WO2025185243A1 - 基于三层优先目标的配电网多级电压协同控制方法及系统 - Google Patents

基于三层优先目标的配电网多级电压协同控制方法及系统

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Publication number
WO2025185243A1
WO2025185243A1 PCT/CN2024/135261 CN2024135261W WO2025185243A1 WO 2025185243 A1 WO2025185243 A1 WO 2025185243A1 CN 2024135261 W CN2024135261 W CN 2024135261W WO 2025185243 A1 WO2025185243 A1 WO 2025185243A1
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Prior art keywords
distribution network
constraints
node
net load
level
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PCT/CN2024/135261
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English (en)
French (fr)
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WO2025185243A8 (zh
WO2025185243A9 (zh
Inventor
刘科研
贾东梨
梁英
杨晓雨
叶学顺
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China Electric Power Research Institute Co Ltd CEPRI
State Grid Shandong Electric Power Co Ltd
Linyi Power Supply Co of State Grid Shandong Electric Power Co Ltd
State Grid Corp of China SGCC
Original Assignee
China Electric Power Research Institute Co Ltd CEPRI
State Grid Shandong Electric Power Co Ltd
Linyi Power Supply Co of State Grid Shandong Electric Power Co Ltd
State Grid Corp of China SGCC
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Application filed by China Electric Power Research Institute Co Ltd CEPRI, State Grid Shandong Electric Power Co Ltd, Linyi Power Supply Co of State Grid Shandong Electric Power Co Ltd, State Grid Corp of China SGCC filed Critical China Electric Power Research Institute Co Ltd CEPRI
Publication of WO2025185243A1 publication Critical patent/WO2025185243A1/zh
Publication of WO2025185243A8 publication Critical patent/WO2025185243A8/zh
Publication of WO2025185243A9 publication Critical patent/WO2025185243A9/zh
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; 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
    • H02J3/12—Arrangements for adjusting voltage in AC networks by changing a characteristic of the network load
    • H02J3/14—Arrangements for adjusting voltage in AC networks by changing a characteristic of the network load by switching loads on to, or off from, the networks, e.g. progressively balanced loading
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; 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
    • H02J3/12—Arrangements for adjusting voltage in AC networks by changing a characteristic of the network load
    • H02J3/16—Arrangements for adjusting voltage in AC networks by changing a characteristic of the network load by adjustment of reactive power
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; 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
    • H02J3/17—Demand-responsive operation of AC power transmission or distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; 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
    • H02J3/38—Arrangements for feeding a single network from two or more generators or sources in parallel; Arrangements for feeding already energised networks from additional generators or sources in parallel
    • H02J3/46—Controlling the sharing of generated power between the generators, sources or networks
    • H02J3/48—Controlling the sharing of active power
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; 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
    • H02J3/38—Arrangements for feeding a single network from two or more generators or sources in parallel; Arrangements for feeding already energised networks from additional generators or sources in parallel
    • H02J3/46—Controlling the sharing of generated power between the generators, sources or networks
    • H02J3/50—Controlling the sharing of reactive power
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2101/00—Supply or distribution of decentralised, dispersed or local electric power generation
    • H02J2101/20—Dispersed power generation using renewable energy sources
    • H02J2101/22—Solar energy
    • H02J2101/24—Photovoltaics
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2101/00—Supply or distribution of decentralised, dispersed or local electric power generation
    • H02J2101/20—Dispersed power generation using renewable energy sources
    • H02J2101/28—Wind energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2103/00—Details of circuit arrangements for mains or AC distribution networks
    • H02J2103/30—Simulating, planning, modelling, reliability check or computer assisted design [CAD] of electric power networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2103/00—Details of circuit arrangements for mains or AC distribution networks
    • H02J2103/30—Simulating, planning, modelling, reliability check or computer assisted design [CAD] of electric power networks
    • H02J2103/35—Grid-level management of power transmission or distribution systems, e.g. load flow analysis or active network management
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/30—Reactive power compensation

Definitions

  • this application proposes a distribution network multi-level voltage coordinated control method based on three-level priority objectives, including:
  • the three-layer optimization target programming model is solved using affine theory, duality theory, power circle linearization, and absolute value linearization methods to obtain the maximum node acceptable net load disturbance domain, the minimum total distribution network operation cost, and the minimum expected voltage deviation;
  • Various reactive devices in the distribution network are collaboratively controlled by the distribution network operation parameters corresponding to maximizing the net load disturbance domain that the node can accept, minimizing the total cost of distribution network operation, and minimizing the expected voltage deviation;
  • the three-layer optimization target planning model is constructed by maximizing the node's acceptable net load disturbance domain, minimizing the total cost of distribution network operation, and minimizing the expected voltage deviation as the three-layer objective function, and setting constraints for the three-layer objective function.
  • the first-level optimization objective is to maximize the node's acceptable net load disturbance domain
  • the second-level optimization objective is to minimize the total cost of distribution network operation
  • the third-level optimization objective is to minimize the expected voltage deviation
  • the constraints under the net load forecast value include: total power balance constraints under the net load forecast value, line flow constraints, branch capacity constraints, node voltage upper and lower limit constraints, unit output constraints, energy storage constraints, upper-level power grid power supply constraints, solar power curtailment, wind power curtailment, load shedding constraints, grouped capacitor switching constraints, and static VAR compensator (SVC) constraints;
  • the first-layer optimization objective is expressed as follows:
  • Z1 is the first layer objective function, is the net load disturbance range that the upstream node can accept, is the net load disturbance domain that the downstream node can accept, NT is the set of optimization time periods; NI is the set of nodes; i is the node number; t is the optimization time period;
  • Coper is the operating cost
  • Ccut is the load shedding cost of wind and solar power curtailment
  • Cess is the energy storage cost
  • Z 3 is the third layer objective function, is the predicted value of node voltage, and Vi ,t is the actual node voltage.
  • the three-layer optimization target programming model based on the distribution network operating parameters and the pre-constructed model is solved by using affine theory, duality theory, power circle linearization and absolute value linearization methods to obtain the maximum node acceptable net load disturbance domain, the minimum distribution network operation total cost and the minimum expected voltage deviation, including:
  • Affine theory and duality theory are used to transform the uncertainty formula in the three-level optimization target programming model into a deterministic formula
  • the power circle linearization and absolute value linearization methods are used to transform the nonlinear formula in the three-level optimization target programming model into a linear formula.
  • the use of affine theory and duality theory to convert the uncertainty formula in the three-level optimization target programming model into a deterministic formula includes:
  • Affine theory is used to transform the node voltage uncertainty in the three-level optimization target programming model by introducing an auxiliary variable to obtain a deterministic variable.
  • the uncertainty of unit regulation disturbance output is transformed by duality theory, and the net load disturbance domain that can be accepted by the node is obtained.
  • the absolute value linearization method is used to linearize the absolute value in the uncertainty formula.
  • the feasible domain of the uncertainty formula is the interior of the circle, and the regular polygon inscribed in the circle is approximated as the circle corresponding to the feasible domain of the uncertainty formula;
  • the area enclosed by the regular polygon is used to approximately replace the area enclosed by the circle, and the uncertainty formula is converted into a calculation formula for the area of the regular polygon.
  • the distribution network operating parameters include: distribution network topology, distribution network line capacity and resistance and reactance values, time-of-use electricity prices, controllable generator data, upper-level power grid data, and load data.
  • the present application also provides a distribution network multi-level voltage coordinated control system based on three-level priority targets, including:
  • a target solving module is used to solve the three-layer optimization target programming model based on the distribution network operating parameters and a pre-built three-layer optimization target programming model using affine theory, duality theory, power circle linearization and absolute value linearization methods to obtain the maximum node acceptable net load disturbance domain, the minimum distribution network operation total cost and the minimum expected voltage deviation;
  • a control module is used to coordinately control various reactive devices in the distribution network according to the distribution network operation parameters corresponding to maximizing the node's acceptable net load disturbance domain, minimizing the total cost of distribution network operation, and minimizing the expected voltage deviation;
  • the three-layer optimization target planning model is constructed by maximizing the node's acceptable net load disturbance domain, minimizing the total cost of distribution network operation, and minimizing the expected voltage deviation as the three-layer objective function, and setting constraints for the three-layer objective function.
  • a model building module is further included for:
  • the first-level optimization objective is to maximize the node's acceptable net load disturbance domain
  • the second-level optimization objective is to minimize the total cost of distribution network operation
  • the constraint conditions include: constraints under the net load forecast value and constraints under the net load disturbance.
  • the constraints under the net load forecast value include: total power balance constraints under the net load forecast value, line flow constraints, branch capacity constraints, node voltage upper and lower limit constraints, unit output constraints, energy storage constraints, upper-level power grid power supply constraints, solar power curtailment, wind power curtailment, load shedding constraints, grouped capacitor switching constraints, and static VAR compensator (SVC) constraints;
  • the constraints under the net load disturbance include: total power balance constraints under net load disturbance, net load acceptance domain constraints, line flow constraints, line capacity constraints, node voltage upper and lower limit constraints, generator-related constraints, energy storage constraints, upper-level power grid power supply constraints, curtailment of solar and wind power, load shedding constraints, grouped capacitor switching constraints, and static VAR compensator constraints.
  • the first-layer optimization objective is expressed as follows:
  • Z1 is the first layer objective function, is the net load disturbance range that the upstream node can accept, is the net load disturbance domain that the downstream node can accept, NT is the set of optimization time periods; NI is the set of nodes; i is the node number; t is the optimization time period;
  • the goal solving module includes:
  • the deterministic conversion submodule is used to convert the uncertainty formula in the three-level optimization target programming model into a deterministic formula using affine theory and duality theory;
  • a linearization submodule is used to convert the nonlinear formula in the three-level optimization target programming model into a linear formula using power circle linearization and absolute value linearization methods;
  • the solving submodule is used to solve the converted three-layer optimization target programming model to obtain the maximum node acceptable net load disturbance domain, the minimum total cost of distribution network operation and the minimum expected voltage deviation.
  • the deterministic conversion submodule is specifically configured to:
  • Affine theory is used to transform the node voltage uncertainty in the three-level optimization target programming model by introducing an auxiliary variable to obtain a deterministic variable.
  • the uncertainty of unit regulation disturbance output is transformed by duality theory, and the net load disturbance domain that can be accepted by the node is obtained.
  • the uncertainty formula that can be piecewise linearized in the three-level optimization target programming model is linearized using the power circle linearization method
  • the absolute value linearization method is used to linearize the absolute value in the uncertainty formula.
  • the specific implementation steps of linearizing the uncertainty formula that can be piecewise linearized in the three-layer optimization target programming model using the power circle linearization method in the linearization submodule include:
  • the present application further provides a computing device comprising: at least one processor and a memory;
  • the memory is used to store one or more programs
  • the present invention has the following advantages:
  • the present application provides a distribution network multi-level voltage collaborative control method based on three-layer priority objectives, including: obtaining distribution network operating parameters; based on the distribution network operating parameters and a pre-constructed three-layer optimization target programming model, using affine theory, duality theory, power circle linearization and absolute value linearization methods to solve the three-layer optimization target programming model to obtain the maximized node acceptable net load disturbance domain, minimized distribution network operation total cost and minimized expected voltage deviation; various reactive equipment in the distribution network are collaboratively controlled according to the distribution network operating parameters corresponding to the maximized node acceptable net load disturbance domain, minimized distribution network operation total cost and minimized expected voltage deviation; wherein, the three-layer optimization target programming model is constructed with maximizing the node acceptable net load disturbance domain, minimizing the distribution network operation total cost and minimizing the expected voltage deviation as the three-layer objective function, and setting constraints for the three-layer objective function.
  • FIG1 is a flow chart of a method for coordinated multi-level voltage control of a distribution network based on three-tier priority targets of the present application
  • FIG2 is a flow chart of a method for coordinated control of multi-level voltages in a distribution network according to a specific embodiment of the present application
  • FIG4 is a schematic diagram of the piecewise linearization of the unit operating cost of the present application.
  • FIG5 is a schematic diagram of the linearization of branch capacity constraints and unit capacity constraints of the present application.
  • This application proposes a multi-level voltage coordinated control method and system for distribution networks based on three-level priority objectives.
  • a three-level priority objective programming model is constructed.
  • the first-level optimization objective is to maximize the net load disturbance domain that a node can accommodate.
  • This optimization model uses an optimizable interval endpoint to more flexibly describe the uncertainty of distributed power generation output, reduce the impact of distributed power generation disturbances on system voltage, and maximize the absorption of distributed power generation output, thereby reducing the cost of traditional energy output and meeting the dual-carbon goals.
  • the second-level optimization objective is to minimize the total operating cost of the distribution network, meeting the requirements of power system economic operation.
  • the third-level optimization objective is to minimize the expected voltage deviation.
  • node voltages should be kept within a reasonable range.
  • the voltages at each node of the distribution network are optimized by adjusting various reactive devices in the system.
  • a power flow expression is constructed using the generation load transfer factor of the decoupled linearized power flow to more accurately describe the voltage variations in the distribution network.
  • the distribution network multi-level voltage coordinated control method based on three-level priority objectives is shown in Figure 1 and includes:
  • Step 2 Based on the distribution network operating parameters and a pre-built three-layer optimization target programming model, the three-layer optimization target programming model is solved using affine theory, duality theory, power circle linearization, and absolute value linearization methods to obtain the maximum node acceptable net load disturbance domain, the minimum total distribution network operation cost, and the minimum expected voltage deviation;
  • Step 3 Coordinated control of various reactive devices in the distribution network is performed based on the distribution network operation parameters corresponding to maximizing the node's acceptable net load disturbance domain, minimizing the total distribution network operation cost, and minimizing the expected voltage deviation;
  • the present application proposes a method and system for coordinated control of multi-level voltage in distribution networks based on three-level priority objectives.
  • the method adopts the three-level objective programming theory, reasonably considers the safety, economy and reliability of the distribution network according to the priority of actual needs, introduces the node's acceptable net load disturbance domain into the distribution network operation optimization, and can achieve good mutual assistance between the load side and the power supply side.
  • the system operation constraints are constructed by the power generation load transfer factor based on the decoupled linearized flow, and the joint optimization of the active flow, reactive flow and voltage is realized.
  • the constraints are linearized by power circle linearization and 0/1 variable linearization, and the large-scale MINLP (Mixed Integer Nonlinear Programming) problem of the distribution network is converted into a MIP (Mixed Integer Linear Programming) problem.
  • MINLP Mated Integer Nonlinear Programming
  • MIP Mated Integer Linear Programming
  • step 1 a three-layer optimization target programming model is also constructed.
  • the construction process of the three-layer optimization target programming model is as follows:
  • the cost of electricity purchased by the distribution network from the upper power grid is the unit operating cost, is the load shedding cost, is the cost of wind curtailment, is the cost of curtailed solar power;
  • NT is the set of optimized time periods;
  • NI is the set of nodes;
  • NF is the set of upstream power stations;
  • NG is the set of generators;
  • NW is the set of wind turbines;
  • NPV is the set of photovoltaics;
  • NE is the set of energy storage devices; and are the cost coefficients for the upstream power station f to provide active power and reactive power in time period t, respectively; and They represent the active power and reactive power generated by the upper-level power grid f during time period t when the net load is the predicted value; represents the active power output of generator g during time period t when the net load is the predicted value, that is, the active power operation base point;
  • a g , b g , and c g are the operating cost coefficients of generator
  • constraints in the distribution network multi-level voltage coordinated control method model based on three-level priority objectives include constraints under net load forecast values and constraints under net load disturbances.
  • It represents the active power output of generator g during time period t when the net load is the predicted value, i.e. the active power operation base point; is the predicted active power value of the net load of node i in time period t, It represents the reactive output of generator g in time period t under the net load forecast value, that is, the reactive operation base point; represents the predicted reactive power value of the net load of node i in time period t.
  • NSVC represents the set of static VAR compensators svc; It represents the reactive power provided by the static VAR compensator SVC during the time period t under the net load forecast value;
  • NCB represents the set of capacitor banks CB; It represents the reactive power provided by capacitor bank cb during time period t under the predicted net load value;
  • g is the generator number;
  • Formula (7) ensures that in any time period, the sum of the total generator output (active or reactive) and the upper grid output (active or reactive) changes is equal to the sum of the total net load forecast value (active or reactive) changes.
  • ref is the parameter and variable under the net load forecast value
  • rer is the reference node, They represent the active power flow and reactive power flow of line ij in time period t under the net load forecast value respectively
  • b f is a binary variable, 1 indicates that the upper grid is directly connected to node k, and 0 indicates that the upper grid is not directly connected to node k
  • NL ⁇ NL none ,NL end ,NL start ⁇ ;
  • NL none represents the set of branches that are not directly connected to the reference node;
  • NL start represents the set of branches that start with the reference node; and
  • NL end represents the set of branches that end with the reference node.
  • Gk (ref) is the predicted conductance value of the line connected to node k
  • Vref is the predicted voltage amplitude value
  • Bk (ref) is the predicted susceptance value of the line connected to node k
  • ⁇ ref is the predicted voltage phase angle value
  • ⁇ ref is the predicted voltage phase angle value
  • bij is the susceptance of line ij
  • gij is the conductance of line ij.
  • Formula (10) indicates that the total power flow on the branch cannot exceed the branch capacity limit.
  • Vimax and Vimin represent the upper and lower limits of the voltage amplitude at node i, respectively; ⁇ imax and ⁇ imin represent the upper and lower limits of the voltage phase angle at node i , respectively;
  • Gk(ref) is the predicted conductance of the line connected to node k
  • Vref is the predicted voltage amplitude
  • ⁇ ref is the predicted voltage phase angle
  • Bk (ref) is the predicted susceptance of the line connected to node k.
  • Equation (11) and the middle term of Equation (12) are the node voltage amplitude and node voltage phase angle calculated based on the generation load transfer factor of the decoupled linearized power flow, respectively, and both must be within the specified upper and lower limits.
  • Equation (14) indicates that the active output of generator g must be within the specified upper and lower limits, and the second formula indicates that the total output of the generator must be less than the upper limit of the generator set capacity.
  • Equation (15) provides upper and lower limits for the upward and downward reserve of generator set g.
  • Equation (16) provides the upward and downward ramp rate constraints for generator set g.
  • Ee ,t represents the energy stored in energy storage e in time period t
  • Ee ,t-1 represents the energy stored in energy storage e in time period t-1
  • ⁇ c and ⁇ d represent the charging and discharging efficiencies of energy storage, respectively
  • E start and E end represent the initial and final energies within a charging cycle, respectively. This constraint limits the variability of the energy storage device's charge and discharge power and stored energy.
  • Q cb,t represents the reactive output of capacitor bank cb during time period t
  • h represents the gear position
  • NC cb,h,t represents the number of capacitor banks put into operation when capacitor bank cb is in gear position h
  • x cb,h,t is a 0/1 variable indicating whether capacitor bank cb is in gear position h at time t.
  • a value of 1 indicates that capacitor bank cb is in gear position h at time t, and vice versa. This formula indicates that during time period t, capacitor bank cb can only be in one gear position.
  • B cb,t is a 0 ⁇ 1 variable. When the value is 0, it means that cb maintains its original state and does not operate. When the value is 1, it means that cb is adjusted.
  • Q cb,t-1 represents the reactive power output of capacitor bank cb in the t-1 period. Indicates the reactive output of cb unit, Indicates the maximum number of gears that cb can adjust at time t, Indicates the maximum number of operations of group switching capacitor banks within the optimization period.
  • the model needs to satisfy the constraints under the net load disturbance, which are similar to the constraints under the net load forecast value:
  • It represents the perturbation range that the net load of node i can tolerate at time t. It represents the perturbation range that the upward net load of node i can accept at time t, and They represent the upper and lower limits of the net load disturbance at node i in time period t, which are determined by the load characteristics and the natural disturbance law of distributed energy output. They are parameters and can be obtained through probabilistic prediction methods.
  • Formula (24) indicates that the size of the acceptable net load disturbance domain is within the upper and lower limits of the net load disturbance (as shown in Figure 2).
  • Pij ,t and Qij ,t represent the active and reactive power flows of line ij during time period t under net load disturbance;
  • Pg ,t represents the active output of generator g during time period t under disturbance;
  • Pf ,t represents the active output of upper-level grid f during time period t under disturbance.
  • Equation (25) when the net load fluctuates within the acceptable net load disturbance domain, the power flow on the line also fluctuates randomly.
  • Equations (27) and (28) indicate that when the net load fluctuates within the acceptable net load disturbance domain, the node voltage amplitude and phase angle must be within the specified upper and lower limits.
  • Equation (29) indicates that when the net load fluctuates within the acceptable net load disturbance domain, the generator's active power output must be within the upper and lower active power limits, and the total output must be less than the capacity limit.
  • Equation (30) indicates that when the net load fluctuates within the acceptable net load disturbance domain, the reserve provided by the generator should be greater than the reserve required by the system.
  • constraints (23)-(30) are random when considering the net load disturbance, so there are countless constraints involved.
  • Step 1 Obtain distribution network operating parameters
  • Distribution network operating parameters include:
  • Distribution network topology Distribution network line capacity and resistance and reactance values, time-of-use electricity prices, controllable generator data, upper-level power grid data, and load data.
  • Step 2 Based on the distribution network operating parameters and the pre-built three-layer optimization target programming model, the three-layer optimization target programming model is solved using affine theory, duality theory, power circle linearization, and absolute value linearization methods to obtain the maximum node acceptable net load disturbance domain, the minimum distribution network operation total cost, and the minimum expected voltage deviation, including:
  • Affine theory and duality theory are used to transform the uncertainty formula in the three-level optimization target programming model into a deterministic formula
  • the power circle linearization and absolute value linearization methods are used to transform the nonlinear formula in the three-level optimization target programming model into a linear formula.
  • the transformed three-layer optimization objective programming model is solved to obtain the maximum node acceptable net load disturbance domain, the minimum total cost of distribution network operation and the minimum expected voltage deviation.
  • affine theory and duality theory to transform the uncertainty formula in the three-level optimization target programming model into a deterministic formula includes:
  • Affine theory is used to transform the node voltage uncertainty in the three-level optimization target programming model by introducing an auxiliary variable to obtain a deterministic variable.
  • the uncertainty of unit regulation disturbance output is transformed by duality theory, and the net load disturbance domain that can be accepted by the node is obtained.
  • the power circle linearization and absolute value linearization methods are used to convert the nonlinear formula in the three-level optimization target programming model into a linear formula, including:
  • the uncertainty formula that can be piecewise linearized in the three-level optimization target programming model is linearized using the power circle linearization method
  • the absolute value linearization method is used to linearize the absolute value in the uncertainty formula.
  • the uncertainty formula that can be piecewise linearized in the three-layer optimization target programming model is linearized using a power circle linearization method, including:
  • the feasible domain of the uncertainty formula is the interior of the circle, and the regular polygon inscribed in the circle is approximated as the circle corresponding to the feasible domain of the uncertainty formula;
  • the area enclosed by the regular polygon is used to approximately replace the area enclosed by the circle, and the uncertainty formula is converted into a calculation formula for the area of the regular polygon.
  • step 2 The specific steps of step 2 are as follows:
  • the operating cost part of the objective function of this model can be piecewise linearized, as shown in Figure 3.
  • the second formula of Equations (10) and (14), and the second formula of (26) and (29) are quadratic constraints.
  • the power circle linearization method is used to linearize the above constraints and effectively solve them.
  • M is the penalty coefficient, which is infinite in principle
  • ⁇ cb,t is an auxiliary 0 ⁇ 1 variable
  • B cb,t is a 0 ⁇ 1 variable indicating whether the capacitor bank cb is operating.
  • This model accounts for the uncertainty of net load disturbances and the resulting uncertainty in unit output regulation and node voltage changes. It contains countless uncertain equality and inequality constraints, making direct solutions difficult. To address this issue, this disclosure employs an affine strategy to handle the uncertain variables in the constraints. This affine strategy is also known as affine theory.
  • Equation (35) states that under a net load disturbance, the total output of the generator, upstream grid, grouped switching capacitors, and static VAR compensator is the operating base plus the random output adjustment.
  • the random output adjustment is the product of the participation factor of each device and the total net load disturbance of the system. Since the sum of the output adjustments of these devices must be equal to the sum of the net load disturbance values, Equation (36) ensures that the total participation factor of these devices is 1.
  • They represent the active participation factor and reactive participation factor of unit g in time period t respectively; They represent the active participation factor and reactive participation factor of the upper grid f in time period t respectively; represents the reactive participation factor of the static VAR compensator svc in time period t; Represents the reactive participation factor of the group switching capacitor cb in time period t; They represent the active power output adjustment and reactive power output adjustment of unit g in time period t respectively; They represent the active power output adjustment and reactive power output adjustment of the upper power grid f in time period t respectively; represents the random active power change at node i during time period t, Represents the random reactive power change at node i during time period t. They represent the reactive output adjustment of the static VAR compensator and the group switching capacitor in time period t respectively.
  • the uncertain model is converted into a deterministic model.
  • the reserve provided by each unit is determined by its participation factor and the total net load acceptance range of the system.
  • the reserve in the upward and downward directions required by unit g is expressed as follows:
  • the uncertain model can be converted into a deterministic model by introducing an auxiliary variable z i,t .
  • the perturbation domain that the random net load can accommodate can be explicitly expressed as:
  • ⁇ is a proportional coefficient, assuming that the node active net load disturbance is proportional to the node reactive net load disturbance.
  • Equation (35) Substituting Equation (35) into the constraints based on net load disturbance, the random adjustment of the generator, the random adjustment of the upper power grid, the random adjustment of the reactive compensation equipment, the random node voltage amplitude and phase angle, the random line flow, etc. can be used as the random acceptable net load disturbance value Represented. Then for the transformed formula (31), The formula should hold true for any random change within its range. Since the formula holds true for the worst realization of the random quantity, it holds true for any realization of the uncertain quantity. Therefore, formula (31) can be transformed into:
  • ⁇ i,g,t,u is the dual variable, is the upper limit of the output capacity of generator g.
  • formula (39) After substituting formula (38) into formula (39), using the duality theory, formula (39) can be transformed into:
  • equations (26)-(28) can be transformed similarly, which will not be repeated here.
  • this model is transformed into a deterministic linear programming model, which can be solved using mature commercial solvers.
  • the priority target programming method is used to convert the model into a three-layer model so that the model has a clear hierarchical order.
  • the first-layer model has priority
  • the second-layer model has one more constraint condition than the first-layer model to ensure that the value of the total net load acceptance domain obtained after optimization is not less than the optimized scheduling result of the first-layer model, as shown in formula (41).
  • ⁇ is the conservative control factor, which is used to control the conservatism of the optimization results.
  • a larger value indicates that the system optimization results are more inclined to the security of the power system, and a smaller value indicates that the system optimization results are more inclined to the economy of the power system.
  • the third-layer model specifically takes the minimum expected voltage deviation as the objective function.
  • V i,t is the actual voltage of each node in the system at node i at time t
  • V i,t is the ideal voltage of each node in the system at node i at time t.
  • ZP is an auxiliary variable
  • the third-level model has one more constraint than the second-level model, which ensures that the total distribution network operation cost obtained after optimization is not less than the optimized scheduling result of the second-level model, as shown in formula (44).
  • C oper is the distribution network operating cost
  • C cut is the load shedding cost of the distribution network due to wind and solar power curtailment
  • C ess is the energy storage operating cost.
  • ⁇ is the conservative control factor, which is used to control the conservatism of the optimization result. Its function is the same as that of the conservative control factor ⁇ and will not be repeated here.
  • Step 3 Various reactive devices in the distribution network are collaboratively controlled based on the distribution network operation parameters corresponding to maximizing the node's acceptable net load disturbance domain, minimizing the total distribution network operation cost, and minimizing the expected voltage deviation.
  • the present invention has the following advantages:
  • This application constructs a model of a multi-level voltage coordinated control method for distribution networks based on three-level priority targets. It uses the node's acceptable net load disturbance domain to quantitatively describe the node's ability to accept distributed energy and the system's ability to resist load disturbances. It comprehensively considers the coordinated interaction capabilities between distributed energy output, energy storage systems, and load disturbances, and explores the comprehensive regulation potential of a new multi-target and multi-agent power system.
  • This application constructs a three-layer model based on the priority target programming theory.
  • the first layer model is to maximize the net load disturbance domain that the node can accept
  • the second layer model is to minimize the distribution network operation cost
  • the third layer is to minimize the expected voltage deviation.
  • the optimal result of the objective function determined by the first layer model is introduced into the second layer model. After reasonable relaxation, it is used as a restrictive constraint.
  • the objectives determined by the first and second layer models are both introduced into the third layer model. After solving the third layer model as a restrictive constraint, the optimal operation strategy of the distribution network is obtained. This model ensures that the distribution network system comprehensively considers safety, economy and reliability, and meets the requirements of safe and economic operation of the system.
  • This application adopts the relevant constraints of the power generation load transfer factor based on the decoupled linearized current that is more suitable for the operation of the distribution network to construct the system, carefully considers the relationship between the line resistance and the line reactance of the distribution network, and carefully considers the voltage amplitude change and the influence of reactive power of the distribution network. It can achieve the coordinated optimization of active current, reactive current and node voltage, significantly reduce the current calculation error, increase the reliability of the distribution network optimization results, and improve the practical engineering application value of the distribution network optimization.
  • This application adopts an affine strategy to process the uncertain variables in the model, and further adopts the duality theory to quantify the explicit representation of the net load disturbance domain that the node can accept, converting the disclosed model into a deterministic model, and then adopts power circle linearization and large M method to linearize the nonlinear part of the model, converting the model into a deterministic linearized model, thereby improving the computational efficiency and practical application value of the model.
  • the present application adopts the following technical solutions:
  • the present application provides a method for coordinated multi-level voltage control of a distribution network based on three-layer priority targets, comprising:
  • the first-level objective is to maximize the net load disturbance domain that a node can accommodate
  • the second-level objective is to minimize the distribution network operating cost
  • the third-level objective is to minimize the distribution network's expected voltage deviation.
  • Energy storage system models and multiple reactive power regulation device models are introduced into the dispatch model. System operation constraints are established using a generation load transfer factor based on decoupled linearized power flows.
  • This model also known as a three-level optimization objective planning model, is a multi-level voltage coordinated control method for the distribution network.
  • the affine strategy and duality theory are used to transform the node voltage uncertainty and the unit regulation disturbance output uncertainty into deterministic ones, and the model is converted into a deterministic model.
  • the power circle linearization method is used to linearly express the branch capacity constraints
  • the large M method is used to linearly express the adjustment number constraints of the capacitor bank and the on-load tap-changing transformer, so that the model is converted into a linearized model.
  • the linear programming algorithm is used to solve the distribution network multi-level voltage coordinated control method model based on the three-layer priority target, and the operation and scheduling scheme of each subject of the power system is obtained.
  • the node can accept the net load disturbance domain including the upstream node can accept the net load disturbance domain and the downstream node can accept the net load disturbance domain;
  • the distribution network operating cost includes the unit operating cost, the upper power grid purchase cost, the wind and solar power abandonment and load shedding cost, and the energy storage scheduling cost;
  • the expected voltage deviation is the sum of the differences between the actual voltage of all nodes of the 35kv-10kv-0.38kv distribution network and the predicted voltage value of each node in 24 hours.
  • the constraints of the distribution network multi-level voltage coordinated control method model based on the three-layer priority target include total power balance constraint, node acceptable net load disturbance domain capacity constraint, active power flow constraint, reactive power flow constraint, node voltage constraint and unit output constraint, upper grid power supply constraint, photovoltaic wind turbine output constraint, wind and solar curtailment load shedding constraint, capacitor bank constraint, energy storage operation constraint, static VAR compensator constraint;
  • the nonlinear terms and nonlinear constraints in the objective function are linearized through a linearization method.
  • the uncertainty variables in the constraints are processed based on an affine strategy, and the model is further converted into a deterministic model using duality theory. Finally, the entire model is converted into a deterministic linear programming model, which can be solved using a mature commercial solver to obtain an optimized distribution network scheduling plan.
  • the maximization node can accept the net load disturbance domain including the upward acceptable disturbance domain and the downward acceptable disturbance domain;
  • the total cost of distribution network operation includes the operating cost, the cost of wind and solar power curtailment and load shedding, and the energy storage operation cost;
  • the minimum expected deviation includes the total expected deviation of the three-level voltage of the distribution network 35kv-10kv-0.38kv.
  • the specific solution process includes:
  • the distribution network generation load transfer factor based on decoupled linearized power flow is used to quantitatively represent the amplitude and phase angle of branch power flow and node voltage.
  • the calculation formula for the amplitude and phase angle of branch power flow and node voltage expressed by the generation load transfer factor is obtained, which meets the requirement of the distribution network to accurately consider the changes in voltage amplitude and reactive power.
  • the affine strategy and duality theory are used to eliminate the uncertainty variables in the model, quantify the net load disturbance domain that the node can accept, and transform the disclosed model into a deterministic model.
  • This application takes maximizing the node's acceptable net load disturbance domain, minimizing the total cost of distribution network operation, and minimizing the expected voltage deviation as the three-layer objective function. It can more clearly characterize the uncertainty of distributed power output and the impact of distributed power output on the system's backup capacity, and improve the problem of vague description of distributed power output characteristics; it optimizes the distribution network voltage deviation in a targeted manner, and effectively improves the multi-level voltage over-limit and voltage fluctuation problems of the distribution network caused by large-scale access of distributed power sources; it comprehensively considers the safety, economy and reliability of distribution network operation, making the optimization results more practical in engineering.
  • the present application based on the same inventive concept also provides a distribution network multi-level voltage coordinated control system based on three-layer priority targets, including:
  • Parameter acquisition module used to obtain distribution network operating parameters
  • a target solving module is used to solve the three-layer optimization target programming model based on the distribution network operating parameters and a pre-built three-layer optimization target programming model using affine theory, duality theory, power circle linearization and absolute value linearization methods to obtain the maximum node acceptable net load disturbance domain, the minimum distribution network operation total cost and the minimum expected voltage deviation;
  • a control module is used to coordinately control various reactive devices in the distribution network according to the distribution network operation parameters corresponding to maximizing the node's acceptable net load disturbance domain, minimizing the total cost of distribution network operation, and minimizing the expected voltage deviation;
  • the three-layer optimization target planning model is constructed by maximizing the node's acceptable net load disturbance domain, minimizing the total cost of distribution network operation, and minimizing the expected voltage deviation as the three-layer objective function, and setting constraints for the three-layer objective function.
  • model building module for:
  • the first-level optimization objective is to maximize the node's acceptable net load disturbance domain
  • the second-level optimization objective is to minimize the total cost of distribution network operation
  • the third-level optimization objective is to minimize the expected voltage deviation
  • the constraint conditions include: constraints under the net load forecast value and constraints under the net load disturbance.
  • constraints under the net load forecast value include: total power balance constraints under the net load forecast value, line flow constraints, branch capacity constraints, node voltage upper and lower limit constraints, unit output constraints, energy storage constraints, upper-level power grid power supply constraints, curtailment of solar power, curtailment of wind power, load shedding constraints, group switching of capacitors constraints, and static VAR compensator (SVC) constraints;
  • SVC static VAR compensator
  • the constraints under the net load disturbance include: total power balance constraints under net load disturbance, net load acceptance domain constraints, line flow constraints, line capacity constraints, node voltage upper and lower limit constraints, generator-related constraints, energy storage constraints, upper-level power grid power supply constraints, curtailment of solar and wind power, load shedding constraints, grouped capacitor switching constraints, and static VAR compensator constraints.
  • the first-layer optimization objective is as follows:
  • Z1 is the first layer objective function, is the net load disturbance range that the upstream node can accept, is the net load disturbance domain that the downstream node can accept, NT is the set of optimization time periods; NI is the set of nodes; i is the node number; t is the optimization time period;
  • Coper is the operating cost
  • Ccut is the load shedding cost of wind and solar power curtailment
  • Cess is the energy storage cost
  • Z 3 is the third layer objective function, is the predicted value of node voltage, and Vi ,t is the actual node voltage.
  • the target solving module includes:
  • the deterministic conversion submodule is used to convert the uncertainty formula in the three-level optimization target programming model into a deterministic formula using affine theory and duality theory;
  • a linearization submodule is used to convert the nonlinear formula in the three-level optimization target programming model into a linear formula using power circle linearization and absolute value linearization methods;
  • the solving submodule is used to solve the converted three-layer optimization target programming model to obtain the maximum node acceptable net load disturbance domain, the minimum total cost of distribution network operation and the minimum expected voltage deviation.
  • deterministic conversion submodule is specifically used to:
  • Affine theory is used to transform the node voltage uncertainty in the three-level optimization target programming model by introducing an auxiliary variable to obtain a deterministic variable.
  • the uncertainty of unit regulation disturbance output is transformed by duality theory, and the net load disturbance domain that can be accepted by the node is obtained.
  • linearization submodule is specifically used to:
  • the uncertainty formula that can be piecewise linearized in the three-level optimization target programming model is linearized using the power circle linearization method
  • the absolute value linearization method is used to linearize the absolute value in the uncertainty formula.
  • the feasible domain of the uncertainty formula is the interior of the circle, and the regular polygon inscribed in the circle is approximated as the circle corresponding to the feasible domain of the uncertainty formula;
  • the present application also provides a computer device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium.
  • the processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • the present application also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data.
  • the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device.
  • the computer-readable storage medium provides a storage space that stores the operating system of the terminal.
  • one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more computer programs (including program codes).
  • the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory.
  • the processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the distribution network multi-level voltage coordinated control method based on three-level priority targets in the above embodiment.
  • the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.
  • a computer-usable storage media including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.
  • each flow process and/or box in the flow chart and/or block diagram and the combination of the flow process and/or box in the flow chart and/or block diagram can be realized by computer program instructions.
  • These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processing machine or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for realizing the function specified in one flow chart flow or multiple flows and/or one box or multiple boxes of the block diagram.
  • These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.
  • These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.
  • the present application provides a method and system for coordinated control of multi-level voltage in a distribution network based on three-layer priority objectives, the method comprising: obtaining distribution network operating parameters; based on the distribution network operating parameters and a pre-constructed three-layer optimization target planning model, using affine theory, duality theory, power circle linearization and absolute value linearization methods to solve the three-layer optimization target planning model, and obtaining the domain of net load disturbance that can be accepted by the node, minimizing the total cost of distribution network operation and minimizing the expected voltage deviation; coordinated control of various reactive devices in the distribution network is performed based on the distribution network operating parameters corresponding to maximizing the domain of net load disturbance that can be accepted by the node, minimizing the total cost of distribution network operation and minimizing the expected voltage deviation.
  • the present application can more clearly characterize the uncertainty of the output of distributed power sources and the impact of the output of distributed power sources on the system's backup capacity, and improve the problems of multi-level voltage over-limit and voltage fluctuation in the distribution network caused by the fuzzy description of the output characteristics of distributed power sources and large-scale access.

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Abstract

本申请提供了一种基于三层优先目标的配电网多级电压协同控制方法及系统，该方法包括：获取配电网运行参数；基于配电网运行参数和预先构建的三层优化目标规划模型，采用仿射理论、对偶理论、功率圆线性化和绝对值线性化方法对三层优化目标规划模型进行求解，得到最大化节点可接纳净负荷扰动域、最小化配电网运行总成本和最小化期望电压偏差；由最大化节点可接纳净负荷扰动域、最小化配电网运行总成本和最小化期望电压偏差对应的配电网运行参数对配电网的各种无功设备进行协同控制。本申请可以更清晰的刻画分布式电源出力的不确定性，以及分布式电源出力对系统备用容量的影响，改善分布式电源出力特性描述模糊、大规模接入造成的配电网多级电压越限和电压波动的问题。

Description

基于三层优先目标的配电网多级电压协同控制方法及系统
相关申请的交叉引用
本申请基于申请号为202410264658.7、申请日为2024年03月08日、申请名称为“基于三层优先目标的配电网多级电压协同控制方法及系统”的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此引入本申请作为参考。
技术领域
本申请涉及电力系统配电网优化调度领域，具体涉及基于三层优先目标的配电网多级电压协同控制方法及系统。
背景技术
随着风电、光伏等分布式能源在配电网中的占比不断提高，分布式电源出力的波动性造成配电网的电能质量日益恶化。为解决分布式能源接入下带来的不确定性，提高配电网运行的安全性、经济性和可靠性，需要对配电网进行多电压等级的协调控制和优化。
目前，大部分研究通过网络损耗最小或电压波动最小来实现配电网电压控制，且只考虑单级电压。现有技术中存在以下几种情况：(1)在理论上分析分布式光伏接入对配电网电能质量产生的影响，并进行仿真分析。(2)利用改进型原始对偶内点法，研究多级协调的配电网电压质量优化方法。(3)研究基于分布式光伏集群的配电网电压多级协调控制策略。(4)以最小化网络损耗成本为目标函数，通过粒子群优化求解无功电压多级控制模型。
此外，传统的以区间或概率分布描述分布式电源出力波动的方式，无法准确表达分布式电源随天气变化带来的不确定性。
发明内容
为了解决现有技术通过网络损耗最小或电压波动最小来实现配电网电压控制，且只考虑单级电压，以及以区间或概率分布描述分布式电源出力波动的方式，无法准确表达分布式电源随天气变化带来的不确定性的问题，本申请提出了基于三层优先目标的配电网多级电压协同控制方法，包括：
获取配电网运行参数；
基于所述配电网运行参数和预先构建的三层优化目标规划模型，采用仿射理论、对偶理论、功率圆线性化和绝对值线性化方法对所述三层优化目标规划模型进行求解，得到最大化节点可接纳净负荷扰动域、最小化配电网运行总成本和最小化期望电压偏差；
由所述最大化节点可接纳净负荷扰动域、最小化配电网运行总成本和最小化期望电压偏差对应的配电网运行参数对配电网的各种无功设备进行协同控制；
其中，三层优化目标规划模型是以最大化节点可接纳净负荷扰动域，最小化配电网运行总成本，最小化期望电压偏差作为三层目标函数，并为所述三层目标函数设置约束条件构建的。
在一些实施例中，所述三层优化目标规划模型的构建包括：
以最大化节点可接纳净负荷扰动域构建第一层优化目标；
以最小化配电网运行总成本构建第二层优化目标；
以最小化期望电压偏差构建第三层优化目标；
为所述第一层优化目标、第二层优化目标和第三层优化目标设置约束条件；
所述约束条件包括：净负荷预测值下的约束和净负荷扰动下的约束。
在一些实施例中，所述净负荷预测值下的约束包括：净负荷预测值下的总功率平衡约束、线路潮流约束、支路容量约束、节点电压上下限约束、机组出力约束、储能约束、上级电网供电约束、弃光、弃风、切负荷约束、分组投切电容器约束、静止无功补偿器SVC约束；
所述净负荷扰动下的约束包括：净负荷扰动下的总功率平衡约束、净负荷可接纳域约束、线路潮流约束、线路容量约束、节点电压上下限约束、发电机相关约束、储能约束、上级电网供电约束、弃光弃风切负荷约束、分组投切电容器约束、静止无功补偿器约束。
在一些实施例中，所述第一层优化目标如下式所示：
式中，Z1为第一层目标函数，为向上节点可接纳净负荷扰动域，为向下节点可接纳净负荷扰动域，NT表示优化时段的集合；NI表示节点的集合；i为节点编号；t为优化时段；
所述第二层优化目标如下式所示：
Z2＝min(Coper+Ccut+Cess)
式中，Z2为第二层目标函数，Coper为运行成本，Ccut为弃风弃光切负荷成本，Cess为储能成本；
所述第三层优化目标如下式所示：
式中，Z3为第三层目标函数，为节点电压预测值，Vi,t为节点实际电压。
在一些实施例中，所述基于所述配电网运行参数和预先构建的三层优化目标规划模型，采用仿射理论、对偶理论、功率圆线性化和绝对值线性化方法对所述三层优化目标规划模型进行求解，得到最大化节点可接纳净负荷扰动域、最小化配电网运行总成本和最小化期望电压偏差，包括：
采用仿射理论和对偶理论将三层优化目标规划模型中的不确定性公式转化为确定性公式；
采用功率圆线性化和绝对值线性化方法将三层优化目标规划模型中的非线性公式转化为线性公式；
对转化后的三层优化目标规划模型进行求解，得到最大化节点可接纳净负荷扰动域、最小化配电网运行总成本和最小化期望电压偏差。
在一些实施例中，所述采用仿射理论和对偶理论将三层优化目标规划模型中的不确定性公式转化为确定性公式，包括：
采用仿射理论通过引入一个辅助变量对三层优化目标规划模型中的节点电压不确定性进行转化，得到确定性变量；
采用对偶理论对机组调节扰动出力不确定性进行转化，得到表示节点可接纳净负荷扰动域。
在一些实施例中，所述采用功率圆线性化和绝对值线性化方法将三层优化目标规划模型中的非线性公式转化为线性公式，包括：
将三层优化目标规划模型中可以进行分段线性化的不确定性公式采用功率圆线性化方法进行线性化；
采用绝对值线性化方法对所述不确定性公式中的绝对值进行线性化处理。
在一些实施例中，所述将三层优化目标规划模型中可以进行分段线性化的不确定性公式采用功率圆线性化方法进行线性化，包括：
所述不确定性公式的可行域均为圆的内部，将圆内接正多边形近似为所述不确定性公式的可行域对应的圆；
将所述正多边形围成的面积近似代替圆形围成的面积，将所述不确定性公式转化为所述正多边形的面积计算式。
在一些实施例中，配电网运行参数包括：配电网拓扑结构、配电网线路容量和电阻电抗值、分时电价、可控发电机数据、上级电网数据、负荷数据。
再一方面本申请还提供了基于三层优先目标的配电网多级电压协同控制系统，包括：
参数获取模块，用于获取配电网运行参数；
目标求解模块，用于基于所述配电网运行参数和预先构建的三层优化目标规划模型，采用仿射理论、对偶理论、功率圆线性化和绝对值线性化方法对所述三层优化目标规划模型进行求解，得到最大化节点可接纳净负荷扰动域、最小化配电网运行总成本和最小化期望电压偏差；
控制模块，用于由所述最大化节点可接纳净负荷扰动域、最小化配电网运行总成本和最小化期望电压偏差对应的配电网运行参数对配电网的各种无功设备进行协同控制；
其中，三层优化目标规划模型是以最大化节点可接纳净负荷扰动域，最小化配电网运行总成本，最小化期望电压偏差作为三层目标函数，并为所述三层目标函数设置约束条件构建的。
在一些实施例中，还包括模型构建模块，用于：
以最大化节点可接纳净负荷扰动域构建第一层优化目标；
以最小化配电网运行总成本构建第二层优化目标；
以最小化期望电压偏差构建第三层优化目标；
为所述第一层优化目标、第二层优化目标和第三层优化目标设置约束条件；
所述约束条件包括：净负荷预测值下的约束和净负荷扰动下的约束。
在一些实施例中，所述净负荷预测值下的约束包括：净负荷预测值下的总功率平衡约束、线路潮流约束、支路容量约束、节点电压上下限约束、机组出力约束、储能约束、上级电网供电约束、弃光、弃风、切负荷约束、分组投切电容器约束、静止无功补偿器SVC约束；
所述净负荷扰动下的约束包括：净负荷扰动下的总功率平衡约束、净负荷可接纳域约束、线路潮流约束、线路容量约束、节点电压上下限约束、发电机相关约束、储能约束、上级电网供电约束、弃光弃风切负荷约束、分组投切电容器约束、静止无功补偿器约束。
在一些实施例中，所述第一层优化目标如下式所示：
式中，Z1为第一层目标函数，为向上节点可接纳净负荷扰动域，为向下节点可接纳净负荷扰动域，NT表示优化时段的集合；NI表示节点的集合；i为节点编号；t为优化时段；
所述第二层优化目标如下式所示：
Z2＝min(Coper+Ccut+Cess)
式中，Z2为第二层目标函数，Coper为运行成本，Ccut为弃风弃光切负荷成本，Cess为储能成本；
所述第三层优化目标如下式所示：
式中，Z3为第三层目标函数，为节点电压预测值，Vi,t为节点实际电压。
在一些实施例中，目标求解模块包括：
确定性转化子模块，用于采用仿射理论和对偶理论将三层优化目标规划模型中的不确定性公式转化为确定性公式；
线性化子模块，用于采用功率圆线性化和绝对值线性化方法将三层优化目标规划模型中的非线性公式转化为线性公式；
求解子模块，用于对转化后的三层优化目标规划模型进行求解，得到最大化节点可接纳净负荷扰动域、最小化配电网运行总成本和最小化期望电压偏差。
在一些实施例中，确定性转化子模块具体用于：
采用仿射理论通过引入一个辅助变量对三层优化目标规划模型中的节点电压不确定性进行转化，得到确定性变量；
采用对偶理论对机组调节扰动出力不确定性进行转化，得到表示节点可接纳净负荷扰动域。
在一些实施例中，线性化子模块具体用于：
将三层优化目标规划模型中可以进行分段线性化的不确定性公式采用功率圆线性化方法进行线性化；
采用绝对值线性化方法对所述不确定性公式中的绝对值进行线性化处理。
在一些实施例中，所述线性化子模块中的将三层优化目标规划模型中可以进行分段线性化的不确定性公式采用功率圆线性化方法进行线性化的具体实现步骤包括：
所述不确定性公式的可行域均为圆的内部，将圆内接正多边形近似为所述不确定性公式的可行域对应的圆；
将所述正多边形围成的面积近似代替圆形围成的面积，将所述不确定性公式转化为所述正多边形的面积计算式。
再一方面，本申请还提供了一种计算设备，包括：至少一个处理器和存储器；
所述存储器，用于存储一个或多个程序；
当所述一个或多个程序被所述至少一个处理器执行时，实现如上述所述的基于三层优先目标的配电网多级电压协同控制方法。
再一方面，本申请还提供了一种计算机可读存储介质，其上存有计算机程序，所述计算机程序被执行时，实现如上述所述的基于三层优先目标的配电网多级电压协同控制方法。
与现有技术相比，本申请的有益效果为：
本申请提供了基于三层优先目标的配电网多级电压协同控制方法，包括：获取配电网运行参数；基于所述配电网运行参数和预先构建的三层优化目标规划模型，采用仿射理论、对偶理论、功率圆线性化和绝对值线性化方法对所述三层优化目标规划模型进行求解，得到最大化节点可接纳净负荷扰动域、最小化配电网运行总成本和最小化期望电压偏差；由所述最大化节点可接纳净负荷扰动域、最小化配电网运行总成本和最小化期望电压偏差对应的配电网运行参数对配电网的各种无功设备进行协同控制；其中，三层优化目标规划模型是以最大化节点可接纳净负荷扰动域，最小化配电网运行总成本，最小化期望电压偏差作为三层目标函数，并为所述三层目标函数设置约束条件构建的。本申请以最大化节点可接纳净负荷扰动域，最小化配电网运行总成本，最小化期望电压偏差作为三层目标函数，可以更清晰的刻画分布式电源出力的不确定性，以及分布式电源出力对系统备用容量的影响，改善分布式电源出力特性描述模糊的问题；针对性优化配电网电压偏差，有效改善分布式电源大规模接入造成的配电网多级电压越限和电压波动问题；统筹考虑配电网运行的安全性、经济性和可靠性，使优化结果更具有工程实用性。
附图说明
图1为本申请的基于三层优先目标的配电网多级电压协同控制方法流程图；
图2为本申请的具体实施例的配电网多级电压协同控制方法流程图；
图3为本申请的节点可接纳净负荷扰动域范围示意图；
图4为本申请的机组运行成本分段线性化示意图；
图5为本申请的支路容量约束和机组容量约束线性化示意图。
具体实施方式
本申请提出基于三层优先目标的配电网多级电压协同控制方法及系统，构造三层优先目标规划模型，第一层优化目标为最大化节点可接纳净负荷扰动域，以区间端点可优化的形式，更灵活的描述分布式电源出力的不确定性，降低分布式电源扰动对系统电压的影响，以最大化消纳分布式电源出力为目标，减少传统能源出力成本，满足双碳目标；第二层优化目标为最小化配电网运行总成本，满足电力系统运行经济性的要求；第三层优化目标为最小化期望电压偏差，当采用常规控制技术增加分布式发电时，每个节点的电压变化就会成为一个难题，为了使电压变化不损害用户的设备和电力系统的其他装置，应将节点电压保持在合理的范围之内，通过调节系统中各种无功设备，优化配电网各节点电压。同时采用解耦线性化潮流的发电负荷转移因子构建潮流表达式，更准确的描述配电网电压的变化。通过三层目标函数，统筹考虑配电网的安全性、经济性和可靠性，以更合理的方式实现配电网多级电压协同控制。
本申请采用仿射理论和对偶理论将不确定性模型转化为确定性模型，采用功率圆线性化和绝对值线性化方法将非线性模型转化为线性模型，提高了计算效率和求解准确度。
实施例1：
基于三层优先目标的配电网多级电压协同控制方法，如图1所示，包括：
步骤1：获取配电网运行参数；
步骤2：基于所述配电网运行参数和预先构建的三层优化目标规划模型，采用仿射理论、对偶理论、功率圆线性化和绝对值线性化方法对所述三层优化目标规划模型进行求解，得到最大化节点可接纳净负荷扰动域、最小化配电网运行总成本和最小化期望电压偏差；
步骤3：由所述最大化节点可接纳净负荷扰动域、最小化配电网运行总成本和最小化期望电压偏差对应的配电网运行参数对配电网的各种无功设备进行协同控制；
其中，三层优化目标规划模型是以最大化节点可接纳净负荷扰动域，最小化配电网运行总成本，最小化期望电压偏差作为三层目标函数，并为所述三层目标函数设置约束条件构建的。
本申请提出的一种基于三层优先目标的配电网多级电压协同控制方法及系统，采用三层目标规划理论，按照实际需求的优先级，合理统筹考虑配电网的安全性、经济性和可靠性，将节点可接纳净负荷扰动域引入配电网运行优化中，能够实现负荷侧与电源侧的良好互济，采用基于解耦线性化潮流的发电负荷转移因子构建系统运行约束，实现有功潮流、无功潮流和电压的联合优化，同时对约束采用功率圆线性化和0/1变量线性化进行线性化转换，将配电网的大规模MINLP(Mixed Integer Nonlinear Programming，混合整数非线性规划)问题转化为MIP(Mixed Integer Linear Programming，混合整数线性规划)问题，使用仿射策略和对偶理论消除模型中的不确定性，实现模型的高效求解。
在步骤1之前还包括构建三层优化目标规划模型，该三层优化目标规划模型的构建过程具体如下：
1.1目标函数：

Z2＝min(Coper+Ccut+Cess)       (2)
该模型有三层目标函数，第一层目标函数Z1为最大化节点可接纳净负荷扰动域，如式(1)所示，包括向上节点可接纳净负荷扰动域和向下节点可接纳净负荷扰动域第二层目标函数Z2为最小化配电网运行总成本，如式(2)所示，包括运行成本Coper、弃风弃光切负荷成本Ccut、储能成本Cess；第三层目标函数Z3为最小化期望电压偏差，如式(3)所示期望电压偏差为配电网所有节点实际电压Vi,t与各节点电压预测值的差值的绝对值之和，i为节点编号，t为优化时段。第二层目标函数成本可以明确表示如下：
式中，为配电网向上级电网购电成本，为机组运行成本，为切负荷成本，为弃风成本，为弃光成本；NT表示优化时段的集合；NI表示节点的集合；NF表示上级电站的集合；NG表示发电机组的集合；NW表示风机的集合；NPV表示光伏的集合；NE表示储能装置的集合；和分别为上级电站f在t时间段内提供有功功率和无功功率的成本系数；和分别表示净负荷为预测值时t时间段内上级电网f发出的有功功率和无功功率；表示净负荷为预测值时t时间段内发电机g的有功出力，即有功运行基点；ag、bg、cg为发电机g的运行成本系数；和分别表示t时间段内发电机g可以提供的向上备用和向下备用；分别表示发电机提供向上备用和向下备用的成本；σL、σw和σpv分别为切负荷、弃风和弃光的惩罚成本系数；ΔPi,t和ΔQi,t分别为t时段内节点i的有功和无功切负荷量；ΔPw,t和ΔPpv,t分别为t时段内风机w的弃风量和光伏pv的弃光量；ρE为储能充放电成本系数；和分别为t时段内储能e的充电功率和放电功率，e为储能装置的编号，f为上级电站或电网的编号。
1.2约束条件
基于三层优先目标的配电网多级电压协同控制方法模型中的约束包括净负荷预测值下的约束和净负荷扰动下的约束。
净负荷预测值下的约束：
(1)总功率平衡式
式中，表示净负荷为预测值时t时间段内发电机g的有功出力，即有功运行基点；为t时间段内节点i的净负荷预测有功值，表示在净负荷预测值下t时间段内发电机g的无功出力，即无功运行基点；表示t时间段内节点i的净负荷预测无功值。NSVC表示静止无功补偿器svc的集合；表示在净负荷预测值下t时间段内静止无功补偿器svc提供的无功功率；NCB表示电容器组cb的集合；表示在净负荷预测值下t时间段内电容器组cb提供的无功功率；g为发电机的编号；
式(7)确保在任一时间段内，总的发电机出力(有功或无功)和上级电网出力(有功或无功)变化之和等于总的净负荷预测值(有功或无功)变化之和。
(2)线路潮流计算式
式中，ref为净负荷预测值下的参数和变量，rer为参考节点，分别表示净负荷预测值下时间段t内线路ij的有功潮流和无功潮流；loc(g)＝k表示节点k上连接的发电机g；分别表示有功潮流关于节点k上的有功和无功功率注入的发电负荷转移因子；分别表示无功潮流关于节点k上的有功和无功功率注入的发电负荷转移因子；bf为二进制变量，为1时表示上级电网与节点k直接连接，为0时表示上级电网不与节点k直接连接；loc(svc)＝k表示节点k上连接的静止无功补偿器；loc(cb)＝k表示节点k上连接的电容器组；为t时间段内节点k上连接的发电机g的有功出力，为t时间段内发电机g的无功出力，为t时间段内节点k上连接的发电机g的无功出力，为t时间段内静止无功补偿器svc的无功出力，为t时间段内节点k上连接的静止无功补偿器svc的无功出力，为t时间段内电容器组b的无功出力，为t时间段内节点k上连接的电容器组cb的无功出力，g为发电机组编号，i和j均为节点编号，NE为储能装置的集合，e为储能装置的编号，t为优化时段，分别为有功参数和无功参数，满足式(9)：
式中，NL＝{NLnone,NLend,NLstart}；NLnone表示不与参考节点直接连接的支路集合；NLstart表示以参考节点为起始节点的支路集合；NLend表示以参考节点为末端节点的支路集合；为有功潮流相对于节点k上的有功功率注入的发电负荷转移因子，Gk(ref)为节点k相连线路的电导预测值，Vref为电压幅值预测值，Bk(ref)为节点k相连线路的电纳预测值，θref为电压相角预测值，为有功潮流相对于节点k上的无功功率注入的发电负荷转移因子，为无功潮流相对于节点k上的有功功率注入的发电负荷转移因子，为无功潮流相对于节点k上的无功功率注入的发电负荷转移因子，bij为线路ij电纳，gij为线路ij的电导。
(3)支路容量约束：
式中，表示支路ij的容量上限。
式(10)表示支路上的总潮流不能超过支路容量限制。
(4)节点电压上下限约束
式中，Vi max和Vi min分别表示节点i的电压幅值上限和电压幅值下限；θi max、θi min分别表示节点i的电压相角上限和电压相角下限；式中，分别表示节点i的电压幅值关于节点k上的有功功率和无功功率注入的发电负荷转移因子；分别表示节点i的电压相角关于节点k上的有功功率和无功功率注入的发电负荷转移因子；和分别为电压幅值相关参数和电压相角相关参数，满足式(13)：
式中，分别表示节点i的电压幅值关于节点k上的有功功率和无功功率注入的发电负荷转移因子；Gk(ref)为节点k相连线路的电导预测值，Vref为电压幅值预测值，θref为电压相角预测值，Bk(ref)为节点k相连线路的电纳预测值。
式(11)的中间项和式(12)的中间项分别是基于解耦线性化潮流的发电负荷转移因子计算的节点电压幅值和节点电压相角，均需在规定的上下限范围内。
(5)机组出力约束
式中，和分别表示机组g的有功最小出力值和最大出力值；表示机组g的出力容量上限；和分别为机组g的向上爬坡容量和向下爬坡容量；Δt表示时间段t的持续时间；URg为机组g的向上爬坡速率，DRg为机组g的向下爬坡速率；表示净负荷为预测值时t时间段内发电机g的有功出力，即有功运行基点；表示净负荷为预测值时t-1时间段内发电机g的有功出力。
受发电机组自身物理特性的限制，机组出力均是有限的。式(14)的第一个公式表示发电机g的有功出力需在规定的上下限范围内，第二个公式表示发电机的总出力应需小于发电机组容量上限。式(15)为发电机组g提供向上备用和向下备用的上下限范围约束。式(16)为机组g向上爬坡速率和向下爬坡速率约束。
(6)储能约束
式中，和分别表示储能e的最大充电功率和最大放电功率；Ee,t表示储能e在时间段t储存的能量；Ee,t-1表示储能e在时间段t-1储存的能量；ηc和ηd分别表示储能的充、放电效率；和分别表示储能e在时间段t中储存能量的上限值和下限值；Estart和Eend分别表示一个充电周期内的初始能量和最终能量。本约束限制了储能装置的充放电功率和储存能量的变化。
(7)上级电网供电约束
式中，和分别表示上级电网f提供的有功功率和无功功率的最大值。该式约束了上级电网出力应在上下限范围内。
(8)弃光、弃风、切负荷约束
式中，和分别为t时间段光伏pv和风机w有功出力预测值；和分别为t时间段节点i的有功负荷和无功负荷；ΔPw,t为t时间段风机w的弃风量，ΔPpv,t为t时间段光伏pv的弃光量，为t时间段节点i的有功弃负荷量，为t时间段节点i的无功弃负荷量。
(9)分组投切电容器(CB)约束
式中，Qcb,t表示电容器组cb在t时段的无功出力，h表示挡位，NCcb,h,t表示当电容器组cb处于档位h时所投入电容器的组数，是参数；xcb,h,t为表示电容器组cb在t时刻是否处于档位h的0/1变量，该值为1表示电容器组cb在t时刻处于档位h，反之则不在；该式表明在t时段cb仅能处在某一个档位上。
式中，Bcb,t为0\1变量，当值为0时表示cb维持原状态不动作，当值为1时表示cb进行调节；Qcb,t-1为表示电容器组cb在t-1时段的无功出力；表示cb单位无功出力，表示t时刻cb最多可以调节的挡位数，表示在优化时段内，分组投切电容器组的最多动作次数。
(12)静止无功补偿器SVC约束
式中，和分别表示静止无功补偿器出力的上下限。
为了保证当净负荷随机波动时，备用能够成功和有效地传输，模型需要满足净负荷扰动下的约束，这些约束与净负荷预测值下的约束类似：
1)总功率平衡式
式中，和分别表示在t时间段内净负荷扰动下机组的随机有功和无功出力，和分别表示在t时间段内净负荷扰动下上级电网的随机有功和无功出力，表示在t时间段内净负荷扰动下静止无功补偿器的随机无功出力，表示在t时间段内净负荷扰动下分组投切电容器的随机无功出力，和分别表示在t时间段内节点i上随机的可接纳有功和无功净负荷。该式说明当节点可接纳净负荷波动时，为满足供需平衡，配电网出力也是随机波动的。
2)净负荷可接纳域约束
式中，表示t时刻i节点向下净负荷可接纳扰动域，表示t时刻i节点向上净负荷可接纳扰动域，和分别表示在t时间段内节点i上的净负荷扰动的上限和下限，这是由负荷特性和分布式能源出力的自然扰动规律决定的，是参数，可以通过概率预测方法来获取。
式(24)表示可接纳净负荷扰动域的大小在净负荷扰动的上下限范围之内(如图2所示)。
3)线路潮流计算式
式中，Pij,t和Qij,t表示净负荷扰动下t时间段内线路ij的有功和无功潮流；Pg,t表示扰动状态下t时间段内发电机g的有功出力，Pf,t表示扰动状态下t时间段内上级电网f的有功出力，表示扰动状态下t时间段内节点k上连接的发电机g的有功出力，Qg,t表示扰动状态下t时间段内发电机g的无功出力，表示扰动状态下t时间段内节点k上连接的发电机g的无功出力，Qsvc,t表示扰动状态下t时间段内静止无功补偿器svc的无功出力，Qsvck,t表示扰动状态下t时间段内节点k上连接的静止无功补偿器svc的无功出力，Qcb,t表示扰动状态下t时间段内电容器组b的无功出力，Qcbk,t表示扰动状态下t时间段内节点k上连接的电容器组cb的无功出力。根据式(25)，当净负荷在可接纳净负荷扰动域内波动时，线路上的潮流也是随机波动的。
4)线路容量约束
式中，为扰动状态下线路容量。
根据式(26)，当净负荷在可接纳净负荷扰动域内波动时，线路上的总潮流不能超过线路的容量上限。
5)节点电压上下限约束

式中，和分别为电压幅值相关参数和电压相角相关参数，满足式(13)。
式(27)和(28)表示，当净负荷在可接纳净负荷扰动域内波动时，节点电压幅值和相角需要在规定的上下限范围之内。
6)发电机相关约束
式中，表示发电机组g的出力容量上限，t表示优化时段。
式(29)表示，当净负荷在可接纳净负荷扰动域内波动时，发电机的有功出力需要在有功上下限范围之内，总出力需要小于容量上限。式(30)表示，当净负荷在可接纳净负荷扰动域内波动时，发电机提供的备用应大于系统所需求的备用。
储能约束、上级电网供电约束、弃光弃风切负荷约束、分组投切电容器约束、静止无功补偿器约束与净负荷预测值下的相关约束一样，不再赘述。
可以看出，在考虑了净负荷扰动时约束(23)-(30)中含有的一些参数和变量是随机的，因此其中包含有无数个约束。
步骤1：获取配电网运行参数；
配电网运行参数包括：
配电网拓扑结构、配电网线路容量和电阻电抗值、分时电价、可控发电机数据、上级电网数据、负荷数据。
步骤2：基于所述配电网运行参数和预先构建的三层优化目标规划模型，采用仿射理论、对偶理论、功率圆线性化和绝对值线性化方法对所述三层优化目标规划模型进行求解，得到最大化节点可接纳净负荷扰动域、最小化配电网运行总成本和最小化期望电压偏差，包括：
采用仿射理论和对偶理论将三层优化目标规划模型中的不确定性公式转化为确定性公式；
采用功率圆线性化和绝对值线性化方法将三层优化目标规划模型中的非线性公式转化为线性公式；
对转化后的三层优化目标规划模型进行求解，得到最大化节点可接纳净负荷扰动域、最小化配电网运行总成本和最小化期望电压偏差。
进一步的，所述采用仿射理论和对偶理论将三层优化目标规划模型中的不确定性公式转化为确定性公式，包括：
采用仿射理论通过引入一个辅助变量对三层优化目标规划模型中的节点电压不确定性进行转化，得到确定性变量；
采用对偶理论对机组调节扰动出力不确定性进行转化，得到表示节点可接纳净负荷扰动域。
进一步的，所述采用功率圆线性化和绝对值线性化方法将三层优化目标规划模型中的非线性公式转化为线性公式，包括：
将三层优化目标规划模型中可以进行分段线性化的不确定性公式采用功率圆线性化方法进行线性化；
采用绝对值线性化方法对所述不确定性公式中的绝对值进行线性化处理。
进一步的，所述将三层优化目标规划模型中可以进行分段线性化的不确定性公式采用功率圆线性化方法进行线性化，包括：
所述不确定性公式的可行域均为圆的内部，将圆内接正多边形近似为所述不确定性公式的可行域对应的圆；
将所述正多边形围成的面积近似代替圆形围成的面积，将所述不确定性公式转化为所述正多边形的面积计算式。
步骤2的具体步骤如下：
(1)模型的线性化处理
该模型目标函数的运行成本部分可以分段线性化，如图3所示；式(10)、(14)的第二个公式、(26)、(29)的第二个公式为二次约束，采用功率圆线性化方法将上述约束进行线性化，进行有效求解。
以式(29)的第二个公式为例，该式的可行域均为圆的内部，将圆内接正多边形近似该圆，考虑到精度和模型的计算效率，本公开采用圆内接十二边形围成的面积近似代替圆形围成的面积，如图4所示，最终式(29)可转化为式(31)：
式中，和为线性化功率圆约束对应的系数，随着划分的正多边形的边数而变化；α、β分别表示圆内接等u边形的两个相邻顶点的弧度角；u为圆内接多边形的边数；cb调节次数线性化，令中间变量εcb,t＝|Qcb,t-Qcb,t-1|，Qcb,t表示扰动状态下t时间段内电容器组b的无功出力，εcb,t为电容器组cb在t-1时刻到t时刻的无功出力变化值，Qcb,t-1为表示扰动状态下t-1时间段内电容器组b的无功出力，对式(21)中的绝对值进行线性化处理，可得:
式中，M为惩罚系数，原则上取无限大的数，δcb,t为辅助0\1变量，Bcb,t为表示电容器组cb是否动作的0\1变量，为电容器组cb的单位无功出力，为t时刻电容器组cb最多可调节的挡位数，。
(2)模型的确定性转化
该模型考虑了净负荷扰动的不确定性和因此造成的机组出力调节量变化、节点电压变化的不确定性，包含有无数个带有不确定性的等式约束和不等式约束，直接求解困难。为解决此问题，本公开采用仿射策略处理约束中的不确定性变量。这里的仿射策略又称为仿射理论。
仿射策略如式(35)所示，净负荷扰动状态下，发电机、上级电网、分组投切电容器、静止无功补偿器的总出力均为运行基点加上随即出力调整量，随即出力调整量为各项设配的参与因子和系统总净负荷扰动的乘积。由于上述设备的出力调整量总和需和净负荷扰动值总和相等，所以式(36)确保上述设备的总参与因子为1。
式中，分别表示机组g在时间段t的有功参与因子和无功参与因子；分别表示上级电网f在时间段t的有功参与因子和无功参与因子；表示静止无功补偿器svc在时间段t的无功参与因子；表示分组投切电容器cb在时间段t的无功参与因子； 分别表示机组g在时间段t的有功出力调整和无功出力调整；分别表示上级电网f在时间段t的有功出力调整和无功出力调整；表示在t时间段内节点i上随机的有功变化量，表示在t时间段内节点i上随机的无功变化量。分别表示静止无功补偿器和分组投切电容器在时间段t的无功出力调整。
以发电机提供的备用为例，将不确定模型转化为确定性模型。
各机组提供的备用由其参与因子和系统总的净负荷可接纳域决定，则机组g所需提供的向上、向下两个方向的备用分别表示为：
式中，为t时刻节点i的向上净负荷可接纳扰动域，为t时刻节点i的向下净负荷可接纳扰动域。
上级电网与无功补偿设备可提供的备用同理。
采用仿射策略后，总功率平衡式(7)、(23)的第一个公式、(30)自动满足，因此可以省略。可通过引入一个辅助变量zi,t，将不确定模型转为确定性模型。随机的净负荷可接纳扰动域可以显化表示为：
式中，ρ是一个比例系数，假设节点有功净负荷扰动与节点无功净负荷扰动为比例关系。
将式(35)代入基于净负荷扰动下的约束中，此时，发电机的随机调整量、上级电网随机调整量、无功补偿设备随机调整量、随机的节点电压幅值和相角、随机的线路潮流等可用随机的可接纳净负荷扰动值表示。则对于转化后的式(31)，在其范围内随意变化，该式都应成立。由于该式对于随机量最劣实现下成立，则对于不确定量的任何实现都成立，因此式(31)可转化为：
式中，ηi,g,t,u为对偶变量，为发电机g的出力容量上限。
将式(38)代入式(39)后，利用对偶理论，式(39)可转化为：
式中，为机组g在时间段t的有功参与因子，为机组g在时间段t的无功参与因子，ηi,g,t,u为对偶变量，ρ为一个比例系数，假设节点有功净负荷扰动与节点无功净负荷扰动为比例关系。
采用相同的方法，式(26)-(28)可以做相似的转化，此处不再赘述。通过上述转化，本模型转化为确定性的线性规划模型，可使用成熟的商业求解器进行求解
由于在配电网运行调度中，系统的安全性比经济性更为重要，因此采用优先目标规划方法将模型转为三层模型，使模型具有明显的等级顺序，第一层模型具有优先级，第二层模型比第一层模型多一个约束条件，保证优化后得到的总的净负荷可接纳域的值不小于第一层模型的优化调度结果，如式(41)所示。
式中，λ为保守度控制因子，用来控制优化结果的保守度，较大的值表示系统优化结果更倾向于电力系统安全性，较小的值表示系统优化结果更倾向于电力系统经济性，λ＝1时，第二层优化得到的总净负荷可接纳域与第一层优化得到的结果相同，λ＝0时，模型简化为确定性的经济调度问题；为第一层模型的目标函数优化结果。
因分布式电源接入为配电网电压带来严峻挑战，为解决电压越限问题，第三层模型针对性的以期望电压偏差最小为目标函数。
式中，Vi,t为t时刻i节点系统各节点实际电压，为t时刻i节点系统各节点理想电压。
可对绝对值进行线性化，公式(42)转化为：
式中，ZP为辅助变量。
第三层模型比第二层模型多一个约束条件，保证优化后得到的总的配电网运行成本的值不小于第二层模型的优化调度结果，如式(44)所示。
式中，Coper为配电网运行成本，Ccut为配电网弃风弃光切负荷成本，Cess储能运行成本为，为第二层模型优化后的配电网运行成本，π为保守度控制因子，用来控制优化结果的保守度，其作用与保守度控制因子λ相同，不再赘述。
步骤3：由所述最大化节点可接纳净负荷扰动域、最小化配电网运行总成本和最小化期望电压偏差对应的配电网运行参数对配电网的各种无功设备进行协同控制。
与现有技术相比，本申请的有益效果为：
(1)本申请构建了基于三层优先目标的配电网多级电压协同控制方法模型，采用节点可接纳净负荷扰动域量化描述节点可接纳分布式能源的能力与系统抵抗负荷扰动的能力，统筹考虑了分布式能源出力、储能系统和负荷扰动之间的协同互动能力，挖掘了多目标多主体新型电力系统的综合调节潜力。
(2)本申请基于优先目标规划理论构建了三层模型，第一层模型为最大化节点可接纳净负荷扰动域，第二层模型为最小化配电网运行成本，第三层为最小化期望电压偏差，同时将第一层模型决策出的目标函数最优结果引入第二层模型，合理松弛后作为限制性约束，将第一层与第二层模型决策出的目标均引入第三层模型，作为限制性约束后求解第三层模型，得到配电网最优运行策略。该模型确保了配电网系统统筹考虑安全性、经济性与可靠性，满足系统安全经济运行的要求。
(3)本申请采用了更适用于配电网运行的基于解耦线性化潮流的发电负荷转移因子构建系统的相关约束，精细考虑配电网线路电阻与线路电抗的大小关系，精细考虑配电网的电压幅值变化和无功功率的影响，能够实现对有功潮流无功潮流和节点电压的协同优化，明显降低潮流计算误差，增加了配电网优化结果的可靠性，提高了配电网优化的实际工程应用价值。
(4)本申请采用仿射策略处理模型中的不确定性变量，并进一步采用对偶理论量化显性表示节点可接纳净负荷扰动域，将本公开模型转化为确定性模型，又采用功率圆线性化和大M法对模型的非线性部分进行线性化处理，将模型转化为确定的线性化模型，提高了模型的计算效率和实际应用价值。
实施例2：
根据一些实施例，本申请采用如下技术方案：
第一方面，本申请提供一种基于三层优先目标的配电网多级电压协同控制方法，包括：
以节点可接纳净负荷扰动域最大为第一层目标，以配电网运行成本最小为第二层目标，以配电网期望电压偏差最小为第三层目标，并在调度模型中引入储能系统模型和多种无功调节设备模型，采用基于解耦线性化潮流的发电负荷转移因子构建系统运行约束，构建基于三层优先目标的配电网多级电压协同控制方法模型。这里的配电网多级电压协同控制方法模型又称为三层优化目标规划模型。
运用仿射策略和对偶理论将节点电压不确定性和机组调节扰动出力不确定性进行确定性转换，将模型转化为确定性模型，同时采用功率圆线性化方法将支路容量约束线性化表达，采用大M法对电容器组和有载调压变压器调节次数约束进行线性表达，使模型转化为线性化模型，最终通过线性规划算法求解基于三层优先目标的配电网多级电压协同控制方法模型，得到电力系统各主体的运行调度方案。
作为可选择的实施方式，节点可接纳净负荷扰动域包括向上节点可接纳净负荷扰动域和向下节点可接纳净负荷扰动域；配电网运行成本包括机组运行成本，上级电网购电成本，弃风、弃光及切负荷成本，储能调度成本；期望电压偏差为24小时配电网35kv-10kv-0.38kv所有节点实际电压与各节点电压预测值的差值之和。
作为可选择的实施方式，基于三层优先目标的配电网多级电压协同控制方法模型的约束条件包括总功率平衡约束、节点可接纳净负荷扰动域容量约束、有功潮流约束、无功潮流约束、节点电压约束和机组出力约束、上级电网供电约束、光伏风机出力约束、弃风弃光切负荷约束、电容器组约束、储能运行约束、静止无功补偿器约束；
作为可选择的实施方式，对目标函数中的非线性项以及非线性约束如机组运行成本、支路容量约束，机组容量约束通过线性化方法将其线性化，基于仿射策略处理约束中的不确定性变量并进一步采用对偶理论将模型转化为确定性模型，最终整个模型转化为确定性的线性规划模型，可以采用成熟的商业求解器进行求解，得到配电网优化调度方案。
作为可选择的实施方式，最大化节点可接纳净负荷扰动域包括向上可接纳扰动域和向下可接纳扰动域；配电网运行总成本包括运行成本、弃风弃光切负荷成本、储能运行成本；期望偏差最小包括配电网35kv-10kv-0.38kv三级电压总期望偏差。
作为可选择的实施方式，求解的具体过程包括：
(1)使用基于解耦线性化潮流的配电网发电负荷转移因子，定量表示支路潮流、节点电压的幅值和相角，得到支路潮流、节点电压的幅值和相角用发电负荷转移因子表示的计算公式，满足配电网需要精确考虑电压幅值变化和无功功率变化的要求。
(2)采用仿射策略和对偶理论消除模型中的不确定性变量，量化显性表示节点可接纳净负荷扰动域，将本公开模型转化为确定性模型。
(3)采用功率圆线性化的方法将支路容量约束和机组容量约束中的平方项线性化，利用大M法将电容器组的调节次数约束线性化，得到相应的线性化表达式。从而将优化调度模型改写为线性规划模型，使用线性规划求解器求解，其中支路容量约束和机组容量约束线性化示意图如图5所示。
本申请以最大化节点可接纳净负荷扰动域，最小化配电网运行总成本，最小化期望电压偏差作为三层目标函数，可以更清晰的刻画分布式电源出力的不确定性，以及分布式电源出力对系统备用容量的影响，改善分布式电源出力特性描述模糊的问题；针对性优化配电网电压偏差，有效改善分布式电源大规模接入造成的配电网多级电压越限和电压波动问题；统筹考虑配电网运行的安全性、经济性和可靠性，使优化结果更具有工程实用性。
实施例3：
基于同一发明构思的本申请还提供了基于三层优先目标的配电网多级电压协同控制系统，包括：
参数获取模块，用于获取配电网运行参数；
目标求解模块，用于基于所述配电网运行参数和预先构建的三层优化目标规划模型，采用仿射理论、对偶理论、功率圆线性化和绝对值线性化方法对所述三层优化目标规划模型进行求解，得到最大化节点可接纳净负荷扰动域、最小化配电网运行总成本和最小化期望电压偏差；
控制模块，用于由所述最大化节点可接纳净负荷扰动域、最小化配电网运行总成本和最小化期望电压偏差对应的配电网运行参数对配电网的各种无功设备进行协同控制；
其中，三层优化目标规划模型是以最大化节点可接纳净负荷扰动域，最小化配电网运行总成本，最小化期望电压偏差作为三层目标函数，并为所述三层目标函数设置约束条件构建的。
进一步的，还包括模型构建模块，用于：
以最大化节点可接纳净负荷扰动域构建第一层优化目标；
以最小化配电网运行总成本构建第二层优化目标；
以最小化期望电压偏差构建第三层优化目标；
为所述第一层优化目标、第二层优化目标和第三层优化目标设置约束条件；
所述约束条件包括：净负荷预测值下的约束和净负荷扰动下的约束。
进一步的，所述净负荷预测值下的约束包括：净负荷预测值下的总功率平衡约束、线路潮流约束、支路容量约束、节点电压上下限约束、机组出力约束、储能约束、上级电网供电约束、弃光、弃风、切负荷约束、分组投切电容器约束、静止无功补偿器SVC约束；
所述净负荷扰动下的约束包括：净负荷扰动下的总功率平衡约束、净负荷可接纳域约束、线路潮流约束、线路容量约束、节点电压上下限约束、发电机相关约束、储能约束、上级电网供电约束、弃光弃风切负荷约束、分组投切电容器约束、静止无功补偿器约束。
进一步的，所述第一层优化目标如下式所示：
式中，Z1为第一层目标函数，为向上节点可接纳净负荷扰动域，为向下节点可接纳净负荷扰动域，NT表示优化时段的集合；NI表示节点的集合；i为节点编号；t为优化时段；
所述第二层优化目标如下式所示：
Z2＝min(Coper+Ccut+Cess)
式中，Z2为第二层目标函数，Coper为运行成本，Ccut为弃风弃光切负荷成本，Cess为储能成本；
所述第三层优化目标如下式所示：
式中，Z3为第三层目标函数，为节点电压预测值，Vi,t为节点实际电压。
进一步的，目标求解模块包括：
确定性转化子模块，用于采用仿射理论和对偶理论将三层优化目标规划模型中的不确定性公式转化为确定性公式；
线性化子模块，用于采用功率圆线性化和绝对值线性化方法将三层优化目标规划模型中的非线性公式转化为线性公式；
求解子模块，用于对转化后的三层优化目标规划模型进行求解，得到最大化节点可接纳净负荷扰动域、最小化配电网运行总成本和最小化期望电压偏差。
进一步的，确定性转化子模块具体用于：
采用仿射理论通过引入一个辅助变量对三层优化目标规划模型中的节点电压不确定性进行转化，得到确定性变量；
采用对偶理论对机组调节扰动出力不确定性进行转化，得到表示节点可接纳净负荷扰动域。
进一步的，线性化子模块具体用于：
将三层优化目标规划模型中可以进行分段线性化的不确定性公式采用功率圆线性化方法进行线性化；
采用绝对值线性化方法对所述不确定性公式中的绝对值进行线性化处理。
进一步的，所述线性化子模块中的将三层优化目标规划模型中可以进行分段线性化的不确定性公式采用功率圆线性化方法进行线性化的具体实现步骤包括：
所述不确定性公式的可行域均为圆的内部，将圆内接正多边形近似为所述不确定性公式的可行域对应的圆；
将所述正多边形围成的面积近似代替圆形围成的面积，将所述不确定性公式转化为所述正多边形的面积计算式。
实施:4：
基于同一种发明构思，本申请还提供了一种计算机设备，该计算机设备包括处理器以及存储器，所述存储器用于存储计算机程序，所述计算机程序包括程序指令，所述处理器用于执行所述计算机存储介质存储的程序指令。处理器可能是中央处理单元(Central Processing Unit，CPU)，还可以是其他通用处理器、数字信号处理器(Digital Signal Processor、DSP)、专用集成电路(Application SpecificIntegrated Circuit，ASIC)、现成可编程门阵列(Field-Programmable GateArray，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等，其是终端的计算核心以及控制核心，其适于实现一条或一条以上指令，具体适于加载并执行计算机存储介质内一条或一条以上指令从而实现相应方法流程或相应功能，以实现上述实施例中基于三层优先目标的配电网多级电压协同控制方法的步骤。
实施例5：
基于同一种发明构思，本申请还提供了一种存储介质，具体为计算机可读存储介质(Memory)，所述计算机可读存储介质是计算机设备中的记忆设备，用于存放程序和数据。可以理解的是，此处的计算机可读存储介质既可以包括计算机设备中的内置存储介质，当然也可以包括计算机设备所支持的扩展存储介质。计算机可读存储介质提供存储空间，该存储空间存储了终端的操作系统。并且，在该存储空间中还存放了适于被处理器加载并执行的一条或一条以上的指令，这些指令可以是一个或一个以上的计算机程序(包括程序代码)。需要说明的是，此处的计算机可读存储介质可以是高速RAM存储器，也可以是非不稳定的存储器(non-volatile memory)，例如至少一个磁盘存储器。可由处理器加载并执行计算机可读存储介质中存放的一条或一条以上指令，以实现上述实施例中基于三层优先目标的配电网多级电压协同控制方法的步骤。
本领域内的技术人员应明白，本申请的实施例可提供为方法、系统、或计算机程序产品。因此，本申请可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本申请可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本申请是参照根据本申请实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
以上仅为本申请的实施例而已，并不用于限制本申请，凡在本申请的精神和原则之内，所做的任何修改、等同替换、改进等，均包含在发明待批的本申请的权利要求范围之内。
工业实用性
本申请提供了一种基于三层优先目标的配电网多级电压协同控制方法及系统，该方法包括：获取配电网运行参数；基于配电网运行参数和预先构建的三层优化目标规划模型，采用仿射理论、对偶理论、功率圆线性化和绝对值线性化方法对三层优化目标规划模型进行求解，得到最大化节点可接纳净负荷扰动域、最小化配电网运行总成本和最小化期望电压偏差；由最大化节点可接纳净负荷扰动域、最小化配电网运行总成本和最小化期望电压偏差对应的配电网运行参数对配电网的各种无功设备进行协同控制。本申请可以更清晰的刻画分布式电源出力的不确定性，以及分布式电源出力对系统备用容量的影响，改善分布式电源出力特性描述模糊、大规模接入造成的配电网多级电压越限和电压波动的问题。

Claims (12)

  1. 基于三层优先目标的配电网多级电压协同控制方法，包括：
    获取配电网运行参数；
    基于所述配电网运行参数和预先构建的三层优化目标规划模型，采用仿射理论、对偶理论、功率圆线性化和绝对值线性化方法对所述三层优化目标规划模型进行求解，得到最大化节点可接纳净负荷扰动域、最小化配电网运行总成本和最小化期望电压偏差；
    由所述最大化节点可接纳净负荷扰动域、最小化配电网运行总成本和最小化期望电压偏差对应的配电网运行参数对配电网的各种无功设备进行协同控制；
    其中，三层优化目标规划模型是以最大化节点可接纳净负荷扰动域，最小化配电网运行总成本，最小化期望电压偏差作为三层目标函数，并为所述三层目标函数设置约束条件构建的。
  2. 如权利要求1所述的方法，所述三层优化目标规划模型的构建，包括：
    以最大化节点可接纳净负荷扰动域构建第一层优化目标；
    以最小化配电网运行总成本构建第二层优化目标；
    以最小化期望电压偏差构建第三层优化目标；
    为所述第一层优化目标、第二层优化目标和第三层优化目标设置约束条件；
    所述约束条件包括：净负荷预测值下的约束和净负荷扰动下的约束。
  3. 如权利要求2所述的方法，所述净负荷预测值下的约束包括：净负荷预测值下的总功率平衡约束、线路潮流约束、支路容量约束、节点电压上下限约束、机组出力约束、储能约束、上级电网供电约束、弃光、弃风、切负荷约束、分组投切电容器约束、静止无功补偿器SVC约束；
    所述净负荷扰动下的约束包括：净负荷扰动下的总功率平衡约束、净负荷可接纳域约束、线路潮流约束、线路容量约束、节点电压上下限约束、发电机相关约束、储能约束、上级电网供电约束、弃光弃风切负荷约束、分组投切电容器约束、静止无功补偿器约束。
  4. 如权利要求2所述的方法，所述第一层优化目标如下式所示：
    式中，Z1为第一层目标函数，为向上节点可接纳净负荷扰动域，为向下节点可接纳净负荷扰动域，NT表示优化时段的集合；NI表示节点的集合，i为节点编号；t为优化时段；
    所述第二层优化目标如下式所示：
    Z2＝min(Coper+Ccut+Cess)
    式中，Z2为第二层目标函数，Coper为运行成本，Ccut为弃风弃光切负荷成本，Cess为储能成本；
    所述第三层优化目标如下式所示：
    式中，Z3为第三层目标函数，为节点电压预测值，Vi,t为节点实际电压。
  5. 如权利要求1所述的方法，所述基于所述配电网运行参数和预先构建的三层优化目标规划模型，采用仿射理论、对偶理论、功率圆线性化和绝对值线性化方法对所述三层优化目标规划模型进行求解，得到最大化节点可接纳净负荷扰动域、最小化配电网运行总成本和最小化期望电压偏差，包括：
    采用仿射理论和对偶理论将三层优化目标规划模型中的不确定性公式转化为确定性公式；
    采用功率圆线性化和绝对值线性化方法将三层优化目标规划模型中的非线性公式转化为线性公式；
    对转化后的三层优化目标规划模型进行求解，得到最大化节点可接纳净负荷扰动域、最小化配电网运行总成本和最小化期望电压偏差。
  6. 如权利要求5所述的方法，所述采用仿射理论和对偶理论将三层优化目标规划模型中的不确定性公式转化为确定性公式，包括：
    采用仿射理论通过引入一个辅助变量对三层优化目标规划模型中的节点电压不确定性进行转化，得到确定性变量；
    采用对偶理论对机组调节扰动出力不确定性进行转化，得到表示节点可接纳净负荷扰动域。
  7. 如权利要求5所述的方法，所述采用功率圆线性化和绝对值线性化方法将三层优化目标规划模型中的非线性公式转化为线性公式，包括：
    将三层优化目标规划模型中可以进行分段线性化的不确定性公式采用功率圆线性化方法进行线性化；
    采用绝对值线性化方法对所述不确定性公式中的绝对值进行线性化处理。
  8. 如权利要求7所述的方法，所述将三层优化目标规划模型中可以进行分段线性化的不确定性公式采用功率圆线性化方法进行线性化，包括：
    所述不确定性公式的可行域均为圆的内部，将圆内接正多边形近似为所述不确定性公式的可行域对应的圆；
    将所述正多边形围成的面积近似代替圆形围成的面积，将所述不确定性公式转化为所述正多边形的面积计算式。
  9. 基于三层优先目标的配电网多级电压协同控制系统，包括：
    参数获取模块，用于获取配电网运行参数；
    目标求解模块，用于基于所述配电网运行参数和预先构建的三层优化目标规划模型，采用仿射理论、对偶理论、功率圆线性化和绝对值线性化方法对所述三层优化目标规划模型进行求解，得到最大化节点可接纳净负荷扰动域、最小化配电网运行总成本和最小化期望电压偏差；
    控制模块，用于由所述最大化节点可接纳净负荷扰动域、最小化配电网运行总成本和最小化期望电压偏差对应的配电网运行参数对配电网的各种无功设备进行协同控制；
    其中，三层优化目标规划模型是以最大化节点可接纳净负荷扰动域，最小化配电网运行总成本，最小化期望电压偏差作为三层目标函数，并为所述三层目标函数设置约束条件构建的。
  10. 如权利要求9所述的系统，还包括模型构建模块，用于：
    以最大化节点可接纳净负荷扰动域构建第一层优化目标；
    以最小化配电网运行总成本构建第二层优化目标；
    以最小化期望电压偏差构建第三层优化目标；
    为所述第一层优化目标、第二层优化目标和第三层优化目标设置约束条件；
    所述约束条件包括：净负荷预测值下的约束和净负荷扰动下的约束。
  11. 一种计算机设备，包括：至少一个处理器和存储器；
    所述存储器，用于存储一个或多个程序；
    当所述一个或多个程序被所述至少一个处理器执行时，实现如权利要求1至8中任一项所述的基于三层优先目标的配电网多级电压协同控制方法。
  12. 一种计算机可读存储介质，其上存有计算机程序，所述计算机程序被执行时，实现如权利要求1至8中任一项所述的基于三层优先目标的配电网多级电压协同控制方法。
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