Multi-level coordinated voltage control method and system for power distribution network based on three-tier priority objectives

Abstract

The present application provides a multi-level coordinated voltage control method and system for a power distribution network based on three-tier priority objectives. The method comprises: acquiring operation parameters of a power distribution network; on the basis of the operation parameters of the power distribution network and a pre-constructed three-tier optimization objective planning model, using an affine theory, a duality theory, and power circle linearization and absolute value linearization methods to solve the three-tier optimization objective planning model to obtain maximization of an admissible net-load disturbance domain at each node, minimization of the total operation cost of the power distribution network and minimization of an expected voltage deviation; and performing coordinated control on various reactive devices of the power distribution network by means of the operation parameters of the power distribution network corresponding to the maximization of the admissible net-load disturbance domain at each node, the minimization of the total operation cost of the power distribution network and the minimization of the expected voltage deviation. The present application can more clearly characterize the uncertainty of distributed generation power output and the impact of the distributed generation power output on system reserve capacity, and mitigate the problems of ambiguous characterization of distributed generation power output characteristics, and multi-level voltage violation and voltage fluctuation of the power distribution network caused by large-scale distributed generation integration.

Description

Distribution network multi-level voltage coordinated control method and system based on three-level priority targets

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application is based on the Chinese patent application with application number 202410264658.7, application date March 8, 2024, and application name “Multi-level voltage coordinated control method and system for distribution network based on three-layer priority targets”, and claims the priority of the Chinese patent application. The entire content of the Chinese patent application is hereby introduced into this application as a reference. Technical Field

[0003] The present application relates to the field of optimized dispatching of power system distribution networks, and specifically to a method and system for coordinated multi-level voltage control of distribution networks based on three-layer priority targets. Background Art

[0004] As the proportion of distributed energy sources such as wind power and photovoltaics in distribution networks continues to increase, the volatility of distributed power output is causing the power quality of distribution networks to deteriorate. To address the uncertainty brought about by the integration of distributed energy resources and improve the safety, economy, and reliability of distribution network operations, coordinated control and optimization of distribution networks at multiple voltage levels is necessary.

[0005] At present, most studies achieve distribution network voltage control by minimizing network loss or voltage fluctuation, and only consider single-stage voltage. The following situations exist in the existing technology: (1) The impact of distributed photovoltaic access on the power quality of the distribution network is analyzed theoretically and simulated. (2) The improved primal dual interior point method is used to study the multi-stage coordinated distribution network voltage quality optimization method. (3) The multi-stage coordinated control strategy of the distribution network voltage based on distributed photovoltaic clusters is studied. (4) The multi-stage reactive voltage control model is solved by particle swarm optimization with the objective function of minimizing network loss cost.

[0006] In addition, the traditional method of describing the output fluctuations of distributed power sources using intervals or probability distributions cannot accurately express the uncertainty brought about by distributed power sources with weather changes. Summary of the Invention

[0007] To address the problem that existing technologies achieve distribution network voltage control by minimizing network loss or voltage fluctuation, only consider single-level voltage, and describe the output fluctuation of distributed power generation using intervals or probability distribution, which cannot accurately express the uncertainty brought by distributed power generation due to weather changes, this application proposes a distribution network multi-level voltage coordinated control method based on three-level priority objectives, including:

[0008] Obtain distribution network operating parameters;

[0009] Based on the distribution network operating parameters and a pre-constructed 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;

[0010] 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;

[0011] Among them, 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.

[0012] In some embodiments, the construction of the three-layer optimization target programming model includes:

[0013] The first-level optimization objective is to maximize the node's acceptable net load disturbance domain;

[0014] The second-level optimization objective is to minimize the total cost of distribution network operation;

[0015] The third-level optimization objective is to minimize the expected voltage deviation;

[0016] Setting constraints for the first-level optimization objective, the second-level optimization objective, and the third-level optimization objective;

[0017] The constraint conditions include: constraints under the net load forecast value and constraints under the net load disturbance.

[0018] In some embodiments, 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;

[0019] 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.

[0020] In some embodiments, the first-layer optimization objective is expressed as follows:

[0021] Where 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;

[0022] The second-level optimization objective is as follows: Z2 = min(C oper +C cut +C ess )

[0023] Where Z2 is the second layer objective function, C oper is the operating cost, C cut is the load shedding cost of wind and solar curtailment, C ess is the energy storage cost;

[0024] The third-level optimization objective is as follows:

[0025] Where Z3 is the third layer objective function, is the node voltage prediction value, V i,t is the actual node voltage.

[0026] In some embodiments, 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:

[0027] Affine theory and duality theory are used to transform the uncertainty formula in the three-level optimization target programming model into a deterministic formula;

[0028] 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.

[0029] 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.

[0030] In some embodiments, 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:

[0031] 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.

[0032] 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.

[0033] In some embodiments, the use of power circle linearization and absolute value linearization methods to convert the nonlinear formula in the three-level optimization target programming model into a linear formula includes:

[0034] The uncertainty formula that can be piecewise linearized in the three-level optimization target programming model is linearized using the power circle linearization method;

[0035] The absolute value linearization method is used to linearize the absolute value in the uncertainty formula.

[0036] In some embodiments, the step of linearizing the uncertainty formula that can be piecewise linearized in the three-layer optimization target programming model using a power circle linearization method includes:

[0037] 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;

[0038] 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.

[0039] In some embodiments, 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.

[0040] On the other hand, the present application also provides a distribution network multi-level voltage coordinated control system based on three-level priority targets, including:

[0041] Parameter acquisition module, used to obtain distribution network operating parameters;

[0042] 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;

[0043] 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;

[0044] Among them, 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.

[0045] In some embodiments, a model building module is further included for:

[0046] The first-level optimization objective is to maximize the node's acceptable net load disturbance domain;

[0047] The second-level optimization objective is to minimize the total cost of distribution network operation;

[0048] The third-level optimization objective is to minimize the expected voltage deviation;

[0049] Setting constraints for the first-level optimization objective, the second-level optimization objective, and the third-level optimization objective;

[0050] The constraint conditions include: constraints under the net load forecast value and constraints under the net load disturbance.

[0051] In some embodiments, 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;

[0052] 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.

[0053] In some embodiments, the first-layer optimization objective is expressed as follows:

[0054] Where 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;

[0055] The second-level optimization objective is as follows: Z2 = min(C oper +C cut +C ess )

[0056] Where Z2 is the second layer objective function, C oper is the operating cost, C cut is the load shedding cost of wind and solar curtailment, C ess is the energy storage cost;

[0057] The third-level optimization objective is as follows:

[0058] Where Z3 is the third layer objective function, is the node voltage prediction value, V i,t is the actual node voltage.

[0059] In some embodiments, the goal solving module includes:

[0060] 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;

[0061] 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;

[0062] 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.

[0063] In some embodiments, the deterministic conversion submodule is specifically configured to:

[0064] 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.

[0065] 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.

[0066] In some embodiments, the linearization submodule is specifically configured to:

[0067] The uncertainty formula that can be piecewise linearized in the three-level optimization target programming model is linearized using the power circle linearization method;

[0068] The absolute value linearization method is used to linearize the absolute value in the uncertainty formula.

[0069] In some embodiments, 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:

[0070] 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;

[0071] 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.

[0072] In another aspect, the present application further provides a computing device comprising: at least one processor and a memory;

[0073] The memory is used to store one or more programs;

[0074] When the one or more programs are executed by the at least one processor, the distribution network multi-level voltage coordinated control method based on three-layer priority targets as described above is implemented.

[0075] On the other hand, the present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed, it implements the distribution network multi-level voltage coordinated control method based on three-layer priority targets as described above.

[0076] Compared with the prior art, the present invention has the following advantages:

[0077] 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. 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] 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;

[0079] 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;

[0080] FIG3 is a schematic diagram of the range of the net load disturbance domain that a node can accept in the present application;

[0081] FIG4 is a schematic diagram of the piecewise linearization of the unit operating cost of the present application;

[0082] FIG5 is a schematic diagram of the linearization of branch capacity constraints and unit capacity constraints of the present application. DETAILED DESCRIPTION

[0083] 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. When conventional control techniques are used to increase distributed generation, voltage variations at each node become a problem. To ensure that voltage variations do not damage user equipment and other devices in the power system, 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. Through this three-level objective function, the safety, economy, and reliability of the distribution network are comprehensively considered, achieving multi-level voltage coordinated control of the distribution network in a more reasonable manner.

[0084] This application adopts affine theory and duality theory to transform the uncertainty model into a deterministic model, and adopts power circle linearization and absolute value linearization methods to transform the nonlinear model into a linear model, thereby improving the computational efficiency and solution accuracy.

[0085] Example 1:

[0086] The distribution network multi-level voltage coordinated control method based on three-level priority objectives is shown in Figure 1 and includes:

[0087] Step 1: Obtain distribution network operating parameters;

[0088] 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;

[0089] 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;

[0090] Among them, 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.

[0091] The present application proposes a distribution network multi-level voltage coordinated control method and system based on three-level priority objectives. The method adopts the three-level objective programming theory, reasonably and comprehensively 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. At the same time, 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. The affine strategy and duality theory are used to eliminate the uncertainty in the model and achieve efficient solution of the model.

[0092] Before 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:

[0093] 1.1 Objective Function: Z2=min(C oper +C cut +C ess ) (2)

[0094] The model has three layers of objective functions. The first layer objective function Z1 is to maximize the net load disturbance domain that the node can accept, as shown in formula (1), including the net load disturbance domain that the upward node can accept and the net load disturbance domain that the downstream node can accept The second-level objective function Z2 is to minimize the total cost of distribution network operation, as shown in formula (2), including the operating cost C oper , wind and solar curtailment load shedding cost C cut , energy storage cost C ess The third layer objective function Z3 is to minimize the expected voltage deviation, as shown in formula (3): the expected voltage deviation is the actual voltage V of all nodes in the distribution network. i,t and the predicted voltage value of each node The sum of the absolute values ​​of the differences, i is the node number, and t is the optimization period. The second-level objective function cost can be clearly expressed as follows:

[0095] Where, 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; Indicates 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; a g 、b g 、c g is the operating cost coefficient of generator g; and They represent the upward reserve and downward reserve that generator g can provide during time period t; They represent the cost of the generator providing upward reserve and downward reserve respectively; σ L , σ w and σ pv are the penalty cost coefficients for load shedding, wind curtailment, and solar curtailment respectively; ΔP i,t and ΔQ i,t are the active and reactive load shedding of node i in period t; ΔP w,t and ΔP pv,t are the amount of wind power abandoned by wind turbine w and the amount of solar power abandoned by photovoltaic power pv in period t respectively; ρ E is the energy storage charging and discharging cost coefficient; and are the charging power and discharging power of energy storage e during time period t, e is the number of the energy storage device, and f is the number of the upper power station or power grid.

[0096] 1.2 Constraints

[0097] The 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.

[0098] Constraints under the net load forecast value:

[0099] (1) Total power balance

[0100] Where, 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;

[0101] 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.

[0102] (2) Line flow calculation formula

[0103] Where, 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; loc(g)=k represents the generator g connected to node k; denote the generation load transfer factors of active power flow with respect to active and reactive power injection at node k, respectively; are the generation load transfer factors of reactive power flow with respect to active and reactive power injection at node k; b fis a binary variable. When it is 1, it indicates that the upper power grid is directly connected to the node k. When it is 0, it indicates that the upper power grid is not directly connected to the node k. loc(svc)=k indicates the static VAR compensator connected to the node k. loc(cb)=k indicates the capacitor bank connected to the node k. is the active power output of generator g connected to node k during time period t, is the reactive power output of generator g during time period t, is the reactive power output of generator g connected to node k during time period t, is the reactive power output of the static VAR compensator SVC during the time period t, is the reactive power output of the static VAR compensator SVC connected to node k during time period t, is the reactive power output of capacitor group b during time period t, is the reactive power output of the capacitor bank cb connected to node k during time period t, g is the generator group number, i and j are both node numbers, NE is the set of energy storage devices, e is the number of the energy storage device, t is the optimization period, are active parameters and reactive parameters respectively, satisfying formula (9):

[0104] Where, NL={NL none ,NL end ,NL start};NL none Indicates the set of branches that are not directly connected to the reference node; NL start Indicates the branch set with the reference node as the starting node; NL end represents the set of branches with the reference node as the terminal node; is the generation load transfer factor of the active power flow relative to the active power injection at node k, G k(ref) is the predicted conductance value of the line connected to node k, V ref is the predicted value of voltage amplitude, B k(ref) is the predicted susceptance value of the line connected to node k, θ ref is the voltage phase angle prediction value, is the generation load transfer factor of active power flow relative to reactive power injection at node k, is the generation load transfer factor of reactive power flow relative to active power injection at node k, is the generation load transfer factor of reactive power flow relative to reactive power injection at node k, b ij is the line ij susceptance, g ij is the conductance of circuit ij.

[0105] (3) Branch capacity constraints:

[0106] Where, Indicates the upper capacity limit of branch ij.

[0107] Formula (10) indicates that the total power flow on the branch cannot exceed the branch capacity limit.

[0108] (4) Node voltage upper and lower limit constraints

[0109] Where V i max and V i min Respectively represent the upper and lower limits of the voltage amplitude of node i; θ i max ,θ i min Respectively represent the upper limit and lower limit of the voltage phase angle of node i; where, denote the generation load transfer factors of the voltage amplitude at node i with respect to the active power and reactive power injected at node k, respectively; denote the generation load transfer factors of the voltage phase angle at node i with respect to the active power and reactive power injected at node k, respectively; and are the voltage amplitude related parameters and the voltage phase angle related parameters, respectively, satisfying formula (13):

[0110] Where, are the voltage amplitude at node i with respect to the generation load transfer factor of active power and reactive power injected at node k; G k(ref) is the predicted conductance value of the line connected to node k, V ref is the predicted value of voltage amplitude, θ ref is the voltage phase angle prediction value, B k(ref) is the predicted susceptance value of the line connected to node k.

[0111] The middle term of 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.

[0112] (5) Unit output constraints

[0113] Where, and They represent the minimum and maximum active output values ​​of unit g respectively; Indicates the upper limit of the output capacity of unit g; and are the upward and downward climbing capacities of unit g, respectively; Δt represents the duration of time period t; UR g is the upward ramp rate of unit g, DR g is the downward climbing rate of unit g; 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; It represents the active power output of generator g during the time period t-1 when the net load is the predicted value.

[0114] Due to the physical characteristics of the generator set itself, the unit output is limited. The first formula in 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.

[0115] (6) Energy storage constraints

[0116] Where, and They represent the maximum charging power and maximum discharging power of energy storage e respectively; E e,t represents the energy stored in the energy storage e during the time period t; E e,t-1 represents the energy stored in the energy storage e in the time period t-1; η c and η d Respectively represent the charging and discharging efficiency of energy storage; and They represent the upper and lower limits of the energy stored in the energy storage e in the time period t; E start and E end They represent the initial energy and final energy in a charging cycle, respectively. This constraint limits the change in the charging and discharging power and stored energy of the energy storage device.

[0117] (7) Power supply constraints of the upper power grid

[0118] Where, and They represent the maximum active power and reactive power provided by the upper grid f. This formula constrains the output of the upper grid to be within the upper and lower limits.

[0119] (8) Constraints on curtailing solar power, curtailing wind power, and load shedding

[0120] Where, and They are the predicted active output values ​​of photovoltaic power (PV) and wind turbine power (W) in time period t respectively; and are the active load and reactive load of node i in time period t; ΔP w,t is the abandoned air volume of fan w in time period t, ΔP pv,t is the amount of abandoned photovoltaic power in time period t, is the active load shedding amount of node i in time period t, is the reactive load shedding amount of node i in time period t.

[0121] (9) Group switching capacitor (CB) constraints

[0122] Where Q cb,t It represents the reactive output of capacitor bank cb in period t, h represents the gear position, NC cb,h,t It indicates the number of capacitor groups put into operation when the capacitor group cb is in gear h, which is a parameter; x cb,h,t is a 0 / 1 variable indicating whether the capacitor bank cb is in gear h at time t. A value of 1 indicates that the capacitor bank cb is in gear h at time t, otherwise it is not. This formula shows that cb can only be in one gear during time period t.

[0123] Where B cb,t It is a 0\1 variable. When the value is 0, it means that cb maintains the original state and does not act. When the value is 1, it means that cb is adjusted. cb,t-1 represents the reactive power output of capacitor bank cb during period t-1; 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.

[0124] (12) Static Var Compensator (SVC) Constraints

[0125] Where, and They represent the upper and lower limits of the static VAR compensator output respectively.

[0126] To ensure that the reserve can be successfully and efficiently transferred when the net load fluctuates randomly, the model needs to satisfy the constraints under the net load disturbance, which are similar to the constraints under the net load forecast value:

[0127] 1) Total power balance

[0128] Where, and They represent the random active and reactive outputs of the unit under the net load disturbance in the time period t, and They represent the random active and reactive outputs of the upper power grid under the net load disturbance in the time period t, represents the random reactive output of the static VAR compensator under the net load disturbance in the time period t, represents the random reactive output of grouped switched capacitors under net load disturbance during time period t, and are the random acceptable active and reactive net loads at node i during time period t. This formula shows that when the acceptable net load of a node fluctuates, the output of the distribution network also fluctuates randomly to meet the supply and demand balance.

[0129] 2) Net load acceptable range constraints

[0130] Where, 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.

[0131] 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).

[0132] 3) Line flow calculation formula

[0133] Where, P ij,t and Q ij,t represents the active and reactive power flows of line ij during time period t under net load disturbance; P g,t P represents the active output of generator g during the time period t under the disturbance state, f,t It represents the active power output of the upper power grid f during the time period t under the disturbance state, represents the active output of generator g connected to node k during time period t under disturbance state, Q g,t represents the reactive output of generator g during the time period t under disturbance state, represents the reactive output of generator g connected to node k during time period t under disturbance state, Q svc,t It represents the reactive power output of the static VAR compensator SVC in the time period t under the disturbance state, Q svck,trepresents the reactive power output of the static VAR compensator SVC connected to node k during the time period t under disturbance state, Q cb,t It represents the reactive power output of capacitor group b during the time period t under disturbance state, Q cbk,t represents the reactive output of the capacitor bank cb connected to node k during the time period t under the disturbance state. According to formula (25), when the net load fluctuates within the acceptable net load disturbance domain, the power flow on the line also fluctuates randomly.

[0134] 4) Line capacity constraints

[0135] Where, is the line capacity under disturbance conditions.

[0136] According to formula (26), when the net load fluctuates within the acceptable net load disturbance domain, the total power flow on the line cannot exceed the capacity upper limit of the line.

[0137] 5) Node voltage upper and lower limit constraints

[0138] Where, and are the voltage amplitude related parameters and the voltage phase angle related parameters, respectively, satisfying formula (13).

[0139] 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.

[0140] 6) Generator-related constraints

[0141] Where, represents the upper limit of the output capacity of generator set g, and t represents the optimization period.

[0142] 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.

[0143] Energy storage constraints, upper-level grid power supply constraints, curtailment of solar and wind power, grouped capacitor switching constraints, and static VAR compensator constraints are the same as those under the net load forecast value and will not be repeated here.

[0144] It can be seen that some parameters and variables contained in constraints (23)-(30) are random when considering the net load disturbance, so there are countless constraints involved.

[0145] Step 1: Obtain distribution network operating parameters;

[0146] Distribution network operating parameters include:

[0147] 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.

[0148] 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:

[0149] Affine theory and duality theory are used to transform the uncertainty formula in the three-level optimization target programming model into a deterministic formula;

[0150] 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.

[0151] 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.

[0152] Furthermore, the use of affine theory and duality theory to transform the uncertainty formula in the three-level optimization target programming model into a deterministic formula includes:

[0153] 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.

[0154] 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.

[0155] Furthermore, 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:

[0156] The uncertainty formula that can be piecewise linearized in the three-level optimization target programming model is linearized using the power circle linearization method;

[0157] The absolute value linearization method is used to linearize the absolute value in the uncertainty formula.

[0158] Furthermore, 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:

[0159] 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;

[0160] 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.

[0161] The specific steps of step 2 are as follows:

[0162] (1) Linearization of the model

[0163] 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.

[0164] Taking the second formula of formula (29) as an example, the feasible domain of this formula is the interior of the circle. The circle is approximated by a regular polygon inscribed in the circle. Considering the accuracy and computational efficiency of the model, the present disclosure uses the area enclosed by the dodecagon inscribed in the circle to approximate the area enclosed by the circle. As shown in FIG4 , the final formula (29) can be transformed into formula (31):

[0165] Where, and is the coefficient corresponding to the linearized power circle constraint, which changes with the number of sides of the divided regular polygon; α and β represent the arc angles of two adjacent vertices of the inscribed equal u-gon respectively; u is the number of sides of the inscribed polygon; cb adjusts the number of times linearized, and the intermediate variable ε is set. cb,t =|Q cb,t -Q cb,t-1 |, Q cb,t represents the reactive output of capacitor bank b during the time period t under disturbance state, ε cb,t is the reactive output change of capacitor bank cb from time t-1 to time t, Q cb,t-1 To express the reactive output of capacitor group b in the time period t-1 under the disturbance state, the absolute value in equation (21) is linearized to obtain:

[0166] Where M is the penalty coefficient, which is infinite in principle, and δ cb,t is an auxiliary 0\1 variable, B cb,t is a 0\1 variable indicating whether the capacitor bank cb is operating. is the unit reactive power output of capacitor bank cb, is the maximum number of adjustable gears of capacitor bank cb at time t.

[0167] (2) Deterministic transformation of the model

[0168] 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.

[0169] The affine strategy, as shown in 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.

[0170] Where, 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.

[0171] Taking the backup provided by generators as an example, the uncertain model is converted into a deterministic model.

[0172] 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:

[0173] Where, is the perturbation domain that the upward net load of node i can accept at time t, is the perturbation range that the downward net load of node i can accommodate at time t.

[0174] The same applies to the backup that can be provided by the superior power grid and reactive power compensation equipment.

[0175] After adopting the affine strategy, the total power balance equation (7), the first equation of (23), and (30) are automatically satisfied and can be omitted. This can be achieved by introducing an auxiliary variable z i,t , transforming the uncertain model into a deterministic model. The perturbation domain that the random net load can tolerate can be explicitly expressed as:

[0176] Where ρ is a proportional coefficient, assuming that the node active net load disturbance is proportional to the node reactive net load disturbance.

[0177] 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:

[0178] Where η i,g,t,u is the dual variable, is the upper limit of the output capacity of generator g.

[0179] After substituting formula (38) into formula (39), using the duality theory, formula (39) can be transformed into:

[0180] Where, is the active participation factor of unit g in time period t, is the reactive participation factor of unit g in time period t, η i,g,t,u is the dual variable, ρ is a proportional coefficient, and it is assumed that the node active net load disturbance is proportional to the node reactive net load disturbance.

[0181] Using the same method, equations (26)-(28) can be transformed similarly, which will not be repeated here. Through the above transformation, this model is transformed into a deterministic linear programming model, which can be solved using mature commercial solvers.

[0182] Since the safety of the system is more important than the economy in the operation and scheduling of the distribution network, 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, and 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).

[0183] Where λ 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. When λ = 1, the total net load acceptance domain obtained by the second-level optimization is the same as that obtained by the first-level optimization. When λ = 0, the model is simplified to a deterministic economic dispatch problem. Optimize the objective function of the first layer model.

[0184] Because the access of distributed power sources brings severe challenges to the distribution network voltage, in order to solve the voltage limit problem, the third-layer model specifically takes the minimum expected voltage deviation as the objective function.

[0185] Where V i,t is the actual voltage of each node in the i-node system at time t, is the ideal voltage of each node in the system at node i at time t.

[0186] The absolute value can be linearized, and formula (42) is transformed into:

[0187] Where ZP is an auxiliary variable.

[0188] 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).

[0189] Where C oper is the distribution network operation cost, C cut is the load shedding cost of wind and solar curtailment in the distribution network, C ess The operating cost of energy storage is, is the distribution network operation cost after optimization of the second-layer model. π 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 λ and will not be repeated here.

[0190] 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.

[0191] Compared with the prior art, the present invention has the following advantages:

[0192] (1) 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.

[0193] (2) 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, and the third layer is to minimize the expected voltage deviation. At the same time, 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.

[0194] (3) 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.

[0195] (4) 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.

[0196] Example 2:

[0197] According to some embodiments, the present application adopts the following technical solutions:

[0198] In a first aspect, the present application provides a method for coordinated multi-level voltage control of a distribution network based on three-layer priority targets, comprising:

[0199] 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, and 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.

[0200] 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. At the same time, the power circle linearization method is used to linearly express the branch capacity constraints, and 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. Finally, 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.

[0201] As an optional implementation method, 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.

[0202] As an optional implementation method, 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;

[0203] As an optional implementation method, the nonlinear terms and nonlinear constraints in the objective function, such as unit operating costs and branch capacity constraints, 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.

[0204] As an optional implementation method, 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.

[0205] As an optional implementation method, the specific solution process includes:

[0206] (1) 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.

[0207] (2) 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.

[0208] (3) The squared terms in the branch capacity constraints and unit capacity constraints are linearized using the power circle linearization method. The large-M method is used to linearize the capacitor bank adjustment times constraint, resulting in the corresponding linearized expressions. The optimization scheduling model is then rewritten as a linear programming model and solved using a linear programming solver. The linearization diagram for the branch capacity constraints and unit capacity constraints is shown in Figure 5.

[0209] 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.

[0210] Example 3:

[0211] 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:

[0212] Parameter acquisition module, used to obtain distribution network operating parameters;

[0213] 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;

[0214] 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;

[0215] Among them, 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.

[0216] Furthermore, it also includes a model building module for:

[0217] The first-level optimization objective is to maximize the node's acceptable net load disturbance domain;

[0218] The second-level optimization objective is to minimize the total cost of distribution network operation;

[0219] The third-level optimization objective is to minimize the expected voltage deviation;

[0220] Setting constraints for the first-level optimization objective, the second-level optimization objective, and the third-level optimization objective;

[0221] The constraint conditions include: constraints under the net load forecast value and constraints under the net load disturbance.

[0222] Furthermore, 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, curtailment of solar power, curtailment of wind power, load shedding constraints, group switching of capacitors constraints, and static VAR compensator (SVC) constraints;

[0223] 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.

[0224] Furthermore, the first-layer optimization objective is as follows:

[0225] Where 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;

[0226] The second-level optimization objective is as follows: Z2 = min(C oper +C cut +C ess )

[0227] Where Z2 is the second layer objective function, C oper is the operating cost, C cut is the load shedding cost of wind and solar curtailment, C ess is the energy storage cost;

[0228] The third-level optimization objective is as follows:

[0229] Where Z3 is the third layer objective function, is the node voltage prediction value, V i,t is the actual node voltage.

[0230] Furthermore, the target solving module includes:

[0231] 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;

[0232] 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;

[0233] The solution 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.

[0234] Furthermore, the deterministic conversion submodule is specifically used to:

[0235] 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.

[0236] 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.

[0237] Furthermore, the linearization submodule is specifically used to:

[0238] The uncertainty formula that can be piecewise linearized in the three-level optimization target programming model is linearized using the power circle linearization method;

[0239] The absolute value linearization method is used to linearize the absolute value in the uncertainty formula.

[0240] Furthermore, 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:

[0241] 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;

[0242] 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.

[0243] Implementation:4:

[0244] Based on the same inventive concept, the present application also provides a computer device, which includes a processor and a memory, the memory being used to store a computer program, the computer program including program instructions, and the processor being 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 gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of the distribution network multi-level voltage coordinated control method based on the three-layer priority target in the above embodiment.

[0245] Example 5:

[0246] Based on the same inventive concept, 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. It can be understood that 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, which stores the operating system of the terminal. In addition, 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). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a 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.

[0247] Those skilled in the art will appreciate that 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.

[0248] The present application is described with reference to the flow chart and / or block diagram of the method, device (system), and computer program product according to the embodiment of the present application. It should be understood that 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.

[0249] 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.

[0250] 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.

[0251] The above are merely embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application are included in the scope of the claims of the present application to be approved. Industrial Applicability

[0252] 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.

Claims

1. A multi-level voltage coordinated control method for distribution networks based on three-level priority objectives, including: Obtain distribution network operating parameters; Based on the distribution network operating parameters and a pre-constructed 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; 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; Among them, 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.

2. The method according to claim 1, wherein the construction of the three-layer optimization target programming model comprises: 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; Setting constraints for the first-level optimization objective, the second-level optimization objective, and the third-level optimization objective; The constraint conditions include: constraints under the net load forecast value and constraints under the net load disturbance.

3. The method according to claim 2, wherein the constraints under the net load forecast value include: Total power balance constraints under net load forecast values, line flow constraints, branch capacity constraints, node voltage upper and lower limit constraints, unit output constraints, energy storage constraints, upper 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; 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.

4. The method according to claim 2, wherein the first layer optimization objective is as follows: Where 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 second-layer optimization objective is as follows: Z2=min(C oper +C cut +C ess ) Where Z2 is the second layer objective function, C oper is the operating cost, C cut is the load shedding cost of wind and solar curtailment, C ess is the energy storage cost; The third-level optimization objective is as follows: Where Z3 is the third layer objective function, is the node voltage prediction value, V i,t is the actual node voltage.

5. The method according to claim 1, wherein the three-layer optimization target programming model is solved 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 a method for maximizing the node's acceptable net load disturbance domain, minimizing the total distribution network operating cost, and minimizing the 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.

6. The method according to claim 5, wherein the method of converting the uncertainty formula in the three-level optimization target programming model into a deterministic formula by using affine theory and duality theory comprises: 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.

7. The method according to claim 5, wherein the converting of the nonlinear formula in the three-level optimization target programming model into a linear formula by using power circle linearization and absolute value linearization methods comprises: 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.

8. The method according to claim 7, wherein the step of linearizing the uncertainty formula in the three-level optimization objective programming model that can be piecewise linearized using a power circle linearization method comprises: 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.

9. A multi-level voltage coordinated control system for distribution networks based on three-tier 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; Among them, 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.

10. The system of claim 9, further comprising a 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; Setting constraints for the first-level optimization objective, the second-level optimization objective, and the third-level optimization objective; The constraint conditions include: constraints under the net load forecast value and constraints under the net load disturbance.

11. A computer device comprising: at least one processor and memory; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, the distribution network multi-level voltage coordinated control method based on three-layer priority targets as described in any one of claims 1 to 8 is implemented.

12. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, the method for coordinated control of multi-level voltages in a distribution network based on three-layer priority targets as claimed in any one of claims 1 to 8 is implemented.

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