A dispatching method, system, electronic device and medium for coping with power shortage

Abstract

The present invention provides a dispatching method, system, electronic device and medium for coping with power shortages, belonging to the technical field of demand-side resource invocation and optimal dispatching. The method includes the following steps: S1. Classify the demand-side adjustable resources; S2. According to the classification results of the demand-side adjustable resources, construct a day-ahead and intra-day optimal dispatching mechanism for the demand-side resources to participate in system regulation; S3. Model the demand-side adjustable resources; S4. Aggregate the regulation capabilities of type-II loads; S5. Based on the total cost of purchasing electricity from other power grids and the total cost of invoking load-side resources, construct an intra-day optimal dispatching model. By classifying the demand-side resources and constructing an optimal dispatching mechanism, the present invention effectively invokes different types of regulation resources to participate in optimal dispatching, weighs the costs of purchasing electricity from outside the province and invoking resources within the province, and considers the uncertainty of the adjustable capabilities of distributed resources, making the model more accurate and practical, thereby reducing the dispatching cost during power shortages.

Description

Technical Field

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

[0002] With the acceleration of the transition to clean energy, the power system is increasingly integrating more renewable energy sources, particularly photovoltaic and wind power. The generation capacity of these renewable energy sources is highly dependent on local, real-time weather conditions. The variability and uncertainty of weather, particularly the frequent extreme weather events in recent years, have greatly increased the difficulty of achieving source-load balance in new power systems with a high proportion of renewable energy integration, potentially leading to significant gaps in daily power supply and demand. When the system's internal reserve regulation capacity is stretched thin, it has to rely on real-time spot markets to purchase electricity from other power grids at exorbitant prices to maintain normal load operation. This undoubtedly poses a severe challenge to the economic efficiency of dispatch.

[0003] Against this backdrop, the demand side has attracted increasing attention due to its enormous regulatory potential. Although research has made some progress in modeling and aggregating demand-side adjustable resources, constructing economically optimized scheduling models, and evaluating system supply and demand balance, these studies remain largely theoretical, failing to fully explore the characteristics of demand-side adjustable resources or constructing targeted deployment mechanisms and optimized scheduling methods applicable to actual scheduling systems.

[0004] As the proportion of renewable energy in the power system continues to increase, forecasting power output from the source side becomes increasingly difficult. Uncertainty on both the source and load sides further exacerbates the challenges of maintaining power balance. Current solutions to potential daily power shortages primarily include utilizing system reserves, purchasing power from other grids, and implementing orderly power consumption. However, increasing system reserve capacity is not an overnight undertaking. When reserve capacity is insufficient to cover significant power shortages, high power purchase costs become an unavoidable issue.

[0005] Numerous studies have demonstrated that the demand side of the power system holds enormous regulatory potential, providing strong support for the system's flexible regulation. However, current research primarily focuses on modeling and aggregating demand-side adjustable resources, as well as constructing optimized dispatch models. Effective dispatch mechanisms tailored to the unique characteristics of various demand-side regulatory resources have yet to be established, nor have optimized dispatch methods that can be directly applied to actual dispatch systems been developed. Therefore, future research should further explore the potential of demand-side adjustable resources and develop more precise and efficient dispatch mechanisms and methods to address the challenges posed by the increasing proportion of renewable energy access. Summary of the Invention

[0006] The objective of the present invention is to provide a dispatching method, system, electronic device and medium for coping with power shortages. By classifying demand-side resources and constructing an optimized dispatching mechanism, different types of regulating resources can be effectively called to participate in the optimized dispatching, weighing the costs of purchasing electricity from other provinces and calling on in-province resources, and considering the uncertainty of the adjustable capacity of distributed resources, making the model more accurate and practical, thereby reducing the dispatching cost during power shortages.

[0007] To achieve the above objective, the present invention provides a dispatching method for coping with power shortages, including the following steps:

[0008] S1. Classify the adjustable resources on the demand side, including industrial loads and residential and general industrial and commercial loads. Among them, industrial loads are Class I loads, and residential and general industrial and commercial loads are Class II loads;

[0009] S2. According to the classification results of the adjustable resources on the demand side, construct a day-ahead and intra-day optimized dispatching mechanism for the demand-side resources to participate in system regulation;

[0010] S3. Model the adjustable resources on the demand side;

[0011] S4. Aggregate the adjustment capabilities of Class II loads;

[0012] S5. Based on the total cost of purchasing electricity from other power grids and the total cost of calling on load-side resources, construct an intra-day optimized dispatching model.

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

[0014] In the day-ahead stage, the dispatching center infers the probability of source-load balance and the standby capacity that may need to be called based on the new energy and load forecasting results, combined with the probabilities of different weather conditions; sends the forecasting results and standby capacity information to the marketing department, and the marketing department splits the shortage into two parts, which are respectively used as the declaration basis for Class I loads and Class II loads; the dispatching center performs optimized calculations based on the declaration situations of the two types of loads and issues the calculation results to the users;

[0015] In the intra-day stage, when it is necessary to call on user-side resources, the users are notified to respond according to the issued calculation results; when it is not necessary for the user-side resources to participate, the users are not notified.

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

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

[0018] Power balance constraint:

[0019]

[0020] Power regulation constraints:

[0021]

[0022] Actual required power constraints:

[0023]

[0024] Hill climbing constraints:

[0025]

[0026] Energy consumption constraints:

[0027]

[0028] in, 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; 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 at node i participating in demand response; They represent the upper and lower limits of the actual power demand of class I load s at 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 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 the steel load; SiC represents the silicon carbide industrial load; Cement represents the cement processing load;

[0029] Class 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 the user at node i participating in the 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] Among them, respectively represent the active and reactive powers actually participating in regulation of the type-II load h on node i at time t; respectively represent the maximum active and reactive regulation powers that the type-II load h on node i can participate in regulation at time t;

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

[0034]

[0035] Among them, respectively represent the active and reactive power demands of the type-II load h on node i when not participating in regulation at time t; respectively represent the actual active and reactive power demands of the type-II load h on node i after participating in regulation at time t;

[0036] For the air-conditioning load model, it is assumed that when the air-conditioning users on node i fully participate in regulation at time t, the corresponding indoor temperature is At this time, the adjustment amount of the user's indoor temperature is Among them, represents the indoor temperature of the air-conditioning users on node i when not participating in regulation at time t, and the change amount of the state of charge corresponding to the indoor temperature adjustment amount and the change amount of the power consumption The relationship is:

[0037]

[0038] In the formula, represents the state of charge of the air-conditioning users on node i when not participating in regulation at time t; represents the state of charge of the air-conditioning users on node i when fully participating in regulation at time t; T i,max and T i,min respectively represent the highest adjustable temperature and the lowest adjustable temperature of the air conditioner on node i; represents the change amount of the power of the air-conditioning load on node i participating in regulation at time t; a1, a2, and a3 all represent model parameters; and respectively represent and The states at time t + 1;

[0039] Considering that the probability of user participation in regulation is affected by the psychological differences of individual users, when introducing the participation probability of the user, the actual power consumption of the air-conditioning load is:

[0040]

[0041] in, represents the power required by the air conditioning load at 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] Where, 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 resident water heater load at node i is fully regulated at time t The corresponding water heater charge state; a t and b t Represents the unit parameters of the water heater modular unit; and Respectively and The state at time t+1.

[0046] Preferably, in step S4, the regulation capability 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 region of projected convex polygon;

[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 ) represents the direction vector in space, which is determined by the normal vector of the starting convex polygon boundary; represents the feasible region inside the point, P pcc and Q pcc respectively represent the active power and reactive power of the projection on the connection line between the aggregate and the power grid;

[0052] The constraint conditions for solving the projection of the feasible region are the adjustable output constraints of each flexibility resource and the connection network power flow constraints;

[0053] To solve the optimization problem, find new vertices by moving the starting polygon along the normal vector direction of each side. When the distance l k between the new vertex and the original side is less than the fixed value l δ , end the solution. The end condition is:

[0054]

[0055] where, represents the new vertex coordinates; the parameters M, N, and C are determined by the original side equation M·P pcc0 + N·Q pcc0 + C = 0; P pcc0 and Q pcc0 respectively represent the newly solved active power and reactive power of the projection on the connection line between the aggregate and the power grid.

[0056] Preferably, in step S5, the intra-day optimal 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 invoking load-side resources; j1, j2, and j3 respectively represent the node sets connected to other power grids, type-I loads, and type-II loads; and respectively represent the electricity purchase unit prices from other power grids, type-I loads, and type-II loads; and respectively represent the electricity purchase quantities from other power grids, type-I loads, and type-II loads; P lack represents the power shortage of the system; and respectively represent the upper limits of the regulation capabilities of other power grids, type-I loads, and type-II loads; and respectively represent the lower limits of the regulation capabilities of type-I loads and type-II loads; Pj and Q j respectively represent the active power and reactive power injected into node j; 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 respectively represent the voltage, maximum voltage and minimum voltage of node i; I ij 、I max and I min respectively represent the carrying current, maximum carrying current and minimum carrying current of line ij; represents the set of upstream nodes 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, including:

[0060] A resource classification module for classifying adjustable resources on the demand side, including type-I loads and type-II loads;

[0061] A dispatching mechanism construction module for constructing a day-ahead and intra-day optimal dispatching mechanism for the participation of demand-side resources in system regulation according to the resource classification results;

[0062] A resource modeling module for modeling adjustable resources on the demand side, including establishing a type-I load model and a type-II load model;

[0063] An adjustment capacity aggregation module for aggregating the adjustment capacity of type-II loads;

[0064] An intra-day optimal dispatching model module for constructing an intra-day optimal dispatching model based on the total cost of purchasing electricity from other power grids and the total cost of invoking load-side resources.

[0065] The present invention also provides a computer device, including: a memory and a processor; the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned dispatching method for coping with power shortage are implemented.

[0066] The present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned dispatching 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 coping with power shortage, and the beneficial technical effects are as follows: The present invention classifies the demand-side adjustable resources according to their characteristics, and constructs a calling mechanism for the demand-side resources to participate in actual dispatching based on the classification results. It can accept different types of adjustable resources to participate in the optimal dispatching, and the calling mechanism can better fit the characteristics of the resources. Compared with the existing economic optimal dispatching method considering the adjustable capacity of the demand side, it weighs the cost of purchasing electricity from other provinces and calling on the 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. Description of the Drawings

[0068] Figure 1 It is a flowchart of a dispatching method for coping with power shortage according to the present invention;

[0069] Figure 2 It is a clearing framework diagram for demand-side resources to participate in the spinning reserve market;

[0070] Figure 3 It is the optimal dispatching cost of each resource; among them, Figure 3 (a) in it is the total dispatching cost; Figure 3 (b) in it is the cost of purchasing electricity from other power grids; Figure 3 (c) in it is the cost of purchasing electricity from Class I loads; Figure 3 (d) in it is the cost of purchasing electricity from Class II loads. Detailed Embodiment

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

[0072] Unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those of ordinary skill in the field to which the present invention belongs.

[0073] Embodiment 1

[0074] As Figure 1 shown, it is a flowchart of a dispatching method for coping with power shortage according to the present invention, including the following steps:

[0075] S1. Classify the demand-side adjustable resources, including industrial loads and residential and general industrial and commercial loads, where industrial loads are Class I loads and residential and general industrial and commercial loads are Class II loads.

[0076] Specifically, the demand-side flexibility resources are widely distributed and rich in types, mainly including industrial loads, residential and general industrial and commercial loads, and agricultural loads, etc. Agricultural loads mainly depend on agricultural production needs and have weak adjustment capabilities. Therefore, demand-side response mainly considers industrial loads and residential and general industrial and commercial loads.

[0077] Industrial loads are relatively concentrated in the power grid, have large individual regulation capabilities, and are easily managed centrally. These loads are designated as Class I loads. These loads can participate in demand response as individuals. Residential and general industrial and commercial loads are characterized by a wide distribution, a large base number, weak individual regulation capabilities, and diverse participation intentions. These loads are designated as Class II loads. Therefore, when Class II loads participate in demand response, their regulation capabilities must first be aggregated, and then they can participate in market scheduling as an aggregate.

[0078] For Class II loads, the user response power that can be obtained is related to factors such as incentive price and environment. Under a certain weather condition σ and other environmental conditions γ (including factors such as policies and individual users), 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 is 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 intraday optimization scheduling mechanism is established for demand-side resources to participate in system regulation.

[0083] When the power system has a power shortage, 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 the supply guarantee problem, 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 phase, the dispatch center uses renewable energy and load forecasts, combined with the probability of different weather conditions, to infer the probability of source-load balance and the amount of reserve capacity that may need to be deployed. The forecast results and reserve capacity information are then sent to the marketing department, which then splits the shortfall into two parts, one for Class I load and the other for Class II load declarations. The dispatch center then performs optimization calculations based on the declarations for both types of load and distributes the results to users.

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

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

[0087] Modeling the adjustable resources on the demand side includes establishing a Type-I load model and a Type-II load model;

[0088] Industrial loads account for a large proportion in the electricity consumption composition, and the electricity demand of the loads is relatively stable. Among them, some industrial loads, such as those in the chemical, railway, mining and other industries, do not have the adjustment ability 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 their high energy consumption characteristics, such loads will be equipped with reactive power compensation devices to avoid the assessment of the power factor by the power system operator. Therefore, when modeling for the power system, the impact of reactive power on the power system is not considered, and the adjustment ability of Type-I loads is described by the active power adjustment ability, adjustment rate, and electricity demand. The constraint conditions of the Type-I load model are as follows:

[0090] Power balance constraint:

[0091]

[0092] Power adjustment constraint:

[0093]

[0094] Actual demand power constraint:

[0095]

[0096] Ramp constraint:

[0097]

[0098] Energy consumption constraint:

[0099]

[0100] Among them, represents the adjustment power of Type-I load s at node i participating in demand response at time t; represents the power demand of Type-I load s at node i when not participating in demand response at time t; represents the actual demand power of Type-I load s at node i after participating in the adjustment at time t; represents the actual demand power of Type-I load s at node i after participating in the adjustment at time t-1; respectively represent the upper and lower limits of the regulation power of the type-I load s on node i participating in demand response; respectively represent the upper and lower limits of the actual demand power of the type-I load s on node i, which depend on the maximum transmission power of the line; respectively represent the upper and lower limits of the regulation rate of the type-I load s on node i; and respectively represent N the electric energy required for the maximum and minimum production plans of the load during the time period from t0 to t; N is the number of calculation moments; k is the moment number; t k is the k-th moment; t k-1 is the (k - 1)-th moment; s ∈ {Steel, SiC, Cement}, where Steel represents the steel load; SiC represents the silicon carbide industrial load; Cement represents the cement processing load.

[0101] At the grid-connected node of the factory, a Static Var Compensator (SVC) is installed accordingly, and its model is as follows:

[0102]

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

[0104] The type-II load model includes a general resource model, an air-conditioning composite model, and a residential water heater load model;

[0105] where, for the general resource model, the probability that users on node i participate in the power system dispatching under policy incentives is then the power of users on node i participating in the power system regulation at time t is:

[0106]

[0107]

[0108] where respectively represent the active and reactive powers of the type-II load h on node i actually participating in regulation at time t; respectively represent the maximum active and reactive regulation powers that the type-II load h on node i can participate in regulation at time t;

[0109] The actual grid-connected load of users on node i is:

[0110]

[0111] Among them, respectively represent the active and reactive power demands of the type-II load h on node i when not participating in regulation at time t; respectively represent the actual active and reactive power demand powers of the type-II load h on node i after participating in regulation at time t;

[0112] For the air-conditioning load model, assume that when the air-conditioning users on node i fully participate in regulation at time t, the corresponding indoor temperature is At this time, the adjustment amount of the user's indoor temperature is Among them, represents the indoor temperature of the air-conditioning users on node i when not participating in regulation at time t, and the change amount of the state of charge corresponding to the indoor temperature adjustment amount and the change amount of the power consumption The relationship is:

[0113]

[0114] In the formula, represents the state of charge of the air-conditioning users on node i when not participating in regulation at time t; represents the state of charge of the air-conditioning users on node i when fully participating in regulation at time t; T i,max and T i,min respectively represent the highest adjustable temperature and the lowest adjustable temperature of the air conditioner on node i; represents the change amount of the power of the air-conditioning load on node i participating in regulation at time t; a1, a2, and a3 all represent model parameters; and respectively represent and The states at time t + 1;

[0115] Considering that the probability of user participation is affected by the psychological differences of individual users, when introducing the participation probability of the user, the actual power consumption of the air-conditioning load is:

[0116]

[0117] Among them, represents the power required by the air-conditioning load on node i when not participating in demand response at time t;

[0118] For the residential water heater load model, assume that when the residential water heater load users on node i fully participate in regulation at time t, the heating upper limit temperature of the water heater is the upper limit temperature of the water heater At this time, the temperature adjustment amount participated by the residential water heater load on node i at time t is Among them, Denote the upper limit temperature of the water heater set by the user with the residential water heater load at node i when not participating in regulation at time t, and the change in the state of charge corresponding to the temperature regulation amount of the residential water heater and the change in power consumption The relationship between them is as follows:

[0119]

[0120] In the formula, Denote the state of charge of the water heater corresponding to the residential water heater load at node i when not participating in regulation at time t ; Denote the state of charge of the water heater corresponding to the residential water heater load at node i when fully participating in regulation at time t ; a t and b t Denote the unit parameters of the water heater modular unit; and Respectively denote and The states at time t + 1.

[0121] S4. Aggregate the regulation capabilities of type II loads, and the specific operations are as follows:

[0122] Type II loads have a wide variety, large quantity, and wide distribution range, and the regulation ability of a single resource is small. When participating in demand response, it is necessary to participate in the power system dispatching uniformly through the method of resource aggregation. The space formed by the power regulation range of the type II load aggregator is where n is the number of flexibility resources. The willingness of users to participate Does not affect the linear nature of the resources. Therefore, within the aggregation range, all resource constraints and network constraints are linear constraints, and in The space is manifested as a high-dimensional convex polyhedron. The projection of this convex polyhedron on the connection line between the aggregator and the power grid is A finite-edge convex polygon in the space, and the point set constituting this convex polygon is denoted as

[0123] Use the vertex search method to solve the feasible region of the projected convex polygon;

[0124] By changing the optimization direction of the objective function, solve the optimization problems under different objective functions, and gradually extrapolate to obtain the boundary of the convex polygon. The objective function for solving the projection of the feasible region is:

[0125]

[0126] Among them, μ = (μ P , μ Q ) represents The direction vector in space is determined by the normal vector of the starting convex polygon boundary; Represents the feasible region Points within, P pcc And Q pcc Respectively represent the active power and reactive power of the projection on the connection line between the aggregate and the power grid;

[0127] The constraint conditions for solving the projection of the feasible region are the adjustable output constraints of each flexibility resource and the power flow constraints of the connection network;

[0128] Solve the optimization problem. Search for new vertices along the normal vector direction of each side of the starting polygon. When the distance l between the new vertex and the original side k Is less than the fixed value l δ End the solution. The end condition is:

[0129]

[0130] Among them, Represents the new vertex coordinates; The parameters M, N, and C are determined by the original side equation M·P pcc0 +N·Q pcc0 +C = 0; P pcc0 And Q pcc0 Respectively represent the newly solved active power and reactive power of the projection on the connection line between the aggregate and the power grid.

[0131] S5. Based on the total cost of purchasing electricity from other power grids and the total cost of invoking load-side resources, construct an intraday optimal scheduling model.

[0132] When there is a power shortage in the system intraday due to inaccurate day-ahead weather prediction, the optimization problem can be solved to optimize the electricity purchase quota from other places and the load-side resource response quota, ensuring the power balance between the source and the load while achieving the best economy. The intraday optimal 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 invoking load-side resources; j1, j2, and j3 respectively represent the node sets connected to other power grids, type-I loads, and type-II loads; And Respectively represent the electricity purchase unit prices from other power grids, type-I loads, and type-II loads; And Respectively represent the electricity purchase quantities from other power grids, type-I loads, and type-II loads; Plack Indicates the system power deficit; and respectively represent the upper limits of the regulation capabilities of other power grids, Class-I loads, and Class-II loads; and respectively represent the lower limits of the regulation capabilities of Class-I loads and Class-II loads; P j and Q j respectively represent the active power and reactive power injected into node j; 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 respectively represent the voltage, maximum voltage, and minimum voltage of node i; I ij 、I max and I min respectively represent the carrying current, maximum carrying current, and minimum carrying current of line ij; represents the set of upstream nodes 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 structure, and set and The bases of are 50 yuan / MW, 30 yuan / MW, and 35 yuan / MW respectively, and random numbers within the upper and lower 50% intervals are generated according to the Gaussian distribution as the values of each moment and . Based on the proposed optimization method, the economic optimization scheduling results as shown in Figure 3 can be obtained. Among them, Figure 3 in (a) is the total cost after optimization, Figure 3 in (b), Figure 3 in (c), and Figure 3 in (d) are the power purchase costs from other power grids, Class-I loads, and Class-II loads respectively. It can be seen from the optimization results that when there is a power deficit during the day, the participation of demand-side flexibility resources can effectively reduce the power purchase volume from other power grids and can effectively reduce the cost of making up for the power deficit.

[0138] Embodiment 2

[0139] The present invention also provides a scheduling system for coping with power deficits, including:

[0140] A resource classification module, which is used to classify the adjustable resources on the demand side, including type-I loads and type-II loads;

[0141] A scheduling mechanism construction module, which is used to construct a day-ahead and intra-day optimal scheduling mechanism for the demand-side resources to participate in system regulation according to the resource classification results;

[0142] A resource modeling module, which is used to model the adjustable resources on the demand side, including establishing a type-I load model and a type-II load model;

[0143] An adjustment capacity aggregation module, which is used to aggregate the adjustment capacities of type-II loads;

[0144] An intra-day optimal scheduling model module, which is used to construct an intra-day optimal scheduling model based on the total cost of purchasing electricity from other power grids and the total cost of invoking load-side resources.

[0145] If the above functions are implemented in the form of software function 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, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

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

[0147] More specific examples (nonexhaustive list) of computer-readable media include the following: electrical connections (electronic devices) having one or more wirings, portable computer diskettes (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber devices, and portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or, if necessary, other suitable processing, and then storing it in a computer memory.

[0148] It should be noted that the content not elaborated in detail in the present invention is all prior art and is well known to those skilled in the art.

[0149] Therefore, the present invention adopts the above-mentioned dispatching method, system, electronic device and medium for coping with power shortage. By classifying demand-side resources and constructing an optimized dispatching mechanism, it effectively invokes different types of regulating resources to participate in the optimized dispatching, weighs the cost of purchasing electricity from other provinces and invoking resources within the province, and considers the uncertainty of the adjustable capacity of distributed resources, making the model more accurate and practical, thereby reducing the dispatching cost during power shortage.

[0150] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that they can still modify or equivalently replace the technical solutions of the present invention, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A dispatching method for coping with power shortage, characterized in that It includes the following steps: S1. Classify the adjustable resources on the demand side, including industrial loads and residential and general industrial and commercial loads. Among them, industrial loads are Class I loads, and residential and general industrial and commercial loads are Class II loads; S2. According to the classification results of the adjustable resources on the demand side, construct a day-ahead and intra-day optimal scheduling mechanism for the participation of demand-side resources in system regulation; S3. Model the adjustable resources on the demand side; S4. Aggregate the regulation capabilities 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, construct an intra-day optimal scheduling model; The day-ahead and intra-day optimal scheduling mechanism for the participation of demand-side resources in system regulation is as follows: In the day-ahead stage, the dispatching center infers the probability of source-load balance and the standby capacity that may need to be called according to the new energy and load forecasting results, combined with the probabilities of different weather conditions; sends the forecasting results and standby capacity information to the marketing department, and the marketing department splits the deficit into two parts, which are used as the declaration bases for Class I loads and Class II loads respectively; the dispatching center conducts optimal calculations according to the declaration situations of the two types of loads and issues the calculation results to users; In the intra-day stage, when it is necessary to call user-side resources, notify the users to respond according to the issued calculation results. When user-side resources do not need to participate, do not notify the users; In step S5, the intra-day optimal scheduling model is as follows: ； ； ； ； ； ； ； ； ； Wherein, represents the total cost at a certain moment; represents the total cost of purchasing electricity from other power grids; represents the total cost of calling load - side resources; 、 and respectively represent the sets of nodes connected to other power grids, type - I loads, and type - II loads; 、 and respectively represent the unit prices of purchasing electricity from other power grids, type - I loads, and type - II loads; 、 and respectively represent the electricity purchase quantities from other power grids, type - I loads, and type - II loads; represents the power shortage of the system; 、 and respectively represent the upper limits of the regulation capabilities of other power grids, type - I loads, and type - II loads; and respectively represent the lower limits of the regulation capabilities of type - I loads and type - II loads; and respectively represent the active power and reactive power injected into node ; and respectively represent the active power and reactive power injected into line ; 、 and respectively represent the unit resistance, unit reactance, and length of line ; 、 and respectively represent the voltage, maximum voltage, and minimum voltage of node ; 、 and respectively represent the carrying current, maximum carrying current, and minimum carrying current of line ; represents the set of upstream nodes of node ; represents the set of downstream nodes of node .

2. The dispatching method for coping with power shortage according to claim 1, wherein In step S3, model the adjustable resources on the demand side, including establishing a Class I load model and a Class II load model; Among them, the constraint conditions of the Class I load model are as follows: Power balance constraint: ； Power regulation constraint: ； Actual demand power constraint: ； Ramp constraint: ； Energy consumption constraint: ； Among them, represents the regulating power of Class-I load on node at time participating in demand response; represents the power demand of Class-I load on node at time when not participating in demand response; represents the actual demand power of Class-I load on node at time after participating in regulation; represents the actual demand power of Class-I load on node at time after participating in regulation; and respectively represent the upper and lower limits of the regulating power of Class-I load on node participating in demand response; and respectively represent the upper and lower limits of the actual demand power of Class-I load on node , depending on the maximum transmission power of the line; and respectively represent the upper and lower limits of the regulation rate of Class-I load on node ; and respectively represent to the electric energy required for the maximum and minimum production plans of the load during the period; is the number of calculation times; is the time number; is the th time; is the th time; , represents the steel load; represents the silicon carbide industrial load; represents the cement processing load; The Class II load model includes a general resource model, an air-conditioning composite model, and a residential water heater load model; Among them, for the general resource model, under the policy incentive, the probability that users on node participate in the power system scheduling is . Then at time , the power of users on node ； ； ； Among them, and respectively represent the active and reactive powers actually participating in regulation of the Class-II load at node at time ; and respectively represent the maximum active and reactive regulation powers that the Class-II load at node can participate in regulation at time ; Node The actual Internet access load of the user is: ； ； Among them, , respectively represent the Class-II loads on node when they do not participate in regulation at time for active and reactive power demands; , respectively represent the actual active and reactive power demand powers of the Class-II loads on node after participating in regulation at time ; For the air-conditioning load model, it is assumed that when the air-conditioning users on node fully participate in the regulation at time, the corresponding indoor temperature is . At this time, the adjustment amount of the indoor temperature of the user is , where represents the indoor temperature when the air-conditioning users on node do not participate in the regulation at time. The change in the state of charge corresponding to the adjustment amount of the indoor temperature and the change in the power consumption are related as follows: ； ； In the formula, represents the node The state of charge of the upper air conditioner user when not participating in regulation at the moment; represents the node The state of charge of the upper air conditioner user when fully participating in regulation at the moment; and respectively represent the highest adjustable temperature and the lowest adjustable temperature of the upper air conditioner at the node ; represents the node The power change amount of the upper air conditioner load participating in regulation at the moment; , and all represent model parameters; , , and respectively represent , , and at the moment; Considering that the probability of user participation in regulation is affected by the psychological differences of individual users, when introducing the participation probability of users the actual power consumption of the air-conditioning load is as follows: ； Among them, represents the node The required power of the upper air conditioning load when it does not participate in demand response at the moment; For the residential water heater load model, assume that the residential water heater load users on the node fully participate in the regulation at the moment. At this time, the heating upper limit temperature of the water heater is the upper limit temperature of the water heater . At this time, the temperature regulation amount participated by the residential water heater load on the node at the moment is , where represents the set upper limit temperature of the water heater when the residential water heater load users on the node do not participate in the regulation at the . The relationship between the change in the state of charge corresponding to the temperature regulation amount of the residential water heater and the change in the power consumption is as follows: ； ； In the formula, represents the node when the residential water heater load on it is not participating in regulation at the corresponding state of charge of the water heater; represents the node when the residential water heater load on it fully participates in regulation at the corresponding state of charge of the water heater; and represent the unit parameters of the water heater modular unit; 、 and respectively represent 、 and at the state at the moment.

3. The dispatching method for coping with power shortage according to claim 2, characterized in that In step S4, aggregate the regulation capabilities of Class II loads. The specific operations are as follows: The space formed by determining the aggregated power regulation range of Class II loads is , where is the number of flexibility resources; Use the vertex search method to solve the feasible region of the projected convex polygon; By changing the optimization direction of the objective function, solve the optimization problems under different objective functions, and gradually extrapolate to obtain the boundary of the convex polygon. The objective function for solving the projection of the feasible region is: ； Among them, represents the direction vector in space, which is determined by the normal vector of the starting convex polygon boundary; represents the point in the feasible region and respectively represent the active power and reactive power of the projection on the connection line between the aggregate and the power grid; The constraint conditions for solving the projection of the feasible region are the adjustable output constraints of each flexibility resource and the connecting network power flow constraints; Solve the optimization problem, find new vertices along the normal vector direction of each side of the starting polygon. When the distance between the new vertex and the original side is less than a fixed value stop the solution. The termination condition is: ； Among them, represents the new vertex coordinates; the parameter , , are determined by the original edge equation ; and respectively represent the newly solved projected active power and reactive power on the connection line between the aggregate and the power grid.

4. A dispatching system for coping with power shortages, characterized in that, For implementing the method according to any one of claims 1-3, it includes: A resource classification module for classifying the adjustable resources on the demand side, including Class I loads and Class II loads; A scheduling mechanism construction module for constructing a day-ahead and intra-day optimal scheduling mechanism for the participation of demand-side resources in system regulation according to the resource classification results; A resource modeling module for modeling the adjustable resources on the demand side, including establishing a Class I load model and a Class II load model; A regulation capacity aggregation module for aggregating the regulation capabilities of Class II loads; An intra-day optimal scheduling model module for constructing an intra-day optimal scheduling model based on the total cost of purchasing electricity from other power grids and the total cost of calling load-side resources.

5. A computer device, comprising: A memory and a processor; The memory stores a computer program, characterized in that when the processor executes the computer program, it implements the steps of the scheduling method for coping with power deficits according to any one of claims 1-3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, The steps of the dispatching method for coping with power shortage described in any one of claims 1-3 are implemented when the computer program is executed by a processor.

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