Power grid intelligent dispatching method and related device for multiple demand sides

Abstract

The present application proposes a method and related devices for intelligent dispatching of power grids for multiple demand sides. By implementing the embodiments of the present application, an intelligent dispatching model for power grids for multiple demand sides is constructed; multiple predicted demand side parameter sets and multiple real-time demand side parameter sets are obtained according to the intelligent dispatching model for power grids; a first target dispatching data set and a second target dispatching data set are obtained by solving the multiple predicted demand side parameter sets, multiple demand side sub-models, and the first objective function and the second objective function; a parameter dispatching scheme for the power grid is determined according to the first target dispatching data set and the second target dispatching data set. The present application considers the collaborative interaction between multiple demand sides at multiple time scales, and according to the dispatching scheme determined by the model based on the first time scale and the second time scale, it is conducive to fully combining the interaction between multiple resources and power grids, improving the reliability, safety and economy of power grid regulation, and improving user experience.

Description

Technical Field

[0001] The present invention relates to the field of power grid technology, and in particular to a power grid intelligent dispatching method and related devices for multiple demand sides. Background Art

[0002] The power system is developing towards a high proportion of new energy vehicles and the coordinated interaction of "source-grid-load-storage". On the one hand, the randomness, volatility, intermittency and instability brought about by the large-scale connection of new energy vehicles to the grid have caused time mismatches between power generation and power consumption in the power system, making peak load and frequency regulation more difficult, and traditional loads are no longer sufficient to meet the needs of power grid development; on the other hand, the power equipment on the load side is becoming more and more diversified, and the access of flexible and adjustable loads such as electric vehicles, energy storage facilities and air conditioners has increased the means of regulation of the system. However, the current scheme only considers a single type of large-scale load resources, and has not studied the simulated interaction between multi-resource aggregates and the power grid, which makes the grid regulation still have the disadvantage of not being comprehensive enough. Summary of the invention

[0003] The embodiments of the present application provide a method and related devices for intelligent dispatching of power grids for multiple demand sides, which can be beneficial to fully combine the interaction of multiple resources and power grids, and improve the reliability, safety and economy of power grid regulation.

[0004] In a first aspect, an embodiment of the present application provides a method for intelligent dispatching of a power grid for a multi-demand side, which is applied to a server of a power system, and the method includes:

[0005] Constructing a power grid intelligent dispatching model for multiple demand sides, the power grid intelligent dispatching model includes multiple demand side sub-models and multiple objective functions corresponding to multiple different time scales, the multiple objective functions include a first objective function corresponding to a first time scale and a second objective function corresponding to a second time scale;

[0006] According to the power grid intelligent dispatching model, multiple predicted demand side parameter sets and multiple real-time demand side parameter sets are obtained, and each demand side sub-model corresponds to a predicted demand side parameter set and a real-time demand side parameter set;

[0007] Solving the first target scheduling data set according to the multiple predicted demand side parameter sets, the multiple demand side sub-models and the first objective function, the first target scheduling data set is used to indicate the scheduling direction of the power grid parameters at the first time scale;

[0008] A second target scheduling data set is obtained by solving the plurality of real-time demand side parameter sets, the plurality of demand side sub-models and the second objective function, wherein the first target scheduling data set is used to indicate a scheduling direction of the power grid parameters at a second time scale;

[0009] A parameter dispatching scheme of the power grid is determined according to the first target dispatching data set and the second target dispatching data set. The parameter dispatching scheme of the power grid is a scheme for adjusting power grid parameters based on the first time scale and the second time scale, and is used for intelligent dispatching of the power grid.

[0010] In a possible embodiment, the first time scale includes a day-ahead scale, the second time scale includes an intraday scale, the multiple demand-side sub-models include at least one of an energy storage sub-model, a new energy vehicle sub-model, an air conditioning sub-model, and a distribution network sub-model; and constructing a power grid intelligent dispatching model for multiple demand sides includes:

[0011] Building an energy storage sub-model according to the charging and discharging parameters of the energy storage device, the energy storage sub-model including a first constraint condition, the first constraint condition being used to constrain the power of the energy storage device;

[0012] A new energy vehicle sub-model is constructed according to the charging and discharging parameters of the new energy vehicle. The new energy vehicle sub-model includes a second constraint condition and a third constraint condition. The second constraint condition is used to constrain the charging and discharging power, and the third constraint condition is used to constrain the battery power of the new energy vehicle.

[0013] Construct an air conditioning sub-model of the air conditioning equipment, where the air conditioning sub-model is used to simulate the operation of the air conditioning equipment and constrain the operating power of the air conditioning equipment;

[0014] A distribution network sub-model is constructed according to electrical parameters of the distribution network, wherein the distribution network sub-model includes a fourth constraint condition, and the fourth constraint condition is used to constrain power and voltage characteristics of the distribution network;

[0015] A first objective function is constructed based on the energy storage sub-model, the new energy vehicle sub-model, the air conditioning sub-model and the distribution network sub-model; and a second objective function is constructed based on the energy storage sub-model, the new energy vehicle sub-model, the air conditioning sub-model and the distribution network sub-model.

[0016] In a possible embodiment, the first time scale represents the prediction scale, the first objective function includes a load fluctuation objective function and a first cost objective function, and the first objective function is constructed based on the energy storage sub-model, the new energy vehicle sub-model, the air conditioning sub-model, and the distribution network sub-model, including:

[0017] Obtaining load parameters, the load parameters including a first load parameter of the energy storage device, a second load parameter of the new energy vehicle, a third load parameter of the air conditioning device, and a fourth load parameter of the distribution network; and constructing a load fluctuation objective function based on the first load parameter, the second load parameter, the third load parameter, and the fourth load parameter;

[0018] A first cost parameter of the distribution network and a second cost parameter of the energy storage device are obtained; and a first cost objective function is constructed based on the first cost parameter and the second cost parameter.

[0019] In a possible embodiment, the second time scale represents the real-time scale, the second objective function includes a user satisfaction objective function and a second cost objective function, and the second objective function is constructed based on the energy storage sub-model, the new energy vehicle sub-model, the air conditioning sub-model, and the distribution network sub-model, including:

[0020] Get the initial temperature value of the air-conditioning equipment, which is the temperature value before scheduling;

[0021] Construct the user satisfaction objective function based on the air conditioning sub-model and the initial temperature value;

[0022] A second cost objective function is constructed based on the real-time first load parameter of the new energy vehicle and the real-time first cost parameter of the distribution network.

[0023] In a possible embodiment, the load fluctuation objective function includes the following formula:

[0024] ,

[0025] ,

[0026] ,

[0027] in, represents the standard deviation of the load curve, is the second load parameter in period t, is the third load parameter, is the fourth load parameter, and is the first load parameter, represents the wind power output in period t, A represents a scheduling duration, and T is a scheduling cycle;

[0028] The first cost objective function includes the following formula:

[0029] ,

[0030] in, is the first cost parameter, is the second cost parameter, λ is the unit wind abandonment penalty coefficient, is the purchased electricity price of the distribution network during period t, Represents the charging and discharging cost of energy storage equipment.

[0031] In a possible embodiment, the user satisfaction objective function includes the following formula:

[0032] ,

[0033] in, is the initial temperature value, is the target temperature value, which is the temperature value to be adjusted;

[0034] The second cost objective function includes the following formula:

[0035] ,

[0036] in, For new energy vehicle load, is the real-time first load parameter, N is the number of vehicles using new energy vehicles, and μ is the unit penalty coefficient for the power interaction deviation between the distribution network and the upper-level power grid.

[0037] In a possible embodiment, determining a parameter scheduling scheme of a power grid according to a first target scheduling data set and a second target scheduling data set includes:

[0038] Performing a calibration based on the first target scheduling data set and the first preset calibration standard to obtain a first calibration result, wherein the first calibration result includes satisfying the requirement;

[0039] Performing a calibration based on the second target scheduling data set and the second preset calibration standard to obtain a second calibration result, wherein the second calibration result includes satisfying the requirements;

[0040] If the first verification result meets the requirements and the second verification result meets the requirements, a parameter scheduling plan of the power grid is determined according to the first target scheduling data set and the second target scheduling data set.

[0041] In a second aspect, an embodiment of the present application provides a power grid intelligent dispatching device for multiple demand sides, which is applied to a server of a power system, and the device includes:

[0042] A construction module, used to construct a power grid intelligent dispatching model for multiple demand sides, the power grid intelligent dispatching model includes multiple demand side sub-models and multiple objective functions corresponding to multiple different time scales, the multiple objective functions include a first objective function corresponding to a first time scale and a second objective function corresponding to a second time scale;

[0043] An acquisition module is used to acquire multiple predicted demand side parameter sets and multiple real-time demand side parameter sets according to the power grid intelligent dispatching model, and each demand side sub-model corresponds to a predicted demand side parameter set and a real-time demand side parameter set;

[0044] A first solving module, used for solving according to a plurality of predicted demand side parameter sets, a plurality of demand side sub-models and a first objective function to obtain a first target scheduling data set, wherein the first target scheduling data set is used for indicating a scheduling direction of a power grid parameter at a first time scale;

[0045] A second solving module is used to solve a second target scheduling data set according to multiple real-time demand side parameter sets, multiple demand side sub-models and a second objective function, and the first target scheduling data set is used to indicate the scheduling direction of the power grid parameters at a second time scale;

[0046] A determination module is used to determine a parameter scheduling scheme of a power grid according to a first target scheduling data set and a second target scheduling data set. The parameter scheduling scheme of the power grid is a scheme for adjusting power grid parameters based on a first time scale and a second time scale, and is used for intelligent scheduling of the power grid.

[0047] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing some or all of the steps described in the first aspect of the embodiment of the present application.

[0048] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. The computer storage medium stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, the processor executes some or all of the steps described in the first aspect.

[0049] In a fifth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in the first aspect of the embodiment of the present application. The computer program product may be a software installation package.

[0050] By implementing the embodiments of the present application, a power grid intelligent dispatching model for multiple demand sides is constructed, the power grid intelligent dispatching model includes multiple demand side sub-models and multiple objective functions corresponding to multiple different time scales, the multiple objective functions include a first objective function corresponding to a first time scale and a second objective function corresponding to a second time scale; multiple predicted demand side parameter sets and multiple real-time demand side parameter sets are obtained according to the power grid intelligent dispatching model, each demand side sub-model corresponds to a predicted demand side parameter set and a real-time demand side parameter set; a first target dispatching data set is obtained by solving the multiple predicted demand side parameter sets, the multiple demand side sub-models and the first objective function, and the first target dispatching data set is used to indicate the dispatching direction of the power grid parameters at the first time scale; a second target dispatching data set is obtained by solving the multiple real-time demand side parameter sets, the multiple demand side sub-models and the second objective function, and the first target dispatching data set is used to indicate the dispatching direction of the power grid parameters at the second time scale; a parameter dispatching scheme of the power grid is determined according to the first target dispatching data set and the second target dispatching data set, and the parameter dispatching scheme of the power grid is a scheme for adjusting the power grid parameters at the first time scale and the second time scale, and is used for intelligent dispatching of the power grid. This application considers the coordinated interaction between multiple demand sides at multiple time scales, constructs a power grid intelligent regulation model, and then determines the scheduling plan based on the first time scale and the second time scale according to the model, which is conducive to fully combining the interaction between multiple resources and power grids, improving the reliability, safety and economy of power grid regulation, and improving user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the background technology, the drawings required for use in the embodiments of the present invention or the background technology will be described below.

[0052] Figure 1 It is a schematic diagram of the architecture of an intelligent power system provided in an embodiment of the present application;

[0053] Figure 2 It is a flow chart of a method for intelligent dispatching of a power grid for multiple demand sides provided in an embodiment of the present application;

[0054] Figure 3 It is a structural schematic diagram of a power grid intelligent dispatching device for multiple demand sides provided in an embodiment of the present application;

[0055] Figure 4 It is a structural schematic diagram of another power grid intelligent dispatching device for multiple demand sides provided in an embodiment of the present application;

[0056] Figure 5 It is a structural schematic diagram of an electronic device proposed in an embodiment of the present application. DETAILED DESCRIPTION

[0057] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0058] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or electronic device that includes a series of steps or units is not limited to the listed steps or units, but in an optional example also includes steps or units that are not listed, or in an optional example also includes other steps or units inherent to these processes, methods, products, or electronic devices.

[0059] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0060] The intelligent dispatching method and related device for the power grid on the multiple demand side provided in the embodiments of the present application can be applied to Figure 1 In the smart power system shown, see Figure 1 , Figure 1 1 is a schematic diagram of the architecture of a smart power system provided in an embodiment of the present application. The smart power system 100 includes a demand-side device 110 and a server 120 .

[0061] In this solution, the demand-side equipment 110 may include but is not limited to load equipment such as energy storage equipment, new energy vehicles, air-conditioning equipment, and distribution networks, and the demand-side equipment can be increased or decreased according to actual needs.

[0062] The server 120 refers to a remote computer used to process a large number of computing tasks and store data. In this solution, a power grid intelligent dispatching model is deployed on the server 120 to determine the parameter dispatching scheme of the power grid for the power grid on the multi-demand side. The server 120 can also be used to collect data during the use of the model to facilitate subsequent optimization. The server 120 also includes a solver for performing model calculations, and the specific solver is not limited here.

[0063] Based on this, the present application provides a method and related devices for intelligent dispatching of power grids for multiple demand sides. The present application is described in detail below in conjunction with the accompanying drawings.

[0064] See also Figure 2 , Figure 2 is a flow chart of a method for intelligent dispatching of a power grid for multiple demand sides provided in an embodiment of the present application, such as Figure 2 As shown, the method comprises the following steps:

[0065] S210, constructing a power grid intelligent dispatching model for multiple demand sides, the power grid intelligent dispatching model includes multiple demand side sub-models and multiple objective functions corresponding to multiple different time scales, the multiple objective functions include a first objective function corresponding to a first time scale and a second objective function corresponding to a second time scale.

[0066] Among them, the construction of the intelligent dispatching model of the power grid is based on the modeling of multiple demand sides or the boundary adjustment of parameters. The intelligent dispatching model of the power grid includes multiple demand side sub-models. The demand side sub-model can be a model built based on the demand side, specifically one or more of the boundary conditions and simulation models. The demand side may include but is not limited to one or more of the load devices such as energy storage equipment, new energy vehicles, air conditioning equipment, and distribution networks, and the demand side equipment can be increased or reduced according to actual needs. The above time scale is used to describe the dispatching plan of different time spans in the power system. The multiple different time scales may include multiple time scales, specifically including the first time scale and the second time scale. The objective function is a constraint condition for optimizing the dispatching of the power grid, which can be in the form of an expression. The objective function can be linear or nonlinear, depending on the complexity and needs of the specific problem. A time scale can correspond to one or more objective functions. Specifically, the first time scale corresponds to the first objective function, and the second time scale corresponds to the second objective function.

[0067] In a possible embodiment, the first time scale includes a day-ahead scale, the second time scale includes an intraday scale, and the multiple demand-side sub-models include at least one of an energy storage sub-model, a new energy vehicle sub-model, an air conditioning sub-model, and a distribution network sub-model; constructing a power grid intelligent dispatching model for multiple demand sides includes: constructing an energy storage sub-model according to the charging and discharging parameters of the energy storage device, the energy storage sub-model includes a first constraint condition, and the first constraint condition is used to constrain the power of the energy storage device; constructing a new energy vehicle sub-model according to the charging and discharging parameters of the new energy vehicle, the new energy vehicle sub-model includes a second constraint condition and a third constraint condition, the second constraint condition is used to constrain the charging and discharging power, and the third constraint condition is used to constrain the battery power of the new energy vehicle; constructing an air conditioning sub-model of the air conditioning equipment, the air conditioning sub-model is used to simulate the operation of the air conditioning equipment and constrain the operating power of the air conditioning equipment; constructing a distribution network sub-model according to the electrical parameters of the distribution network, the distribution network sub-model includes a fourth constraint condition, and the fourth condition is used to constrain the power and voltage characteristics of the distribution network; constructing a first objective function based on the energy storage sub-model, the new energy vehicle sub-model, the air conditioning sub-model, and the distribution network sub-model; and constructing a second objective function based on the energy storage sub-model, the new energy vehicle sub-model, the air conditioning sub-model, and the distribution network sub-model.

[0068] The charging and discharging parameters of the energy storage device may include the charging and discharging power of the energy storage device, or the storage capacity of the energy storage device. The first constraint condition may include the following formula:

[0069] , (Formula 1),

[0070] , (Formula 2),

[0071] , (Formula 3),

[0072] , (Formula 4),

[0073] in, Indicates the storage capacity of the energy storage system, in kWh; and are the charging and discharging power of the energy storage device, in kW; , Respectively represent the charging and discharging efficiency of energy storage; , It is the charge and discharge status mark of the battery. 0 means shutdown and 1 means operation. It also satisfies the mutual exclusion constraint and charge and discharge frequency constraint, that is:

[0074] , (Formula 5).

[0075] Among them, the charging and discharging parameters of new energy vehicles may include charging and discharging power, specifically, the charging and discharging power of n electric vehicles in time period t, and may also include the battery charge of new energy vehicles. Specifically, the new energy vehicle sub-model may include a second constraint and a third constraint, the second constraint is used to constrain the charging and discharging power of new energy vehicles, the third constraint is used to constrain the value range of the battery charge of new energy vehicles, and the second constraint is a full value constraint.

[0076] Specifically, the first constraint condition may include the following formula:

[0077] , (Formula 6),

[0078] , (Formula 7),

[0079] , (Formula 8),

[0080] in, and They represent the charge and discharge power of the nth new energy vehicle in period t, and They represent the maximum allowed charge and discharge power of the nth new energy vehicle in period t, and The status flags for charging and discharging are Boolean variables. Boolean variables are used to indicate the current charging or discharging status of the vehicle. For example, 1 and 0 can be used to indicate charging and discharging. Ensure that charging and discharging are not performed at the same time.

[0081] Specifically, the second constraint condition may include the following formula:

[0082] , (Formula 9),

[0083] , (Formula 10),

[0084] , (Formula 11),

[0085] in, It represents the change value of SOC of the nth new energy vehicle from the beginning of charging and discharging to the time period t; and Respectively represent the SOC value of the nth new energy vehicle when charging starts and ends; and They represent the charging and discharging efficiency of the nth new energy vehicle, represents the battery capacity of the nth new energy vehicle, and They respectively represent the time when the nth new energy vehicle connects to the charging pile and the time when it leaves the charging pile.

[0086] The air conditioner sub-model may be a state expression for simulating the air conditioner when the air conditioner is running, which may include the state of the air conditioner itself and the state of the environment, wherein the state of the air conditioner itself may include the power during operation, and the air conditioner environment state may include the impact of the operation on the ambient temperature. Specifically, the temperature of the air conditioner may be affected by the power of the electric energy supplied to the air conditioner.

[0087] Specifically, the air conditioning sub-model can include the following:

[0088] , (Formula 12),

[0089] , , (Formula 13),

[0090] , (Formula 14),

[0091] , (Formula 15),

[0092] , (Formula 16),

[0093] , (Formula 17),

[0094] , (Formula 18),

[0095] in, , , represent the indoor temperature, building wall temperature and outdoor temperature at time t respectively; and They represent the equivalent thermal resistance between indoor air and outdoor, and between indoor air and wall respectively. and They represent the equivalent thermal resistance between indoor air and outdoor, and between indoor air and wall, respectively. and are the equivalent heat capacities of the room and the wall respectively, Q represents the heat exchange between the air conditioning load and the room, Indicates the on / off status of the air conditioning equipment load at the moment. The on / off calculation method of the air conditioning equipment load at the moment t+1 is:

[0096] , (Formula 19),

[0097] in, Indicates the air conditioning load set temperature; Indicates the temperature dead zone, Indicates that the air conditioning equipment load is turned on. Indicates that the air conditioning equipment load is off. When the indoor temperature is greater than the maximum temperature limit, the load is on; when the indoor temperature is less than the minimum temperature limit, the load is off.

[0098] Among them, when the influence of the wall is ignored, the expression of the air conditioning equipment is as follows:

[0099] When the air conditioning load is turned off,

[0100] , (Formula 20),

[0101] Among them, when the air conditioning load is turned off, the indoor temperature The change is mainly affected by the ambient temperature and the thermal characteristics of the system (thermal resistance and heat capacity ). How does the indoor temperature gradually approach the ambient temperature without additional heat input, that is, without load power?

[0102] Among them, when the air conditioning load is turned on,

[0103] , (Formula 21),

[0104] Among them, when the air conditioning load is turned on, in addition to the ambient temperature and the thermal characteristics of the system, the heat provided by the air conditioning must also be considered How does the indoor temperature change when there is additional heat input, that is, when there is load power. Load power will affect the cooling or heating effect of the air conditioner, which will directly affect the change of indoor temperature.

[0105] in, ,Right now , P is the electric power of air conditioning load, is the energy efficiency ratio. Based on the above formula 20 and formula 21, the opening time of the air conditioning equipment load can be obtained and closing time :

[0106] , (Formula 22),

[0107] , (Formula 23).

[0108] The air conditioning sub-model also includes the following content about the aggregate power during operation:

[0109] , (Formula 24),

[0110] in, Used to indicate the power on / off status of air conditioning equipment. It indicates the power of a single air conditioner load. It can be seen that the aggregate power of the air conditioner load is only related to the air conditioner load that is turned on. When the set temperature of each air conditioner load does not change, the duty cycle of the air conditioner load can be used to obtain the average power of a single air conditioner load. and Formula, the duty cycle can be expressed as:

[0111] , (Formula 25).

[0112] When each air conditioning equipment load operates independently and contains enough individual air conditioning equipment loads, the aggregate power of the air conditioning equipment loads can be approximately expressed as:

[0113] , (Formula 26),

[0114] , (Formula 27).

[0115] Among them, the distribution network sub-model in this scheme is constructed based on the Distflow power flow model. In this scheme, the operation of the distribution network satisfies the distribution network sub-model, and the distribution network sub-model includes the following formulas:

[0116] , (Formula 28),

[0117] , (Formula 29),

[0118] , (Formula 30),

[0119] in, and They represent the active power and reactive power transmitted on the inter-group line jl, and Indicates the resistance and reactance of the branch. and are the active power and reactive power of the load at node j, and Represents the voltage at nodes i and j.

[0120] Among them, the first objective function is constructed based on the constraints of the above-mentioned energy storage sub-model, new energy vehicle sub-model, air conditioning sub-model and distribution network sub-model. The first objective function is used to optimize some parameters at the first time scale on the basis of satisfying the constraints of the energy storage sub-model, new energy vehicle sub-model, air conditioning sub-model and distribution network sub-model to optimize the dispatch of the power grid. The second objective function is similar to the first objective function and is used to optimize some parameters at the second time scale. Specifically, the first objective function and the second objective function are used to further optimize the parameters under the condition of satisfying Formulas 1 to 30, so that the dispatch of the power grid satisfies Formulas 1 to 30 and the first objective function and the second objective function.

[0121] It can be seen that in this embodiment, multiple demand-side models and a first objective function and a second objective function are given. The first objective function and the second objective function are related to multiple demand-side models, which can fully combine the interaction of multiple resources and power grids to improve the reliability and safety of power grid regulation.

[0122] In a possible embodiment, the first time scale represents the prediction scale, the first objective function includes a load fluctuation objective function and a first cost objective function, and the first objective function is constructed based on the energy storage sub-model, the new energy vehicle sub-model, the air conditioning sub-model and the distribution network sub-model, including: obtaining load parameters, the load parameters include a first load parameter of the energy storage device, a second load parameter of the new energy vehicle, a third load parameter of the air conditioning device, and a fourth load parameter of the distribution network; and, constructing a load fluctuation objective function based on the first load parameter, the second load parameter, the third load parameter and the fourth load parameter; obtaining a first cost parameter of the distribution network and a second cost parameter of the energy storage device; and, constructing a first cost objective function based on the first cost parameter and the second cost parameter.

[0123] The first time scale represents the prediction scale, which can be the day-ahead scale, which refers to the dispatch plan made one day or more in advance, based on the plan for calling the electric power used by different demand sides in the future. The first objective function includes two directions, including an objective function for reducing the load fluctuation of the power grid and a first cost objective function for improving the economic efficiency of the operation of the smart power system.

[0124] Among them, the first load parameter of the energy storage device, the second load parameter of the new energy vehicle, the third load parameter of the air conditioning device, and the fourth load parameter of the distribution network can be the load parameters of the energy storage device, the new energy vehicle, the air conditioning device, and the distribution network in the previous process before the current time period, or can be the current time period load parameter obtained. Specifically, the above load parameters can be the power used by each demand-side device, and examples can be the load power of new energy vehicles, the aggregated power of air conditioning equipment, the load power of the distribution network, and the load power of energy storage devices.

[0125] Specifically, when constructing the first cost objective function, it is also necessary to obtain the first cost parameter and the second cost parameter related to the price, which may include the purchased electricity price or sold electricity price of the distribution network, the unit wind abandonment penalty coefficient, the charging and discharging cost of the energy storage equipment, etc.

[0126] Specifically, the load fluctuation objective function includes the following formula:

[0127] , (Formula 31),

[0128] , (Formula 32),

[0129] , (Formula 33),

[0130] in, represents the standard deviation of the load curve, is the second load parameter in period t, is the third load parameter, is the fourth load parameter, and is the first load parameter, represents the wind power output in period t, A represents a scheduling duration, For period t , , , , and The sum of, T is a scheduling cycle, For multiple The average value of

[0131] The first cost objective function includes the following formula:

[0132] , (Formula 34),

[0133] in, is the first cost parameter, is the second cost parameter, λ is the unit wind abandonment penalty coefficient, is the purchased electricity price of the distribution network during period t, Represents the charging and discharging cost of energy storage equipment.

[0134] in, , (Formula 35).

[0135] Among them, please refer to the specific meanings in Formula 1 to Formula 30.

[0136] It can be seen that in this embodiment, the load fluctuation objective function in the first objective function is constructed based on the load power sum of multiple demand sides, and the first cost objective function in the first objective function is constructed based on the cost parameters of multiple demand sides, which is conducive to fully combining the interaction of various resources and power grids and improving the reliability, safety and economy of power grid regulation.

[0137] In a possible embodiment, the second time scale represents the real-time scale, the second objective function includes a user satisfaction objective function and a second cost objective function, and the second objective function is constructed based on the energy storage sub-model, the new energy vehicle sub-model, the air conditioning sub-model and the distribution network sub-model, including: obtaining the initial temperature value of the air-conditioning equipment, the initial temperature value is the temperature value before scheduling; constructing the user satisfaction objective function based on the air conditioning sub-model and the initial temperature value; constructing the second cost objective function based on the real-time first load parameter of the new energy vehicle and the real-time first cost parameter of the distribution network.

[0138] Among them, the second time scale represents the real-time scale, which can be an intraday scale. The intraday scale is based on the above-mentioned day-ahead scale optimization and is adjusted and corrected according to the real-time changes in actual operation. The second objective function includes two directions, including a user satisfaction objective function for improving the comfort of air-conditioning users while keeping the output of the energy storage facility obtained by day-ahead optimization unchanged, and a second cost objective function for reducing the charging cost of new energy vehicles, the cost of wind curtailment, and the penalty cost caused by the power interaction deviation between the distribution network and the upper-level power grid.

[0139] The initial temperature value may be the indoor temperature of the air conditioner in the previous process before the current period, or the current indoor temperature obtained, corresponding to the above The air conditioning sub-model is used to constrain the relationship between temperature and load power. Based on the constraints of the air conditioning sub-model and the current temperature, i.e., the initial temperature, a user satisfaction objective function is constructed to optimize the power regulation of the air conditioning equipment.

[0140] Specifically, the user satisfaction objective function is used to limit the indoor temperature to not exceed the temperature limit that makes the user dissatisfied. The user dissatisfaction can be expressed by the following formula:

[0141] , (Formula 36),

[0142] in, is a positive proportionality coefficient, is the temperature after scheduling, is the initial temperature value, that is, the temperature the user expects to maintain.

[0143] For example, users feel uncomfortable when the temperature changes by more than 3 degrees Celsius, so the user target satisfaction objective function is used to constrain the regulation of the load power of the air-conditioning equipment to minimize the discomfort to the user, that is, to make the indoor temperature change less than 3 degrees Celsius.

[0144] Specifically, the user satisfaction objective function includes the following formula:

[0145] , (Formula 37),

[0146] in, is the initial temperature value, is the target temperature value, which is the temperature value to be adjusted;

[0147] The second cost objective function includes the following formula:

[0148] , (Formula 38),

[0149] in, For new energy vehicle load, is the real-time first load parameter, N is the number of vehicles using new energy vehicles, and μ is the unit penalty coefficient for the power interaction deviation between the distribution network and the upper-level power grid.

[0150] It should be noted that the above-mentioned wind power output is the power demand and supply generated by the wind power generation system connected to the power grid, which is equivalent to another source of electricity connected to the power grid.

[0151] It can be seen that in this embodiment, the user satisfaction objective function in the second objective function at the intraday scale is constructed based on the air-conditioning sub-model and the initial temperature of the air-conditioning equipment, and the second cost objective function in the second objective function at the intraday scale is constructed based on multiple demand-side cost parameters, which is conducive to fully combining the interaction of multiple resources and power grids, improving the reliability, safety and economy of power grid regulation, and improving user satisfaction.

[0152] S220, obtaining multiple predicted demand side parameter sets and multiple real-time demand side parameter sets according to the power grid intelligent dispatching model, each demand side sub-model corresponds to a predicted demand side parameter set and a real-time demand side parameter set.

[0153] Among them, when the power grid intelligent dispatching model is determined for use, at the first time scale, part or all of the corresponding data required in the power grid intelligent dispatching model on each demand side is obtained, that is, the demand side parameter set is predicted.

[0154] When the intelligent dispatching of the power grid is performed in real time, that is, at the second time scale, part or all of the corresponding data required in the intelligent dispatching model of the power grid on each demand side is obtained, that is, the real-time demand side parameter set.

[0155] The data required to be obtained for the predicted demand side parameter set and the real-time demand side parameter set may be the same or different. When the first objective function at the first time scale is different from the second objective function at the second time scale, the data required to be obtained is changed according to different parts of the first objective function and the second objective function.

[0156] Specifically, when the first objective function includes the load fluctuation objective function and the first cost objective function, the obtained forecast demand side parameter set needs to include the parameters required by the load fluctuation objective function and the first cost objective function in addition to the parameters required by the energy storage sub-model, the new energy vehicle sub-model, the air conditioning sub-model, and the distribution network sub-model. When the second objective function includes the user satisfaction objective function and the second cost objective function, the obtained forecast demand side parameter set needs to include the parameters required by the user satisfaction objective function and the second cost objective function in addition to the parameters required by the energy storage sub-model, the new energy vehicle sub-model, the air conditioning sub-model, and the distribution network sub-model.

[0157] S230, solving a first target scheduling data set according to a plurality of predicted demand side parameter sets, a plurality of demand side sub-models and a first objective function, wherein the first target scheduling data set is used to indicate a scheduling direction of power grid parameters at a first time scale.

[0158] Among them, at the first time scale, that is, at the day-ahead scale, a first target scheduling data set is obtained by solving according to multiple predicted demand-side parameter sets, multiple demand-side sub-models and the first objective function. Specifically, the solution can be obtained by a solver. The specific solver can be CPLEX, GLPK, lpsolve, CMIP, MATLAB solver, etc., which is not limited here.

[0159] After the solution, a first target scheduling data set instructing the power grid to perform day-ahead scheduling may be obtained, wherein the first target scheduling data set may be a power regulation target obtained after the solution.

[0160] S240, solving a second target scheduling data set according to multiple real-time demand side parameter sets, multiple demand side sub-models and a second objective function, wherein the first target scheduling data set is used to indicate a scheduling direction of power grid parameters at a second time scale.

[0161] Among them, at the second time scale, that is, the intraday scale, the first target scheduling data set is obtained by solving according to multiple predicted demand-side parameter sets, multiple demand-side sub-models and the first objective function. Specifically, it can be solved by a solver. The specific solver can be CPLEX, GLPK, lpsolve, CMIP, MATLAB solver, etc., which is not limited here.

[0162] After the solution, a second target scheduling data set instructing the power grid to perform intraday scheduling can be obtained, wherein the first target scheduling data set can be a power regulation target obtained after the solution.

[0163] Among them, intraday scheduling and day-ahead scheduling can be different hierarchical scheduling, that is, day-ahead scheduling can be performed first and then intraday scheduling can be performed on the basis of day-ahead scheduling, or the power grid intelligent scheduling model can be solved at the day-ahead scale first to obtain the first target scheduling data set, and then the power grid intelligent scheduling model can be solved at the day-ahead scale to obtain the second target scheduling data set, and the first target scheduling data set and the second scheduling data set can be integrated to perform unified scheduling.

[0164] S250, determining a parameter scheduling scheme of the power grid according to the first target scheduling data set and the second target scheduling data set, wherein the parameter scheduling scheme of the power grid is a scheme for adjusting power grid parameters based on the first time scale and the second time scale, and is used for intelligent scheduling of the power grid.

[0165] After obtaining the first target scheduling data set and the second target scheduling data set, a complete scheduling plan for the power grid is obtained by synthesizing the first target scheduling data set and the second target scheduling data set.

[0166] Specifically, after obtaining the first target scheduling data set and the second target scheduling data, in order to ensure the safety of the power grid, the scheduling data also needs to be verified.

[0167] In a possible embodiment, a parameter scheduling scheme of a power grid is determined based on a first target scheduling data set and a second target scheduling data set, including: calibrating based on the first target scheduling data set and a first preset calibration standard to obtain a first calibration result, the first calibration result includes satisfying the requirements; calibrating based on the second target scheduling data set and the second preset calibration standard to obtain a second calibration result, the second calibration result includes satisfying the requirements; if the first calibration result satisfies the requirements and the second calibration result satisfies the requirements, then determining the parameter scheduling scheme of the power grid based on the first target scheduling data set and the second target scheduling data set.

[0168] The first preset calibration standard and the second preset calibration standard can be a preset standard range for calibration, a verification method for a range threshold of each output result, or other methods, which are not limited here. The first calibration result and the second calibration result both include not meeting the requirements.

[0169] Among them, when the first verification result and the second verification result outputted by the outputted first target scheduling data set and the second target scheduling data set both meet the requirements, the parameter scheduling scheme of the power grid is determined according to the first target scheduling data set and the second target scheduling data set. When the outputted first verification result and the second verification result do not meet the requirements, the solver solution process is re-executed, that is, step S230 and step S240, or step S230 or step S240. When the first target scheduling data set and the second target scheduling data set are obtained at the same time, when the outputted first verification result and the second verification result do not meet the requirements, the solver solution process is re-executed, that is, step S230 and step S240. When the first target scheduling data set and the second target scheduling data set are obtained at different times, that is, before the day and within the day, when the outputted first verification result or the second verification result does not meet the requirements, the solver solution process is re-executed, that is, step S230 or step S240.

[0170] It can be seen that after solving the problem, the output first target scheduling data set and the second target scheduling data set are verified to determine whether the output result is safe and executable, which is beneficial to improving the reliability and safety of power grid regulation.

[0171] It can be seen that by implementing the embodiments of the present application, a power grid intelligent dispatching model for multiple demand sides is constructed, and the power grid intelligent dispatching model includes multiple demand side sub-models and multiple objective functions corresponding to multiple different time scales, and the multiple objective functions include a first objective function corresponding to a first time scale and a second objective function corresponding to a second time scale; according to the power grid intelligent dispatching model, multiple predicted demand side parameter sets and multiple real-time demand side parameter sets are obtained, and each demand side sub-model corresponds to a predicted demand side parameter set and a real-time demand side parameter set; according to the multiple predicted demand side parameter sets, the multiple demand side sub-models and the first objective function, a first target dispatching data set is obtained by solving, and the first target dispatching data set is used to indicate the dispatching direction of the power grid parameters at the first time scale; according to the multiple real-time demand side parameter sets, the multiple demand side sub-models and the second objective function, a second target dispatching data set is obtained by solving, and the first target dispatching data set is used to indicate the dispatching direction of the power grid parameters at the second time scale; according to the first target dispatching data set and the second target dispatching data set, a parameter dispatching scheme of the power grid is determined, and the parameter dispatching scheme of the power grid is a scheme for adjusting the power grid parameters at the first time scale and the second time scale, which is used for intelligent dispatching of the power grid. This application considers the coordinated interaction between multiple demand sides at multiple time scales, constructs a power grid intelligent regulation model, and then determines the scheduling plan based on the first time scale and the second time scale according to the model, which is conducive to fully combining the interaction between multiple resources and power grids, improving the reliability, safety and economy of power grid regulation, and improving user experience.

[0172] See also Figure 3, Figure 3 It is a structural diagram of a power grid intelligent dispatching device for multiple demand sides proposed in an embodiment of the present application. The power grid intelligent dispatching device 300 for multiple demand sides includes: a construction module 310, an acquisition module 320, a first solution module 330, a second solution module 340, and a determination module 350, wherein:

[0173] A construction module 310 is used to construct a power grid intelligent dispatching model for multiple demand sides, the power grid intelligent dispatching model includes multiple demand side sub-models and multiple objective functions corresponding to multiple different time scales, the multiple objective functions include a first objective function corresponding to a first time scale and a second objective function corresponding to a second time scale;

[0174] An acquisition module 320 is used to acquire multiple predicted demand side parameter sets and multiple real-time demand side parameter sets according to the power grid intelligent dispatching model, and each demand side sub-model corresponds to a predicted demand side parameter set and a real-time demand side parameter set;

[0175] A first solving module 330, configured to solve a first target scheduling data set according to a plurality of predicted demand side parameter sets, a plurality of demand side sub-models and a first objective function, wherein the first target scheduling data set is used to indicate a scheduling direction of a power grid parameter at a first time scale;

[0176] A second solving module 340 is used to solve according to multiple real-time demand side parameter sets, multiple demand side sub-models and a second objective function to obtain a second target scheduling data set, wherein the first target scheduling data set is used to indicate a scheduling direction of the power grid parameters at a second time scale;

[0177] The determination module 350 is used to determine the parameter scheduling scheme of the power grid according to the first target scheduling data set and the second target scheduling data set. The parameter scheduling scheme of the power grid is a scheme for adjusting the power grid parameters based on the first time scale and the second time scale, and is used for intelligent scheduling of the power grid.

[0178] In a possible embodiment, the first time scale includes a day-ahead scale, the second time scale includes an intraday scale, and the multiple demand-side sub-models include at least one of an energy storage sub-model, a new energy vehicle sub-model, an air conditioning sub-model, and a distribution network sub-model; the construction module 310 is specifically used to construct a power grid intelligent dispatching model for multiple demand sides:

[0179] Building an energy storage sub-model according to the charging and discharging parameters of the energy storage device, the energy storage sub-model including a first constraint condition, the first constraint condition being used to constrain the power of the energy storage device;

[0180] A new energy vehicle sub-model is constructed according to the charging and discharging parameters of the new energy vehicle. The new energy vehicle sub-model includes a second constraint condition and a third constraint condition. The second constraint condition is used to constrain the charging and discharging power, and the third constraint condition is used to constrain the battery power of the new energy vehicle.

[0181] Construct an air conditioning sub-model of the air conditioning equipment, where the air conditioning sub-model is used to simulate the operation of the air conditioning equipment and constrain the operating power of the air conditioning equipment;

[0182] A distribution network sub-model is constructed according to electrical parameters of the distribution network, wherein the distribution network sub-model includes a fourth constraint condition, and the fourth constraint condition is used to constrain power and voltage characteristics of the distribution network;

[0183] A first objective function is constructed based on the energy storage sub-model, the new energy vehicle sub-model, the air conditioning sub-model and the distribution network sub-model; and a second objective function is constructed based on the energy storage sub-model, the new energy vehicle sub-model, the air conditioning sub-model and the distribution network sub-model.

[0184] In a possible embodiment, the first time scale represents the prediction scale, the first objective function includes a load fluctuation objective function and a first cost objective function, and the construction module 310 is specifically used to construct the first objective function and the second objective function based on the energy storage sub-model, the new energy vehicle sub-model, the air conditioning sub-model, and the distribution network sub-model:

[0185] Obtaining load parameters, the load parameters including a first load parameter of the energy storage device, a second load parameter of the new energy vehicle, a third load parameter of the air conditioning device, and a fourth load parameter of the distribution network; and constructing a load fluctuation objective function based on the first load parameter, the second load parameter, the third load parameter, and the fourth load parameter;

[0186] A first cost parameter of the distribution network and a second cost parameter of the energy storage device are obtained; and a first cost objective function is constructed based on the first cost parameter and the second cost parameter.

[0187] In a possible embodiment, the second time scale represents a real-time scale, the second objective function includes a user satisfaction objective function and a second cost objective function, and the construction module 310 is specifically used to construct the second objective function based on the energy storage sub-model, the new energy vehicle sub-model, the air conditioning sub-model, and the distribution network sub-model:

[0188] Get the initial temperature value of the air-conditioning equipment, which is the temperature value before scheduling;

[0189] Construct the user satisfaction objective function based on the air conditioning sub-model and the initial temperature value;

[0190] A second cost objective function is constructed based on the real-time first load parameter of the new energy vehicle and the real-time first cost parameter of the distribution network.

[0191] In a possible embodiment, the load fluctuation objective function includes the following formula:

[0192] ,

[0193] ,

[0194] ,

[0195] in, represents the standard deviation of the load curve, is the second load parameter in period t, is the third load parameter, is the fourth load parameter, and is the first load parameter, represents the wind power output in period t, A represents a scheduling duration, and T is a scheduling cycle;

[0196] The first cost objective function includes the following formula:

[0197] ,

[0198] in, is the first cost parameter, is the second cost parameter, λ is the unit wind abandonment penalty coefficient, is the purchased electricity price of the distribution network during period t, Represents the charging and discharging cost of energy storage equipment.

[0199] In a possible embodiment, the user satisfaction objective function includes the following formula:

[0200] ,

[0201] in, is the initial temperature value, is the target temperature value, which is the temperature value to be adjusted;

[0202] The second cost objective function includes the following formula:

[0203] ,

[0204] in, For new energy vehicle load, is the real-time first load parameter, N is the number of vehicles using new energy vehicles, and μ is the unit penalty coefficient for the power interaction deviation between the distribution network and the upper-level power grid.

[0205] In a possible embodiment, the determination module 350 is specifically used to determine the parameter scheduling scheme of the power grid according to the first target scheduling data set and the second target scheduling data set:

[0206] Performing a calibration based on the first target scheduling data set and the first preset calibration standard to obtain a first calibration result, wherein the first calibration result includes satisfying the requirement;

[0207] Performing a calibration based on the second target scheduling data set and the second preset calibration standard to obtain a second calibration result, wherein the second calibration result includes satisfying the requirements;

[0208] If the first verification result meets the requirements and the second verification result meets the requirements, a parameter scheduling plan of the power grid is determined according to the first target scheduling data set and the second target scheduling data set.

[0209] It can be seen that in this embodiment, a power grid intelligent dispatching model for multiple demand sides is constructed, and the power grid intelligent dispatching model includes multiple demand side sub-models and multiple objective functions corresponding to multiple different time scales, and the multiple objective functions include a first objective function corresponding to a first time scale and a second objective function corresponding to a second time scale; multiple predicted demand side parameter sets and multiple real-time demand side parameter sets are obtained according to the power grid intelligent dispatching model, and each demand side sub-model corresponds to a predicted demand side parameter set and a real-time demand side parameter set; a first target dispatching data set is obtained by solving the multiple predicted demand side parameter sets, the multiple demand side sub-models and the first objective function, and the first target dispatching data set is used to indicate the dispatching direction of the power grid parameters at the first time scale; a second target dispatching data set is obtained by solving the multiple real-time demand side parameter sets, the multiple demand side sub-models and the second objective function, and the first target dispatching data set is used to indicate the dispatching direction of the power grid parameters at the second time scale; a parameter dispatching scheme of the power grid is determined according to the first target dispatching data set and the second target dispatching data set, and the parameter dispatching scheme of the power grid is a scheme for adjusting the power grid parameters at the first time scale and the second time scale, which is used for intelligent dispatching of the power grid. This application considers the coordinated interaction between multiple demand sides at multiple time scales, constructs a power grid intelligent regulation model, and then determines the scheduling plan based on the first time scale and the second time scale according to the model, which is conducive to fully combining the interaction between multiple resources and power grids and improving the reliability, safety and economy of power grid regulation.

[0210] It is worth pointing out that the specific functional implementation of the intelligent dispatching device for the power grid on the multi-demand side can be found in the above Figure 2The description of the intelligent dispatching method for the power grid on the multiple demand side shown in the figure, for example, the construction module 310 is used to implement the relevant content of executing S210. The various units or modules in the intelligent dispatching device 300 for the power grid on the multiple demand side can be respectively or completely merged into one or several other units or modules to constitute, or one (some) of the units or modules can be further divided into multiple functionally smaller units or modules to constitute, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present invention. The above-mentioned units or modules are divided based on logical functions. In actual applications, the functions of one unit (or module) are implemented by multiple units (or modules), or the functions of multiple units (or modules) are implemented by one unit (or module).

[0211] It can be seen that the power grid intelligent dispatching device for multiple demand sides described in the embodiment of the present application constructs a power grid intelligent dispatching model for multiple demand sides, and the power grid intelligent dispatching model includes multiple demand side sub-models and multiple objective functions corresponding to multiple different time scales, and the multiple objective functions include a first objective function corresponding to a first time scale and a second objective function corresponding to a second time scale; according to the power grid intelligent dispatching model, multiple predicted demand side parameter sets and multiple real-time demand side parameter sets are obtained, and each demand side sub-model corresponds to a predicted demand side parameter set and a real-time demand side parameter set; according to the multiple predicted demand side parameter sets, the multiple demand side sub-models and the first objective function, a first target dispatching data set is obtained by solving, and the first target dispatching data set is used to indicate the dispatching direction of the power grid parameters at the first time scale; according to the multiple real-time demand side parameter sets, the multiple demand side sub-models and the second objective function, a second target dispatching data set is obtained by solving, and the first target dispatching data set is used to indicate the dispatching direction of the power grid parameters at the second time scale; according to the first target dispatching data set and the second target dispatching data set, a parameter dispatching scheme of the power grid is determined, and the parameter dispatching scheme of the power grid is a scheme for adjusting the power grid parameters of the first time scale and the second time scale, which is used for intelligent dispatching of the power grid. This application considers the coordinated interaction between multiple demand sides at multiple time scales, constructs a power grid intelligent regulation model, and then determines the scheduling plan based on the first time scale and the second time scale according to the model, which is conducive to fully combining the interaction between multiple resources and power grids, improving the reliability, safety and economy of power grid regulation, and improving user experience.

[0212] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the above-mentioned units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0213] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0214] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0215] In the case of integrated units, see Figure 4 , Figure 4 is a structural diagram of another intelligent dispatching device for a power grid on a multiple demand side provided in an embodiment of the present application, such as Figure 4 As shown, the smart dispatching device 300 for the power grid on the multiple demand side includes: a processing module 301 and a communication module 302. The processing module 301 is used to control and manage the actions of the smart dispatching device 300 for the power grid on the multiple demand side, for example, executing the steps of the construction module 310, the acquisition module 320, the first solution module 330, the second solution module 340, the determination module 350, and / or other processes for executing the technology described herein. The communication module 302 is used for data exchange between the smart dispatching device for the power grid on the multiple demand side and other load sides. Figure 4 As shown, the intelligent dispatching device 300 for the power grid on the multiple demand side may further include a storage module 303, and the storage module 303 is used to store program codes and data of the intelligent dispatching device 300 for the power grid on the multiple demand side.

[0216] Among them, the processing module 301 can be a processor or a controller, for example, a central processing unit (CPU), a general processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can implement or execute various exemplary logic boxes, modules and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements a computing function, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like. The communication module 302 can be a transceiver, an RF circuit or a communication interface, etc. The storage module 303 can be a memory.

[0217] Among them, all relevant contents of each scenario involved in the above method embodiment can be referred to the functional description of the corresponding functional module, which will not be repeated here. The above-mentioned intelligent dispatching device 300 for the multi-demand side can execute the above-mentioned Figure 2 The intelligent dispatching method of the power grid for multiple demand sides is shown.

[0218] See also Figure 5 , Figure 5 It is a structural diagram of an electronic device proposed in an embodiment of the present application. As shown in the figure, the electronic device 500 includes a processor 510, a memory 520, a communication interface 530 and one or more programs 521. The one or more programs 521 are stored in the memory 520 and are configured to be executed by the processor 510.

[0219] The processor 510, the memory 520, and the communication interface 530 are connected to each other and perform communication work with each other;

[0220] The memory 520 may be a volatile memory such as a dynamic random access memory DRAM, or a non-volatile memory such as a mechanical hard disk. The memory 520 is used to store a set of executable program codes, and the processor 510 is used to call one or more programs 521 stored in the memory 520 to execute the above-mentioned Figure 2 Any part or all of the steps of the smart dispatching method for a power grid on a multiple demand side recorded in the embodiments.

[0221] Among them, the electronic device 500 may include smart phones (such as Android phones, iOS phones, Windows Phone phones, etc.), tablet computers, PDAs, driving recorders, vehicle-mounted electronic devices, servers, laptops, mobile Internet electronic devices (MID, Mobile Internet Devices) or wearable electronic devices (such as smart watches, Bluetooth headsets), etc. The above are only examples and not exhaustive, including but not limited to the above electronic devices.

[0222] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for intelligent scheduling of power grids for multiple demand sides, and the computer program enables a computer to execute part or all of the steps of any method recorded in the above method embodiments, and the above computer includes an electronic device.

[0223] The embodiment of the present application also provides a computer program product, the computer program product includes a non-transitory computer-readable storage medium storing a computer program, the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment. The computer program product may be a software installation package, and the computer includes an electronic device.

[0224] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0225] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0226] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory, including a number of instructions to enable a computer electronic device (which can be a personal computer, electronic device or network electronic device, etc.) to perform all or part of the steps of the above-mentioned methods in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0227] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable memory, which may include a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0228] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for intelligent dispatching of power grids for multiple demand sides, characterized in that: Applied to a server of an electric power system, the method comprises: Constructing a power grid intelligent dispatching model for multiple demand sides, the power grid intelligent dispatching model includes multiple demand side sub-models and multiple objective functions corresponding to multiple different time scales, the multiple objective functions include a first objective function corresponding to a first time scale and a second objective function corresponding to a second time scale, the multiple demand side sub-models include at least one of an energy storage sub-model, a new energy vehicle sub-model, an air conditioning sub-model and a distribution network sub-model, the first time scale includes a day-ahead scale, the second time scale includes an intraday scale, the first objective function and the second objective function are associated with the multiple demand side sub-models, the first objective function is used to constrain the load degree of the multiple demand side sub-models and the cost at the first time scale, the second objective function is used to constrain the user satisfaction of the multiple demand side sub-models and the cost at the second time scale; the constructing of the power grid intelligent dispatching model for multiple demand sides includes: constructing the energy storage sub-model according to the charging and discharging parameters of the energy storage device, the energy storage sub-model includes the first time scale, the second time scale includes the second time scale, the first objective function and the second objective function are associated with the multiple demand side sub-models. a constraint condition, wherein the first constraint condition is used to constrain the power of the energy storage device; and / or, constructing the new energy vehicle sub-model according to the charging and discharging parameters of the new energy vehicle, the new energy vehicle sub-model includes a second constraint condition and a third constraint condition, the second constraint condition is used to constrain the charging and discharging power, and the third constraint condition is used to constrain the battery power used by the new energy vehicle; and / or, constructing the air conditioning sub-model of the air conditioning equipment, the air conditioning sub-model is used to simulate the operation of the air conditioning equipment and constrain the operating power of the air conditioning equipment; and / or, constructing the distribution network sub-model according to the electrical parameters of the distribution network, the distribution network sub-model includes a fourth constraint condition, and the fourth constraint condition is used to constrain the power and voltage characteristics of the distribution network; constructing the first objective function based on the energy storage sub-model, the new energy vehicle sub-model, the air conditioning sub-model and the distribution network sub-model; and constructing the second objective function based on the energy storage sub-model, the new energy vehicle sub-model, the air conditioning sub-model and the distribution network sub-model; Acquire multiple predicted demand side parameter sets and multiple real-time demand side parameter sets according to the power grid intelligent dispatching model, each of the demand side sub-models corresponding to one predicted demand side parameter set and one real-time demand side parameter set; Solving the first target scheduling data set according to the multiple predicted demand side parameter sets, the multiple demand side sub-models and the first objective function, wherein the first target scheduling data set is used to indicate a scheduling direction of a power grid parameter at a first time scale; Solving the plurality of real-time demand-side parameter sets, the plurality of demand-side sub-models and the second objective function to obtain a second target scheduling data set, wherein the first target scheduling data set is used to indicate a scheduling direction of the power grid parameters at a second time scale; A parameter scheduling scheme for a power grid is determined according to the first target scheduling data set and the second target scheduling data set. The parameter scheduling scheme for the power grid is a scheme for adjusting power grid parameters on a comprehensive first time scale and a second time scale, and is used for intelligent scheduling of the power grid.

2. The method according to claim 1, characterized in that The first time scale represents a prediction scale, the first objective function includes a load fluctuation objective function and a first cost objective function, and the first objective function is constructed based on the energy storage sub-model, the new energy vehicle sub-model, the air conditioning sub-model and the distribution network sub-model, including: Obtaining load parameters, the load parameters including a first load parameter of the energy storage device, a second load parameter of the new energy vehicle, a third load parameter of the air conditioning device, and a fourth load parameter of the distribution network; and constructing the load fluctuation objective function based on the first load parameter, the second load parameter, the third load parameter, and the fourth load parameter; Acquire a first cost parameter of the distribution network and a second cost parameter of the energy storage device; and construct the first cost objective function based on the first cost parameter and the second cost parameter.

3. The method according to claim 2, characterized in that The second time scale represents a real-time scale, the second objective function includes a user satisfaction objective function and a second cost objective function, and the second objective function is constructed based on the energy storage sub-model, the new energy vehicle sub-model, the air conditioning sub-model and the distribution network sub-model, including: Obtaining an initial temperature value of the air-conditioning equipment, where the initial temperature value is the temperature value before scheduling; Constructing the user satisfaction objective function based on the air conditioning sub-model and the initial temperature value; The second cost objective function is constructed based on the real-time first load parameter of the new energy vehicle and the real-time first cost parameter of the distribution network.

4. The method according to claim 2, characterized in that: The load fluctuation objective function includes the following formula: ， ， ， in, represents the standard deviation of the load curve, is the second load parameter during period t, is the third load parameter, is the fourth load parameter, and is the first load parameter, represents the wind power output during period t, For the period t , , , , and The sum of, T is a scheduling cycle, For multiple The average value of The first cost objective function includes the following formula: , in, is the first cost parameter, is the second cost parameter, λ is the unit wind abandonment penalty coefficient, is the purchased electricity price of the distribution network in period t, Represents the charging and discharging cost of energy storage equipment.

5. The method according to claim 3, characterized in that: The user satisfaction objective function includes the following formula: , in, is the initial temperature value, is a target temperature value, and the target temperature value is a temperature value to be adjusted; The second cost objective function includes the following formula: , in, For new energy vehicle load, is the real-time first load parameter, N is the number of vehicles using the new energy vehicle, and μ is the unit penalty coefficient of the power interaction deviation between the distribution network and the upper-level power grid.

6. The method according to claim 1, wherein determining a parameter scheduling scheme of a power grid according to the first target scheduling data set and the second target scheduling data set comprises: Performing a calibration based on the first target scheduling data set and a first preset calibration standard to obtain a first calibration result, wherein the first calibration result includes satisfying a requirement; Performing a calibration based on the second target scheduling data set and the second preset calibration standard to obtain a second calibration result, wherein the second calibration result includes satisfying the requirement; If the first verification result satisfies the requirement and the second verification result satisfies the requirement, a parameter scheduling scheme of the power grid is determined according to the first target scheduling data set and the second target scheduling data set.

7. A smart dispatching device for a power grid targeting multiple demand sides, characterized in that: A server applied to an electric power system, the device comprising: A construction module is used to construct a power grid intelligent dispatching model for multiple demand sides, the power grid intelligent dispatching model includes multiple demand side sub-models and multiple objective functions corresponding to multiple different time scales, the multiple objective functions include a first objective function corresponding to a first time scale and a second objective function corresponding to a second time scale, the multiple demand side sub-models include at least one of an energy storage sub-model, a new energy vehicle sub-model, an air conditioning sub-model and a distribution network sub-model, the first time scale includes a day-ahead scale, the second time scale includes an intraday scale, the first objective function and the second objective function are associated with the multiple demand side sub-models, the first objective function is used to constrain the load degree of the multiple demand side sub-models and the cost under the first time scale, the second objective function is used to constrain the user satisfaction of the multiple demand side sub-models and the cost under the second time scale; the construction of the power grid intelligent dispatching model for multiple demand sides includes: constructing the energy storage sub-model according to the charging and discharging parameters of the energy storage device, the energy storage sub-model The model includes a first constraint condition, which is used to constrain the power of the energy storage device; and / or, the new energy vehicle submodel is constructed according to the charging and discharging parameters of the new energy vehicle, and the new energy vehicle submodel includes a second constraint condition and a third constraint condition, the second constraint condition is used to constrain the charging and discharging power, and the third constraint condition is used to constrain the battery power used by the new energy vehicle; and / or, the air conditioning submodel of the air conditioning equipment is constructed, and the air conditioning submodel is used to simulate the operation of the air conditioning equipment and constrain the operating power of the air conditioning equipment; and / or, the distribution network submodel is constructed according to the electrical parameters of the distribution network, and the distribution network submodel includes a fourth constraint condition, and the fourth constraint condition is used to constrain the power and voltage characteristics of the distribution network; the first objective function is constructed based on the energy storage submodel, the new energy vehicle submodel, the air conditioning submodel and the distribution network submodel; and the second objective function is constructed based on the energy storage submodel, the new energy vehicle submodel, the air conditioning submodel and the distribution network submodel; An acquisition module, used for acquiring a plurality of predicted demand side parameter sets and a plurality of real-time demand side parameter sets according to the power grid intelligent dispatching model, wherein each of the demand side sub-models corresponds to one predicted demand side parameter set and one real-time demand side parameter set; A first solving module, configured to solve according to the multiple predicted demand side parameter sets, the multiple demand side sub-models and the first objective function to obtain a first target scheduling data set, wherein the first target scheduling data set is used to indicate a scheduling direction of a power grid parameter at a first time scale; A second solving module, configured to solve according to the multiple real-time demand side parameter sets, the multiple demand side sub-models and the second objective function to obtain a second target scheduling data set, wherein the first target scheduling data set is used to indicate a scheduling direction of the power grid parameters at a second time scale; A determination module is used to determine a parameter scheduling scheme for a power grid based on the first target scheduling data set and the second target scheduling data set. The parameter scheduling scheme for the power grid is a scheme for adjusting power grid parameters based on a comprehensive first time scale and a second time scale, and is used for intelligent scheduling of the power grid.

8. A computer-readable storage medium, characterized in that: A smart grid dispatching program for multiple demand sides is stored, including execution instructions. When a processor of an electronic device executes the execution instructions, the processor executes the method as described in any one of claims 1 to 6.

9. An electronic device, characterized in that: The method comprises a processor and a memory storing execution instructions, wherein the memory stores one or more programs; when the processor executes the execution instructions stored in the memory, the processor executes the method according to any one of claims 1 to 6.

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