A multi-level voltage collaborative optimization method for distribution network and related devices

Abstract

The present invention discloses a multi-level voltage coordinated optimization method for a distribution network and a related device, which obtains day-ahead forecast data, intra-day multi-cycle rolling forecast data, and a multi-objective cross-layer voltage optimization model including a substation layer, a feeder layer, and an equivalent area layer that considers power quality and economic operation; the day-ahead forecast data is input into the multi-objective cross-layer voltage optimization model, and a first control instruction for adjusting the substation layer, the feeder layer, and the equivalent area voltage layer is output, the first control instruction includes a switch action control instruction and an active-reactive control instruction; the decision variables and constraints of the multi-objective cross-layer voltage optimization model are reduced in the time domain by using the time scale of the intra-day multi-cycle rolling forecast data, and the intra-day multi-cycle rolling optimization model is obtained; the intra-day multi-cycle rolling optimization model is solved by using a distributed primal dual subgradient method, and a second control instruction for adjusting the substation layer, the feeder layer, and the equivalent area voltage layer is obtained, and the second control instruction includes an active-reactive control instruction; and the multi-level voltage coordinated optimization of the distribution network is performed according to the first control instruction and / or the second control instruction. The purpose of the present invention is to take into account both the power quality and economic benefits of the distribution network, avoid voltage over-limit and make better use of multiple regulation resources to achieve the highest efficiency, and adopt a distributed algorithm to reduce the amount of calculation and improve the calculation speed.

Description

Technical Field

[0001] The present invention belongs to the field of energy system and distribution system planning, and specifically relates to a multi-level voltage coordinated optimization method for a distribution network and related devices. Background Art

[0002] With the increasing global awareness of environmental protection and the transformation of energy structure, especially the clear proposal of the "dual carbon" (carbon peak and carbon neutrality) goals, the development and utilization of new energy has entered an unprecedented rapid development stage. As a representative of distributed energy, the proportion of photovoltaic power connected to the power grid has increased year by year, which has greatly promoted the optimization of energy structure and the utilization of renewable energy. This trend has also brought new challenges to the operation and management of distribution networks.

[0003] The traditional distribution network design is relatively simple, mainly serving load demand, and long-term voltage regulation is performed through the on-load voltage regulation of the substation. However, with the widespread access to distributed energy sources such as photovoltaic power stations and wind power plants, the structure and operation characteristics of the distribution network have undergone fundamental changes. These distributed energy sources not only supply power to the grid as power sources, but may also cause significant reverse currents, which in turn cause problems such as voltage over-limit, seriously threatening the power quality of users and the stable operation of the grid. In addition, the impedance characteristics of the distribution network are complex, and there is a close coupling relationship between voltage, active power and reactive power, which makes voltage problems often propagate across levels, and the regulation resources are massive and diverse. With the access to fast regulation resources such as energy storage systems and electric vehicles, the regulation means of the distribution network are more abundant, but at the same time, the complexity of the problem and the difficulty of solving it are also increased. Traditional research methods and optimization strategies for passive distribution networks can no longer effectively cope with the challenges faced by current active distribution networks.

[0004] At present, although some methods for voltage support of new distribution networks have been proposed, most of them still remain within the framework of reactive power regulation voltage adopted by high-voltage transmission lines, and fail to fully consider the impact of active power on voltage, nor conduct in-depth research on the characteristics of problems at different levels and the regulation characteristics of regulation resources. At the same time, traditional centralized algorithms are often difficult to achieve online rapid optimization when faced with massive data and complex computing requirements, and cannot meet the needs of actual operation. Summary of the invention

[0005] In view of the problems existing in the prior art, the present invention provides a multi-level voltage collaborative optimization method and related devices for a distribution network, the purpose of which is to take into account both the power quality and economic benefits of the distribution network, avoid voltage over-limit and better utilize a variety of regulation resources to achieve the highest efficiency, and adopt a distributed algorithm to reduce the amount of calculation and improve the calculation speed.

[0006] In order to solve the above technical problems, the present invention is implemented by the following technical solutions:

[0007] According to a first aspect of the present invention, a method for coordinated optimization of multi-level voltage in a distribution network is provided, comprising:

[0008] Obtain day-ahead forecast data, intraday multi-period rolling forecast data, and a multi-objective cross-layer voltage optimization model that considers power quality and economic operation, including the substation layer, feeder layer, and equivalent area layer;

[0009] Input the day-ahead forecast data into the multi-objective cross-layer voltage optimization model, and output a first control instruction for adjusting the substation layer-feeder layer-equivalent area voltage layer, wherein the first control instruction includes a switch action control instruction and an active-reactive power control instruction;

[0010] The decision variables and constraints of the multi-objective cross-layer voltage optimization model are reduced in the time domain by using the time scale of the intra-day multi-period rolling forecast data to obtain an intra-day multi-period rolling optimization model;

[0011] The distributed primal-dual subgradient method is used to solve the intraday multi-period rolling optimization model to obtain a second control instruction for adjusting the substation layer-feeder layer-equivalent area voltage layer, wherein the second control instruction includes an active-reactive control instruction;

[0012] Multi-level voltage coordinated optimization of the distribution network is performed according to the first control instruction and / or the second control instruction.

[0013] In a possible implementation manner of the first aspect, the objective function of the multi-objective cross-layer voltage optimization model including the substation layer, the feeder layer, and the equivalent area layer is:

[0014]

[0015] T={t1,t2,…t N}

[0016] in:

[0017] The objective function f1(C) of the substation layer is:

[0018]

[0019] Where: C is the set of decision variables for tap switching; t∈T is the time series; λ1 is the penalty coefficient for the on-load tap changer gear switching at the substation level; is the tap position switching instruction; λ2 is the penalty coefficient for the substation-level shunt capacitor position switching; It is the gear switching instruction of the parallel capacitor;

[0020] The constraints of the substation layer include gear constraints and bidirectional power flow constraints;

[0021] The objective function f2(V,P,Q) of the feeder layer is:

[0022]

[0023] Where: V is the set of decision variables for the voltage amplitude at the feeder layer; P is the set of active decision variables; Q is the set of reactive decision variables; i∈I is the number of resources; is the line loss; V i,t is the voltage amplitude; V ref is the voltage reference value; c deg The cost of electricity; is the energy storage charging and discharging power; γ is the electric vehicle incentive cost; is the charging and discharging power of electric vehicles; λ is the penalty coefficient for abandoned light; is the maximum photovoltaic output power; is the actual photovoltaic output power;

[0024] The constraints of the feeder layer include energy storage constraints, electric vehicle constraints, photovoltaic power station regulation capacity constraints, voltage constraints, current constraints and three-phase imbalance constraints;

[0025] The objective function f3(P,Q) of the equivalent station layer is:

[0026]

[0027] Where: c1 is the equivalent area layer active power response cost; P i,t is the equivalent area layer response active power; c2 is the equivalent area layer reactive power response cost; Q i,t is the equivalent area layer response reactive power;

[0028] The constraints of the equivalent station layer include energy storage constraints, electric vehicle constraints and photovoltaic power station regulation capacity constraints.

[0029] In a possible implementation manner of the first aspect, the gear constraint is specifically:

[0030]

[0031] Where: S TAP (t) is the on-load voltage regulator switching instruction at time t; S is the remaining number of switchable times of the on-load voltage regulator at time t; CAP (t) is the parallel capacitor switching instruction at time t; is the remaining number of switchable times of the parallel capacitor at time t;

[0032] The bidirectional power flow constraints are specifically:

[0033]

[0034] Where: P import (t) is the forward power of the equilibrium node at time t; is the maximum forward power; P export (t) is the reverse power of the balanced node at time t; is the maximum reverse power;

[0035] The energy storage constraints are specifically:

[0036]

[0037] Where: N ES is the amount of energy storage; P i ES (t) is the active power of energy storage i at time t; is the maximum active power of energy storage i; is the minimum active power of energy storage i; is the reactive power of energy storage i at time t; is the maximum reactive power of energy storage i; is the minimum reactive power of energy storage i; is the state of charge of energy storage i at time t; is the maximum state of charge of energy storage i; is the minimum state of charge of energy storage i;

[0038] The electric vehicle constraints are specifically:

[0039]

[0040] Where: N EV is the number of electric vehicles; P i EV (t) is the active power of electric vehicle i at time t; is the maximum active power of electric vehicle i; is the minimum active power of electric vehicle i; is the state of charge of electric vehicle i at time t; is the maximum state of charge of electric vehicle i; is the minimum state of charge of electric vehicle i;

[0041] The photovoltaic constraints are specifically:

[0042]

[0043] Where: N PV is the number of photovoltaic cells; P i PV (t) is the active power of PV i at time t; is the reactive power of PV i at time t; is the inverter capacity of PV i; is the power factor angle of photovoltaic i at time t; is the photovoltaic power factor angle limit; P i MPPT (t) is the active power of PV i under the MPPT model at time t;

[0044] The voltage constraint is specifically:

[0045]

[0046] Where: V i (t) is the voltage amplitude of node i at time t; is the maximum voltage amplitude of node i; V is the minimum voltage amplitude of node i; N is the number of grid nodes;

[0047] The current constraint is specifically:

[0048]

[0049] Where: I i (t) is the current amplitude of line i at time t; is the maximum current amplitude of line i; I is the minimum current amplitude of line i; N line is the number of grid branches;

[0050] The three-phase unbalance constraint is specifically:

[0051]

[0052] Where: i (t) is the three-phase unbalance degree of voltage at node i at time t; is the maximum value of the three-phase unbalance degree of the voltage at node i; Λ It is the minimum value of the three-phase unbalance of the voltage at node i.

[0053] In a possible implementation manner of the first aspect, the voltage calculation method of the voltage constraint includes:

[0054] The linearized voltage equation is used for calculation, and the linearized voltage equation is as follows:

[0055] |v|=Ts+a

[0056]

[0057] Where: v is the approximate node voltage obtained after linear equation calculation, v is the initial node voltage, |v| is the node voltage amplitude, For the coefficient matrix, Y LL is the three-phase admittance matrix; diag is the diagonal function; s is the injected three-phase power vector of the node ((p Y ) T ,(q Y ) T ) T , a is the steady-state value of the voltage vector and amplitude before power is injected into the node,

[0058] In a possible implementation manner of the first aspect, the objective function of the intraday multi-period rolling optimization model is:

[0059]

[0060] T mc ={T1,T2,T3}

[0061]

[0062] Where: T mc It is a multi-period prediction domain, from the current time to the end of 24 hours; T1 is a 15-minute prediction domain; T2 is a 30-minute prediction domain; T3 is a 2-hour prediction domain.

[0063] In a possible implementation of the first aspect, the distributed primal dual subgradient method is used to solve the intraday multi-period rolling optimization model, specifically:

[0064] Construct the dual problem of the original problem:

[0065]

[0066] Where: L(u,p,q,λ) is the Lagrangian function of the global optimization problem; n is the number of subproblems; f i (u,p,q) is the ith subproblem; u is the voltage control command; p is the active power control command; q is the reactive power control command; λ is the Lagrange multiplier; is a constraint condition; L i (u,p,q,λ) is the Lagrangian function of the ith subproblem; g(λ) is the dual function;

[0067] The average consensus algorithm is used to update the sub-problems:

[0068]

[0069] Where: k is the number of iterations; W ij >0 is the weight coefficient; is the solution to the ith subproblem; (u, p, q) j is the solution to the jth subproblem; is the Lagrange multiplier of the i-th Lagrangian function; is the Lagrange multiplier of the jth Lagrangian function;

[0070] Project the solution and the Lagrange multiplier respectively into their definition space:

[0071]

[0072] Where: is the Euclidean projection operator of the solution on its space X; (u,p,q) i is the solution to the ith subproblem; α>0 is a fixed step size; is the subgradient of the solution; is the projection operator of the Lagrange multiplier in its space Λ; is the Lagrange multiplier of the i-th Lagrangian function; is the Lagrange multiplier of the jth Lagrangian function; is the super gradient of the Lagrange multiplier.

[0073] According to a second aspect of the present invention, there is provided a distribution network multi-level voltage coordinated optimization device, comprising:

[0074] The acquisition module is used to obtain the day-ahead forecast data, the intraday multi-period rolling forecast data, and the multi-objective cross-layer voltage optimization model including the substation layer, the feeder layer, and the equivalent area layer considering the power quality and economic operation;

[0075] A day-ahead prediction module, used for inputting the day-ahead prediction data into the multi-objective cross-layer voltage optimization model, and outputting a first control instruction for adjusting the substation layer-feeder layer-equivalent area voltage layer, wherein the first control instruction includes a switch action control instruction and an active-reactive power control instruction;

[0076] A reduction module, used to reduce the decision variables and constraints of the multi-objective cross-layer voltage optimization model in the time domain by using the time scale of the intra-day multi-period rolling forecast data to obtain an intra-day multi-period rolling optimization model;

[0077] An intraday prediction module, used for solving the intraday multi-period rolling optimization model by using a distributed primal-dual subgradient method, and obtaining a second control instruction for adjusting the substation layer-feeder layer-equivalent area voltage layer, wherein the second control instruction includes an active-reactive control instruction;

[0078] An optimization module is used to perform multi-level voltage coordinated optimization of the distribution network according to the first control instruction and / or the second control instruction.

[0079] In a possible implementation manner of the second aspect, the objective function of the multi-objective cross-layer voltage optimization model including the substation layer-feeder layer-equivalent station layer is:

[0080]

[0081] T={t1,t2,…t N}

[0082] in:

[0083] The objective function f1(C) of the substation layer is:

[0084]

[0085] Where: C is the set of decision variables for tap switching; t∈T is the time series; λ1 is the penalty coefficient for the on-load tap changer gear switching at the substation level; is the tap position switching instruction; λ2 is the substation-level shunt capacitor position switching penalty coefficient; It is the gear switching instruction of the parallel capacitor;

[0086] The constraints of the substation layer include gear constraints and bidirectional power flow constraints;

[0087] The objective function f2(V,P,Q) of the feeder layer is:

[0088]

[0089] Where: V is the set of decision variables for the voltage amplitude at the feeder layer; P is the set of active decision variables; Q is the set of reactive decision variables; i∈I is the number of resources; is the line loss; V i,t is the voltage amplitude; V ref is the voltage reference value; c deg The cost of electricity; is the energy storage charging and discharging power; γ is the electric vehicle incentive cost; is the charging and discharging power of electric vehicles; λ is the penalty coefficient for abandoned light; is the maximum photovoltaic output power; is the actual photovoltaic output power;

[0090] The constraints of the feeder layer include energy storage constraints, electric vehicle constraints, photovoltaic power station regulation capacity constraints, voltage constraints, current constraints and three-phase imbalance constraints;

[0091] The objective function f3(P,Q) of the equivalent station layer is:

[0092]

[0093] Where: c1 is the equivalent area layer active power response cost; P i,t is the equivalent area layer response active power; c2 is the equivalent area layer reactive power response cost; Q i,t is the equivalent area layer response reactive power;

[0094] The constraints of the equivalent station layer include energy storage constraints, electric vehicle constraints and photovoltaic power station regulation capacity constraints.

[0095] According to a third aspect of the present invention, there is provided a device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for coordinated multi-level voltage optimization in a distribution network when executing the computer program.

[0096] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the method for coordinated multi-level voltage optimization in a distribution network.

[0097] Compared with the prior art, the present invention has at least the following beneficial effects:

[0098] The present invention provides a method for coordinated optimization of multi-level voltage in a distribution network. The method is based on a multi-objective cross-layer voltage optimization model including a substation layer, a feeder layer, and an equivalent area layer that considers power quality and economic operation. It considers the characteristics of multi-level problems in the distribution network, the coupling relationship between voltage and active / reactive power, and a variety of regulation resources, and takes into account both power quality and economic benefits. Through the day-ahead forecast data and the intra-day multi-period rolling forecast data, the day-ahead-intra-day rolling two-stage optimization is adopted to accurately control the regulation resources at different time scales. The multi-objective cross-layer voltage optimization model is reduced in the time domain by using the intra-day multi-period rolling forecast data to reduce the model scale and improve the calculation speed. The method is applicable to new multi-level distribution networks, conforms to the actual operation of existing distribution networks, and in view of the complex model solution and long solution time caused by the massive and diverse regulation resources, the distributed primal dual subgradient method is adopted to achieve rapid solution.

[0099] Furthermore, the linearized voltage equation is used to calculate the voltage of the voltage constraint, which further improves the solution speed.

[0100] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0101] In order to more clearly illustrate the technical solutions in the specific implementation modes of the present invention, the drawings required for use in the description of the specific implementation modes will be briefly introduced below. Obviously, the drawings described below are some implementation modes of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0102] Figure 1 This is a flow chart of a method for coordinated optimization of multi-level voltage in a distribution network according to an embodiment of the present invention;

[0103] Figure 2 This is a schematic diagram of intraday rolling iterative optimization according to an embodiment of the present invention;

[0104] Figure 3 This is a schematic diagram of multi-cycle voltage rolling optimization according to an embodiment of the present invention;

[0105] Figure 4 This is a schematic diagram of a distributed primal-dual subgradient solution according to an embodiment of the present invention;

[0106] Figure 5 This is a schematic diagram of distributed information solution interaction of the present invention. DETAILED DESCRIPTION

[0107] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0108] It should be noted that the multi-level distribution network in the present invention includes three levels: substation level, feeder level and equivalent substation level, and the substation is connected to the feeder using an equivalent substation model.

[0109] like Figure 1 As shown, the embodiment of the present invention provides a multi-level voltage coordinated optimization method for a distribution network, which aims to solve the voltage over-limit, power quality degradation, massive resources, and complex calculation problems caused by the access of a high proportion of distributed energy to the distribution network through refined management and control. The following are the specific implementation steps of this embodiment:

[0110] S1. Obtain day-ahead forecast data, intraday multi-period rolling forecast data, and a multi-objective cross-layer voltage optimization model including substation layer, feeder layer, and equivalent area layer considering power quality and economic operation.

[0111] It should be noted that the multi-objective cross-layer voltage optimization model including substation layer, feeder layer and equivalent area layer considering power quality and economic operation is pre-built. The model comprehensively considers power quality and economic operation objectives. In the multi-objective cross-layer voltage optimization model, the substation layer is responsible for global voltage regulation, the feeder layer is responsible for feeder-level voltage management, and the equivalent area layer represents the voltage and power balance of the area (i.e., the area served by the distribution transformer).

[0112] It should be understood that the day-ahead forecast data includes weather forecasts, load forecasts, distributed energy generation forecasts, etc., which are used to plan voltage control strategies in advance.

[0113] It should also be understood that the intraday multi-period rolling forecast data is updated based on a shorter time scale (such as every hour or every half hour) to reflect more accurate load and distributed energy generation conditions.

[0114] In one embodiment, the objective function of the multi-objective cross-layer voltage optimization model including the substation layer, feeder layer, and equivalent area layer is:

[0115]

[0116] T={t1,t2,…t N}

[0117] in:

[0118] The objective function f1(C) of the substation layer is:

[0119]

[0120] Where: C is the set of decision variables for tap switching; t∈T is the time series; λ1 is the penalty coefficient for the on-load tap changer gear switching at the substation level; is the tap position switching instruction; λ2 is the penalty coefficient for the substation-level shunt capacitor position switching; It is the gear switching instruction of the parallel capacitor.

[0121] The constraints at the substation level include gear constraints and bidirectional power flow constraints.

[0122] The gear constraints are specifically:

[0123]

[0124] Where: S TAP (t) is the on-load voltage regulator switching instruction at time t; S is the remaining number of switchable times of the on-load voltage regulator at time t; CAP (t) is the parallel capacitor switching instruction at time t; is the remaining number of switchable times of the parallel capacitor at time t.

[0125] The bidirectional power flow constraints are specifically:

[0126]

[0127] Where: P import (t) is the forward power of the equilibrium node at time t; is the maximum forward power; P export (t) is the reverse power of the balanced node at time t; is the maximum reverse power.

[0128] The objective function f2(V,P,Q) of the feeder layer is:

[0129]

[0130] Where: V is the set of decision variables for the voltage amplitude at the feeder layer; P is the set of active decision variables; Q is the set of reactive decision variables; i∈I is the number of resources; is the line loss; V i,t is the voltage amplitude; V ref is the voltage reference value; c deg The cost of electricity; is the energy storage charging and discharging power; γ is the electric vehicle incentive cost; is the charging and discharging power of electric vehicles; λ is the penalty coefficient for abandoned light; is the maximum photovoltaic output power; is the actual photovoltaic output power.

[0131] The constraints of the feeder layer include energy storage constraints, electric vehicle constraints, photovoltaic power station regulation capacity constraints, voltage constraints, current constraints and three-phase imbalance constraints.

[0132] The energy storage constraints are specifically:

[0133]

[0134] Where: N ES is the amount of energy storage; P i ES (t) is the active power of energy storage i at time t; is the maximum active power of energy storage i; is the minimum active power of energy storage i; is the reactive power of energy storage i at time t; is the maximum reactive power of energy storage i; is the minimum reactive power of energy storage i; is the state of charge of energy storage i at time t; is the maximum state of charge of energy storage i; is the minimum state of charge of energy storage i.

[0135] The electric vehicle constraints are specifically:

[0136]

[0137] Where: N EV is the number of electric vehicles; P i EV (t) is the active power of electric vehicle i at time t; is the maximum active power of electric vehicle i; is the minimum active power of electric vehicle i; is the state of charge of electric vehicle i at time t; is the maximum state of charge of electric vehicle i; is the minimum state of charge of electric vehicle i.

[0138] The photovoltaic constraints are specifically:

[0139]

[0140] Where: N PV is the number of photovoltaic cells; P i PV (t) is the active power of PV i at time t; is the reactive power of PV i at time t; is the inverter capacity of PV i; is the power factor angle of photovoltaic i at time t; is the photovoltaic power factor angle limit; P i MPPT (t) is the active power of PV i under the MPPT model at time t.

[0141] The voltage constraint is specifically:

[0142]

[0143] Where: N is the number of grid nodes; V i (t) is the voltage amplitude of node i at time t; is the maximum voltage amplitude of node i; V is the minimum voltage amplitude at node i.

[0144] The current constraint is specifically:

[0145]

[0146] Where: N line is the number of power grid branches; I i(t) is the current amplitude of line i at time t; is the maximum current amplitude of line i; I is the minimum current amplitude of line i.

[0147] The three-phase unbalance constraint is specifically:

[0148]

[0149] Where: i (t) is the three-phase unbalance degree of voltage at node i at time t; is the maximum value of the three-phase unbalance degree of the voltage at node i; Λ It is the minimum value of the three-phase unbalance of the voltage at node i.

[0150] The objective function f3(P,Q) of the equivalent station layer is:

[0151]

[0152] Where: c1 is the equivalent area layer active power response cost; P i,t is the equivalent area layer response active power; c2 is the equivalent area layer reactive power response cost; Q i,t is the equivalent area layer response reactive power;

[0153] The constraints of the equivalent station layer include energy storage constraints, electric vehicle constraints and photovoltaic power station regulation capacity constraints.

[0154] S2. Input the day-ahead forecast data into the multi-objective cross-layer voltage optimization model, and output a first control instruction for adjusting the substation layer-feeder layer-equivalent area voltage layer, wherein the first control instruction includes a switch action control instruction and an active-reactive control instruction.

[0155] That is to say, the day-ahead forecast data is input into the multi-objective cross-layer voltage optimization model. The model calculates and outputs the first control instructions based on the input data, including switch action control instructions (such as capacitor bank switching and transformer tap adjustment) and active-reactive control instructions (such as active and reactive output adjustment of distributed energy).

[0156] S3. Using the time scale of the intra-day multi-period rolling forecast data, the decision variables and constraints of the multi-objective cross-layer voltage optimization model are reduced in the time domain to obtain an intra-day multi-period rolling optimization model.

[0157] It should be noted that this operation is intended to simplify the model and reduce the amount of calculation while maintaining the accuracy of the model.

[0158] It should also be noted that the decision variables of the multi-objective cross-layer voltage optimization model can include on-load voltage regulation gear switching, shunt capacitor gear switching, active / reactive power of energy storage, active / reactive power of photovoltaic power stations and active power of electric vehicles.

[0159] Combination Figure 2 and Figure 3 Shown are the intraday rolling iterative optimization schematic diagram and the multi-period voltage rolling optimization schematic diagram.

[0160] In one embodiment, the objective function of the intraday multi-period rolling optimization model is:

[0161]

[0162] T mc ={T1,T2,T3}

[0163]

[0164] Where: T mc It is a multi-period prediction domain, from the current time to the end of 24 hours; T1 is a 15-minute prediction domain; T2 is a 30-minute prediction domain; T3 is a 2-hour prediction domain.

[0165] S4. Use the distributed primal-dual subgradient method to solve the intraday multi-period rolling optimization model to obtain a second control instruction for adjusting the substation layer-feeder layer-equivalent area voltage layer, wherein the second control instruction includes an active-reactive control instruction.

[0166] Specifically, the distributed primal-dual subgradient method decomposes the model and assigns the computing tasks to different computing nodes to achieve parallel computing, thereby greatly improving the computing speed. After the solution is completed, the second control instructions are obtained, including active-reactive control instructions for the substation layer, feeder layer and equivalent area layer. These instructions are used to adjust the voltage and power balance of each layer in real time to ensure the stable operation of the distribution network and the quality of power.

[0167] In one embodiment, in combination Figure 4 and Figure 5 As shown, the distributed primal dual subgradient method is used to solve the intraday multi-period rolling optimization model, specifically:

[0168] Construct the dual problem of the original problem:

[0169]

[0170] Where: L(u,p,q,λ) is the Lagrangian function of the global optimization problem; n is the number of subproblems; f i(u,p,q) is the ith subproblem; u is the voltage control command; p is the active power control command; q is the reactive power control command; λ is the Lagrange multiplier; is a constraint condition; L i (u,p,q,λ) is the Lagrangian function of the ith subproblem; g(λ) is the dual function;

[0171] The average consensus algorithm is used to update the sub-problems:

[0172]

[0173] Where: k is the number of iterations; W ij >0 is the weight coefficient; is the solution to the ith subproblem; (u, p, q) j is the solution to the jth subproblem; is the Lagrange multiplier of the i-th Lagrangian function; is the Lagrange multiplier of the jth Lagrangian function;

[0174] Project the solution and the Lagrange multiplier respectively into their definition space:

[0175]

[0176] Where: is the Euclidean projection operator of the solution on its space X; (u,p,q) i is the solution to the ith subproblem; α>0 is a fixed step size; is the subgradient of the solution; is the projection operator of the Lagrange multiplier in its space Λ; is the Lagrange multiplier of the i-th Lagrangian function; is the Lagrange multiplier of the jth Lagrangian function; is the super gradient of the Lagrange multiplier.

[0177] S5. Perform multi-level voltage coordinated optimization of the distribution network according to the first control instruction and / or the second control instruction.

[0178] That is to say, the first control instruction and / or the second control instruction are sent to the corresponding control equipment and system to execute the voltage control strategy.

[0179] Through the above implementation methods, the present invention realizes the coordinated optimization of multi-level voltages in the distribution network, effectively solves the problems of voltage over-limit and power quality degradation, and at the same time makes full use of various regulation resources to improve economic benefits and computing efficiency.

[0180] In a preferred embodiment, a linearized voltage equation is used to calculate the voltage of the voltage constraint, and the linearized voltage equation is specifically as follows:

[0181] |v|=Ts+a

[0182]

[0183] Where: v is the approximate node voltage obtained after linear equation calculation; v is the initial node voltage, |v| is the node voltage amplitude, For the coefficient matrix, Y LL is the three-phase admittance matrix; diag is the diagonal function; s is the injected three-phase power vector of the node a is the steady-state value of the voltage vector and amplitude before power is injected into the node,

[0184] In another embodiment of the present invention, a distribution network multi-level voltage collaborative optimization device is provided, which is used to implement the distribution network multi-level voltage collaborative optimization method provided in the above embodiment, specifically comprising:

[0185] The acquisition module is used to obtain the day-ahead forecast data, the intraday multi-period rolling forecast data, and the multi-objective cross-layer voltage optimization model including the substation layer, the feeder layer, and the equivalent area layer considering the power quality and economic operation.

[0186] The day-ahead prediction module is used to input the day-ahead prediction data into the multi-objective cross-layer voltage optimization model, and output a first control instruction for adjusting the substation layer-feeder layer-equivalent substation voltage layer, wherein the first control instruction includes a switch action control instruction and an active-reactive control instruction.

[0187] The reduction module is used to reduce the decision variables and constraints of the multi-objective cross-layer voltage optimization model in the time domain by using the time scale of the intra-day multi-period rolling prediction data to obtain the intra-day multi-period rolling optimization model.

[0188] The intraday prediction module is used to solve the intraday multi-period rolling optimization model using a distributed primal-dual subgradient method to obtain a second control instruction for adjusting the substation layer-feeder layer-equivalent area voltage layer, wherein the second control instruction includes an active-reactive control instruction.

[0189] An optimization module is used to perform multi-level voltage coordinated optimization of the distribution network according to the first control instruction and / or the second control instruction.

[0190] All relevant contents of each step involved in the aforementioned embodiment of a distribution network multi-level voltage collaborative optimization method can be referred to the functional description of the functional module corresponding to a distribution network multi-level voltage collaborative optimization device in the embodiment of the present invention, and will not be repeated here. The division of modules in the embodiment of the present invention is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional module in each embodiment of the present invention can be integrated into a processor, or it can exist physically separately, or two or more modules can be integrated into one module. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of software functional modules.

[0191] In another embodiment of the present invention, a computer device is provided, the computer device including a processor and a memory, 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 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., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions in a computer storage medium to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of a multi-level voltage collaborative optimization method for a distribution network.

[0192] In another embodiment of the present invention, the present invention 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 is understandable 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, and 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 corresponding steps of the multi-level voltage collaborative optimization method for a distribution network in the above embodiment.

[0193] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0194] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented 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 processor, 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 generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0195] These computer program instructions may also be stored in a computer-readable memory capable of directing 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 an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0196] 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, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0197] In the present invention, the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" etc. mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the described specific features, structures, materials or characteristics may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0198] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for coordinated optimization of multi-level voltage in a distribution network, characterized in that: include: Obtain day-ahead forecast data, intraday multi-period rolling forecast data, and a multi-objective cross-layer voltage optimization model that considers power quality and economic operation, including the substation layer, feeder layer, and equivalent area layer; The objective function of the multi-objective cross-layer voltage optimization model containing the substation layer, feeder layer and equivalent area layer is: T={t1,t2,…t N } in: The objective function f1(C) of the substation layer is: Where: C is the set of decision variables for tap switching; t∈T is the time series; λ1 is the penalty coefficient for the on-load tap changer gear switching at the substation level; is the tap position switching instruction; λ2 is the substation-level shunt capacitor position switching penalty coefficient; It is the gear switching instruction of the parallel capacitor; The constraints of the substation layer include gear constraints and bidirectional power flow constraints; The objective function f2(V,P,Q) of the feeder layer is: Where: V is the set of decision variables for the voltage amplitude at the feeder layer; P is the set of active decision variables; Q is the set of reactive decision variables; i∈I is the number of resources; is the line loss; V i,t is the voltage amplitude; V ref is the voltage reference value; c deg The cost of electricity; is the energy storage charging and discharging power; γ is the electric vehicle incentive cost; is the charging and discharging power of electric vehicles; λ is the penalty coefficient for abandoned light; is the maximum photovoltaic output power; is the actual photovoltaic output power; The constraints of the feeder layer include energy storage constraints, electric vehicle constraints, photovoltaic power station regulation capacity constraints, voltage constraints, current constraints and three-phase imbalance constraints; The objective function f3(P,Q) of the equivalent station layer is: Where: c1 is the equivalent area layer active power response cost; P i,t is the equivalent area layer response active power; c2 is the equivalent area layer reactive power response cost; Q i,t is the equivalent area layer response reactive power; The constraints of the equivalent station layer include energy storage constraints, electric vehicle constraints and photovoltaic power station regulation capacity constraints; Input the day-ahead forecast data into the multi-objective cross-layer voltage optimization model, and output a first control instruction for adjusting the substation layer-feeder layer-equivalent area voltage layer, wherein the first control instruction includes a switch action control instruction and an active-reactive power control instruction; The decision variables and constraints of the multi-objective cross-layer voltage optimization model are reduced in the time domain by using the time scale of the intra-day multi-period rolling forecast data to obtain an intra-day multi-period rolling optimization model; The distributed primal-dual subgradient method is used to solve the intraday multi-period rolling optimization model to obtain a second control instruction for adjusting the substation layer-feeder layer-equivalent area voltage layer, wherein the second control instruction includes an active-reactive control instruction; Multi-level voltage coordinated optimization of the distribution network is performed according to the first control instruction and / or the second control instruction.

2. A method for coordinated optimization of multi-level voltage in a distribution network according to claim 1, characterized in that: The gear constraints are specifically: Where: S TAP (t) is the on-load voltage regulator switching instruction at time t; S is the remaining number of switchable times of the on-load voltage regulator at time t; CAP (t) is the parallel capacitor switching instruction at time t; is the remaining number of switchable times of the parallel capacitor at time t; The bidirectional power flow constraints are specifically: Where: P import (t) is the forward power of the equilibrium node at time t; is the maximum forward power; P export (t) is the reverse power of the balanced node at time t; is the maximum reverse power; The energy storage constraints are specifically: Where: N ES is the amount of energy storage; P i ES (t) is the active power of energy storage i at time t; is the maximum active power of energy storage i; is the minimum active power of energy storage i; is the reactive power of energy storage i at time t; is the maximum reactive power of energy storage i; is the minimum reactive power of energy storage i; is the state of charge of energy storage i at time t; is the maximum state of charge of energy storage i; is the minimum state of charge of energy storage i; The electric vehicle constraints are specifically: Where: N EV is the number of electric vehicles; P i EV (t) is the active power of electric vehicle i at time t; is the maximum active power of electric vehicle i; is the minimum active power of electric vehicle i; is the state of charge of electric vehicle i at time t; is the maximum state of charge of electric vehicle i; is the minimum state of charge of electric vehicle i; The photovoltaic constraints are as follows: Where: N PV is the number of photovoltaic cells; P i PV (t) is the active power of PV i at time t; is the reactive power of PV i at time t; is the inverter capacity of PV i; is the power factor angle of photovoltaic i at time t; is the photovoltaic power factor angle limit; P i MPPT (t) is the active power of PV i under the MPPT model at time t; The voltage constraint is specifically: Where: V i (t) is the voltage amplitude of node i at time t; is the maximum voltage amplitude of node i; V is the minimum voltage amplitude of node i; N is the number of grid nodes; The current constraint is specifically: Where: I i (t) is the current amplitude of line i at time t; is the maximum current amplitude of line i; I is the minimum current amplitude of line i; Nline is the number of grid branches; The three-phase unbalance constraint is specifically: Where: i (t) is the three-phase unbalance degree of voltage at node i at time t; is the maximum value of the three-phase unbalance of the voltage at node i; Λ It is the minimum value of the three-phase unbalance of the voltage at node i.

3. A method for coordinated optimization of multi-level voltage in a distribution network according to claim 2, characterized in that: The voltage calculation method of the voltage constraint includes: The linearized voltage equation is used for calculation, and the linearized voltage equation is as follows: Where: is the approximate node voltage obtained after linear equation calculation, v is the initial node voltage, is the node voltage amplitude, For the coefficient matrix, Y LL is the three-phase admittance matrix; diag is the diagonal function; s is the injected three-phase power vector of the node ((p Y ) T ,(q Y ) T ) T , a is the steady-state value of the voltage vector and amplitude before power is injected into the node, 4. A method for coordinated optimization of multi-level voltage in a distribution network according to claim 1, characterized in that: The objective function of the intraday multi-period rolling optimization model is: <h2 style=";text-align:left;direction:ltr">T<h2 style=";text-align:left;direction:ltr"> mc <h2 style=";text-align:left;direction:ltr"> (T1,T2,T3) Where: T mc It is a multi-period prediction domain, from the current time to the end of 24 hours; T1 is a 15-minute prediction domain; T2 is a 30-minute prediction domain; T3 is a 2-hour prediction domain.

5. A method for coordinated optimization of multi-level voltage in a distribution network according to claim 4, characterized in that: The distributed primal dual subgradient method is used to solve the intraday multi-period rolling optimization model, specifically: Construct the dual problem of the original problem: Where: L(u,p,q,λ) is the Lagrangian function of the global optimization problem; n is the number of subproblems; f i (u,p,q) is the ith subproblem; u is the voltage control command; p is the active power control command; q is the reactive power control command; λ is the Lagrange multiplier; is a constraint condition; L i (u,p,q,λ) is the Lagrangian function of the ith subproblem; g(λ) is the dual function; The average consensus algorithm is used to update the sub-problems: Where: k is the number of iterations; W ij >0 is the weight coefficient; is the solution to the ith subproblem; is the solution to the jth subproblem; is the Lagrange multiplier of the i-th Lagrangian function; is the Lagrange multiplier of the jth Lagrangian function; Project the solution and the Lagrange multiplier respectively into their definition space: Where: is the Euclidean projection operator of the solution onto its space X; (u,p,q) i is the solution to the ith subproblem; α>0 is a fixed step size; is the subgradient of the solution; is the projection operator of the Lagrange multiplier in its space Λ; is the Lagrange multiplier of the i-th Lagrangian function; is the Lagrange multiplier of the jth Lagrangian function; is the super gradient of the Lagrange multiplier.

6. A multi-level voltage coordinated optimization device for a distribution network, characterized in that: include: The acquisition module is used to obtain the day-ahead forecast data, the intraday multi-period rolling forecast data, and the multi-objective cross-layer voltage optimization model including the substation layer, the feeder layer, and the equivalent area layer considering the power quality and economic operation; The objective function of the multi-objective cross-layer voltage optimization model containing the substation layer, feeder layer and equivalent area layer is: T={t1,t2,…t N } in: The objective function f1(C) of the substation layer is: Where: C is the set of decision variables for tap switching; t∈T is the time series; λ1 is the penalty coefficient for the on-load tap changer gear switching at the substation level; is the tap position switching instruction; λ2 is the substation-level shunt capacitor position switching penalty coefficient; It is the gear switching instruction of the parallel capacitor; The constraints of the substation layer include gear constraints and bidirectional power flow constraints; The objective function f2(V,P,Q) of the feeder layer is: Where: V is the set of decision variables for the voltage amplitude at the feeder layer; P is the set of active decision variables; Q is the set of reactive decision variables; i∈I is the number of resources; is the line loss; V i,t is the voltage amplitude; V ref is the voltage reference value; c deg The cost of electricity; is the energy storage charging and discharging power; γ is the electric vehicle incentive cost; is the charging and discharging power of electric vehicles; λ is the penalty coefficient for abandoned light; is the maximum photovoltaic output power; is the actual photovoltaic output power; The constraints of the feeder layer include energy storage constraints, electric vehicle constraints, photovoltaic power station regulation capacity constraints, voltage constraints, current constraints and three-phase imbalance constraints; The objective function f3(P,Q) of the equivalent station layer is: Where: c1 is the equivalent area layer active power response cost; P i,t is the equivalent area layer response active power; c2 is the equivalent area layer reactive power response cost; Q i,t is the equivalent area layer response reactive power; The constraints of the equivalent station layer include energy storage constraints, electric vehicle constraints and photovoltaic power station regulation capacity constraints; A day-ahead prediction module, used for inputting the day-ahead prediction data into the multi-objective cross-layer voltage optimization model, and outputting a first control instruction for adjusting the substation layer-feeder layer-equivalent area voltage layer, wherein the first control instruction includes a switch action control instruction and an active-reactive power control instruction; A reduction module, used to reduce the decision variables and constraints of the multi-objective cross-layer voltage optimization model in the time domain by using the time scale of the intra-day multi-period rolling forecast data to obtain an intra-day multi-period rolling optimization model; An intraday prediction module, used for solving the intraday multi-period rolling optimization model by using a distributed primal dual subgradient method, and obtaining a second control instruction for adjusting the substation layer-feeder layer-equivalent area voltage layer, wherein the second control instruction includes an active-reactive control instruction; An optimization module is used to perform multi-level voltage coordinated optimization of the distribution network according to the first control instruction and / or the second control instruction.

7. A computing device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for coordinated optimization of multi-level voltage in a distribution network as described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of a distribution network multi-level voltage coordinated optimization method as described in any one of claims 1 to 5 are implemented.

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