Scheduling method and system for coping with power vacancy, electronic equipment and medium

Abstract

The invention provides a scheduling method and system for coping with power vacancy, electronic equipment and a medium, and belongs to the technical field of demand side resource calling and optimal scheduling, and the method comprises the following steps: S1, classifying demand side adjustable resources; s2, according to a demand side adjustable resource classification result, constructing a day-ahead-intra-day optimization scheduling mechanism for demand side resources to participate in system adjustment; s3, modeling is carried out on the demand side adjustable resources; s4, carrying out regulation capability aggregation on the class II load; and S5, based on the total cost of purchasing electricity from other power grids and the total cost of calling load side resources, constructing an intra-day optimization scheduling model. According to the method, the resources on the demand side are classified, the optimal scheduling mechanism is constructed, different types of adjustment resources are effectively called to participate in optimal scheduling, the cost of purchasing electricity from the outside of the province and the cost of calling resources in the province are balanced, the uncertainty of the adjustable capacity of the distributed resources is considered, the model is more accurate and practical, and therefore the scheduling cost during power shortage is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of demand-side resource calling and optimization scheduling, and in particular to a scheduling method, system, electronic equipment and medium for coping with power shortage. Background Art

[0002] With the acceleration of the transformation to clean energy, more new energy sources are increasingly being integrated into the power system source side, especially photovoltaic and wind power. The power generation capacity of these new energy sources is highly dependent on the local real-time weather conditions. The variability and uncertainty of the weather, especially the frequent extreme weather events in recent years, have greatly increased the difficulty of achieving source-load power balance in the new power system with a high proportion of new energy access, which may lead to a significant gap in daily power supply and demand. When the system's internal reserve regulation capacity is stretched, in order to maintain the normal operation of the load, it has to rely on the real-time spot market to purchase electricity from other power grids at high prices, which undoubtedly poses a severe challenge to the economic efficiency of dispatch.

[0003] In this context, the demand side has received increasing attention due to its huge regulatory potential. Although some progress has been made in the modeling, aggregation, construction of economic optimization scheduling models, and system supply and demand balance evaluation systems of demand-side adjustable resources, most of these studies remain at the theoretical level and fail to fully explore the characteristics of demand-side adjustable resources, nor to specifically construct a call mechanism and optimization scheduling method suitable for actual scheduling systems.

[0004] As the proportion of renewable energy in the power system continues to increase, the difficulty of predicting the output of the source side is increasing, and the uncertainty of the source-load side further exacerbates the difficulty of power balance. In the face of possible power shortages during the day, the current practical solutions mainly include calling system reserves, purchasing electricity from other power grids, and implementing orderly power consumption. However, increasing the system's reserve capacity is not an overnight achievement. When the reserve capacity is not enough to make up for the large power shortage, the high cost of purchasing electricity becomes an unavoidable problem.

[0005] A large number of studies have shown that the demand side of the power system has huge regulation potential and can provide strong support for the flexible regulation of the system. However, current research still focuses on the modeling, aggregation and construction of optimal dispatch models of demand-side adjustable resources. An effective dispatch mechanism has not yet been established based on the unique characteristics of various demand-side regulation resources, nor has an optimal dispatch method that can be directly applied to the actual dispatch system been formed. Therefore, future research should further explore the potential of demand-side adjustable resources and build more accurate and efficient dispatch mechanisms and methods to meet the challenges brought by the increase in the proportion of new energy access. Summary of the invention

[0006] The purpose of the present invention is to provide a scheduling method, system, electronic device and medium for dealing with power shortages. By classifying demand-side resources and building an optimized scheduling mechanism, different types of regulating resources can be effectively called upon to participate in the optimized scheduling, the costs of purchasing electricity from outside the province and calling on resources within the province can be weighed, and the uncertainty of the adjustable capacity of distributed resources can be considered to make the model more accurate and practical, thereby reducing the scheduling cost when there is a power shortage.

[0007] To achieve the above object, the present invention provides a dispatching method for coping with power shortage, comprising the following steps:

[0008] S1. Classify the adjustable resources on the demand side, including industrial load and residential and general industrial and commercial load, among which industrial load is Class I load and residential and general industrial and commercial load is Class II load;

[0009] S2. Based on the classification results of demand-side adjustable resources, a day-ahead and intra-day optimization scheduling mechanism is established for demand-side resources to participate in system regulation;

[0010] S3, modeling demand-side adjustable resources;

[0011] S4, aggregate the regulating capacity of Class II loads;

[0012] S5. Based on the total cost of purchasing electricity from other power grids and the total cost of calling load-side resources, an intraday optimization scheduling model is constructed.

[0013] Preferably, the day-ahead and day-intraday optimization scheduling mechanism for demand-side resources to participate in system regulation is as follows:

[0014] In the day-ahead stage, the dispatch center infers the probability of source-load balance and the spare capacity that may need to be called up based on the forecast results of new energy and load, combined with the probability of different weather conditions; the forecast results and spare capacity information are sent to the marketing department, and the marketing department divides the shortage into two parts, which are used as the basis for the declaration of Class I load and Class II load respectively; the dispatch center optimizes the calculation according to the declaration of the two types of loads, and sends the calculation results to users;

[0015] During the intraday stage, when it is necessary to call user-side resources, the user will be notified to respond based on the calculation results issued. When user-side resources are not required to participate, the user will not be notified.

[0016] Preferably, in step S3, modeling is performed on the demand-side adjustable resources, including establishing a Class I load model and a Class II load model;

[0017] Among them, the constraints of Class I load model are as follows:

[0018] Power balance constraints:

[0019]

[0020] Power regulation constraints:

[0021]

[0022] Actual power requirement constraints:

[0023]

[0024] Hill climbing constraints:

[0025]

[0026] Energy Consumption Constraints:

[0027]

[0028] in, It represents the regulation power of class I load s at node i participating in demand response at time t; represents the power demand of class I load s on node i when it does not participate in demand response at time t; It represents the actual power demand of the Class I load s on node i after participating in the regulation at time t; It represents the actual power demand of the Class I load s on node i after participating in the regulation at time t-1; They represent the upper and lower limits of the regulation power of the Class I load s on node i participating in the demand response; They represent the upper and lower limits of the actual power demand of the Class I load s at the node i, respectively, which depends on the maximum transmission power of the line; They represent the upper and lower limits of the regulation rate of the class I load s on node i respectively; and Respectively represent t0~t N The electric energy required for the maximum and minimum production plans during the period; N is the number of calculated moments; k is the moment number; t k is the kth moment; t k-1 is the k-1th moment; s∈{Steel,SiC,Cement}, Steel represents steel load; SiC represents silicon carbide industrial load; Cement represents cement processing load;

[0029] Type II load models include general resource models, air conditioning composite models, and residential water heater load models;

[0030] Among them, for the general resource model, the probability of a user at node i participating in power system dispatch under policy incentives is: Then the power of the user participating in the power system regulation at node i at time t is:

[0031]

[0032] in, They represent the active and reactive power of the class II load h on node i that actually participates in the regulation at time t; They represent the maximum active and reactive power regulation of the class II load h on node i at time t;

[0033] The actual Internet load of the user at node i is:

[0034]

[0035] in, They represent the active and reactive power demands of the class II load h on node i when it does not participate in regulation at time t; They represent the actual active and reactive power requirements of the class II load h on node i after the regulation at time t;

[0036] For the air conditioning load model, it is assumed that when the air conditioning user at node i fully participates in the regulation at time t, the corresponding indoor temperature is At this time, the adjustment amount of the user's indoor temperature is in, Indicates the indoor temperature when the air conditioner user at node i does not participate in the adjustment at time t, and the charge state change corresponding to the indoor temperature adjustment amount Change in power consumption The relationship is:

[0037]

[0038] In the formula, represents the charge state of the air conditioner user at node i when not participating in the regulation at time t; represents the state of charge of the air conditioner user at node i when it fully participates in the regulation at time t; T i,max and T i,min They represent the maximum and minimum temperatures that the air conditioner on node i can adjust respectively; represents the power change of the air conditioning load on node i at time t; a1, a2 and a3 all represent model parameters; and Respectively and The state at time t+1;

[0039] Considering that the probability of user participation in mediation is affected by the individual user's psychological differences in participation, when the user's participation probability is introduced The actual power consumption of air conditioning load is for:

[0040]

[0041] in, represents the power required by the air conditioning load on node i when it does not participate in demand response at time t;

[0042] For the residential water heater load model, assuming that the residential water heater load user at node i fully participates in the regulation at time t, the upper limit temperature of the water heater is the upper limit temperature of the water heater At this time, the temperature regulation amount of the resident water heater load on node i at time t is in, Indicates the upper limit temperature of the water heater set when the user of the residential water heater load at node i does not participate in the regulation at time t, and the charge state change corresponding to the temperature regulation of the residential water heater Change in power consumption The relationship between them is:

[0043]

[0044]

[0045] In the formula, Indicates that the resident water heater load on node i is not regulated at time t The corresponding water heater charge state; Indicates that the residential water heater load at node i fully participates in the regulation at time t The corresponding water heater charge state; a t and b t represents unit parameters of a water heater modular unit; and Respectively and The state at time t+1.

[0046] Preferably, in step S4, the regulation capacity of the Class II load is aggregated, and the specific operations are as follows:

[0047] Determine the space formed by the aggregate power regulation range of Class II loads as Where n is the number of flexibility resources;

[0048] Use vertex search method to solve the feasible domain of projected convex polygons;

[0049] By changing the optimization direction of the objective function, solving the optimization problem under different objective functions, and gradually extrapolating the boundary of the convex polygon, the objective function for solving the feasible domain projection is:

[0050]

[0051] Where, μ=(μ P ,μ Q )express The direction vector in space is determined by the normal vector of the boundary of the starting convex polygon; Represents the feasible domain Points inside, P pcc and Q pcc They represent the projected active power and reactive power on the line connecting the aggregate to the grid, respectively;

[0052] The constraints for solving the feasible domain projection are the adjustable output constraints of each flexible resource and the connection network power flow constraints;

[0053] Solve the optimization problem, find new vertices along the normal vector direction of each edge of the starting polygon, when the distance between the new vertex and the original edge is l k Less than the fixed value l δ When , the solution ends, and the end condition is:

[0054]

[0055] in, Represents the coordinates of the new vertex; the parameters M, N, and C are determined by the original edge equation M·P pcc0 +N·Q pcc0 +C=0Confirm;P pcc0 and Q pcc0 They represent the newly solved projected active power and reactive power on the line connecting the aggregate and the grid, respectively.

[0056] Preferably, in step S5, the intraday optimization scheduling model is as follows:

[0057]

[0058] In the formula, C t represents the total cost at time t; represents the total cost of purchasing electricity from other power grids; represents the total cost of calling load-side resources; j1, j2 and j3 represent the node sets connected to other power grids, Class I loads and Class II loads respectively; and Respectively represent the unit price of electricity purchased from other power grids, Class I loads and Class II loads; and They represent the amount of electricity purchased from other power grids, Class I loads and Class II loads respectively; P lack Indicates system power shortage; and They represent the upper limits of regulation capabilities of other power grids, Class I loads, and Class II loads respectively; and They represent the lower limits of regulation capacity of Class I and Class II loads respectively; Pj and Q j represent the active power and reactive power injected into node j respectively; P ij and Q ij Respectively represent the active power and reactive power injected into line ij; r ij 、x ij and l ij Respectively represent the unit resistance, unit reactance and length of line ij; V i 、V max and V min I represents the voltage, maximum voltage and minimum voltage of node i respectively; ij ,I max and I min They represent the carrying current, maximum carrying current and minimum carrying current of line ij respectively; Represents the upstream node set of node j; Represents the set of downstream nodes of node j.

[0059] The present invention also provides a dispatching system for coping with power shortage, comprising:

[0060] Resource classification module, used to classify demand-side adjustable resources, including Class I load and Class II load;

[0061] The scheduling mechanism construction module is used to construct a day-ahead and intraday optimization scheduling mechanism for demand-side resources to participate in system regulation based on resource classification results;

[0062] Resource modeling module, used to model demand-side adjustable resources, including establishing Class I load model and Class II load model;

[0063] The regulation capacity aggregation module is used to aggregate the regulation capacity of Class II loads;

[0064] The intraday optimization dispatch model module is used to build an intraday optimization dispatch model based on the total cost of purchasing electricity from other power grids and the total cost of calling load-side resources.

[0065] The present invention also provides a computer device, comprising: a memory and a processor; the memory stores a computer program, and the processor implements the steps of the above-mentioned scheduling method for coping with power shortage when executing the computer program.

[0066] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned scheduling method for coping with power shortage are implemented.

[0067] Therefore, the present invention adopts the above-mentioned dispatching method, system, electronic device and medium for dealing with power shortages, and the beneficial technical effect is: the present invention classifies the adjustable resources on the demand side according to their characteristics, and constructs a calling mechanism for the demand-side resources when participating in actual dispatching based on the classification results, which can accept different types of adjustable resources to participate in optimized dispatching, and the calling mechanism can better fit the characteristics of the resources. Compared with the existing economic optimization dispatching method considering the adjustable capacity of the demand side, it weighs the cost of purchasing electricity from outside the province and calling resources within the province, and considers the uncertainty of the adjustable capacity of distributed resources such as air conditioners and residential water heaters, making the constructed model more accurate and practical. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 A flow chart of a dispatching method for coping with power shortage according to the present invention;

[0069] Figure 2 A clearing framework diagram for demand-side resources participating in the spinning reserve market;

[0070] Figure 3 Optimize the scheduling cost for each resource; Figure 3 (a) is the total scheduling cost; Figure 3 (b) in the figure is the cost of purchasing electricity from other power grids; Figure 3 (c) in the formula is the cost of purchasing electricity from Class I loads; Figure 3 (d) in the equation is the cost of purchasing electricity from Class II loads. DETAILED DESCRIPTION

[0071] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0072] Unless otherwise defined, technical or scientific terms used in the present invention shall have the common meanings understood by one having ordinary skills in the field to which the present invention belongs.

[0073] Embodiment 1

[0074] like Figure 1 As shown, it is a flow chart of a dispatching method for coping with power shortage of the present invention, which includes the following steps:

[0075] S1. Classify the adjustable resources on the demand side, including industrial load and residential and general industrial and commercial load. Among them, industrial load is Class I load, and residential and general industrial and commercial load is Class II load.

[0076] Specifically, the demand-side flexibility resources are widely distributed and rich in types, mainly including industrial load, residential and general industrial and commercial load, and agricultural load. Agricultural load mainly depends on agricultural production demand and has weak regulation capacity. Therefore, the demand-side response mainly considers industrial load and residential and general industrial and commercial load.

[0077] Industrial loads are relatively concentrated in the power grid, have large individual regulation capabilities, and are easy to centrally manage. This type of load is recorded as Class I load. This type of load can participate in demand response as an individual. Residential and general industrial and commercial loads are characterized by a wide distribution range, a large base, weak individual regulation capabilities, and diverse willingness to participate. This type of load is recorded as Class II load. Therefore, when Class II loads participate in demand response, they need to first aggregate their regulation capabilities and then participate in market dispatch as an aggregate.

[0078] For Class II loads, the user response power that can be obtained is related to incentive prices, environment and other factors. Under a certain weather condition σ and other environmental conditions γ (including policies, individual users and other factors), when the grid operator provides a certain demand response price plan Z, the flexible resource power P that can be mined from the demand side adj for:

[0079] P adj =f(Z|γ,σ) (1);

[0080] When the power system has a power shortage, it can determine the demand for flexible resource power P adj0 By performing an inverse transformation on equation (1), we can determine the price scheme Z0 in the market:

[0081] Z0=f -1 (P adj0 |γ,σ) (2);

[0082] S2. Based on the classification results of demand-side adjustable resources, a day-ahead and intra-day optimization scheduling mechanism is established for demand-side resources to participate in system regulation.

[0083] When there is a power shortage in the power system, the backup capacity of the local power grid is difficult to provide sufficient support. In order to effectively utilize the flexibility of demand-side resources and increase backup capacity to cope with supply issues, Figure 2 A day-ahead and intraday optimization scheduling framework for demand-side resources participating in spinning reserve is provided.

[0084] In the day-ahead stage, the dispatch center infers the probability of source-load balance and the spare capacity that may need to be called up based on the forecast results of new energy and load, combined with the probability of different weather conditions; the forecast results and spare capacity information are sent to the marketing department, and the marketing department divides the shortage into two parts, which are used as the basis for the declaration of Class I load and Class II load respectively; the dispatch center optimizes the calculation according to the declaration of the two types of loads, and sends the calculation results to users;

[0085] During the day, when user-side resources need to be called, the user will be notified to respond based on the delivery results. When user-side resources do not need to be involved, the user will not be notified.

[0086] S3. Model the demand-side adjustable resources.

[0087] Modeling of demand-side adjustable resources, including establishment of Class I load model and Class II load model;

[0088] Industrial loads account for a large proportion of electricity consumption, and the load electricity demand is relatively stable. Among them, some industrial loads, such as chemical, railway, mining and other industries, do not have the ability to adjust due to the particularity of the industry and the high requirements for the stability and reliability of power supply. The upper and lower limits of the adjustable range of loads such as steel processing, silicon carbide and cement production are relatively clear, with good adjustment potential and the ability to participate in demand response.

[0089] Due to its high energy consumption, this type of load will be equipped with reactive power compensation equipment to avoid the power factor assessment of the power system operator. Therefore, when modeling the power system, the impact of reactive power on the power system is not considered, and the active power regulation capability, regulation rate and power demand are used to describe the regulation capability of Class I loads. The constraints of the Class I load model are as follows:

[0090] Power balance constraints:

[0091]

[0092] Power regulation constraints:

[0093]

[0094] Actual power requirement constraints:

[0095]

[0096] Hill climbing constraints:

[0097]

[0098] Energy Consumption Constraints:

[0099]

[0100] in, It represents the regulation power of class I load s at node i participating in demand response at time t; represents the power demand of class I load s on node i when it does not participate in demand response at time t; It represents the actual power demand of the Class I load s on node i after participating in the regulation at time t; It represents the actual power demand of the Class I load s on node i after participating in the regulation at time t-1; They represent the upper and lower limits of the regulation power of the Class I load s on node i participating in the demand response; They represent the upper and lower limits of the actual power demand of the Class I load s at the node i, respectively, which depends on the maximum transmission power of the line; They represent the upper and lower limits of the regulation rate of the class I load s on node i respectively; and Respectively represent t0~t N The electric energy required for the maximum and minimum production plans during the period; N is the number of calculated moments; k is the moment number; t k is the kth moment; t k-1 is the k-1th moment; s∈{Steel,SiC,Cement}, Steel represents steel load; SiC represents silicon carbide industrial load; Cement represents cement processing load.

[0101] At the factory grid-connected node, a static var compensator (SVC) is installed accordingly, and its model is as follows:

[0102]

[0103] in, represents the reactive power generated by the SVC on node i at time t; They respectively represent the upper and lower limits of the reactive power generated by the SVC.

[0104] Type II load models include general resource models, air conditioning composite models, and residential water heater load models;

[0105] Among them, for the general resource model, the probability of a user at node i participating in power system dispatch under policy incentives is: Then the power of the user participating in the power system regulation at node i at time t is:

[0106]

[0107]

[0108] in, They represent the active and reactive power of the class II load h on node i that actually participates in the regulation at time t; They represent the maximum active and reactive power regulation of the class II load h on node i at time t;

[0109] The actual Internet load of the user at node i is:

[0110]

[0111] in, They represent the active and reactive power demands of the class II load h on node i when it does not participate in regulation at time t; They represent the actual active and reactive power requirements of the class II load h on node i after the regulation at time t;

[0112] For the air conditioning load model, it is assumed that when the air conditioning user at node i fully participates in the regulation at time t, the corresponding indoor temperature is At this time, the adjustment amount of the user's indoor temperature is in, Indicates the indoor temperature when the air conditioner user at node i does not participate in the adjustment at time t, and the charge state change corresponding to the indoor temperature adjustment amount Change in power consumption The relationship is:

[0113]

[0114] In the formula, represents the charge state of the air conditioner user at node i when not participating in the regulation at time t; represents the state of charge of the air conditioner user at node i when it fully participates in the regulation at time t; T i,max and T i,min They represent the maximum and minimum temperatures that the air conditioner on node i can adjust respectively; represents the power change of the air conditioning load on node i at time t; a1, a2 and a3 all represent model parameters; and Respectively and The state at time t+1;

[0115] Considering that the probability of user participation in mediation is affected by the individual user's psychological differences in participation, when the user's participation probability is introduced The actual power consumption of air conditioning load is for:

[0116]

[0117] in, represents the power required by the air conditioning load on node i when it does not participate in demand response at time t;

[0118] For the residential water heater load model, assuming that the residential water heater load user at node i fully participates in the regulation at time t, the upper limit temperature of the water heater is the upper limit temperature of the water heater At this time, the temperature regulation amount of the resident water heater load on node i at time t is in, Indicates the upper limit temperature of the water heater set when the user of the residential water heater load at node i does not participate in the regulation at time t, and the charge state change corresponding to the temperature regulation of the residential water heater Change in power consumption The relationship between them is:

[0119]

[0120] In the formula, Indicates that the resident water heater load on node i is not regulated at time t The corresponding water heater charge state; Indicates that the residential water heater load at node i fully participates in the regulation at time t The corresponding water heater charge state; a t and b t represents unit parameters of a water heater modular unit; and Respectively and The state at time t+1.

[0121] S4. Aggregate the regulation capacity of Class II loads. The specific operations are as follows:

[0122] Class II loads are of many types, large in number, and widely distributed, and the regulation capacity of a single resource is small. When participating in demand response, it is necessary to participate in power system dispatching in a unified manner through resource aggregation. The space formed by the power regulation range of Class II load aggregates is Where n is the number of flexible resources. User participation willingness It does not affect the linear nature of resources. Therefore, within the aggregation range, all resource constraints and network constraints are linear constraints. It is a high-dimensional convex polyhedron in space. The projection of this convex polyhedron on the connection line between the aggregate and the power grid is A finite-edge convex polygon in space, the set of points that constitute the convex polygon is denoted by

[0123] Use vertex search method to solve the feasible domain of projected convex polygons;

[0124] By changing the optimization direction of the objective function, solving the optimization problem under different objective functions, and gradually extrapolating the boundary of the convex polygon, the objective function for solving the feasible domain projection is:

[0125]

[0126] Where, μ=(μ P ,μ Q )express The direction vector in space is determined by the normal vector of the boundary of the starting convex polygon; Represents the feasible domain Points inside, P pcc and Q pcc They represent the projected active power and reactive power on the line connecting the aggregate to the grid, respectively;

[0127] The constraints for solving the feasible domain projection are the adjustable output constraints of each flexible resource and the connection network power flow constraints;

[0128] Solve the optimization problem, find new vertices along the normal vector direction of each edge of the starting polygon, when the distance between the new vertex and the original edge is l k Less than the fixed value l δ When , the solution ends, and the end condition is:

[0129]

[0130] in, Represents the coordinates of the new vertex; the parameters M, N, and C are determined by the original edge equation M·P pcc0 +N·Q pcc0 +C=0Confirm;P pcc0 and Q pcc0 They represent the newly solved projected active power and reactive power on the line connecting the aggregate and the grid, respectively.

[0131] S5. Based on the total cost of purchasing electricity from other power grids and the total cost of calling load-side resources, an intraday optimization scheduling model is constructed.

[0132] When the system has a power shortage due to inaccurate weather forecasts, we can solve the optimization problem to optimize the power purchase quota from other places and the load-side resource response quota to ensure the balance of power source and load while achieving the best economic performance. The intraday optimization scheduling model is as follows:

[0133]

[0134]

[0135] In the formula, C t represents the total cost at time t; represents the total cost of purchasing electricity from other power grids; represents the total cost of calling load-side resources; j1, j2 and j3 represent the node sets connected to other power grids, Class I loads and Class II loads respectively; and Respectively represent the unit price of electricity purchased from other power grids, Class I loads and Class II loads; and They represent the amount of electricity purchased from other power grids, Class I loads and Class II loads respectively; Plack Indicates system power shortage; and They represent the upper limits of regulation capabilities of other power grids, Class I loads, and Class II loads respectively; and They represent the lower limits of regulation capacity of Class I and Class II loads respectively; P j and Q j represent the active power and reactive power injected into node j respectively; P ij and Q ij Respectively represent the active power and reactive power injected into line ij; r ij 、x ij and l ij Respectively represent the unit resistance, unit reactance and length of line ij; V i 、V max and V min I represents the voltage, maximum voltage and minimum voltage of node i respectively; ij ,I max and I min They represent the carrying current, maximum carrying current and minimum carrying current of line ij respectively; Represents the upstream node set of node j; Represents the set of downstream nodes of node j.

[0136] The present invention will be further described below through specific experiments.

[0137] Select the IEEE-33 node system as the simulation topology and set and The bases are 50 yuan / MW, 30 yuan / MW, and 35 yuan / MW respectively. Random numbers within the upper and lower 50% intervals are generated according to Gaussian distribution as the and Based on the proposed optimization method, we can get the following Figure 3 The economic optimization scheduling results are shown in Figure 2. Figure 3 (a) is the total cost after optimization. Figure 3 (b) Figure 3 (c) and Figure 3 (d) in the figure are the electricity purchase costs from other power grids, Class I loads, and Class II loads. From the optimization results, it can be seen that when there is a power shortage during the day, the participation of demand-side flexibility resources can effectively reduce the amount of electricity purchased from other power grids and effectively reduce the cost of making up for the power shortage.

[0138] Embodiment 2

[0139] The present invention also provides a dispatching system for coping with power shortage, comprising:

[0140] Resource classification module, used to classify demand-side adjustable resources, including Class I load and Class II load;

[0141] The scheduling mechanism construction module is used to construct a day-ahead and intraday optimization scheduling mechanism for demand-side resources to participate in system regulation based on resource classification results;

[0142] Resource modeling module, used to model demand-side adjustable resources, including establishing Class I load model and Class II load model;

[0143] The regulation capacity aggregation module is used to aggregate the regulation capacity of Class II loads;

[0144] The intraday optimization dispatch model module is used to build an intraday optimization dispatch model based on the total cost of purchasing electricity from other power grids and the total cost of calling load-side resources.

[0145] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0146] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0147] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0148] It is worth noting that the contents not elaborated in detail in the present invention are all prior art and are well known to those skilled in the art.

[0149] Therefore, the present invention adopts the above-mentioned scheduling method, system, electronic device and medium for dealing with power shortages, classifies demand-side resources and constructs an optimized scheduling mechanism, effectively calls on different types of regulation resources to participate in optimized scheduling, weighs the cost of purchasing electricity from outside the province and calling on resources within the province, and considers the uncertainty of the adjustable capacity of distributed resources, so as to make the model more accurate and practical, thereby reducing the scheduling cost when there is power shortage.

[0150] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A dispatching method for coping with power shortage, characterized in that: The following steps are involved: S1. Classify the adjustable resources on the demand side, including industrial load and residential and general industrial and commercial load, among which industrial load is Class I load and residential and general industrial and commercial load is Class II load; S2. Based on the classification results of demand-side adjustable resources, a day-ahead and intra-day optimization scheduling mechanism is established for demand-side resources to participate in system regulation; S3, modeling demand-side adjustable resources; S4, aggregate the regulating capacity of Class II loads; S5. Based on the total cost of purchasing electricity from other power grids and the total cost of calling load-side resources, an intraday optimization scheduling model is constructed.

2. A dispatching method for coping with power shortage according to claim 1, characterized in that: The day-ahead and intraday optimization scheduling mechanism for demand-side resources to participate in system regulation is as follows: In the day-ahead stage, the dispatch center infers the probability of source-load balance and the spare capacity that may need to be called up based on the forecast results of new energy and load, combined with the probability of different weather conditions; the forecast results and spare capacity information are sent to the marketing department, and the marketing department divides the shortage into two parts, which are used as the basis for the declaration of Class I load and Class II load respectively; the dispatch center optimizes the calculation according to the declaration of the two types of loads, and sends the calculation results to users; During the intraday stage, when it is necessary to call user-side resources, the user will be notified to respond based on the calculation results issued. When user-side resources are not required to participate, the user will not be notified.

3. A dispatching method for coping with power shortage according to claim 2, characterized in that: In step S3, the demand-side adjustable resources are modeled, including establishing a Class I load model and a Class II load model; Among them, the constraints of Class I load model are as follows: Power balance constraints: Power regulation constraints: Actual power requirement constraints: Hill climbing constraints: Energy Consumption Constraints: in, It represents the regulation power of class I load s at node i participating in demand response at time t; represents the power demand of class I load s on node i when it does not participate in demand response at time t; It represents the actual power demand of the Class I load s on node i after participating in the regulation at time t; It represents the actual power demand of the Class I load s on node i after participating in the regulation at time t-1; They represent the upper and lower limits of the regulation power of the Class I load s on node i participating in the demand response; They represent the upper and lower limits of the actual power demand of the Class I load s at the node i, respectively, which depends on the maximum transmission power of the line; They represent the upper and lower limits of the regulation rate of the class I load s on node i respectively; and Respectively represent t0~t N The electric energy required for the maximum and minimum production plans during the period; N is the number of calculated moments; k is the moment number; t k is the kth moment; t k-1 is the k-1th moment; s∈{Steel,SiC,Cement}, Steel represents steel load; SiC represents silicon carbide industrial load; Cement represents cement processing load; Type II load models include general resource models, air conditioning composite models, and residential water heater load models; Among them, for the general resource model, the probability of a user at node i participating in power system dispatch under policy incentives is: Then the power of the user participating in the power system regulation at node i at time t is: in, They represent the active and reactive power of the class II load h on node i that actually participates in the regulation at time t; They represent the maximum active and reactive power regulation of the class II load h on node i at time t; The actual Internet load of the user at node i is: in, They represent the active and reactive power demands of the class II load h on node i when it does not participate in regulation at time t; They represent the actual active and reactive power requirements of the class II load h on node i after the regulation at time t; For the air conditioning load model, it is assumed that when the air conditioning user at node i fully participates in the regulation at time t, the corresponding indoor temperature is At this time, the adjustment amount of the user's indoor temperature is in, Indicates the indoor temperature when the air conditioner user at node i does not participate in the adjustment at time t, and the charge state change corresponding to the indoor temperature adjustment amount Change in power consumption The relationship is: In the formula, represents the charge state of the air conditioner user at node i when not participating in the regulation at time t; represents the state of charge of the air conditioner user at node i when it fully participates in the regulation at time t; T i,max and T i,min They represent the maximum and minimum temperatures that the air conditioner on node i can adjust respectively; represents the power change of the air conditioning load on node i at time t; a1, a2 and a3 all represent model parameters; and Respectively and The state at time t+1; Considering that the probability of user participation in mediation is affected by the individual user's psychological differences in participation, when the user's participation probability is introduced The actual power consumption of air conditioning load is for: in, represents the power required by the air conditioning load on node i when it does not participate in demand response at time t; For the residential water heater load model, assuming that the residential water heater load user at node i fully participates in the regulation at time t, the upper limit temperature of the water heater is the upper limit temperature of the water heater At this time, the temperature regulation amount of the resident water heater load on node i at time t is in, It represents the upper limit temperature of the water heater set when the user of the residential water heater load at node i does not participate in the regulation at time t, and the charge state change corresponding to the temperature regulation of the residential water heater Change in power consumption The relationship between them is: In the formula, Indicates that the resident water heater load on node i is not regulated at time t The corresponding water heater charge state; Indicates that the residential water heater load at node i fully participates in the regulation at time t The corresponding water heater charge state; a t and b t represents unit parameters of a water heater modular unit; and Respectively and The state at time t+1.

4. A dispatching method for coping with power shortage according to claim 3, characterized in that: In step S4, the regulation capacity of class II loads is aggregated, and the specific operations are as follows: Determine the space formed by the aggregate power regulation range of Class II loads as Where n is the number of flexibility resources; Use vertex search method to solve the feasible domain of projected convex polygons; By changing the optimization direction of the objective function, solving the optimization problem under different objective functions, and gradually extrapolating the boundary of the convex polygon, the objective function for solving the feasible domain projection is: Where, μ=(μ P ,μ Q )express The direction vector in space is determined by the normal vector of the boundary of the starting convex polygon; Represents the feasible domain Points inside, P pcc and Q pcc They represent the projected active power and reactive power on the line connecting the aggregate to the grid, respectively; The constraints for solving the feasible domain projection are the adjustable output constraints of each flexible resource and the connection network power flow constraints; Solve the optimization problem, find new vertices along the normal vector direction of each edge of the starting polygon, when the distance between the new vertex and the original edge is l k Less than the fixed value l δ When , the solution ends, and the end condition is: in, Represents the coordinates of the new vertex; the parameters M, N, and C are determined by the original edge equation M·P pcc0 +N·Q pcc0 +C=0Confirm;P pcc0 and Q pcc0 They represent the newly solved projected active power and reactive power on the line connecting the aggregate and the grid, respectively.

5. A dispatching method for coping with power shortage according to claim 4, characterized in that: In step S5, the intraday optimization scheduling model is as follows: In the formula, C t represents the total cost at time t; represents the total cost of purchasing electricity from other power grids; represents the total cost of calling load-side resources; j1, j2 and j3 represent the node sets connected to other power grids, Class I loads and Class II loads respectively; and Respectively represent the unit price of electricity purchased from other power grids, Class I loads and Class II loads; and Respectively represent the amount of electricity purchased from other power grids, Class I loads and Class II loads; P lack Indicates system power shortage; and They represent the upper limits of regulation capabilities of other power grids, Class I loads, and Class II loads respectively; and They represent the lower limits of regulation capacity of Class I and Class II loads respectively; P j and Q j represent the active power and reactive power injected into node j respectively; P ij and Q ij Respectively represent the active power and reactive power injected into line ij; r ij 、x ij and l ij Respectively represent the unit resistance, unit reactance and length of line ij; V i 、V max and V min Represent the voltage, maximum voltage and minimum voltage of node i respectively; I ij ,I max and I min They represent the carrying current, maximum carrying current and minimum carrying current of line ij respectively; Represents the upstream node set of node j; Represents the set of downstream nodes of node j.

6. A dispatching system for dealing with power shortage, characterized in that: include: Resource classification module, used to classify demand-side adjustable resources, including Class I load and Class II load; The scheduling mechanism construction module is used to construct a day-ahead and intraday optimization scheduling mechanism for demand-side resources to participate in system regulation based on resource classification results; Resource modeling module, used to model demand-side adjustable resources, including establishing Class I load model and Class II load model; The regulation capacity aggregation module is used to aggregate the regulation capacity of Class II loads; The intraday optimization dispatch model module is used to build an intraday optimization dispatch model based on the total cost of purchasing electricity from other power grids and the total cost of calling load-side resources.

7. A computer device comprising: Memory and processor; The memory stores a computer program, wherein the processor implements the steps of the scheduling method for coping with power shortage described in any one of claims 1 to 5 when executing the computer program.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the scheduling method for coping with power shortage described in any one of claims 1 to 5 are implemented.

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