CN114977335A - Power distribution network operation optimization method and system with flexible multi-state switch - Google Patents

Power distribution network operation optimization method and system with flexible multi-state switch Download PDF

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CN114977335A
CN114977335A CN202210790023.1A CN202210790023A CN114977335A CN 114977335 A CN114977335 A CN 114977335A CN 202210790023 A CN202210790023 A CN 202210790023A CN 114977335 A CN114977335 A CN 114977335A
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distribution network
power distribution
state switch
flexible multi
optimization
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王华磊
刘新民
田震业
周超群
李长林
王德东
孔海洋
李承伟
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QINGDAO POWER SUPPLY Co OF STATE GRID SHANDONG ELECTRIC POWER Co
State Grid Corp of China SGCC
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QINGDAO POWER SUPPLY Co OF STATE GRID SHANDONG ELECTRIC POWER Co
State Grid Corp of China SGCC
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/48—Controlling the sharing of the in-phase component
    • G—PHYSICS
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/18—Arrangements for adjusting, eliminating or compensating reactive power in networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/50—Controlling the sharing of the out-of-phase component
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
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    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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    • Y02E40/30—Reactive power compensation

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Abstract

The utility model belongs to the technical field of power distribution networks, in particular to a power distribution network operation optimization method and system containing a flexible multi-state switch, which comprises the following steps: connecting a flexible multi-state switch in a power distribution network; constructing an operation model of the power distribution network with the flexible multi-state switch by taking the lowest operation cost and the minimum voltage deviation of the multi-period power distribution network as targets; and performing optimization solution on the constructed operation model of the power distribution network containing the flexible multi-state switch based on the improved particle swarm optimization, so as to realize the optimization operation of the power distribution network. The flexible multi-state switch is adopted to replace the traditional interconnection switch, so that the flexibility of operation control of the power distribution network is improved, and the operation efficiency of the power distribution network is the highest.

Description

Power distribution network operation optimization method and system with flexible multi-state switch
Technical Field
The disclosure belongs to the technical field of power distribution networks, and particularly relates to a power distribution network operation optimization method and system with a flexible multi-state switch.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Due to the randomness of the output and load fluctuation of the distributed power supply, the problems of bidirectional tide, voltage out-of-limit and the like in the power distribution network are aggravated, and the problems are difficult to effectively solve by adopting conventional regulation and control means such as a conventional switch and the like. In recent years, a sudden leap in Power electronic technology provides a chance for solving this problem, and new flexible Power distribution equipment represented by a Dynamic Voltage Restorer (DVR), an Active Power Filter (APF), a Solid State Transformer (SST), and the like is gradually playing a more important role. Among them, the Soft Open Point (SOP) technology of the smart micro grid with a low voltage side facing a high-proportion new energy access is initiating a new research heat tide. The SOP technique aims to replace the traditional breaker-based feeder tie switch with a controllable power electronic converter, thereby realizing the normalized flexible 'soft connection' between feeders, and providing flexible, fast and accurate power exchange control and power flow optimization capabilities.
The flexible interconnection concept based on the SOP is further gradually permeated into all physical layers from a power grid to a user, and an intelligent power distribution network flexible interconnection technology system taking the SOP as a core is formed together; the method not only puts higher requirements on the equipment performance of the SOP, but also puts great challenges on various technical links from planning to operation of the power distribution system.
The flexible switch equipment optimizes the whole network tide distribution by accurately controlling active power exchange at two sides and respectively providing reactive compensation for the two sides according to needs, provides real-time fine tide regulation and optimization capability which is not possessed by the existing distribution automation system, can quickly track dynamic changes of distributed energy and loads, and ensures that a power distribution network is in an optimized running state in real time. Compared with the traditional optimization scheduling means, the flexible switch equipment has great difference in equipment characteristics and performance, and the operation optimization problem of the flexible switch equipment faces great challenges in the aspects of analysis means, optimization strategies, coordination mechanisms and the like.
Disclosure of Invention
In order to solve the problems, the invention provides a method and a system for optimizing the operation of a power distribution network with a flexible multi-state switch.
According to some embodiments, a first aspect of the present disclosure provides a method for optimizing operation of a power distribution network including a flexible multi-state switch, which adopts the following technical scheme:
a method for optimizing the operation of a power distribution network with a flexible multi-state switch comprises the following steps:
connecting a flexible multi-state switch in a power distribution network;
constructing an operation model of the power distribution network with the flexible multi-state switch by taking the lowest operation cost and the minimum voltage deviation of the multi-period power distribution network as targets;
and performing optimization solution on the constructed operation model of the power distribution network containing the flexible multi-state switch based on the improved particle swarm optimization, so as to realize the optimization operation of the power distribution network.
As a further technical limitation, the flexible multi-state switch adopts a back-to-back voltage source type converter structure, the direct current sides of the converter units are connected through a direct current bus, and the alternating current sides of the converter units are connected with different feeder terminals.
As a further technical limitation, the flexible multi-state switch adjusts the state of the power distribution network in real time by controlling the active power and the reactive power of the feeder terminal where the flexible multi-state switch is located.
As a further technical limitation, the sub-objective function with the lowest operation cost of the multi-period power distribution network is related to the traditional power generation cost, the subsidy cost of distributed power generation, the power transmission and distribution cost and the controllable load scheduling cost.
As a further technical definition, the sub-objective function of minimum voltage deviation is related to the node voltage amplitude, and when the voltage exceeds a preset optimization interval, the voltage deviation distribution is reduced through reactive power control.
As a further technical limitation, the constraint conditions of the constructed operation model of the power distribution network with the flexible multi-state switch comprise power distribution network operation constraint and demand response constraint;
the power distribution network operation constraints comprise power flow constraints, node voltage constraints and line power constraints, and the demand response constraints comprise interruption capacity constraints, minimum interruption time constraints, maximum interruption time constraints, interruption duration constraints, interruption times constraints and transfer capacity constraints.
As a further technical limitation, the specific process of performing optimization solution on the constructed operation model of the power distribution network containing the flexible multi-state switch based on the improved particle swarm optimization algorithm is as follows:
(1) setting the total number of particles I, the total dimension D of the particles and the maximum iteration number T max Initializing the particle position x t i Velocity v t i Individual optima pbest t i Global optimum gbest t ；
(2) Calculating to obtain the dynamic radius R of the ith particle i Constructing multiple populations and introducing a topological mechanism V _ topo t i Updating the inertia weight factor w and the learning factor c 1 And c 2 Topological factor c 3 And population exchange factor c 4 ；
(3) Calculating the population optimal value of each sub-population to obtain the population seeds of each sub-populationx t i.seed ；
(4) According to the individual optimum value pbest of the particles t i Global optimum gbest t Topology mechanism V _ topo t i And population seed x t i.seed Updating the particle group velocity v t i And position x t i Calculating a fitness value fitness, and updating the pbest of the ith particle in the tth generation t i And updating the tbest of the t generation t ；
(5) Judging whether the T is reached at the moment max And if not, returning to (2); if so, stopping the algorithm and outputting an optimization result.
According to some embodiments, a second aspect of the present disclosure provides a power distribution network operation optimization system including a flexible multi-state switch, which adopts the following technical solutions:
an operation optimization system for a power distribution network comprising a flexible multi-state switch, comprising:
an access module configured to access a flexible multi-state switch in a power distribution network;
the modeling module is configured to construct an operation model of the power distribution network with the flexible multi-state switch, wherein the operation cost and the voltage deviation of the multi-period power distribution network are minimum;
and the optimization module is configured to perform optimization solution on the constructed operation model of the power distribution network containing the flexible multi-state switch based on the improved particle swarm optimization algorithm, so that the optimized operation of the power distribution network is realized.
According to some embodiments, a third aspect of the present disclosure provides a computer-readable storage medium, which adopts the following technical solutions:
a computer readable storage medium having stored thereon a program which, when executed by a processor, carries out the steps of the method for optimizing the operation of a power distribution network comprising a flexible multi-state switch according to the first aspect of the present disclosure.
According to some embodiments, a fourth aspect of the present disclosure provides an electronic device, which adopts the following technical solutions:
an electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, the processor implementing the steps of the method for optimizing the operation of a power distribution network comprising a flexible multi-state switch according to the first aspect of the present disclosure when executing the program.
Compared with the prior art, the beneficial effect of this disclosure is:
the method establishes a power distribution network operation optimization model containing the three-port flexible multi-state switch, which aims at the lowest multi-period power distribution network operation cost and the lowest voltage deviation, and operates the power distribution network containing the flexible multi-state switch based on the improved particle swarm algorithm, so that the network loss can be effectively reduced, the power distribution network operation cost can be reduced, the voltage level can be improved, and the power distribution network can operate more safely and economically; the controllable load and the flexible multi-state switch are used for coordinated control, the problems that flexible multi-state switch equipment is high in manufacturing cost, limited in capacity and reduced in loss of a power distribution network along with increase of the number of the equipment are solved, the local searching capability of the improved dynamic multi-population particle swarm algorithm is taken into consideration, the population scale of the particle swarm is dynamically adjusted, the diversity is improved, the defect that a standard particle swarm algorithm is prone to falling into local optimization is effectively improved, and an ideal optimizing result is obtained.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
Fig. 1 is a flowchart of a method for optimizing operation of a power distribution network including a flexible multi-state switch according to a first embodiment of the present disclosure;
fig. 2 is a schematic diagram of a three-port flexible multi-state switch accessing a power distribution network in a first embodiment of the disclosure;
FIG. 3 is a flow chart of an improved particle swarm algorithm in one embodiment of the disclosure;
fig. 4 is an exemplary schematic diagram of an IEEE33 node according to a first embodiment of the disclosure;
FIG. 5 is a schematic diagram illustrating comparison before and after optimization of node voltage during static optimization according to a first embodiment of the disclosure;
fig. 6 is a schematic diagram illustrating comparison before and after network loss optimization during static optimization in the first embodiment of the disclosure;
fig. 7(a) is a schematic diagram of active power output by each port of the flexible multi-state switch during static optimization in the first embodiment of the disclosure;
fig. 7(b) is a schematic diagram of reactive power output by each port of the flexible multi-state switch during static optimization in the first embodiment of the disclosure;
FIG. 8 is a schematic diagram illustrating a daily operating curve of a distributed power source and a load according to a first embodiment of the disclosure;
fig. 9(a) is a schematic voltage change diagram of a node 18 where three ports of the flexible multi-state switch are located in one day before and after optimization in the first embodiment of the disclosure;
fig. 9(b) is a schematic voltage change diagram of a node 25 where three ports of the flexible multi-state switch are located in one day before and after optimization in the first embodiment of the disclosure;
fig. 9(c) is a schematic voltage variation of a node 33 where three ports of the flexible multi-state switch are located in one day before and after optimization in the first embodiment of the disclosure;
fig. 10(a) is an active power transmission schematic diagram of a flexible multi-state switch of different nodes in the first embodiment of the disclosure;
fig. 10(b) is a schematic diagram of reactive compensation of a flexible multi-state switch of different nodes in the first embodiment of the disclosure;
fig. 11 is a schematic diagram of comparing network loss before and after optimization in the first embodiment of the disclosure;
FIG. 12 is a diagram illustrating convergence curves of an improved particle swarm algorithm (IPSO) and a standard particle swarm algorithm (PSO) in one embodiment of the disclosure;
FIG. 13 is a schematic diagram of the pre-and post-optimization 20:00 voltage distribution in the first embodiment of the disclosure;
fig. 14 is a block diagram of a power distribution network operation optimization system including a flexible multi-state switch according to a second embodiment of the present disclosure.
Detailed Description
The present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
Example one
The first embodiment of the disclosure introduces a power distribution network operation optimization method with a flexible multi-state switch.
As shown in fig. 1, a method for optimizing the operation of a power distribution network including a flexible multi-state switch includes:
connecting a flexible multi-state switch in a power distribution network;
constructing an operation model of the power distribution network with the flexible multi-state switch by taking the lowest operation cost and the minimum voltage deviation of the multi-period power distribution network as targets;
and optimizing and solving the constructed operation model of the power distribution network containing the flexible multi-state switch based on the improved particle swarm algorithm to realize the optimized operation of the power distribution network.
The flexible multi-state switch is different from the hard connection of a tie switch in the traditional power distribution network and is a soft connection, so that the continuous controllable state of power is increased besides the on and off operation states of the tie switch. The flexible multi-state switch can be flexibly switched in operation mode, the control mode is flexible and various, the function of the flexible multi-state switch is realized by controlling the fully-controlled power electronic device, and the flexible multi-state switch mainly has three types: the power supply system comprises a back-to-back voltage source type converter (B2B VSC), a Unified Power Flow Controller (UPFC) and a series compensator (SSSC).
In this embodiment, the flexible multi-state switch employs a three-port flexible multi-state switch.
A back-to-back voltage source converter structure as shown in fig. 2 is adopted: the direct current sides of the converter units are connected through direct current buses, and the alternating current sides of the converter units are connected with different feeder terminals. Through a corresponding control strategy, the power mutual aid among the ports can be realized, and the consumption of the distributed power supply is improved. The port is adjusted to be idle, voltage fluctuation can be relieved in real time, voltage support is achieved, and the quality of electric energy is improved.
The flexible multi-state switch can control the active power and the reactive power of the feeder terminal where the flexible multi-state switch is located, and therefore the control variable is the active power and the reactive power output by each port converter when the flexible multi-state switch is set.
Selecting PQ/PQ/U under normal operation condition dc In the Q mode, the active loss of each converter is considered, and the operation control of the three-port flexible multi-state switch needs to meet the following constraints:
1) transmission active power constraint:
Figure BDA0003733523320000061
Figure BDA0003733523320000062
in the formula, P f,1 (t)、P f,2 (t)、P f,3 (t) respectively outputting active power and reactive power of the three ports 1,2 and 3 of the flexible multi-state switch in a t period; p loss,m (t) active loss of the mth port of the flexible multi-state switch in a period of t; a. the f,m (t)、P f,m (t)、Q f,m And (t) respectively representing the mth port loss coefficient, the transmission active power and the output reactive power of the flexible multi-state switch.
2) Giving-off reactive power limits
Figure BDA0003733523320000063
In the formula, Q f,m And outputting the upper limit value of the reactive power for the mth port of the flexible multi-state switch.
3) Capacity limitation
Due to the existence of the direct current link, the reactive outputs of the converters are mutually isolated, and only the capacity constraint of each converter needs to be considered during modeling.
Figure BDA0003733523320000064
In the formula, S f,m And the capacity of the m-th port converter of the flexible multi-state switch is accessed.
The access of the flexible multi-state switch can adjust the state of the system in real time, but considering that the investment and operation maintenance cost of the flexible multi-state switch are high, and the improvement of the network loss of the power distribution network is limited along with the increase of the access number of the flexible multi-state switch equipment, the coordination optimization of the flexible multi-state switch needs to be considered. The appearance and the application of the active power distribution network, demand side resources also occupy an important position in the power market gradually, and the safe and reliable operation of a power system is guaranteed through peak clipping and valley filling. Demand Response (DR) refers to marketable participation of a power consumer changing an inherent power usage pattern in response to price signals or incentive mechanisms.
The development of an active power distribution network enables controllable units in the power distribution network to be more flexible, information interaction is rapid and convenient, demand response is used as a control means of a feeder terminal, and more application spaces exist in the active power distribution network. The load on the demand side can be divided into a fixed load, an interruptible load and a transferable load. The present embodiment contemplates regulation of interruptible and transferable loads.
It is assumed that interruptible and transferable loads can be flexibly engaged in day-ahead or day-ahead DR projects with arbitrary interruption or continuous supply of power within contractual limits, depending on the requirements of energy management.
Figure BDA0003733523320000071
Figure BDA0003733523320000072
In the formula, the maximum industrial and residential electricity load of the node i which can be used as DR at the time t is recorded as
Figure BDA0003733523320000073
Figure BDA0003733523320000074
the active load of the node i in the period t is marked as P LD.i (t); the translational response coefficient and the interruption response coefficient of the industrial and residential electric loads are recorded as gamma 1 、γ 2 And η 1 、η 2 And not 0, i.e., 1, is DR of node i during time t as load translatable or interruptible; the ratio of the industrial and residential electric loads to the total load, which can be used as DR, in node i is denoted as lambda i 、μ i 、。
Aiming at different problems, a corresponding objective function is generally selected according to the running actual state of the power distribution network, so that an ideal optimization model is established. Common objective functions in power distribution network operation optimization are as follows: the active loss of the network is reduced, the voltage level is improved, the load of the feeder line is balanced, and the like.
In order to ensure economic and reliable operation of the power distribution network and fully utilize demand response resources, the embodiment takes the lowest operation cost and the minimum voltage deviation of the multi-period power distribution network as optimization targets, and the optimization variables are controllable load and active power and reactive power output by the flexible multi-state switch.
The operation cost of the power distribution network is lowest:
min(f 1 )＝C G +C DG +C L +C DR (7)
wherein
Figure BDA0003733523320000081
Figure BDA0003733523320000082
In the formula, C is used for traditional power generation cost G Represents; DG power generation subsidy cost C DG Represents; line power loss cost, i.e. C for power transmission and distribution L Represents; controllable load scheduling cost is recorded as C DR (ii) a The total time period of optimization is recorded as T; n is the total number of nodes of the system; lambda [ alpha ] t The electricity price is t time period; p loss (t) line power losses, including flexible multi-state switching converter losses; the set of nodes adjacent to node i is denoted as Ω i (ii) a Branch ij resistance is denoted as R ij ；I ij (t) the current amplitude flowing from node i to node j in the period t; k is a load type, 1 represents an industrial load, and 2 represents a residential electricity load; lambda [ alpha ] k CL 、λ k SL Scheduling costs for class k interruptible loads and transferable loads, respectively; p k CL,i (t)、P k SL,i (t)、D k CL,i (t)、D k SL,i (t) capacities and scheduling states of the kth type interruptible load and the transferable load of the t-period node i, respectively (0 is unscheduled, and 1 is scheduled).
2) Minimum voltage offset
When the voltage exceeds the optimization interval, the voltage deviation optimization interval degree is reduced through reactive power control, and therefore the voltage distribution condition is improved.
Figure BDA0003733523320000083
In the formula of U i (t) is the voltage amplitude of node i during time t; u shape max,i 、U min,i Respectively an upper voltage amplitude limit and a lower voltage amplitude limit of the node i.
And determining the respective weights of the two objective functions based on a judgment matrix method, and converting the weights into a single objective function. The synthetic objective function can be expressed as
min F＝min(αf 1 +βf 2 ) (11)
In the formula: alpha and beta are respectively the weight coefficients of each sub-target. f' 1 、f' 2 Are respectively asf 1 、f 2 Normalized (i.e. converted to the interval 0, 1)]The latter value reduces the influence of the difference in unit dimension).
The constraint conditions of the constructed operation model of the power distribution network with the flexible multi-state switch comprise power distribution network operation constraint and demand response constraint:
(1) system operational constraints
The three-port flexible multi-state switch operation constraint adopts a branch power flow form to construct a radial distribution network model, and is described by the following mathematical expression:
1) flow restraint
Figure BDA0003733523320000091
Figure BDA0003733523320000092
Figure BDA0003733523320000093
In the formula, Ψ i A branch terminal node set taking the node i as an initial node; phi ( i A branch starting node set taking the node i as a terminal node; p, Q for node i to flow to node k during time t is denoted as P ik (t)、Q ik (t); the branch ji reactance is denoted X ji ；P i (t)、Q i (t) inject P, Q sum on node i for time period t; p, Q for distributed power and flexible multi-state switch injection on node i during time t is denoted as P DG,i (t)、Q DG,i (t) and P f,i (t)、Q f,i (t)；P DR,i And (t) respectively representing the active power and the reactive power of the controllable load participating in the response on the node i in the period t.
2) Node voltage constraint
U min，i ≤U i (t)≤U max，i (15)
3) Line power constraint
Figure BDA0003733523320000094
In the formula I max,ij The maximum current flowing from node i to node j.
(2) Demand response constraints
The interrupt capacity and interrupt duration of the interruptible load cannot exceed the contractually specified upper limit value, and in a scheduling period, the short-time frequent load interruption is avoided, and the interrupt frequency cannot exceed the maximum value. The transfer power and continuous power supply time of the translatable load cannot exceed the upper limit value, and the translatable load can be scheduled only once a day.
1) Interrupt capacity constraint
Figure BDA0003733523320000101
2) Minimum interrupt time constraint
Figure BDA0003733523320000102
Figure BDA0003733523320000103
3) Maximum interrupt time constraint
Figure BDA0003733523320000104
4) Interrupt duration constraints
Figure BDA0003733523320000105
5) Interruption times constraint
Figure BDA0003733523320000106
6) Transfer capacity constraint
Figure BDA0003733523320000107
7) Continuous supply time constraint
Figure BDA0003733523320000111
8) Constraint of transfer times
Figure BDA0003733523320000112
In the formula: t is k CL,min 、T k CL,max 、T k CL Interrupt duration, minimum and maximum interrupt time, respectively; n is a radical of CL The maximum number of interruptions; t is k SL,max Maximum power supply time for transferable loads; n is a radical of SL The maximum number of times of load transfer can be transferred.
In the embodiment, in order to avoid the algorithm from falling into local optimum, an optimized time sequence model is solved by combining an improved Particle Swarm Optimization (PSO) and a Binary Particle Swarm Optimization (BPSO); in each iteration process, the output power of the flexible multi-state switch is optimized by adopting improved PSO, and the controllable load scheduling state is optimized by adopting improved BPSO.
Assuming that the number of nodes of the power distribution network is N, the nodes of three ports of the three-port flexible multi-state switch are determined, and the particle code consists of four parts of information, namely active power output, reactive power output, controllable load capacity participating in dispatching and dispatching state, of the three ports of the flexible multi-state switch.
x＝[p 1 p 2 p 3 q 1 q 2 q 3 p r1 p r2 …p rn d r1 d r2 ··d rn ] (26)
The first 1-6 dimensional quantity represents that the three-terminal flexible multi-state switch sends active power and transmits reactive power, p r1 p r2 ...p rn Representing the controllable load capacity of n nodes participating in scheduling, d r1 d r2 ...d rn Representing the scheduling state of the controllable load of the n nodes participating in the scheduling.
As shown in fig. 3, the active distribution network multi-objective operation optimization based on the flexible multi-state switch comprises the following steps:
(1) setting the total number of particles I, the total dimension D of the particles and the maximum iteration number T max Initializing the particle position x t i Velocity v t i Individual optima pbest t i Global optimum gbest t ；
(2) Calculating to obtain the dynamic radius R of the ith particle i Constructing multiple populations and introducing a topological mechanism V _ topo t i Updating the inertia weight factor w and the learning factor c 1 And c 2 Topological factor c 3 And population exchange factor c 4 ；
(3) Calculating the population optimal value of each sub-population to obtain the population seed x of each sub-population t i.seed ；
(4) According to the individual optimum value pbest of the particles t i Global optimum gbest t Topology mechanism V _ topo t i And population seed x t i.seed Updating the particle group velocity v t i And position x t i Calculating a fitness value fitness, and updating the pbest of the ith particle in the t generation t i And updating the tbest of the t generation t ；
(5) Judging whether the T is reached at the moment max And if not, returning to (2); if so, stopping the algorithm and outputting an optimization result.
The validity of the method in this example is verified by combining with the analysis of practical examples as follows:
the method comprises the steps of analyzing the condition when the traditional power distribution network without the distributed power supply is connected into the flexible multi-state switch, namely optimizing the static tide of the power distribution network, wherein the fluctuation of the power distribution network is not obvious at the moment, and analyzing the effect of the flexible multi-state switch on a single time section.
The embodiment performs analysis and verification on an improved IEEE33 node algorithm, and the system comprises 37 branches, and the structure is shown in fig. 4. The flexible multi-state switch is in a power continuous controllable state, three ports are respectively connected to nodes 18, 25 and 33, the capacity of each VSC is 6MVA, and the loss coefficient is 0.02. The system voltage level is 12.66kV, the reference power is 100MVA, and the optimization interval of the node voltage amplitude is 0.95-1.05. Learning factor c 1i ＝c 2f ＝2.5，c 1f ＝c 2i The upper and lower limits of the inertial weight w are 0.9 and 0.4, respectively, the maximum number of iterations is 500, the population size is 40, and the particle dimension is 30.
As can be seen from table 1 and fig. 5, in this time section, most of the node voltages before optimization are out-of-limit, where the voltage of the node 18 is obviously the minimum point of the voltage distribution, and the per unit value is 0.913, the normal operation state of the electric device is damaged by too low voltage, and the network loss of the system is also increased. After the three-port flexible multi-state switch is added for optimization, the voltage of each node is obviously limited in an optimization interval, the voltage fluctuation condition is improved clearly, the 18 nodes at the lowest voltage point are changed into the voltage per unit value of 0.974, obvious optimization is obtained, the network loss of the system before optimization is 202.67kW, the network loss after optimization is 114.81kW, and the network loss of the system is also improved, as shown in fig. 6 specifically.
TABLE 1 Voltage per unit value of each node before and after optimization in static optimization
Figure BDA0003733523320000121
Figure BDA0003733523320000131
Figure BDA0003733523320000141
As shown in fig. 7(a) and 7(b), the active power output and the transmitted reactive power of each port of the three-terminal flexible multi-state switch during static optimization are schematic diagrams, it can be seen that the voltages of the node 18 and the node 33 both exceed the lower limit of the optimization interval before optimization, and therefore the flexible multi-state switch mainly sends out reactive power to each node, so that the voltage of each node is increased.
Considering the fluctuation of the distribution network caused by the access of the distributed power supply and the load, and carrying out dynamic power flow optimization on the distribution network with the flexible multi-state switch; verification analysis is also carried out in an IEEE33 node system shown in FIG. 4, parameter setting is consistent with static conditions, 750kW wind power is respectively connected to the nodes 13, 16 and 32, and 500kW photovoltaic power generation is respectively connected to the nodes 7 and 27. The nodes 11,21 and 29 are load nodes capable of being used as DR, and the industrial load and the residential electric load which are used as DR account for 30 percent of the total load, wherein the interruptible load and the transferable load respectively account for 15 percent. The respective scheduling parameters of interruptible load and transferable load in industrial load and residential electric load are shown in table 2.
TABLE 2 controllable load parameters
Figure BDA0003733523320000142
Since the flexible multi-state switch needs to eliminate the voltage threshold in time, the operation optimization of the system in 1 day is considered, a load curve can be obtained by load prediction, one point in 1 hour is taken, a distributed power supply and a load daily operation curve of an IEEE33 node system are shown in figure 8, it can be seen that in one day, the load is in the peak period of coming to midday and evening, the photovoltaic power generation is mainly concentrated in 6:00 to 14:00 points, and the wind power generation is in the fluctuation at the moment, so that the voltage fluctuation and the network loss can be caused when the operation state of the whole power system in one day is in the fluctuation.
As shown in fig. 9(a), 9(b) and 9(c), the schematic diagram of the voltage change conditions of the nodes 18, 25 and 33 where the three ports of the flexible multi-state switch are located before and after optimization, combined with the distributed power supply and the daily load operation curve, it can be known that, when the DG outputs a large amount of power and the load is small, the grid voltage is apt to be higher and lower to optimize the interval upper limit; on the contrary, when the load is large and the DG output is small, the grid voltage is easy to be more optimized to the lower limit of the interval. DG is connected to both node 18 and node 33 on the feeder, so it can be seen that the voltage ripple before optimization is greater than at node 25. After optimization, the voltage fluctuation of the three ports is weakened, the voltage is controlled in an optimization interval, and the voltage deviation is 0.
The active transmission and reactive compensation of the three-port flexible multi-state switch shown in fig. 10(a) and 10(b) are schematically illustrated, wherein the reactive power upper limit is 300kVA, and the reactive power of the distributed power supply is set to 0, so that the reactive power generated by the flexible multi-state switch is mainly consistent with the reactive power change of the load. When the running state of the power distribution network is kept normal, the flexible multi-state switch transmits active power; when the voltage exceeds the minimum value, the flexible multi-state switch mainly sends out reactive power.
As shown in table 3, by comparing the operation costs of the power distribution network before and after the optimization, it can be seen that the total cost before the optimization of the method in this embodiment is 1509 yuan and the total cost after the optimization is 535.2 yuan, so that the operation cost of the power distribution network is reduced and the system is ensured to operate more economically, assuming that the conventional power generation cost and the DG power generation subsidy cost remain unchanged. Wherein the line loss is changed from 1.2575MW before optimization to 0.2865MW after optimization, as shown in FIG. 11, the system network loss is greatly improved.
TABLE 3 comparison of distribution network operating costs before and after optimization
Figure BDA0003733523320000151
Based on the improved dynamic multi-population particle swarm algorithm provided by the embodiment, the improved capability of the improved dynamic multi-population particle swarm algorithm compared with the standard particle swarm algorithm is proved by combining with the test function, the improved dynamic multi-population particle swarm algorithm and the standard particle swarm algorithm are used for calculation at the same time, and the optimization results obtained by the two algorithms are compared and analyzed, as shown in a convergence curve diagram of the improved particle swarm algorithm (IPSO) and the standard particle swarm algorithm (PSO) shown in fig. 12, it can be seen that the IPSO algorithm converges to the fitness value 0.2924, and the standard PSO algorithm converges to the fitness value 0.3184, that is, the improved particle swarm algorithm in the embodiment obtains a better global optimal solution.
As can be seen from table 4, the total cost of the IPSO algorithm and the calculation time of the algorithm provided in this embodiment are both better than those of the PSO algorithm, which proves the superiority of the improved particle swarm optimization in this embodiment.
TABLE 4 comparison of algorithm results before and after improvement
Figure BDA0003733523320000161
To analyze the effect of the demand response in the optimization process, an extreme scenario is selected for analysis, as can be seen from fig. 10(b), at 20: about 00, the reactive power sent by the flexible multi-state switch reaches the upper limit, and the voltage quality cannot be fully optimized. The operating state of the distribution network is improved mainly by means of the adjustment of the controllable load, and the operation is carried out at the speed of 20:00 times are respectively carried out: the operation optimization of the power distribution network based on the flexible multi-state switch in demand response and the operation optimization of the power distribution network based on the coordination effect of the demand response and the flexible multi-state switch are not considered. The results of the two optimized voltage per unit values are shown in fig. 13, and it can be seen that load serving as DR in the nodes 11,21, and 29 is subjected to peak clipping, so that the power flow distribution is improved, and the voltage deviation is further reduced.
The method establishes a power distribution network operation optimization model containing the three-port flexible multi-state switch, which aims at the lowest multi-period power distribution network operation cost and the lowest voltage deviation, and operates the power distribution network containing the flexible multi-state switch based on the improved particle swarm algorithm, so that the network loss can be effectively reduced, the power distribution network operation cost can be reduced, the voltage level can be improved, and the power distribution network can operate more safely and economically; the controllable load and the flexible multi-state switch are used for coordinated control, the problems that flexible multi-state switch equipment is high in manufacturing cost, limited in capacity and reduced in loss of a power distribution network along with increase of the number of the equipment are solved, the local searching capability of the improved dynamic multi-population particle swarm algorithm is taken into consideration, the population scale of the particle swarm is dynamically adjusted, the diversity is improved, the defect that a standard particle swarm algorithm is prone to falling into local optimization is effectively improved, and an ideal optimizing result is obtained.
Example two
The second embodiment of the disclosure introduces a power distribution network operation optimization system with a flexible multi-state switch.
Fig. 14 shows a system for optimizing the operation of a power distribution network including a flexible multi-state switch, which includes:
an access module configured to access a power distribution network including a flexible multi-state switch;
the modeling module is configured to construct an operation model of the power distribution network with the flexible multi-state switch, wherein the operation cost and the voltage deviation of the multi-period power distribution network are minimum;
and the optimization module is configured to optimize and solve the constructed operation model of the power distribution network containing the flexible multi-state switch based on the improved particle swarm optimization algorithm, so that the optimized operation of the power distribution network is realized.
The detailed steps are the same as those of the method for optimizing the operation of the power distribution network with the flexible multi-state switch provided in the first embodiment, and are not described herein again.
EXAMPLE III
The third embodiment of the disclosure provides a computer-readable storage medium.
A computer-readable storage medium, on which a program is stored, which when executed by a processor implements the steps in the method for optimizing the operation of a power distribution network including a flexible multi-state switch according to the first embodiment of the present disclosure.
The detailed steps are the same as those of the method for optimizing the operation of the power distribution network with the flexible multi-state switch provided in the first embodiment, and are not described herein again.
Example four
The fourth embodiment of the disclosure provides an electronic device.
An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method for optimizing the operation of a power distribution network including a flexible multi-state switch according to an embodiment of the present disclosure.
The detailed steps are the same as those of the method for optimizing the operation of the power distribution network with the flexible multi-state switch provided in the first embodiment, and are not described herein again.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.

Claims (10)

1. A power distribution network operation optimization method containing a flexible multi-state switch is characterized by comprising the following steps:
connecting a flexible multi-state switch in a power distribution network;
constructing an operation model of the power distribution network with the flexible multi-state switch by taking the lowest operation cost and the smallest voltage deviation of the multi-period power distribution network as targets;
and optimizing and solving the constructed operation model of the power distribution network containing the flexible multi-state switch based on the improved particle swarm algorithm to realize the optimized operation of the power distribution network.
2. The method of claim 1, wherein the flexible multi-state switch is configured as a back-to-back voltage source converter, and wherein the dc side of each converter unit is connected via a dc bus and the ac side is connected to a different feeder terminal.
3. The method of claim 1, wherein the flexible multi-state switch adjusts the state of the distribution network in real time by controlling the active power and the reactive power at the feeder.
4. The method of claim 1, wherein the sub-objective function that minimizes the operating cost of the multi-period distribution network is associated with conventional power generation costs, subsidy costs for distributed power generation, power transmission and distribution costs, and controllable load scheduling costs.
5. The method of claim 1, wherein the subgoal function of minimum voltage deviation is related to node voltage magnitude, and the voltage deviation profile is reduced by reactive power control when the voltage exceeds a predetermined optimization interval.
6. The method for optimizing the operation of the power distribution network comprising the flexible multi-state switch as claimed in claim 1, wherein the constraint conditions of the constructed operation model of the power distribution network comprising the flexible multi-state switch comprise power distribution network operation constraints and demand response constraints;
the power distribution network operation constraints comprise power flow constraints, node voltage constraints and line power constraints, and the demand response constraints comprise interruption capacity constraints, minimum interruption time constraints, maximum interruption time constraints, interruption duration constraints, interruption times constraints and transfer capacity constraints.
7. The method for optimizing the operation of the power distribution network including the flexible multi-state switch according to claim 1, wherein the specific process of performing the optimization solution on the constructed operation model of the power distribution network including the flexible multi-state switch based on the improved particle swarm optimization comprises the following steps:
(1) setting the total number of particles I, the total dimension D of the particles and the maximum iteration number T max Initializing the particle position x t i Velocity v t i Individual optima pbest t i Global optimum gbest t ；
(2) Calculating to obtain the dynamic radius R of the ith particle i Constructing multiple populations and introducing a topological mechanism V _ topo t i Updating the inertia weight factor w and the learning factor c 1 And c 2 Topological factor c 3 And population exchange factor c 4 ；
(3) Calculating the population optimal value of each sub-population to obtain the population seed x of each sub-population t i.seed ；
(4) According to the individual optimum value pbest of the particles t i Global optimum gbest t Topology mechanism V _ topo t i And population seed x t i.seed Updating the particle group velocity v t i And position x t i Calculating a fitness value fitness, and updating the pbest of the ith particle in the t generation t i And updating the tbest of the t generation t ；
(5) Determine whether T is reached at this time max And if not, returning to (2); if so, stopping the algorithm and outputting an optimization result.
8. A power distribution network operation optimization system containing a flexible multi-state switch is characterized by comprising:
an access module configured to access a flexible multi-state switch in a power distribution network;
the modeling module is configured to construct an operation model of the power distribution network with the flexible multi-state switch, wherein the operation cost and the voltage deviation of the multi-period power distribution network are minimum;
and the optimization module is configured to optimize and solve the constructed operation model of the power distribution network containing the flexible multi-state switch based on the improved particle swarm optimization algorithm, so that the optimized operation of the power distribution network is realized.
9. A computer-readable storage medium, on which a program is stored, which program, when being executed by a processor, is adapted to carry out the steps of the method for optimizing the operation of a power distribution network comprising flexible multi-state switches according to any one of claims 1 to 7.
10. An electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of the method of optimizing the operation of a power distribution network including a flexible multi-state switch according to any one of claims 1-7.
CN202210790023.1A 2022-07-06 2022-07-06 Power distribution network operation optimization method and system with flexible multi-state switch Pending CN114977335A (en)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118174386A (en) * 2024-03-05 2024-06-11 兰州理工大学 New energy flexible distribution network reactive power optimization method based on multi-universe algorithm

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118174386A (en) * 2024-03-05 2024-06-11 兰州理工大学 New energy flexible distribution network reactive power optimization method based on multi-universe algorithm

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