Energy management method and system based on hybrid control

Abstract

The invention discloses an energy management method and system based on hybrid control, and relates to the technical field of power resource management. Variable data of each participant in a power system is collected in real time; mapping the variable data into a state vector field, and obtaining a discrete event according to the state vector field; generating a control instruction through a preset energy management strategy according to the discrete event; and sending the control instruction to the power system, and controlling the operation state of the participant. According to the embodiment of the invention, the variable data of the participants in the power system are analyzed to obtain the discrete event and the control instruction, so that the distributed energy resources and the traditional energy equipment can be intelligently and cooperatively controlled, the stability of power supply is ensured, the energy utilization efficiency is improved, and the carbon emission is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power resource management, and in particular to an energy management method and system based on hybrid control. Background Art

[0002] Affected by factors such as policy incentive regulation and industrial transformation pressure, the demand for active load management of small and medium-sized users has increased sharply. Under the condition of relatively weak power grid construction and rich distributed new energy resources at the same time, improving the power and electricity self-balancing ability of the load side through the load side energy management method is an effective way to improve the power supply reliability of users in pastoral areas.

[0003] The centralized control method of the existing power grid energy management system is difficult to control a large number of users and a huge amount of distributed resources on the load side, and the existing distribution network substation integration terminal does not have the energy management function, and can only implement customized simple logic operations and control functions, and cannot meet the autonomous and flexible adjustment needs of distributed resources on the load side under the background of the construction of a new power system. Summary of the Invention

[0004] The technical problem to be solved by the present invention is how to flexibly adjust and apply the distributed resources of substation users under the new power system, and provide an energy management method and system based on hybrid control, which can perform intelligent collaborative control on distributed energy resources and traditional energy equipment, ensure the stability of power supply, improve energy utilization efficiency at the same time, and reduce carbon emissions.

[0005] To solve the above technical problems, an embodiment of the present invention provides an energy management method based on hybrid control, including:

[0006] Real-time collecting variable data of each participant in the power system;

[0007] Mapping the variable data into a state vector field, and obtaining discrete events according to the state vector field;

[0008] Generating a control instruction according to the discrete event through a preset energy management strategy;

[0009] Sending the control instruction to the power system to control the operating state.

[0010] As an improvement of the above solution, the real-time collecting of the variable data of the distributed energy in the power system includes:

[0011] Real-time collecting the first variable data of each participant in the power system;

[0012] Judging whether the data change interval of the first variable data satisfies a preset threshold interval;

[0013] If it is satisfied, use the first variable data as the variable data;

[0014] Otherwise, if it is not satisfied, generate second variable data according to the change trend of the first variable data, and use the second variable data as the variable data; the second variable data satisfies the preset threshold interval.

[0015] As an improvement to the above solution, determining whether the data change interval of the first variable data satisfies the preset threshold interval includes:

[0016] According to the overall optimization goal of the power system area, obtain the satisfactory state set space and the unsatisfactory state set space;

[0017] According to the data change interval of the first variable data, obtain the state change vector of the participant;

[0018] If the state change vector points from the satisfactory state set space to the unsatisfactory state set space, it is considered that the data change interval of the first variable data satisfies the preset threshold interval, otherwise it is considered that the data change interval of the first variable data does not satisfy the preset threshold interval.

[0019] As an improvement to the above solution, mapping the variable data into the state vector field and obtaining discrete events according to the state vector field includes:

[0020] Obtain the control vector of the participant; the control vector includes an internal control vector and an external control vector;

[0021] According to the control vector, establish a state vector field;

[0022] Map the variable data into the state vector field to obtain a state vector;

[0023] According to the continuity of the state vector, obtain the discontinuous points;

[0024] According to the variable data corresponding to the discontinuous points, screen and obtain the current discrete event from the preset discrete events of the power system.

[0025] As an improvement to the above solution, establishing a state vector field according to the control vector includes:

[0026] According to the control vector, obtain the control space;

[0027] Express the state vector field f as the product of the control space and the state space, and obtain f: X * U → X; where X is the state space, and the state space includes state vectors; U is the control space, and the control space includes control vectors.

[0028] As an improvement of the above solution, mapping the variable data into the state vector field to obtain a state vector includes:

[0029] Obtaining the state of the participant according to the variable data;

[0030] Mapping the state of the participant into the state vector field to obtain a state vector x(t), x(t) = f(x(t), u d (t), u(t)) or x(t) = I(f 1 (x(t), u d (t), u(t)), f 2 (x(t), u d (t), u(t)));

[0031] Wherein, f(), f 1 () and f 2 () are state mapping functions of the state vector and the state vector field, and the state mapping function is a smooth function; u d (t) is an internal control vector; u(t) is an external control vector; I() represents a differential inclusion form.

[0032] As an improvement of the above solution, obtaining a discontinuous point according to the continuity of the state vector includes:

[0033] Screening out the jump state vectors with discontinuous jumps from the state vector according to the continuity of the state vector;

[0034] Obtaining the discontinuous point x(t) through x(t + 0) = g(x(t - 0), q, e); wherein, x(t + 0) and x(t - 0) are jump state vectors at corresponding moments; g() is a conditional mapping function, g: X * Q * Σ → X, X is a state space, Q is a state set, Σ is a preset discrete event set of the power system; q is the vector corresponding to the jump state vector, q ∈ Q; e is a discrete event, e ∈ Σ.

[0035] As an improvement of the above solution, generating a control instruction according to the discrete event through a preset energy management strategy includes:

[0036] Merging the discrete events according to the correlation relationship between the discrete events to obtain a first discrete event;

[0037] Queuing the first discrete event according to the priority of the first discrete event to obtain a second discrete event;

[0038] Selecting a control decision from a preset energy management strategy according to the type of the second discrete event;

[0039] Generate a control instruction according to the control decision and the participant.

[0040] As an improvement of the above solution, the selecting a control decision from a preset energy management strategy according to the type of the second discrete event includes:

[0041] Establish a mapping relationship α between the state space Q and the control space U according to a preset energy management strategy, α: Q → U;

[0042] Obtain the state of the participant according to the type of the second discrete event;

[0043] Select a control decision from the control space according to the state of the participant and the mapping relationship α.

[0044] An embodiment of the present invention further provides an energy management system based on hybrid control, including:

[0045] A variable data acquisition module, configured to acquire the variable data of each participant in the power system in real time;

[0046] A discrete event analysis module, configured to map the variable data into a state vector field, and obtain discrete events according to the state vector field;

[0047] A control instruction generation module, configured to generate a control instruction according to the discrete event through a preset energy management strategy;

[0048] An operation and maintenance management module, configured to send the control instruction to the power system to control the operating state of the participant.

[0049] Compared with the prior art, an energy management method and system based on hybrid control disclosed by the present invention collect the variable data of each participant in the power system in real time; map the variable data into a state vector field, and obtain discrete events according to the state vector field; generate a control instruction according to the discrete event through a preset energy management strategy; send the control instruction to the power system to control the operating state of the participant. By adopting the embodiment of the present invention, discrete events and control instructions are obtained by analyzing the variable data of each participant in the power system, and distributed energy resources and traditional energy equipment can be intelligently and cooperatively controlled to ensure the stability of power supply, improve the energy utilization efficiency, and reduce carbon emissions at the same time. Description of the Drawings

[0050] Figure 1 is a schematic flow chart of the steps of an energy management method based on hybrid control provided by an embodiment of the present invention;

[0051] Figure 2It is a schematic structural diagram of the state set space of hybrid control provided by an embodiment of the present invention;

[0052] Figure 3 It is a schematic structural diagram of an energy management system based on hybrid control provided by an embodiment of the present invention. Detailed implementation manners

[0053] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0054] In the description of the specification and claims, it should be understood that the terms first, second, etc. in the specification and claims are only used for the purpose of distinguishing the description of the same technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features, nor necessarily describing the order or time sequence. The terms can be interchanged under appropriate circumstances. Thus, the features defined with "first", "second" may explicitly or implicitly include at least one of the features.

[0055] At present, the extensive development status of the user-side energy control unit faces the problems of high control terminal cost and the inability to tap the adjustment potential of each electrical device. There is a lack of theoretical and equipment support for the excavation of a large number of dormant resources and distributed optimal control. Therefore, it is urgent to carry out the research and development of low-cost, lightweight, easy-to-expand and comprehensive analysis and operation function of the substation area intelligent fusion terminal control technology to support the intelligent perception, aggregation and optimal control of the user's active load.

[0056] Based on the above considerations, an embodiment of the present invention provides an energy management method based on hybrid control. Please refer to Figure 1 , in this embodiment, the energy management method based on hybrid control is specifically executed through steps S1 to S4:

[0057] S1. Real-time collect the variable data of each participant in the power system.

[0058] S2. Map the variable data into the state vector field, and obtain discrete events according to the state vector field.

[0059] It should be noted that in hybrid control, the effect of discrete events on continuous variables is usually manifested as follows: discrete inputs are introduced into partial differential equations or difference equations, so that the equations contain parameters or input quantities reflecting logical states, and the evolution of logical states is driven by discrete events. The effect of continuous variables on the formation of discrete events is as follows: according to the values of continuous variables at a certain moment or the values of "event functions" with these variables as independent variables, they are compared with predefined conditions to determine whether an "event" is formed.

[0060] S3. Generate a control instruction through a preset energy management strategy according to the discrete event.

[0061] It should be noted that the control instruction includes the participant to be controlled, the device to be controlled, and control parameters. Exemplarily, the control instruction includes controlling battery pack 1 in the battery management system of the energy storage system, so that the state of charge of battery pack 1 is within a preset state of charge threshold range. In this control instruction, the participant to be controlled is the energy storage system, and the energy storage system includes distributed energy and traditional energy; the device to be controlled is the battery management system of the energy storage system, and the control parameters are determined according to the preset state of charge threshold range.

[0062] S4. Send the control instruction to the power system to control the operating state of the participant.

[0063] In some preferred embodiments, the same discrete event corresponds to control instructions for several participants and several devices under each participant. A control instruction scheduling system is established to coordinate the conflict states between control instructions.

[0064] It should be noted that the embodiments of the present invention take the intelligent fusion terminal as the core and develop an energy management method based on hybrid control. This method can be applied to edge devices, and can dynamically adjust and optimize the strategy when devices are added or removed or their states change, and is plug-and-play. It can realize edge computing and distributed control functions even in the offline state of communication interruption with the upper layer. Without the user's perception, it always maintains the optimal operation of the transformer substation area, fully meeting the coordinated and unified control requirements of the source-network-load-storage in the new power system. Compared with the existing user-side energy management system, it is a lightweight hybrid control technology with stronger integrated functions, a volume reduction of more than 8 times, and a hardware cost reduction of more than 5 times, which meets the needs of large-scale and refined control of distributed resources on the user side.

[0065] In the above solution, by analyzing the variable data of each participant in the power system, discrete events and control instructions are obtained, which can intelligently coordinate the control of distributed energy resources and traditional energy devices, ensure the stability of power supply, improve energy utilization efficiency, and reduce carbon emissions.

[0066] As a preferred embodiment, in step S1, variable data of each participant in the power system is collected in real time, including:

[0067] Collecting the first variable data of each participant in the power system in real time;

[0068] Judging whether the data change interval of the first variable data meets a preset threshold interval;

[0069] If it meets, the first variable data is used as the variable data;

[0070] Otherwise, if it does not meet, the second variable data is generated according to the change trend of the first variable data, and the second variable data is used as the variable data; the second variable data meets the preset threshold interval.

[0071] In some preferred embodiments, the first variable data is a variable related to the state transition of the electronic devices of each participant in the power system, such as a charge-discharge voltage variable, a temperature variable, and a state of charge (SOC) variable.

[0072] In the embodiments of the present invention, by generating the second variable data, active operation and maintenance management can be realized, that is, variable data is generated when an event has not yet occurred in the actual system to change the current operation mode. When the data change interval of the first variable data meets the preset threshold interval, the operation and maintenance management of each participant in the power system can be directly performed according to the current actual first variable data to achieve passive management.

[0073] Further preferably, judging whether the data change interval of the first variable data meets a preset threshold interval includes:

[0074] According to the overall optimization goal of the power system's substation area, a satisfactory state set space and an unsatisfactory state set space are obtained;

[0075] According to the data change interval of the first variable data, a state change vector of the participant is obtained;

[0076] If the state change vector points from the satisfactory state set space to the unsatisfactory state set space, it is considered that the data change interval of the first variable data meets the preset threshold interval, otherwise it is considered that the data change interval of the first variable data does not meet the preset threshold interval.

[0077] To better understand the specific implementation method of the hybrid control in the embodiments of the present invention, please refer to Figure 2 .X S (T) is the satisfactory state set space at time T, that is, the multi-objective optimization state set space, obtained from the overall optimization goal of the power system's substation area, X S(T) is the set space of dissatisfaction states at time T, X A (T) is the set space of all states at time T. When the operating state point of the participant at time T enters X S (T) set space, that is, it triggers the generation of time E, and generates corresponding control instructions according to event E, so that the state point of the participant returns to the set of satisfactory states X S (T), realizing the multi-objective optimal operation of the power system. In the embodiment of the present invention, all dissatisfied and unsatisfactory states are classified and defined as events, and through control, the overall power system returns to a state without events. Specifically, in the embodiment of the present invention, the conversion of the set space is determined by the state change vector.

[0078] As a preferred implementation manner, in step S2, the variable data is mapped into the state vector field, and discrete events are obtained according to the state vector field, and are executed through steps S21-S25:

[0079] S21. Obtain the control vector of the participant; the control vector includes an internal control vector and an external control vector;

[0080] S22. Establish a state vector field according to the control vector;

[0081] S23. Map the variable data into the state vector field to obtain a state vector;

[0082] S24. Obtain discontinuity points according to the continuity of the state vector;

[0083] S25. Screen and obtain the current discrete event from the preset discrete events of the power system according to the variable data corresponding to the discontinuity points.

[0084] It should be noted that discrete events may be physical events determined by the results of continuous state changes, or may be jointly determined by external input quantities and the results of discrete mechanisms. The former is uncontrollable, while the latter is controllable.

[0085] In some preferred embodiments, the set of total discrete events is represented as ∑ = ∑ u ∪∑ c , where ∑ u is the discrete event determined by the result of continuous state change, and ∑ c is the discrete event jointly determined by external input quantities and the results of discrete mechanisms.

[0086] The mapping from the continuous state space X to the discrete event set ∑ u is represented by γ, denoted as γ: X → ∑ u. γ acts as an event generator consistent with the region transition of continuous states corresponding to different physical modes. Through γ, the continuous state space can be mapped into k different discrete events e 1 , e 2 , …, e k ∈∑ u , and each discrete time has a corresponding continuous state region in X.

[0087] Furthermore, preferably, in step S22, according to the control vector, a state vector field is established, including:

[0088] According to the control vector, a control space is obtained;

[0089] The state vector field f is expressed as the product of the control space and the state space, obtaining f: X * U → X; where X is the state space, and the state space includes state vectors; U is the control space, and the control space includes control vectors.

[0090] It should be noted that for most participants in the power system, the dimensions of their control vectors and state vectors are different, and they are not necessarily in a one-to-one correspondence. The control vector and the state vector act together to cause the state vector to change spontaneously. In the embodiments of the present invention, the state vector field is used to describe the trend and situation of this change.

[0091] Preferably, in step S23, the variable data is mapped into the state vector field to obtain a state vector, including:

[0092] According to the variable data, the state of the participant is obtained;

[0093] The state of the participant is mapped into the state vector field to obtain a state vector x(t), x(t) = f(x(t), u d (t), u(t)) or x(t) = I(f 1 (x(t), u d (t), u(t)), f 2 (x(t), u d (t), u(t)));

[0094] where f(), f 1 () and f 2 () are state mapping functions of the state vector and the state vector field, and the state mapping function is a smooth function; u d (t) is an internal control vector; u(t) is an external control vector; I() represents a differential inclusion form.

[0095] In the above solution, f 1 () and f2 () is a piecewise smooth function. When the state vector is expressed as x(t) = I(f 1 (x(t), u d (t), u(t)), f 2 (x(t), u d (t), u(t))), the transition time of the discrete event should be the moment when its discontinuous point appears, and f 1 () < f 2 (), that is, the time element of the state mapping function f 1 () is less than the time element of the state mapping function f 2 ().

[0096] As a preferred implementation manner, step S24, obtaining the discontinuous point according to the continuity of the state vector, includes:

[0097] Screening out the jump state vectors with discontinuous jumps from the state vector according to the continuity of the state vector;

[0098] Obtaining the discontinuous point x(t) through x(t + 0) = g(x(t - 0), q, e); where x(t + 0) and x(t - 0) are the jump state vectors at the corresponding moments; g() is a conditional mapping function, g: X * Q * Σ → X, X is the state space, Q is the state set, Σ is the preset discrete event set of the power system; q is the vector corresponding to the jump state vector, q ∈ Q; e is the discrete event, e ∈ Σ.

[0099] As a preferred implementation manner, step S4, generating a control instruction according to the discrete event through a preset energy management strategy, includes:

[0100] Performing a merging process on the discrete events according to the correlation relationship between the discrete events to obtain a first discrete event;

[0101] Queueing the first discrete event according to the priority of the first discrete event to obtain a second discrete event;

[0102] Selecting a control decision from a preset energy management strategy according to the type of the second discrete event;

[0103] Generating a control instruction according to the control decision and the participating party.

[0104] It should be noted that several types of energy are integrated during the operation of the power system, and these types of energy can operate in a coordinated manner. Therefore, in some cases, dealing with a certain type of discrete event can eliminate another type of discrete event. In the embodiments of the present invention, through their association relationships, discrete events are pre-merged to reduce the repeated querying and processing of events, effectively improving the efficiency of operation and maintenance management.

[0105] It should also be noted that different events have different priorities. For example, the processing priority of safety-related events is higher than that of economic-related events. In the embodiments of the present invention, by queuing the first discrete event, the processing order of events can be coordinated in advance.

[0106] Further, preferably, selecting a control decision from a preset energy management strategy according to the type of the second discrete event includes:

[0107] According to a preset energy management strategy, a mapping relationship α between a state space Q and a control space U is established, α: Q→U;

[0108] According to the type of the second discrete event, the state of the participant is obtained;

[0109] According to the state of the participant and the mapping relationship α, a control decision is selected from the control space.

[0110] It should be noted that the preset energy management strategy is generally obtained by an energy storage EMS (Energy Management System) based on the operation data of devices, that is, the variable data described in the embodiments of the present invention. The preset energy management strategy includes three categories. The first category is a conventional EMS scheduling, from which a control decision can be obtained; the second category is an optimization control, which gives at least one control decision, and the optimal strategy is selected by a dispatcher or automatically and then issued; the third category is an economic control instruction, where the energy storage EMS formulates a control decision and gives clear suggestions, which are confirmed by the central dispatching person in charge and then issued for execution.

[0111] By using an energy management method based on hybrid control provided by the embodiments of the present invention, discrete events and control instructions are obtained by analyzing the variable data of each participant in the power system, enabling intelligent collaborative control of distributed energy resources and traditional energy devices, ensuring the stability of power supply, while improving energy utilization efficiency and reducing carbon emissions.

[0112] The embodiments of the present invention provide an energy management system based on hybrid control. Please refer to Figure 3 , the energy management system based on hybrid control includes a variable data acquisition module 11, a discrete event analysis module 12, a control instruction generation module 13, and an operation and maintenance management module 14, where:

[0113] The variable data acquisition module 11 is used to acquire the variable data of each participant in the power system in real time;

[0114] The discrete event analysis module 12 is used to map the variable data into a state vector field and obtain discrete events according to the state vector field;

[0115] The control instruction generation module 13 is used to generate control instructions according to the discrete events through a preset energy management strategy;

[0116] The operation and maintenance management module 14 is used to send the control instructions into the power system to control the operation states of the participants.

[0117] As a preferred implementation manner, the variable data acquisition module 11 includes:

[0118] The real-time data acquisition unit is used to acquire the first variable data of each participant in the power system in real time;

[0119] The condition judgment unit is used to judge whether the data change interval of the first variable data meets a preset threshold interval;

[0120] The condition satisfaction execution unit is used to, if satisfied, use the first variable data as the variable data;

[0121] The condition dissatisfaction execution unit is used to, otherwise, if not satisfied, generate second variable data according to the change trend of the first variable data and use the second variable data as the variable data; the second variable data meets the preset threshold interval.

[0122] Further preferably, the condition judgment unit is specifically used for:

[0123] Obtaining a satisfactory state set space and an unsatisfactory state set space according to the overall optimization goal of the power system area;

[0124] Obtaining the state change vector of the participant according to the data change interval of the first variable data;

[0125] If the state change vector points from the satisfactory state set space to the unsatisfactory state set space, it is considered that the data change interval of the first variable data meets the preset threshold interval, otherwise it is considered that the data change interval of the first variable data does not meet the preset threshold interval.

[0126] As a preferred implementation manner, the discrete event analysis module 12 includes:

[0127] A control vector acquisition unit for acquiring the control vector of the participant; the control vector includes an internal control vector and an external control vector;

[0128] A state vector field establishment unit for establishing a state vector field according to the control vector;

[0129] A state vector acquisition unit for mapping the variable data into the state vector field to obtain a state vector;

[0130] A discontinuity point judgment unit for obtaining a discontinuity point according to the continuity of the state vector;

[0131] A current discrete event screening unit for screening a current discrete event from preset discrete events of the power system according to the variable data corresponding to the discontinuity point.

[0132] Further, preferably, the state vector field establishment unit specifically is used for:

[0133] Obtaining a control space according to the control vector;

[0134] Representing the state vector field f as the product of the control space and the state space to obtain f: X*U→X; where X is the state space, the state space includes a state vector; U is the control space, and the control space includes a control vector.

[0135] Preferably, the state vector acquisition unit specifically is used for:

[0136] Obtaining the state of the participant according to the variable data;

[0137] Mapping the state of the participant into the state vector field to obtain a state vector x(t), x(t) = f(x(t), u d (t), u(t)) or x(t) = I(f 1 (x(t), u d (t), u(t)), f 2 (x(t), u d (t), u(t)));

[0138] Wherein, f(), f 1 () and f 2 () are state mapping functions of the state vector and the state vector field, and the state mapping function is a smooth function; u d (t) is an internal control vector; u(t) is an external control vector; I() represents a differential inclusion form.

[0139] Preferably, the discontinuity point judgment unit specifically is used for:

[0140] According to the continuity of the state vector, a jump state vector with discontinuous jumps is screened from the state vector;

[0141] The discontinuous point x(t) is obtained by x(t+0) = g(x(t-0), q, e); where x(t+0) and x(t-0) are the jump state vectors at the corresponding moments; g() is a conditional mapping function, g: X*Q*Σ→X, X is the state space, Q is the state set, Σ is the preset discrete event set of the power system; q is the vector corresponding to the jump state vector, q∈Q; e is a discrete event, e∈Σ.

[0142] As a preferred implementation manner, the control instruction generation module 13 includes:

[0143] An event merging unit, configured to merge the discrete events according to the correlation relationship between the discrete events to obtain a first discrete event;

[0144] An event queue unit, configured to queue the first discrete event according to the priority of the first discrete event to obtain a second discrete event;

[0145] A control decision selection unit, configured to select a control decision from a preset energy management strategy according to the type of the second discrete event;

[0146] A control instruction generation unit, configured to generate a control instruction according to the control decision and the participating party.

[0147] Further, preferably, the control decision selection unit is specifically configured to:

[0148] Establish a mapping relationship α between the state space Q and the control space U according to a preset energy management strategy, α: Q→U;

[0149] Obtain the state of the participant according to the type of the second discrete event;

[0150] Select a control decision from the control space according to the state of the participant and the mapping relationship α.

[0151] By using an energy management system based on hybrid control provided by an embodiment of the present invention, discrete events and control instructions are obtained by analyzing the variable data of each participant in the power system, and distributed energy resources and traditional energy equipment can be intelligently and coordinately controlled to ensure the stability of power supply, improve energy utilization efficiency, and reduce carbon emissions at the same time.

[0152] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0153] The above is the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the technical field, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements are also regarded as the protection scope of the present invention.

Claims

1. An energy management method based on hybrid control, characterized in that: include Collect variable data of each participant in the power system in real time; Mapping the variable data into a state vector field, and obtaining discrete events according to the state vector field; According to the discrete events, a control instruction is generated through a preset energy management strategy; The control instruction is sent to the power system to control the operating status of the participants.

2. The energy management method based on hybrid control according to claim 1, characterized in that: The real-time acquisition of variable data of each participant in the power system includes: Collecting first variable data of each participant in the power system in real time; Determining whether the data change interval of the first variable data meets a preset threshold interval; If satisfied, the first variable data is used as variable data; Otherwise, if it is not satisfied, second variable data is generated according to the change trend of the first variable data, and the second change data is used as variable data; the second variable data satisfies the preset threshold interval.

3. The energy management method based on hybrid control according to claim 2, characterized in that: The determining whether the data change interval of the first variable data satisfies a preset threshold interval includes: According to the overall optimization target of the power system, a satisfactory state set space and an unsatisfactory state set space are obtained; Obtaining a state change vector of the participant according to a data change interval of the first variable data; If the state change vector points from the satisfactory state set space to the unsatisfactory state set space, it is considered that the data change interval of the first variable data meets the preset threshold interval, otherwise it is considered that the data change interval of the first variable data does not meet the preset threshold interval.

4. The energy management method based on hybrid control according to claim 1, characterized in that: Mapping the variable data into a state vector field and obtaining discrete events according to the state vector field includes: Acquire a control vector of the participant; the control vector includes an internal control vector and an external control vector; Establishing a state vector field according to the control vector; Mapping the variable data into the state vector field to obtain a state vector; According to the continuity of the state vector, a discontinuity point is obtained; According to the variable data corresponding to the discontinuity point, the current discrete event is obtained by screening from preset discrete events of the power system.

5. The energy management method based on hybrid control according to claim 4, characterized in that: The step of establishing a state vector field according to the control vector comprises: According to the control vector, a control space is obtained; The state vector field f is expressed as the product of the control space and the state space, and f:X*U→X is obtained; wherein X is the state space, and the state space includes the state vector; U is the control space, and the control space includes the control vector.

6. The energy management method based on hybrid control according to claim 4, characterized in that: Mapping the variable data to the state vector field to obtain a state vector includes: According to the variable data, the state of the participant is obtained; Map the state of the participant to the state vector field to obtain the state vector x(t), x(t) = f(x(t), u d (t),u(t)) or x(t)=I(f1(x(t),u d (t),u(t)),f2(x(t),u d (t),u(t))); Wherein, f(), f1() and f2() are the state mapping functions of the state vector and the state vector field, and the state mapping function is a smooth function; u d (t) is the internal control vector; u(t) is the external control vector; I() represents the differential inclusion form.

7. The energy management method based on hybrid control according to claim 4, characterized in that: The step of obtaining the discontinuity point according to the continuity of the state vector comprises: According to the continuity of the state vector, a transition state vector of discontinuous transition is obtained by screening from the state vector; The discontinuity point x(t) is obtained by x(t+0)=g(x(t-0),q,e); ​​wherein x(t+0) and x(t-0) are the transition state vectors at the corresponding moments; g() is a conditional mapping function, g:X*Q*Σ→X, X is the state space, Q is the state set, Σ is the preset discrete event set of the power system; q is the vector corresponding to the transition state vector, q∈Q; e is a discrete event, e∈Σ.

8. The energy management method based on hybrid control according to claim 1, characterized in that: The generating of control instructions according to the discrete events through a preset energy management strategy includes: According to the association relationship between the discrete events, the discrete events are merged to obtain a first discrete event; According to the priority of the first discrete event, the first discrete event is queued to obtain a second discrete event; Selecting a control decision from a preset energy management strategy according to the type of the second discrete event; A control instruction is generated according to the control decision and the participants.

9. The energy management method based on hybrid control according to claim 8, characterized in that: The step of selecting a control decision from a preset energy management strategy according to the type of the second discrete event includes: According to the preset energy management strategy, a mapping relationship α between the state space Q and the control space U is established, α: Q→U; According to the type of the second discrete event, obtaining the state of the participant; A control decision is selected from the control space according to the state of the participant and the mapping relationship α.

10. An energy management system based on hybrid control, characterized in that: include: Variable data acquisition module, used to collect variable data of each participant in the power system in real time; A discrete event analysis module, used for mapping the variable data into a state vector field, and obtaining discrete events according to the state vector field; A control instruction generation module, used to generate a control instruction according to the discrete event through a preset energy management strategy; The operation and maintenance management module is used to send the control instructions to the power system to control the operating status of the participants.

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