:

[0029]

[0030] Among them, C ACL is the air conditioning load demand response model, c I is the electricity price for industrial air conditioning load, cC is the electricity price for commercial air conditioning load, c R is the electricity price for residential air conditioning load, P I,i,t is the industrial air conditioning load of the i-th node of the power system at time t, dP I,i,t is the industrial air conditioning demand response power at the i-th node of the power system at time t, P C,i,t is the commercial air conditioning load of the i-th node of the power system at time t, dP C,i,t is the commercial air conditioning demand response power at the i-th node of the power system at time t, P R,i,t is the residential air conditioning load at the i-th node of the power system at time t, dP R,i,t is the residential air conditioning demand response power of the i-th node of the power system at time t.

[0031] Furthermore, the load regulation model is:

[0032]

[0033] Among them, C ACL is the load regulation model, c′ I is the electricity price of industrial air conditioning, c′ C is the electricity price of commercial air conditioner, c′ R For residential air conditioning response electricity price, c p is the electricity purchase price of the power system, P g,i is the amount of electricity purchased by the power system from the power plant at time i.

[0034] Furthermore, the constraint set includes: air conditioning load operation constraint conditions, load regulation constraint conditions, load balancing constraint conditions, and response electricity price constraint conditions;

[0035] Under the constraint set, the master-slave game method is used to solve the air conditioning load demand response model and the load regulation model to generate a target demand response plan for the power system in each time period, including:

[0036] According to the master-slave game method, a master-slave game model is constructed with the air-conditioning load demand response model as the leader sub-model and the load regulation model as the follower sub-model;

[0037] A meta-heuristic algorithm is used to solve the master-slave game model under the air-conditioning load operation constraint conditions, the load regulation constraint conditions, the load balancing constraint conditions, and the response electricity price constraint conditions to generate a target demand response plan for the power system in each time period.

[0038] Furthermore, the meta-heuristic algorithm is used to solve the master-slave game model under the air conditioning load operation constraint, the load regulation constraint, the load balancing constraint, and the response electricity price constraint to generate a target demand response plan for the power system in each time period, including:

[0039] Obtain the initial load adjustment electricity price of various air-conditioning loads in each period;

[0040] Repeating the model solving operation on the master-slave game model according to the initial load adjustment electricity price until a target demand response plan of the power system in each time period is generated;

[0041] The model solving operation includes:

[0042] Obtaining the load adjustment electricity price to be evaluated for each type of air-conditioning load in each time period; wherein, initially, the load adjustment electricity price to be evaluated is the initial load adjustment electricity price;

[0043] An ant colony algorithm is used to adjust the electricity price according to the load to be evaluated, under the air conditioning load operation constraints and the load adjustment constraints, with the goal of minimizing the electricity cost of air conditioning load users, to solve the leadership sub-model and determine the maximum load response that can be provided by each type of air conditioning load at each moment;

[0044] Using a particle swarm algorithm, based on the maximum load response, under the load balancing constraint and the response electricity price constraint, and with the goal of minimizing the power system control cost, the follower sub-model is solved to determine the load response to be evaluated and the response electricity price to be evaluated for each type of air-conditioning load at each moment;

[0045] Based on the load regulation price to be evaluated, the response price to be evaluated, and the load response quantity to be evaluated at each moment, evaluate whether the current load regulation cost and electricity cost are converged;

[0046] If so, the load adjustment electricity price to be evaluated, the response electricity price to be evaluated, and the load response amount to be evaluated are used as the target demand response plan of the power system in each time period;

[0047] If not, the load regulation electricity price to be evaluated required for the next round of model solving operation is generated based on the current load regulation cost and electricity cost.

[0048] Another embodiment of the present invention provides a demand response control device for air conditioning load, comprising:

[0049] Data acquisition module, used to obtain historical air conditioning load data of various air conditioning loads;

[0050] a load analysis module for determining, based on the historical air-conditioning load data, load curves, adjustable capacities, safety margins, and reference electricity prices for various types of air-conditioning loads, and constructing a constraint set based on the load curves, adjustable capacities, safety margins, and reference electricity prices for various types of air-conditioning loads;

[0051] A model construction module is used to construct an air-conditioning load demand response model with the goal of minimizing the electricity cost of air-conditioning load users, and a load regulation model with the goal of minimizing the power system regulation cost;

[0052] The model solving module is used to solve the air-conditioning load demand response model and the load regulation model under the constraint set by adopting a master-slave game method according to the load curves, adjustable capacities, safety margins, and reference electricity prices of various air-conditioning loads, so as to generate a target demand response plan for the power system in each time period; wherein the target demand response plan is the load response amount, load regulation electricity price, and demand response electricity price of various air-conditioning loads in a corresponding time period.

[0053] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements a demand response control method for air-conditioning load as described in any one of the embodiments.

[0054] Another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a demand response control method for air conditioning load as described in any of the above embodiments.

[0055] The following beneficial effects are achieved by implementing the present invention:

[0056] The present invention discloses a demand response control method, device, terminal equipment and storage medium for air-conditioning loads. The method analyzes historical air-conditioning load data of various air-conditioning loads to determine the load curves, adjustable capacities, safety margins and reference electricity prices of various air-conditioning loads, and constructs a constraint set so that when solving the model later, the normal air-conditioning electricity consumption behavior of various air-conditioning users can be taken into account. Without affecting the users' electricity comfort and production efficiency, a target demand response plan is generated, and then an air-conditioning load demand response model with the goal of minimizing the electricity cost of air-conditioning load users and a load control model with the goal of minimizing the power system control cost are constructed. Finally, a master-slave game method is adopted to ensure that the target demand response plan generated in each time period can balance the load control cost of the power system and the electricity cost of air-conditioning users in each time period under the premise of considering the normal air-conditioning electricity consumption behavior of various air-conditioning users. Therefore, the present invention can effectively improve the enthusiasm of users to participate in demand response control, thereby improving the stability and safety of power system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 The present invention provides a flowchart of a method for demand response control of air conditioning loads according to an embodiment of the present invention.

[0058] Figure 2 The present invention provides a structural diagram of a demand response control device for air conditioning loads according to an embodiment of the present invention. DETAILED DESCRIPTION

[0059] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions in this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0060] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0061] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is more than two, unless otherwise clearly and specifically defined.

[0062] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0063] In the description of the embodiments of this application, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0064] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0065] In the description of the embodiments of the present application, unless otherwise expressly specified or limited, technical terms such as "installed," "connected," "connected," and "fixed" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integration; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; internal connections between two components or interactions between two components. Those skilled in the art can understand the specific meanings of the above terms in the embodiments of the present application based on specific circumstances.

[0066] See also Figure 1 , is a flow chart of a method for demand response control of air conditioning loads provided by an embodiment of the present invention, comprising:

[0067] S1. Obtain historical air conditioning load data of various air conditioning loads;

[0068] In a preferred embodiment of the present invention, various types of air conditioning loads include commercial air conditioning loads, industrial air conditioning loads, and residential air conditioning loads. Commercial air conditioning loads include the air conditioning loads of various commercial shopping centers, office buildings, and other buildings or parks. Industrial air conditioning loads include the air conditioning loads of large industrial plants. Residential air conditioning loads include the air conditioning loads of residential buildings, residential communities, and apartments. The historical air conditioning load data includes the historical load data, historical regulation capacity, historical safety margin, and historical reference electricity price for each air conditioner in each type of air conditioning load during each historical period.

[0069] S2. Determine, based on the historical air conditioning load data, load curves, adjustable capacities, safety margins, and reference electricity prices for various types of air conditioning loads, and construct a constraint set based on the load curves, adjustable capacities, safety margins, and reference electricity prices for various types of air conditioning loads;

[0070] In a preferred embodiment of the present invention, in order to analyze the electricity usage habits of various air-conditioning users, the load curves, adjustable capacities, safety margins, and reference electricity prices of various air-conditioning loads are determined by analyzing historical air-conditioning load data. Secondly, this embodiment also constructs a constraint set based on the load curves, adjustable capacities, safety margins, and reference electricity prices of various air-conditioning loads, so that when generating a plan later, the usual air-conditioning electricity usage behaviors of various air-conditioning users can be taken into account, and a target demand response plan can be generated without affecting the users' electricity comfort and production efficiency. For example, during the night period, the residential air-conditioning load is higher than the industrial air-conditioning load, so at this time, the industrial air-conditioning load will be mainly used to respond to demand scheduling. Specifically, based on the load curves of various air-conditioning loads, the air-conditioning load operation constraints of various air-conditioning loads at each moment are determined; based on the adjustable capacity, the load regulation constraints of various air-conditioning loads at each moment are determined; based on the safety margin, the load balancing constraints of various air-conditioning loads at each moment are determined; based on the safety margin, the response electricity price constraints of various air-conditioning loads at each moment are determined;

[0071] Specifically, the air conditioning load operation constraints include:

[0072] P I,min ≤P I,i,t ≤P I,max ;

[0073] P C,min ≤P C,i,t ≤P C,max ;

[0074] P R,min ≤P R,i,t ≤P R,max ;

[0075] Where: P I,min 、PC,min and P R,min are the minimum operating power values ​​of industrial, commercial and residential air conditioning loads respectively. I,max 、P C,max and P R,max They are the maximum operating power of industrial, commercial and residential air-conditioning loads respectively.

[0076] Load regulation constraints, including:

[0077] dP I,i,min ≤dP I,i,t ≤dP I,i,max ;

[0078] dP C,i,min ≤dP C,i,t ≤dP C,i,max ;

[0079] dP R,i,min ≤dP R,i,t ≤dP R,i,max ;

[0080] Where: dP I,i,min 、dP C,i,min and dP R,i,min The minimum adjustable power for industrial, commercial and residential air conditioning loads. I,i,max 、dP C,i,max and dP R,i,max They are the minimum adjustable power values ​​for industrial, commercial and residential air-conditioning loads respectively.

[0081] Load balancing constraints, including:

[0082]

[0083] Where: Ω is the set of nodes where the air conditioning load is located.

[0084] Respond to electricity price constraints, including:

[0085] c I,min ≤c I ≤c I,max ;

[0086] c C,min ≤c C ≤c C,max ;

[0087] c R,min ≤c R ≤c R,max ;

[0088] Where: c I,min 、c C,min and c R,minare the minimum electricity prices for industrial, commercial and residential air conditioning loads respectively. I,max 、c C,max and c R,max The maximum electricity prices for industrial, commercial and residential air-conditioning loads respectively.

[0089] Preferably, the historical air-conditioning load data includes historical load data, historical regulation capacity, historical safety margin, and historical reference electricity price of each air-conditioning load in each historical period.

[0090] Determining the load curve, adjustable capacity, safety margin, and reference electricity price of various air conditioning loads based on the historical air conditioning load data includes:

[0091] S21. Calculating the load similarity of each of the historical air conditioning load data for each type of air conditioning load based on the historical air conditioning load data;

[0092] S22. Constructing a clustering matrix of various air-conditioning loads based on the load similarity;

[0093] S23, hierarchically dividing the historical air-conditioning load data according to the clustering matrix to form a plurality of clustering hierarchies under various types of air-conditioning loads;

[0094] In a preferred embodiment of the present invention, since the amount of data on air-conditioning load users is huge, this embodiment uses a hierarchical density clustering algorithm (DBSCAN) to aggregate the air-conditioning load data to improve the efficiency and accuracy of data analysis.

[0095] Preferably, the historical air-conditioning load data is hierarchically divided according to the clustering matrix to form several clustering hierarchies under various types of air-conditioning loads, including:

[0096] S231, traversing each type of air-conditioning load, and randomly extracting a historical air-conditioning load data from the currently traversed target air-conditioning load as an initial cluster center, and repeatedly performing a hierarchical division operation on the plurality of historical air-conditioning load data according to the clustering matrix and the initial cluster center, until the plurality of historical air-conditioning load data under the target air-conditioning load are classified into corresponding cluster levels;

[0097] S232. After the traversal is completed, several clustering levels are formed under various air conditioning loads;

[0098] The hierarchical division operation includes:

[0099] S2311, obtaining a cluster center; wherein, initially, the cluster center is the initial cluster center;

[0100] S2312: Sort the similarities between the cluster centers and other historical air-conditioning load data according to the cluster matrix to generate a similarity sequence, and obtain a preset number of first similarities from the similarity sequence in descending order;

[0101] S2313: Obtain first historical air-conditioning load data corresponding to the first similarity, and calculate the density of the neighboring region to be evaluated between the cluster center and the first historical air-conditioning load data;

[0102] S2314: When it is determined that the density of the neighborhood to be evaluated is not less than a preset density threshold, a clustering hierarchy is generated, and the cluster center and the first historical air-conditioning load data are classified under the clustering hierarchy;

[0103] S2315: After determining that the neighborhood density to be evaluated is less than a preset density threshold, or generating a clustering hierarchy, determining whether there is unclassified historical air-conditioning load data under the target air-conditioning load;

[0104] S2316: If yes, determine a second similarity from the similarity sequence, and use the second historical air-conditioning load data corresponding to the second similarity as the cluster center required for the next hierarchical division operation; wherein the second similarity is the maximum similarity in the similarity sequence except for the first similarities;

[0105] S2317: If not, output several clustering levels of the target air-conditioning load.

[0106] S24. Integrate the historical load data, historical adjustment capacity, historical safety margin, and historical reference electricity price of several historical air-conditioning load data in each of the clustering levels to determine the load curve, adjustable capacity, safety margin, and reference electricity price of each type of air-conditioning load.

[0107] In a preferred embodiment of the present invention, firstly, the load similarity of each of the historical air-conditioning load data in each type of air-conditioning load is calculated to form a clustering matrix;

[0108] Specifically, the load similarity between the historical air conditioning load data is calculated using the following formula:

[0109]

[0110] Among them, d i,j is the i-th historical air conditioning load data P L,i and the jth historical air conditioning load data P L,j The load similarity between is the i-th historical air conditioning load data P L,i The mth dimension of is the jth historical air conditioning load data PL,j The mth dimension of , the dimension including: historical load data, historical regulation capacity, historical safety margin, and historical reference electricity price;

[0111] Therefore, the clustering matrix D is expressed as:

[0112] D=[d i,j ]i,j=1,2,…n。

[0113] Secondly, the historical air-conditioning load data is divided into cluster density levels according to the clustering matrix. Specifically, in the historical air-conditioning load data set P of a class of air-conditioning loads, L0 Randomly select feasible solutions (historical air conditioning load data) P L,i , and regard it as the central object, and cluster P according to the clustering matrix D L,i Sort by distance to other feasible solutions, and select the k most recent first historical air conditioning load data according to the given k value (preset number) to form a set P′ containing k+1 historical air conditioning load data L0 .

[0114] Then, calculate the neighborhood density k of the k+1 historical air conditioning load data within this range respectively. md , and solve these k+1 k md The mean μ and standard deviation σ are as follows:

[0115]

[0116]

[0117] Where: P L,i,j is a feasible solution P L,i The jth dimension of From the jth dimension to the feasible solution P L,i The nearest feasible solution.

[0118] Then respectively set P′ L0 The boundary of the set is considered as the object of the new selected center, and the nearest historical air conditioning load data P′ outside the set is selected L , calculate k md , according to the maximum regulation margin value allowed by the power market, determine k md The threshold is used to judge, and the feasible solutions are divided into p levels according to the neighborhood density.

[0119] Finally, based on the obtained p density levels, hierarchical clustering of several air-conditioning load data under various air-conditioning loads is realized, and the diverse scenario information in the historical load information is obtained. The electricity usage habits of various air-conditioning load users are analyzed, and the load curve, regulation capacity, electricity price information and safety margin of the load are determined, providing a reference for the pricing strategy of the subsequent power market trading mechanism.

[0120] S3. Construct an air-conditioning load demand response model with the goal of minimizing the electricity cost of air-conditioning load users, and a load regulation model with the goal of minimizing the power system regulation cost;

[0121] Preferably, the various types of air conditioning loads include commercial air conditioning loads, industrial air conditioning loads, and residential air conditioning loads; the air conditioning load demand response model is:

[0122]

[0123] Among them, C ACL is the air conditioning load demand response model, c I is the electricity price for industrial air conditioning load, c C is the electricity price for commercial air conditioning load, c R is the electricity price for residential air conditioning load, P I,i,t is the industrial air conditioning load of the i-th node of the power system at time t, dP I,i,t is the industrial air conditioning demand response power at the i-th node of the power system at time t, P C,i,t is the commercial air conditioning load of the i-th node of the power system at time t, dP C,i,t is the commercial air conditioning demand response power at the i-th node of the power system at time t, P R,i,t is the residential air conditioning load at the i-th node of the power system at time t, dP R,i,t is the residential air conditioning demand response power of the i-th node of the power system at time t.

[0124] Preferably, the load regulation model is:

[0125]

[0126] Among them, C ACL is the load regulation model, c′ I is the electricity price of industrial air conditioning, c′ C is the electricity price of commercial air conditioner, c′ R For residential air conditioning response electricity price, c p is the electricity purchase price of the power system, P g,i is the amount of electricity purchased by the power system from the power plant at time i.

[0127] S4. Under the constraint set, the master-slave game method is used to solve the air-conditioning load demand response model and the load regulation model to generate a target demand response plan for the power system in each time period; wherein the target demand response plan is the load response amount, load regulation electricity price, and demand response electricity price of each type of air-conditioning load in the corresponding time period.

[0128] Preferably, the constraint set includes: air conditioning load operation constraint conditions, load regulation constraint conditions, load balancing constraint conditions, and response electricity price constraint conditions;

[0129] Under the constraint set, the master-slave game method is used to solve the air conditioning load demand response model and the load regulation model to generate a target demand response plan for the power system in each time period, including:

[0130] S41. Using a master-slave game method, construct a master-slave game model with the air conditioning load demand response model as a leader sub-model and the load regulation model as a follower sub-model;

[0131] In a preferred embodiment of the present invention, the master-slave game model is as follows:

[0132]

[0133] Where x c and x d are the operational variable sets for the air conditioning load demand response electricity price and load regulation power, respectively. G(*)≤0 and H(*)=0 are the inequality and equality constraints for the upper-level leader power system demand response regulation model, while g(*)≤0 and h(*)=0 are the inequality and equality constraints for the lower-level follower air conditioning load user electricity cost model.

[0134] Equilibrium solution of the master-slave game model:

[0135] When the master-slave game reaches equilibrium, the following conditions are met:

[0136]

[0137] In the formula is the set of strategies that achieve equilibrium.

[0138] S42. Using a meta-heuristic algorithm, the master-slave game model is solved under the air-conditioning load operation constraint conditions, the load regulation constraint conditions, the load balancing constraint conditions, and the response electricity price constraint conditions to generate a target demand response plan for the power system in each time period.

[0139] Preferably, a meta-heuristic algorithm is used to solve the master-slave game model under the air-conditioning load operation constraint, the load regulation constraint, the load balancing constraint, and the response electricity price constraint to generate a target demand response plan for the power system in each time period, including:

[0140] S421. Obtain the initial load adjustment electricity price of each type of air conditioning load in each time period;

[0141] S422. Repeating the model solving operation on the master-slave game model according to the initial load-adjusted electricity price until a target demand response plan for the power system in each time period is generated;

[0142] The model solving operation includes:

[0143] S4221. Obtain the load adjustment electricity price to be evaluated for each type of air-conditioning load in each time period; wherein, initially, the load adjustment electricity price to be evaluated is the initial load adjustment electricity price;

[0144] S4222. Using an ant colony algorithm, based on the load-to-be-assessed electricity price, under the air conditioning load operating constraints and the load adjustment constraints, and with the goal of minimizing the electricity cost of air conditioning load users, solve the leadership sub-model to determine the maximum load response that each type of air conditioning load can provide at each moment.

[0145] S4223. Using a particle swarm algorithm, based on the maximum load response, under the load balancing constraint and the response electricity price constraint, and with the goal of minimizing the power system control cost, solve the follower sub-model to determine the load response to be evaluated and the response electricity price to be evaluated for each type of air-conditioning load at each moment;

[0146] S4224: Evaluate whether the current load regulation cost and electricity cost have converged based on the load regulation electricity price to be evaluated, the response electricity price to be evaluated, and the load response amount to be evaluated at each moment;

[0147] S4225: If yes, use the load adjustment electricity price to be evaluated, the response electricity price to be evaluated, and the load response amount to be evaluated as the target demand response plan of the power system in each time period;

[0148] S4226: If not, generate the load regulation electricity price to be evaluated required for the next round of model solving operation based on the current load regulation cost and electricity cost.

[0149] In a preferred embodiment of the present application, for the above master-slave game model, the ant colony optimization algorithm (ACO) and the particle swarm optimization algorithm (PSO) are used to solve the problems of the followers and the leader respectively. The ant colony optimization algorithm and the particle swarm optimization algorithm have the characteristics of fast solving and good correlation. The air conditioning load users as investors participate in the demand response regulation of the power market by the ACO algorithm to determine the load response amount of the air conditioning load users, and the power market determines the optimal operation scheme of the demand response regulation of the power system by the PSO algorithm to minimize the purchase cost and the demand response regulation cost.

[0150] The embodiment provides a demand response regulation method of an air conditioning load, which determines the load curve, the adjustable capacity, the safety margin and the reference price of various air conditioning loads by analyzing historical air conditioning load data of the various air conditioning loads, so that the subsequent solving of the model can consider the power consumption behavior of the various air conditioning users, and then constructs an air conditioning load demand response model with the minimization of the power consumption cost of the air conditioning load users as the target and a load regulation model with the minimization of the regulation cost of the power system as the target, and finally adopts the master-slave game method to balance the load regulation cost of the power system and the power consumption cost of the air conditioning users in each period under the premise of considering the power consumption behavior of the various air conditioning users, so that the generated target demand response scheme in each period can balance the load regulation cost of the power system and the power consumption cost of the air conditioning users in each period under the premise of considering the power consumption behavior of the various air conditioning users, and therefore, the present application can effectively improve the enthusiasm of the users in participating in the demand response regulation, and further improve the stability and safety of the operation of the power system.

[0151] Referring to Figure 2 , which is a structural schematic diagram of a demand response regulation method and device of an air conditioning load provided by an embodiment of the present application, comprising:

[0152] a data acquisition module configured to acquire historical air conditioning load data of various air conditioning loads;

[0153] a load analysis module configured to determine the load curve, the adjustable capacity, the safety margin and the reference price of various air conditioning loads according to the historical air conditioning load data, and to construct a constraint set according to the load curve, the adjustable capacity, the safety margin and the reference price of various air conditioning loads;

[0154] a model construction module configured to construct an air conditioning load demand response model with the minimization of the power consumption cost of the air conditioning load users as the target and a load regulation model with the minimization of the regulation cost of the power system as the target;

[0155] The model solving module is used to solve the air-conditioning load demand response model and the load regulation model under the constraint set by adopting a master-slave game method according to the load curves, adjustable capacities, safety margins, and reference electricity prices of various air-conditioning loads, so as to generate a target demand response plan for the power system in each time period; wherein the target demand response plan is the load response amount, load regulation electricity price, and demand response electricity price of various air-conditioning loads in a corresponding time period.

[0156] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0157] Those skilled in the art will clearly understand that for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0158] Another preferred embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements a demand response control method for air-conditioning load as described in any one of the above embodiments.

[0159] The terminal device may be a computing device such as a desktop computer, a notebook computer, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0160] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, connecting various parts of the entire terminal device using various interfaces and lines.

[0161] The memory can be used to store the computer program, and the processor realizes various functions of the terminal device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created based on the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0162] Another preferred embodiment of the present invention provides a storage medium, which is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. The computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium.

[0163] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A demand response control method for air conditioning load, characterized in that: include: Obtain historical air conditioning load data for various types of air conditioning loads; Determining, based on the historical air conditioning load data, load curves, adjustable capacities, safety margins, and reference electricity prices for various types of air conditioning loads, and constructing a constraint set based on the load curves, adjustable capacities, safety margins, and reference electricity prices for various types of air conditioning loads; Construct an air conditioning load demand response model that aims to minimize the electricity cost of air conditioning load users, and a load regulation model that aims to minimize the power system regulation cost; According to the master-slave game method, a master-slave game model is constructed with the air-conditioning load demand response model as the leading sub-model and the load regulation model as the following sub-model; the initial load regulation electricity price of each type of air-conditioning load in each time period is obtained; Repeating the model solving operation on the master-slave game model according to the initial load adjustment electricity price until a target demand response plan of the power system in each time period is generated; The target demand response scheme is the load response amount, load adjustment electricity price, and demand response electricity price of each type of air-conditioning load in a corresponding time period. The constraint set includes: air-conditioning load operation constraint conditions, load adjustment constraint conditions, load balancing constraint conditions, and response electricity price constraint conditions. The model solving operation includes: Obtain the load adjustment electricity price to be evaluated for each type of air-conditioning load in each time period; wherein, initially, the load adjustment electricity price to be evaluated is the initial load adjustment electricity price; adopt an ant colony algorithm, according to the load adjustment electricity price to be evaluated, under the air-conditioning load operation constraint condition and the load adjustment constraint condition, with the goal of minimizing the electricity cost of air-conditioning load users, solve the leadership sub-model, and confirm the maximum load response amount that each type of air-conditioning load can provide at each moment; adopt a particle swarm algorithm, according to the maximum load response amount, under the load balancing constraint condition and the response electricity price constraint condition, with the goal of minimizing the power system regulation cost. The goal is to solve the follower sub-model and determine the load response quantity to be evaluated and the response electricity price to be evaluated of each type of air-conditioning load at each moment; based on the load adjustment electricity price to be evaluated, the response electricity price to be evaluated and the load response quantity to be evaluated at each moment, evaluate whether the current load regulation cost and electricity cost converge; if so, use the load adjustment electricity price to be evaluated, the response electricity price to be evaluated, and the load response quantity to be evaluated as the target demand response plan of the power system in each time period; if not, generate the load adjustment electricity price to be evaluated required for the next round of model solving operation based on the current load regulation cost and electricity cost.

2. The method for demand response control of air conditioning load according to claim 1, characterized in that: The historical air-conditioning load data includes the historical load data, historical regulation capacity, historical safety margin, and historical reference electricity price of each air-conditioning load in each historical period. Determining the load curve, adjustable capacity, safety margin, and reference electricity price of various air conditioning loads based on the historical air conditioning load data includes: Calculating the load similarity of each of the historical air conditioning load data in each type of air conditioning load according to the historical air conditioning load data; According to the load similarity, a clustering matrix of various air-conditioning loads is constructed; According to the clustering matrix, the historical air-conditioning load data is hierarchically divided to form a plurality of clustering hierarchies under various types of air-conditioning loads; The historical load data, historical regulating capacity, historical safety margin, and historical reference electricity price of several historical air-conditioning load data in each clustering level are integrated to determine the load curve, adjustable capacity, safety margin, and reference electricity price of each type of air-conditioning load.

3. The demand response control method for air conditioning load according to claim 2, characterized in that: The historical air-conditioning load data is hierarchically divided according to the clustering matrix to form a plurality of clustering hierarchies under various types of air-conditioning loads, including: Traversing each type of air-conditioning load, and randomly extracting a historical air-conditioning load data from the currently traversed target air-conditioning load as an initial cluster center, and repeatedly performing a hierarchical division operation on a plurality of historical air-conditioning load data according to the clustering matrix and the initial cluster center, until the plurality of historical air-conditioning load data under the target air-conditioning load are classified into corresponding cluster levels; After the traversal is completed, several clustering levels are formed under various air conditioning loads; The hierarchical division operation includes: Obtaining a cluster center; wherein, initially, the cluster center is the initial cluster center; According to the clustering matrix, the cluster centers are sorted by similarity with other historical air-conditioning load data to generate a similarity sequence, and a preset number of first similarities are obtained from the similarity sequence from largest to smallest; Acquire first historical air-conditioning load data corresponding to the first similarity, and calculate the neighborhood density to be evaluated between the cluster center and the first historical air-conditioning load data; When it is determined that the density of the neighborhood to be evaluated is not less than a preset density threshold, a clustering hierarchy is generated, and the cluster center and the first historical air-conditioning load data are classified under the clustering hierarchy; After determining that the neighborhood density to be evaluated is less than a preset density threshold, or generating a clustering hierarchy, determining whether there is unclassified historical air-conditioning load data under the target air-conditioning load; If so, determining a second similarity from the similarity sequence, and using the second historical air-conditioning load data corresponding to the second similarity as the cluster center required for the next hierarchical division operation; wherein the second similarity is the maximum similarity in the similarity sequence except for a number of first similarities; If not, several clustering levels of the target air-conditioning load are output.

4. The method for demand response control of air conditioning load according to claim 3, characterized in that: The various types of air conditioning loads include commercial air conditioning loads, industrial air conditioning loads, and residential air conditioning loads. The air conditioning load demand response model is: ； in, is the air conditioning load demand response model, is the electricity price for industrial air conditioning load, is the electricity price for commercial air conditioning load, The electricity price for residential air conditioning load is is the industrial air conditioning load of the i-th node of the power system at time t, is the industrial air conditioning demand response power at the i-th node of the power system at time t, is the commercial air conditioning load of the i-th node of the power system at time t, is the commercial air conditioning demand response power at the i-th node of the power system at time t, is the residential air conditioning load at the i-th node of the power system at time t, is the residential air conditioning demand response power of the i-th node of the power system at time t.

5. The method for demand response control of air conditioning load according to claim 4, characterized in that: The load regulation model is: ； in, is the load regulation model, Responding to electricity prices for industrial air conditioners, Responding to electricity prices for commercial air conditioners, For residential air conditioning respond to electricity prices, is the electricity purchase price of the power system, is the amount of electricity purchased by the power system from the power plant at time i.

6. A demand response control device for air conditioning load, characterized in that: include: Data acquisition module, used to obtain historical air conditioning load data of various air conditioning loads; a load analysis module for determining, based on the historical air-conditioning load data, load curves, adjustable capacities, safety margins, and reference electricity prices for various types of air-conditioning loads, and constructing a constraint set based on the load curves, adjustable capacities, safety margins, and reference electricity prices for various types of air-conditioning loads; A model construction module is used to construct an air-conditioning load demand response model with the goal of minimizing the electricity cost of air-conditioning load users, and a load regulation model with the goal of minimizing the power system regulation cost; A model solving module is used to construct a master-slave game model using a master-slave game method, with the air-conditioning load demand response model as a leading sub-model and the load regulation model as a following sub-model; and obtain the initial load regulation electricity price of each type of air-conditioning load in each time period; Repeating the model solving operation on the master-slave game model according to the initial load adjustment electricity price until a target demand response plan of the power system in each time period is generated; The target demand response scheme is the load response amount, load adjustment electricity price, and demand response electricity price of each type of air-conditioning load in a corresponding time period. The constraint set includes: air-conditioning load operation constraint conditions, load adjustment constraint conditions, load balancing constraint conditions, and response electricity price constraint conditions. The model solving operation includes: Obtain the load adjustment electricity price to be evaluated for each type of air-conditioning load in each time period; wherein, initially, the load adjustment electricity price to be evaluated is the initial load adjustment electricity price; adopt an ant colony algorithm, according to the load adjustment electricity price to be evaluated, under the air-conditioning load operation constraint condition and the load adjustment constraint condition, with the goal of minimizing the electricity cost of air-conditioning load users, solve the leadership sub-model, and confirm the maximum load response amount that each type of air-conditioning load can provide at each moment; adopt a particle swarm algorithm, according to the maximum load response amount, under the load balancing constraint condition and the response electricity price constraint condition, with the goal of minimizing the power system regulation cost. The goal is to solve the follower sub-model and determine the load response quantity to be evaluated and the response electricity price to be evaluated of each type of air-conditioning load at each moment; based on the load adjustment electricity price to be evaluated, the response electricity price to be evaluated and the load response quantity to be evaluated at each moment, evaluate whether the current load regulation cost and electricity cost converge; if so, use the load adjustment electricity price to be evaluated, the response electricity price to be evaluated, and the load response quantity to be evaluated as the target demand response plan of the power system in each time period; if not, generate the load adjustment electricity price to be evaluated required for the next round of model solving operation based on the current load regulation cost and electricity cost.

7. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for demand response control of air conditioning load according to any one of claims 1 to 5 is implemented.

8. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is executed, the device where the storage medium is located is controlled to execute the demand response control method for air-conditioning load according to any one of claims 1 to 5.

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