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CN118213993B - Cooperative deployment and safety control method and device for energy micro-grid-power distribution network - Google Patents

Cooperative deployment and safety control method and device for energy micro-grid-power distribution network Download PDF

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CN118213993B
CN118213993B CN202410377427.7A CN202410377427A CN118213993B CN 118213993 B CN118213993 B CN 118213993B CN 202410377427 A CN202410377427 A CN 202410377427A CN 118213993 B CN118213993 B CN 118213993B
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hmg
emergency
sop
distribution network
state
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CN118213993A (en
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侯恺
曲嘉伟
李红军
赵冬
姜世公
李敬如
柴炜
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Tianjin University
State Grid Shanghai Electric Power Co Ltd
State Grid Economic and Technological Research Institute
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State Grid Shanghai Electric Power Co Ltd
State Grid Economic and Technological Research Institute
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J3/001—Methods to deal with contingencies, e.g. abnormalities, faults or failures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J15/00—Systems for storing electric energy
    • H02J15/008—Systems for storing electric energy using hydrogen as energy vector
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J3/001—Methods to deal with contingencies, e.g. abnormalities, faults or failures
    • H02J3/0012—Contingency detection
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J3/008—Circuit arrangements for AC mains or AC distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Power Engineering (AREA)
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Abstract

The invention discloses a method and a device for cooperative deployment and safety control of an energy micro-grid and a power distribution network. Then, a three-layer safety control device for cooperative deployment-emergency-safety control of HMG and SOP is constructed. And finally, the cooperative control of the hydrogen energy micro-grid and SOP is realized. The method and the device for the cooperative deployment and safety control of the energy micro-grid-power distribution network realize the cooperative control of the hydrogen energy micro-grid and SOP; in the emergency layer, unified database expression of various emergency events is realized; at the security control layer: a three-stage fast control strategy is proposed, the safety control device being capable of control decision millisecond generation.

Description

Cooperative deployment and safety control method and device for energy micro-grid-power distribution network
Technical Field
The invention relates to the technical field of hydrogen energy and smart grids, in particular to a method and a device for collaborative deployment and safety control of an energy micro-grid-power distribution network.
Background
The frequency and intensity of sudden public events such as extreme natural disasters are continuously upgraded, and reliable operation of the power distribution network is seriously threatened. Currently, technologies related to security promotion of power distribution networks are mostly limited to power distribution networks, such as measures of line reinforcement, energy storage deployment, interconnection expansion, and the like. However, with the increasing frequency of long term emergencies and climate-related disasters, limitations in electrical energy storage become more and more apparent when dealing with long-term power outages. And hydrogen energy sources provide a solution for electric power support on a long time scale due to their higher energy density. However, the prior art device cannot meet the effective coordination configuration and safety control of the hydrogen energy micro-grid and the power distribution network.
Disclosure of Invention
The invention aims to provide a method and a device for cooperative deployment and safety control of an energy micro-grid-power distribution network, which realize cooperative control of a hydrogen energy micro-grid and SOP; in the emergency layer, unified database expression of various emergency events is realized; at the security control layer: a three-stage fast control strategy is proposed, the safety control device being capable of control decision millisecond generation.
In order to achieve the above purpose, the invention provides a method and a device for collaborative deployment and safety control of an energy micro-grid-power distribution network, which comprises the following steps:
S1, a state sensing module transmits real-time data of an acquired hydrogen energy micro-grid HMG, an intelligent soft switch SOP and an intelligent power distribution network to an energy deployment module, an emergency triggering module and a safety control module;
S2, the energy deployment module performs optimal energy deployment based on real-time data and historical perception storage numbers;
S3, analyzing the real-time data by the emergency triggering module, judging whether the current state is caused by an emergency, executing the step S4 when the current state is caused by the emergency, otherwise, returning to the step S2;
S4, the safety control module is triggered, a three-stage rapid control strategy is set, and control is performed under the cooperative scheduling of HMG and SOP.
Preferably, in step S1, the state acquisition module includes an HMG state sensing sub-module, an SOP state sensing sub-module, and an intelligent power distribution network state sensing sub-module;
Wherein, the real-time data collection comprises the following steps:
S11, an HMG state sensing submodule is used for sensing power of various energy devices inside the HMG, wherein the energy devices comprise: an electrolyzer, a fuel cell, a photocatalytic device, a photovoltaic, and a spectrum splitting device;
S12, the SOP state sensing submodule acquires active power and reactive power of two ends of SOP running in real time;
And S13, the intelligent distribution network state sensing submodule is used for acquiring voltage and phase angle of a distribution network node, active power and reactive power of each branch.
Preferably, in step S11, the power used by the HMG state sensing submodule to sense the various energy devices inside the HMG is based on formulas (1) to (7):
wherein d is the device index; omega dev is the device set; s d is the capacity of the device d, which is the input power of the device d;
And Respectively the output and input power of the electrolytic tank,A 1、b1 is the working coefficient of the electrolytic cell, se represents an emergency, and omega se represents an emergency library;
And The output and input power of the fuel cell are respectively,The energy conversion coefficient of the fuel cell;
And The output and input power of the photocatalysis hydrogen production device are respectively,The energy conversion coefficient of the photocatalytic hydrogen production device is Q H2 which is the heat value of hydrogen;
And The output and input power of the photovoltaic are respectively,The energy conversion coefficient of the fuel cell;
And Respectively the minimum and maximum reactive power compensation of the photovoltaic,For reactive power compensation of the photovoltaic, S pv is the capacity of the photovoltaic.
Preferably, in step S12, the SOP status sensing submodule obtains the active power and the reactive power of the two ends of the SOP running in real time based on the formulas (8) to (10):
wherein i, j is the index of the nodes of the distribution network, AndActive power of two ports of the SOP of nodes i and j, respectively, Ω n representing a port set of the SOP; Is the maximum reactive power of the SOP; is the reactive power of the SOP of node i; s sop represents the capacity of the SOP.
Preferably, in step S13, the intelligent distribution network state sensing submodule modifies the branch voltage equation to obtain the state in the distribution network by introducing the branch outage coefficient δ i,j based on the Lindistflow model
Wherein U i and U j represent the voltages of nodes i and j, respectively; r ij、xij、Pij and Q ij represent the resistance, reactance, active power and reactive power, respectively, of branch ij, M being an infinite constant.
Preferably, in step S2, the energy deployment module acquires the optimal security configuration of HMG and SOP, and the objective function of the optimal security configuration is to minimize the configuration cost and the load loss cost:
minCall＝wαCinv+wβCrel(12)
Wherein, C inv and C rel are annual configuration cost and load loss cost, respectively, w α represents a conversion coefficient of the configuration cost, and w β represents a conversion coefficient of the load loss cost;
Wherein r represents the discount rate, and l represents the service life of the equipment; omega dev represents the candidate device set, N i is the projected number of devices d; c d,n and S d,n denote a unit capacity configuration cost and capacity of the n-th unit of the device d, respectively;
constraints are imposed on the maximum and minimum number of devices:
Wherein, AndRepresenting the maximum and minimum number allowed, respectively, s d represents the minimum optimizable capacity of device d.
Preferably, in step S3, a unified model including multiple types of emergencies is designed to determine whether the current event is an emergency event:
f(B,D,pk,xk)＝0(15)
Wherein x k represents a state variable of an internal component of the system, and B represents a basic attribute describing occurrence and operation characteristics of an emergency; d represents the direct attribute representing the intensity of the emergency event; p k is the weight of the emergency event, which represents the weight of the kth scene of the specific type of event; p k obtains the conditional probability of occurrence of event P k, the historical occurrence number of emergency event N k and the average annual frequency f of disasters;
Given variables B, D and p k, determine state variables x k for components internal to the system, the states of the components x k including, but not limited to, the probability of failure and the duration of failure;
When B, D, p k and x k together determine that the event is an emergency event, if yes, the event is stored in an emergency event library, and further triggers a safety control module to execute step S4.
Preferably, in step S4, the safety control module takes the output of the state sensing module and the output of the emergency triggering module as inputs: the method comprises the steps of designing a mixed integer L-shaped method three-stage solving algorithm, wherein the mixed integer L-shaped method comprises HMG, SOP, the states of a power distribution network and p k and x k of the current emergency in an emergency library:
Wherein x int represents an integer type control decision vector, comprising: tap state of the distribution network, discrete reactive compensator state and power grid line on-off state; u unc represents an uncertainty variable associated with an incident; y hmg and z hmg represent control decision vectors, wherein: y hmg includes active power, reactive power, electrolyzer power, fuel cell power, and spectrum splitting device power inside the hydrogen energy microgrid, z hmg is a 0-1 variable associated with the hydrogen energy microgrid; se represents an emergency scene, and Ω se is an emergency scene set; a, D, F, G and H are parameter matrixes, b, c, D, F and G are coefficient vectors; p se is the probability of the incident se, which is determined by p k and x k together, and U represents the uncertainty set of the incident scene.
Preferably, in step S4, a three-stage fast control strategy is set, and control is performed under the cooperative scheduling of HMG and SOP, specifically:
The first stage: HMG control, for a given trigger event u unc and the current running state x int of the power distribution network, gives an optimal running strategy of HMG, and given decision vectors x int and u unc, builds a safety control model:
And a second stage: identifying weak points of the power distribution network, identifying weak links of the power distribution network based on HMG control strategies z hmg and y hmg, and solving the optimal solution in the first stage AndSubstituting max model, introducing relaxation variable theta, and constructing dual relaxation problem model
Wherein pi t is a simple multiplier of y hmg, and obtaining a joint optimal solution pi, u unc * and z hmg of the first and second phase models through iterative solution (18) and (19), wherein u unc * represents a weak point of the distribution network when HMG operation is pi and z hmg, and thereafter introducing three phases;
and a third stage: for the weak point of the power distribution network under the influence of an emergency (u unc *), the intelligent power grid control based on the SOP is implemented by the following optimization model for the SOP and the action x int of the on-load voltage regulating tap and the reactive compensator in the power distribution network:
Constraint E l+θ≥el is called optimal cut, and on this basis, an expression for constructing a mixed integer optimal cut is as follows:
the variable pi * is determined by iteratively solving the problems of the first and second phases, p se is the probability of an incident s, and then iteratively solving the safety control model by re-integrating the variable pi * back into the third phase using (21).
The device comprises a state sensing module, an energy deployment module, an emergency triggering module and a safety control module, wherein the state sensing module comprises an HMG state sensing sub-module; an SOP state sensing sub-module; and the intelligent power distribution network state sensing sub-module.
Therefore, the energy micro-grid-power distribution network cooperative deployment and safety control method and device have the following technical effects:
(1) Three layers of control devices of cooperative deployment-emergency-safety control of the hydrogen energy micro-grid and the SOP are constructed, so that the cooperative safety control of the hydrogen energy micro-grid and the SOP is realized, and the control method can reduce 47% of load loss under the condition of the same economic cost.
(2) A unified database of a plurality of incidents is established. Compared with the existing label recording technology of independent emergencies, the technology can reduce redundant memory consumption by more than 80%, and in practical application, manual labeling is not needed.
(3) A three-phase fast response control mechanism for HMG and SOP is proposed. The millisecond-level rapid control under the cooperative scheduling of HMG and SOP is realized.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
FIG. 1 is a schematic flow chart of a method for collaborative deployment and safety control of an energy microgrid-power distribution network;
fig. 2 is a schematic flow chart of a three-stage quick response control mechanism of the energy microgrid-power distribution network cooperative deployment and safety control method of the invention;
FIG. 3 is an island division schematic of an embodiment.
Detailed Description
The technical scheme of the invention is further described below through the attached drawings and the embodiments.
Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this invention belongs.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Furthermore, it should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a separate embodiment, and that this description is provided for clarity only, and that the disclosure is not limited to the embodiments described in detail below, and that the embodiments described in the examples may be combined as appropriate to form other embodiments that will be apparent to those skilled in the art. Such other embodiments are also within the scope of the present invention.
It should also be understood that the above-mentioned embodiments are only for explaining the present invention, the protection scope of the present invention is not limited thereto, and any person skilled in the art should be able to cover the protection scope of the present invention by equally replacing or changing the technical scheme and the inventive concept thereof within the scope of the present invention.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but are intended to be considered part of the specification where appropriate.
The disclosures of the prior art documents cited in the present specification are incorporated by reference in their entirety into the present invention and are therefore part of the present disclosure.
Example 1
The invention provides a device for a hydrogen energy micro-grid-power distribution network cooperative deployment and safety control method, which comprises a state sensing module, an energy deployment module, an emergency triggering module and a safety control module, wherein the state sensing module comprises an HMG state sensing sub-module; an SOP state sensing sub-module; and the intelligent power distribution network state sensing sub-module. An electronic storage medium and an electronic device are also provided to perform and complete the following method.
The invention provides an energy micro-grid-power distribution network cooperative deployment and safety control method, which comprises the following steps:
S1, a state sensing module transmits real-time data of an acquired hydrogen energy micro-grid HMG, an intelligent soft switch SOP and an intelligent power distribution network to an energy deployment module, an emergency triggering module and a safety control module;
the state acquisition module comprises an HMG state sensing sub-module, an SOP state sensing sub-module and an intelligent power distribution network state sensing sub-module;
Wherein, the real-time data collection comprises the following steps:
S11, an HMG state sensing submodule is used for sensing power of various energy devices inside the HMG, wherein the energy devices comprise: an electrolyzer, a fuel cell, a photocatalytic device, a photovoltaic, and a spectrum splitting device;
the HMG state sensing submodule is used for sensing the power of various energy devices in the HMG based on the formulas (1) to (7):
wherein d is the device index; omega dev is the device set; s d is the capacity of the device d, which is the input power of the device d;
And Respectively the output and input power of the electrolytic tank,A 1、b1 is the working coefficient of the electrolytic cell, se represents an emergency, and omega se represents an emergency library;
And The output and input power of the fuel cell are respectively,The energy conversion coefficient of the fuel cell;
And The output and input power of the photocatalysis hydrogen production device are respectively,The energy conversion coefficient of the photocatalytic hydrogen production device is Q H2 which is the heat value of hydrogen;
And The output and input power of the photovoltaic are respectively,The energy conversion coefficient of the fuel cell;
And Respectively the minimum and maximum reactive power compensation of the photovoltaic,For reactive power compensation of the photovoltaic, S pv is the capacity of the photovoltaic.
S12, the SOP state sensing submodule acquires active power and reactive power of two ends of SOP running in real time;
The SOP state sensing submodule is based on the formulas (8) to (10) to obtain active power and reactive power of two ends of SOP running in real time:
wherein i, j is the index of the nodes of the distribution network, AndActive power of two ports of the SOP of nodes i and j, respectively, Ω n representing a port set of the SOP; Is the maximum reactive power of the SOP; is the reactive power of the SOP of node i; s sop represents the capacity of the SOP.
And S13, the intelligent distribution network state sensing submodule is used for acquiring voltage and phase angle of a distribution network node, active power and reactive power of each branch.
Based on Lindistflow model, the intelligent distribution network state sensing submodule introduces branch off operation coefficient delta i,j to modify branch voltage equation to obtain state in distribution network
Wherein U i and U j represent the voltages of nodes i and j, respectively; r ij、xij、Pij and Q ij represent the resistance, reactance, active power and reactive power, respectively, of branch ij, M being an infinite constant.
S2, the energy deployment module performs optimal energy deployment based on real-time data and historical perception storage numbers;
The energy deployment module is used for acquiring the optimal safety configuration of HMG and SOP, and the objective function of the optimal safety configuration is to minimize the configuration cost and the load loss cost:
minCall＝wαCinv+wβCrel(12)
Wherein, C inv and C rel are the annual configuration cost and the load loss cost, respectively, w α represents the conversion coefficient of the configuration cost, w β represents the conversion coefficient of the load loss cost, for balancing the proportion of the configuration cost and the load loss cost;
Wherein r represents the discount rate, and l represents the service life of the equipment; omega dev represents the candidate device set, N i is the projected number of devices d; c d,n and S d,n denote a unit capacity configuration cost and capacity of the n-th unit of the device d, respectively;
constraints are imposed on the maximum and minimum number of devices:
Wherein, AndRepresenting the maximum and minimum number allowed, respectively, s d represents the minimum optimizable capacity of device d.
S3, analyzing the real-time data by the emergency triggering module, judging whether the current state is caused by an emergency, executing the step S4 when the current state is caused by the emergency, otherwise, returning to the step S2;
designing a unified model containing multiple types of emergency events, and judging whether the current event is an emergency event or not:
f(B,D,pk,xk)＝0(15)
Wherein x k represents a state variable of an internal component of the system, and B represents a basic attribute describing occurrence and operation characteristics of an emergency; d represents the direct attribute representing the intensity of the emergency event; p k is the weight of the emergency event, which represents the weight of the kth scene of the specific type of event; p k obtains the conditional probability of occurrence of event P k, the historical occurrence number of emergency event N k and the average annual frequency f of disasters;
Given variables B, D and p k, determine state variables x k for components internal to the system, the states of the components x k including, but not limited to, the probability of failure and the duration of failure;
When B, D, p k and x k together determine that the event is an emergency event, if yes, the event is stored in an emergency event library, and further triggers a safety control module to execute step S4.
S4, the safety control module is triggered, a three-stage rapid control strategy is set, and control is performed under the cooperative scheduling of HMG and SOP.
The safety control module takes the output of the state sensing module and the output of the emergency triggering module as inputs: the method comprises the steps of designing a mixed integer L-shaped method three-stage solving algorithm, wherein the mixed integer L-shaped method comprises HMG, SOP, the states of a power distribution network and p k and x k of the current emergency in an emergency library:
Wherein x int represents an integer type control decision vector, comprising: tap state of the distribution network, discrete reactive compensator state and power grid line on-off state; u unc represents an uncertainty variable associated with an incident; y hmg and z hmg represent control decision vectors, wherein: y hmg includes active power, reactive power, electrolyzer power, fuel cell power, and spectrum splitting device power inside the hydrogen energy microgrid, z hmg is a 0-1 variable associated with the hydrogen energy microgrid; se represents an emergency scene, and Ω se is an emergency scene set; a, D, F, G and H are parameter matrixes, b, c, D, F and G are coefficient vectors; p se is the probability of the incident se, which is determined by p k and x k together, and U represents the uncertainty set of the incident scene.
Setting a three-stage rapid control strategy, and controlling under the cooperative scheduling of HMG and SOP, wherein the method specifically comprises the following steps:
The first stage: HMG control, for a given trigger event u unc and the current running state x int of the power distribution network, gives an optimal running strategy of HMG, and given decision vectors x int and u unc, builds a safety control model:
And a second stage: identifying weak points of the power distribution network, identifying weak links of the power distribution network based on HMG control strategies z hmg and y hmg, and solving the optimal solution in the first stage AndSubstituting max model, introducing relaxation variable theta, and constructing dual relaxation problem model
Where pi t is the simple multiplier of y hmg, and by iteratively solving (18) and (19) to obtain the joint optimal solutions pi, u unc * and z hmg of the first and second phase models, where u unc * represents the weak point of the distribution network (the weak component set most affected by the incident) when HMG is operating at pi and z hmg, this is to ensure control robustness, after which three phases are introduced;
And a third stage: SOP-based intelligent power grid control is used for realizing optimal control of a power distribution network layer by aiming at weak points of the power distribution network under the influence u unc * of an emergency through actions x int of the SOP, on-load voltage regulating taps and reactive compensators in the power distribution network, and an optimization model is as follows:
Constraint E l+θ≥el is called optimal cut, and on this basis, an expression for constructing a mixed integer optimal cut is as follows:
the variable pi * is determined by iteratively solving the problems of the first and second phases, p se is the probability of an incident s, and then iteratively solving the safety control model by re-integrating the variable pi * back into the third phase using (21).
Example two
The method of example one was used to perform embodiment verification on an actual power distribution network. The system consists of 30 bus bars and lines. It is susceptible to two types of incidents: typhoons and earthquakes, in order to analyze the effects of Distributed Generation (DG), it comprises one Wind Turbine (WT) unit and two Photovoltaic (PV) units, and two battery energy storage (BS) units. Considering geographical constraints, we have determined TS1, TS2, and TS3 as candidate locations for SOP, while the planned locations for HMG are not limiting.
Firstly, the beneficial effects of the proposed three-layer control device for collaborative deployment-emergency-safety control of HMG and SOP are compared. The proposed method M3 is compared with the safety control method M1 considering only SOP and the safety control method M2 considering only HMG, respectively:
table 1 comparison of three safety control methods M1, M2, M3
Cinv(103￥) Crel(103￥) Call(103￥)
M1 75.22 99.08 174.3
M2 61.28 82.97 144.25
M3 56.13 71.94 128.07
Wherein: c inv is the configuration cost, C rel is the load loss cost, and C all is the total cost.
The investment cost and the load loss cost of M1 are higher than those of M2 and M3. The construction cost of M3 is 8.4% lower than that of M2. However, SP can significantly improve hydrogen conversion efficiency and provide load support during long power outages. Therefore, the load reduction cost of M3 is only 86.7% of that of M2.
Secondly, the three-stage quick response control mechanism of the HMG and the SOP provided by the invention is verified, and the method M4 provided by the invention is compared with the existing nest-CCG method M5. It can be seen that the proposed method M4 can effectively promote the generation time of the control strategy, and it should be noted that the more the number of scenes is, the more obvious the acceleration effect of the proposed method is.
Table 2 comparison of properties of M4 and M5
Therefore, the method and the device for the cooperative deployment and safety control of the energy micro-grid-power distribution network realize the cooperative control of the hydrogen energy micro-grid and SOP; in the emergency layer, unified database expression of various emergency events is realized; at the security control layer: a three-stage fast control strategy is proposed, the safety control device being capable of control decision millisecond generation.
Example III
The invention also provides an electronic device, comprising: a memory and a processor, the memory for storing a computer program; the processor is used for executing the energy micro-grid-power distribution network cooperative deployment and safety control method when the computer program is called.
Example IV
The invention also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor implements the energy microgrid-power distribution network cooperative deployment and safety control method as described above.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention and not for limiting it, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that: the technical scheme of the invention can be modified or replaced by the same, and the modified technical scheme cannot deviate from the spirit and scope of the technical scheme of the invention.

Claims (2)

1.一种能源微网-配电网协同部署及安全控制方法，其特征在于，包括以下步骤：1. A method for coordinated deployment and safety control of energy microgrid-distribution network, characterized in that it includes the following steps: S1、状态感知模块将采集到的氢能源微网HMG、智能软开关SOP和智能配电网的实时数据传达给能源部署模块、突发事件触发模块以及安全控制模块；S1, the state perception module transmits the collected real-time data of hydrogen energy microgrid HMG, intelligent soft switch SOP and intelligent distribution network to the energy deployment module, emergency trigger module and safety control module; S2、能源部署模块基于实时数据以及历史感知存储数进行最佳能源部署；S2, the energy deployment module performs optimal energy deployment based on real-time data and historical perception storage data; S3、突发事件触发模块对实时数据的分析，判别当前状态是否为突发事件导致的，为突发事件时，执行步骤S4，否则，返回到步骤S2；S3, the emergency trigger module analyzes the real-time data to determine whether the current state is caused by an emergency. If it is an emergency, execute step S4, otherwise, return to step S2; S4、安全控制模块被触发，设置三阶段快速控制策略，在HMG和SOP的协同调度下进行控制；S4, the safety control module is triggered, and a three-stage rapid control strategy is set to perform control under the coordinated scheduling of HMG and SOP; 在步骤S1中，状态采集模块包括HMG状态感知子模块、SOP状态感知子模块、智能配电网状态感知子模块；In step S1, the state acquisition module includes an HMG state perception submodule, an SOP state perception submodule, and an intelligent distribution network state perception submodule; 其中，采集实时数据包括：The real-time data collection includes: HMG状态感知子模块用以感知HMG内部的多种能源设备的功率，其中能源设备包括：电解槽、燃料电池、光催化装置、光伏以及谱分裂装置；The HMG state sensing submodule is used to sense the power of various energy devices inside the HMG, where the energy devices include: electrolyzer, fuel cell, photocatalytic device, photovoltaic and spectrum splitting device; SOP状态感知子模块获取SOP实时运行的两端口有功功率、无功功率；The SOP state perception submodule obtains the active power and reactive power of the two ports of the SOP in real time operation; 智能配电网状态感知子模块用以获取配电网络节点的电压、相角、各支路的有功功率、无功功率；The intelligent distribution network state perception submodule is used to obtain the voltage, phase angle, active power and reactive power of each branch of the distribution network node; 在步骤S11中，HMG状态感知子模块用以感知HMG内部的多种能源设备的功率是基于公式(1)～公式(7)：In step S11, the HMG state sensing submodule is used to sense the power of various energy devices inside the HMG based on formula (1) to formula (7): 其中，d为设备索引；Ωdev为设备集合；为设备d的输入功率，Sd为设备d的容量；Where, d is the device index; Ω dev is the device set; is the input power of device d, S d is the capacity of device d; 和分别为电解槽的输出、输入功率，为电解槽的工作温度，a1、b1为电解槽的工作系数，se表示突发事件，Ωse表示突发事件库； and are the output and input power of the electrolyzer, is the working temperature of the electrolytic cell, a 1 and b 1 are the working coefficients of the electrolytic cell, se represents the emergency event, and Ω se represents the emergency event library; 和分别为燃料电池的输出、输入功率，为燃料电池能源转换系数； and are the output and input power of the fuel cell, is the fuel cell energy conversion coefficient; 和分别为光催化制氢装置的输出、输入功率，为光催化制氢装置的能源转换系数，QH2为氢气的热值； and are the output and input power of the photocatalytic hydrogen production device, is the energy conversion coefficient of the photocatalytic hydrogen production device, Q H2 is the calorific value of hydrogen; 和分别为光伏的输出、输入功率，为燃料电池能源转换系数； and are the output and input power of photovoltaic, is the fuel cell energy conversion coefficient; 和分别为光伏的最小、最大无功补偿功率，为光伏的无功补偿功率，Spv为光伏的容量； and are the minimum and maximum reactive power compensation power of photovoltaic, is the reactive compensation power of photovoltaic, S pv is the capacity of photovoltaic; 在步骤S12中，SOP状态感知子模块基于公式(8)～公式(10)In step S12, the SOP state perception submodule is based on formula (8) to formula (10): 以获取SOP实时运行的两端口有功功率、无功功率：To obtain the real-time active power and reactive power of the two ports of SOP: 其中，i，j为配电网节点的索引，Pi sop和分别表示节点i和j的SOP的两个端口的有功功率，Ωn表示SOP的端口集合；是SOP的最大无功功率；是节点i的SOP的无功功率；Ssop代表SOP的容量；Among them, i, j are the indexes of the distribution network nodes, Pisop and Respectively represent the active power of the two ports of the SOP of nodes i and j, Ω n represents the port set of the SOP; is the maximum reactive power of SOP; is the reactive power of the SOP of node i; S sop represents the capacity of the SOP; 在步骤S13中，智能配电网状态感知子模块基于Lindistflow模型，引入支路停运系数δij修改支路电压方程获取配电网络内的状态In step S13, the smart distribution network state perception submodule introduces the branch outage coefficient δ ij based on the Lindistflow model to modify the branch voltage equation to obtain the state of the distribution network 其中，Ui和Uj分别代表节点i和j的电压；rij、xij、Pij和Qij分别代表支路ij的电阻、电抗、有功功率和无功功率，M是一个无穷大常数；Where U i and U j represent the voltages of nodes i and j respectively; r ij , x ij , Pi ij and Qi ij represent the resistance, reactance, active power and reactive power of branch ij respectively, and M is an infinite constant; 在步骤S2中，能源部署模块是获取HMG和SOP的最佳安全配置，最佳安全配置的目标函数是最小化配置成本和负荷损失成本：In step S2, the energy deployment module obtains the optimal safety configuration of HMG and SOP. The objective function of the optimal safety configuration is to minimize the configuration cost and load loss cost: minCall＝wαCinv+wβCrel (12)minC all ＝w α C inv +w β C rel (12) 其中，Cinv和Crel分别是年配置成本和负荷损失成本，wα表示配置成本的折算系数，wβ表示负荷损失成本的折算系数；Where, C inv and C rel are the annual configuration cost and load loss cost respectively, w α represents the conversion factor of configuration cost, and w β represents the conversion factor of load loss cost; 其中，r代表折现率，l表示设备的使用寿命；Ωdev代表候选设备集合，Nd是设备d的规划数量；cd,n和Sd,n分别表示设备d的第n单元的单位容量配置成本和容量；Where r represents the discount rate, l represents the service life of the equipment; Ω dev represents the candidate equipment set, N d is the planned number of equipment d; c d,n and S d,n represent the unit capacity configuration cost and capacity of the nth unit of equipment d, respectively; 对设备的最大和最小数量施加约束：Imposing constraints on the maximum and minimum number of devices: 其中，和分别表示允许的最大和最小数量，sd表示设备d的最小可优化容量；in, and They represent the maximum and minimum quantities allowed, respectively, and s d represents the minimum optimizable capacity of device d; 在步骤S3中，设计一个包含多种类型突发事件的统一模型，判定当前事件是否为突发事件：In step S3, a unified model including multiple types of emergencies is designed to determine whether the current event is an emergency: f(B,D,pk,xk)＝0 (15)f(B,D,p k ,x k )＝0 (15) 式中，xk代表系统内部组件的状态变量，B表示基本属性描述突发事件的发生和运行特征；D表示直接属性代表突发事件的强度；pk为突发事件的权重，表示特定类型的事件第k个情景发生的权重；pk从事件发生的条件概率Pk、突发事件的历史发生次数Nk和灾害的平均年频率f获取；In the formula, xk represents the state variables of the internal components of the system, B represents the basic attributes describing the occurrence and operation characteristics of the emergency; D represents the direct attribute representing the intensity of the emergency; pk is the weight of the emergency, which represents the weight of the kth scenario of a specific type of event; pk is obtained from the conditional probability of the event Pk , the historical number of occurrences of the emergency Nk, and the average annual frequency of disasters f; 在给定变量B、D和pk的情况下，确定系统内部组件的状态变量xk，组件的状态xk包括但并不局限于故障的概率和故障持续时间；Given variables B, D and pk , determine the state variables xk of the components within the system. The state xk of the components includes but is not limited to the probability of failure and the duration of failure. 当B、D、pk和xk共同确定后，判定该事件是否为突发事件，是突发事件时，将事件存入突发事件库，并进一步触发安全控制模块，执行步骤S4；When B, D, p k and x k are determined together, it is determined whether the event is an emergency event. If it is an emergency event, the event is stored in the emergency event library, and the security control module is further triggered to execute step S4; 在步骤S4中，安全控制模块是以状态感知模块的输出与突发事件触发模块的输出为输入：包括HMG、SOP、配电网络的状态，以及突发事件库中本次突发事件的pk和xk，设计一个混合整数L形方法三阶段求解算法：In step S4, the safety control module takes the output of the state perception module and the output of the emergency trigger module as input, including HMG, SOP, the state of the distribution network, and p k and x k of this emergency in the emergency database, and designs a three-stage solution algorithm of the mixed integer L-shaped method: 其中，xint代表整数型控制决策向量，包括：配电网的分接头状态、离散无功补偿器状态、电网线路的开断状态；uunc代表与突发事件相关的不确定性变量；yhmg和zhmg代表控制决策向量，其中：yhmg包括氢能源微网内部的有功功率、无功功率、电解槽功率、燃料电池功率以及谱分裂装置功率，zhmg是与氢能源微网相关的0-1变量；se代表突发事件场景，Ωse是突发事件场景集合；A，D，F，G，H为参数矩阵，b，c，d，f，g为系数向量；pse为突发事件se的概率，由pk和xk共同确定，U表示突发事件场景的不确定性集合；Among them, x int represents an integer control decision vector, including: the tap state of the distribution network, the discrete reactive compensator state, and the disconnection state of the power grid line; u unc represents the uncertainty variable related to the emergency; y hmg and z hmg represent the control decision vector, among which: y hmg includes the active power, reactive power, electrolyzer power, fuel cell power and spectrum splitting device power inside the hydrogen energy microgrid, and z hmg is a 0-1 variable related to the hydrogen energy microgrid; se represents the emergency scenario, and Ω se is the set of emergency scenarios; A, D, F, G, H are parameter matrices, and b, c, d, f, g are coefficient vectors; p se is the probability of the emergency se, which is determined by p k and x k , and U represents the uncertainty set of the emergency scenario; 在步骤S4中，设置三阶段快速控制策略，在HMG和SOP的协同调度下进行控制，具体为：In step S4, a three-stage fast control strategy is set to perform control under the coordinated scheduling of HMG and SOP, specifically: 第一阶段：HMG控制，对于给定的触发事件uunc，以及配电网当前的运行状态xint，给出HMG的最佳运行策略，给定决策向量xint*和uunc*，构建安全控制模型：The first stage: HMG control, for a given trigger event u unc , and the current operating state of the distribution network x int , the optimal operation strategy of HMG is given. Given the decision vectors x int * and u unc *, a safety control model is constructed: 第二阶段：配电网薄弱点识别，基于HMG的控制策略zhmg和yhmg，识别配电网的薄弱环节，将第一阶段的最优解和代入max模型，引入松弛变量θ，并构建对偶松弛问题模型The second stage: Identification of weak points in the distribution network. Based on the HMG control strategies z hmg and y hmg , the weak links in the distribution network are identified and the optimal solution of the first stage is converted into and Substitute into the max model, introduce the slack variable θ, and construct the dual slack problem model 其中，πt是yhmg的单纯性乘子，通过迭代求解(18)和(19)获得第一、二阶段模型的联合最优解π*、uunc *和zhmg*，其中uunc *表示当HMG运行处于π*和zhmg*时，配电网的薄弱点，在此之后，引入三阶段；Among them, π t is the simplex multiplier of y hmg . By iteratively solving (18) and (19), the joint optimal solutions of the first and second stage models are obtained: π*, u unc * and z hmg *, where u unc * represents the weak point of the distribution network when the HMG operates at π* and z hmg *. After that, the three stages are introduced; 第三阶段：基于SOP的智能电网控制，针对于配电网在突发事件影响uunc *下的薄弱点，通过对于SOP及配电网内有载调压分接头、无功补偿器的动作xint，优化模型如下：The third stage: Smart grid control based on SOP, targeting the weak points of the distribution network under the influence of emergencies u unc * , through the actions x int of SOP and on-load tap changers and reactive power compensators in the distribution network, the optimization model is as follows: 约束El+θ≥el被称为最优切割，在此基础上，构建混合整数最优切割的表达式如下：The constraint El + θ≥el is called the optimal cut. On this basis, the expression of the mixed integer optimal cut is constructed as follows: 变量π*是通过迭代解决第一阶段、第二阶段的问题来确定的，pse为突发事件s的概率，随后，通过使用(21)将变量π*重新整合回第三阶段中，迭代地解决安全控制模型。The variable π * is determined by iteratively solving the problems in the first and second stages, and p se is the probability of the emergency event s. Subsequently, the variable π * is reintegrated back into the third stage by using (21) to iteratively solve the safety control model. 2.根据权利要求1所述的一种氢能源微网-配电网协同部署及安全控制方法的装置，其特征在于，包括状态感知模块、能源部署模块、突发事件触发模块以及安全控制模块，其中状态感知模块包括HMG状态感知子模块；SOP状态感知子模块；智能配电网状态感知子模块。2. According to claim 1, a device for a hydrogen energy microgrid-distribution network collaborative deployment and safety control method is characterized in that it includes a state perception module, an energy deployment module, an emergency trigger module and a safety control module, wherein the state perception module includes an HMG state perception submodule; an SOP state perception submodule; and an intelligent distribution network state perception submodule.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115663818A (en) * 2022-05-23 2023-01-31 天津大学 Distributed convex optimization method and device for AC and DC distribution network
CN116599165A (en) * 2023-06-21 2023-08-15 广东电网有限责任公司 Micro-grid group optimization control method and system based on generalized Benders decomposition method

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CN114172161B (en) * 2021-11-26 2022-11-29 南京邮电大学 Multi-terminal collaborative voltage management method and storage medium for distribution network with high penetration rate photovoltaic access

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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