CN116805178A - Reactive power optimization method, device, equipment and storage medium for power distribution network - Google Patents
Reactive power optimization method, device, equipment and storage medium for power distribution network Download PDFInfo
- Publication number
- CN116805178A CN116805178A CN202310741012.9A CN202310741012A CN116805178A CN 116805178 A CN116805178 A CN 116805178A CN 202310741012 A CN202310741012 A CN 202310741012A CN 116805178 A CN116805178 A CN 116805178A
- Authority
- CN
- China
- Prior art keywords
- power
- distribution network
- reactive power
- reactive
- objective function
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000009826 distribution Methods 0.000 title claims abstract description 116
- 238000005457 optimization Methods 0.000 title claims abstract description 88
- 238000000034 method Methods 0.000 title claims abstract description 41
- 238000010248 power generation Methods 0.000 claims abstract description 42
- 239000003990 capacitor Substances 0.000 claims abstract description 17
- 241000254158 Lampyridae Species 0.000 claims description 53
- 230000006870 function Effects 0.000 claims description 48
- 230000033228 biological regulation Effects 0.000 claims description 46
- 230000006698 induction Effects 0.000 claims description 45
- 238000004422 calculation algorithm Methods 0.000 claims description 34
- 238000004590 computer program Methods 0.000 claims description 25
- 230000006872 improvement Effects 0.000 claims description 8
- 230000005284 excitation Effects 0.000 claims description 5
- 238000004804 winding Methods 0.000 claims description 5
- 230000000739 chaotic effect Effects 0.000 description 9
- 238000010586 diagram Methods 0.000 description 6
- 230000001105 regulatory effect Effects 0.000 description 5
- 239000002245 particle Substances 0.000 description 3
- 239000000243 solution Substances 0.000 description 3
- 230000003068 static effect Effects 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000002347 injection Methods 0.000 description 1
- 239000007924 injection Substances 0.000 description 1
- 238000012804 iterative process Methods 0.000 description 1
- 230000031700 light absorption Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000002028 premature Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000002441 reversible effect Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000009827 uniform distribution Methods 0.000 description 1
Classifications
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/067—Enterprise or organisation modelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- 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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- Power Engineering (AREA)
- Educational Administration (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The application relates to the technical field of intelligent power distribution network power generation, and provides a reactive power optimization method for a power distribution network, which can effectively reduce network loss of the power distribution network and improve voltage quality. The reactive power output, the on-load tap-changer ratio and the parallel capacitor switching capacity of the distributed power generation and distribution network are used as control variables; acquiring an objective function; the objective function minimizes the sum of the expected value of active power loss of the distributed generation power distribution network and the expected value of node voltage deviation; obtaining a fuzzy reactive power optimization model of the distributed power generation and distribution network according to the variables and the objective function; obtaining constraint conditions of a fuzzy reactive power optimization model; and solving the control variable of the reactive power optimization model according to the constraint condition and the objective function, and taking the solving result meeting the condition as a reactive power optimization result of the power distribution network.
Description
Technical Field
The application relates to the technical field of intelligent power distribution network power generation, in particular to a power distribution network reactive power optimization method, a device, computer equipment, a storage medium and a computer program product.
Background
The voltage is an important standard for judging whether the power quality is qualified or not, and the quality of the voltage influences the safety performance and risk guarantee of the whole power system. To have good power quality, it is ensured that the power system has sufficient reactive compensation capacity to ensure that the power system reaches reactive balance. Thus, different voltage regulation measures should be taken for different voltage offset situations.
At present, most of the adopted voltage regulation means depend on the parallel capacitor bank of the transformer substation. The voltage regulating tap that traditional shunt capacitor group had can't play fine effect to solving long-distance tail end voltage regulation problem, receives the influence that equipment possesses unidirectional voltage regulating effect simultaneously, and the improvement voltage only can rely on output reactive power to realize, so, only can appear the condition that can not effectual voltage regulation to the distribution network under specific environment through current voltage regulating measure.
Disclosure of Invention
Based on this, it is necessary to provide a reactive power optimization method, a reactive power optimization device, a reactive power optimization computer device, a reactive power optimization storage medium and a reactive power optimization computer program product for the power distribution network.
The application provides a reactive power optimization method of a power distribution network, which comprises the following steps:
the reactive power output, the on-load tap-changer ratio and the parallel capacitor switching capacity of the distributed power generation and distribution network are used as control variables;
acquiring an objective function; the objective function minimizes the sum of the expected value of active power loss of the distributed generation power distribution network and the expected value of node voltage deviation;
obtaining a fuzzy reactive power optimization model of the distributed power generation and distribution network according to the variable and the objective function;
obtaining constraint conditions of a fuzzy reactive power optimization model; the constraint condition at least comprises any one of the following: the power flow equation constraint, the inequality constraint of the control variable and the inequality constraint of the node voltage of the distributed power generation and distribution network are adopted;
and solving the control variable of the fuzzy reactive power optimization model according to the constraint condition and the objective function, and taking the solving result meeting the condition as a reactive power optimization result of the power distribution network.
In one embodiment, in the case where the photovoltaic power plant passes through the inverter and to the distributed generation power distribution grid, the method further comprises:
analyzing the data according to the reactive power regulation capacity of the photovoltaic power station to obtain an upper limit and a lower limit of the reactive power regulation capacity of the photovoltaic power station;
and obtaining inequality constraint on reactive power output of the distributed generation power distribution network according to the upper limit and the lower limit of reactive power regulation capacity of the photovoltaic power station.
In one embodiment, the data used for reactive power regulation capability analysis of the photovoltaic power plant includes: the photovoltaic array comprises active power and reactive power output by the photovoltaic array, voltage of a connecting point of the inverter and the distributed generation power distribution network, maximum working current amplitude of the distributed generation power distribution network, intermediate direct current voltage of the inverter, inductance and angular frequency of the distributed generation power distribution network.
In one embodiment, in case the doubly-fed induction wind generator is connected to the distributed generation power distribution network through an inverter, the method further comprises:
analyzing the data used according to the reactive power regulation capacity of the doubly-fed induction wind power generator to obtain the upper limit and the lower limit of the reactive power regulation capacity of the doubly-fed induction wind power generator;
and obtaining inequality constraint on reactive power output of the distributed generation power distribution network according to the upper limit and the lower limit of reactive power regulation capacity of the doubly-fed induction wind generator.
In one embodiment, the data used for reactive power regulation capability analysis of the doubly-fed induction wind generator includes: active power and reactive power output by the doubly-fed induction wind driven generator, voltage at the stator side of the doubly-fed induction wind driven generator, maximum current of a converter at the rotor side of the doubly-fed induction wind driven generator, maximum current of a winding at the stator side of the doubly-fed induction wind driven generator, stator leakage reactance, excitation reactance and slip ratio of the doubly-fed induction wind driven generator.
In one embodiment, solving the control variable of the fuzzy reactive optimization model according to the constraint condition and the objective function comprises:
acquiring a firefly algorithm based on chaos sequence improvement;
and solving the control variable of the fuzzy reactive power optimization model according to the constraint condition and the objective function by utilizing a firefly algorithm based on chaos sequence improvement.
The application provides a reactive power optimization device of a power distribution network, which comprises the following components:
the control variable acquisition module is used for taking reactive power output, an on-load tap-changer ratio and a parallel capacitor switching capacity of the distributed power generation and distribution network as control variables;
the objective function acquisition module is used for acquiring an objective function; the objective function minimizes the sum of the expected value of active power loss of the distributed generation power distribution network and the expected value of node voltage deviation;
the model acquisition module is used for obtaining a fuzzy reactive power optimization model of the distributed generation power distribution network according to the variable and the objective function;
the constraint condition acquisition module is used for acquiring constraint conditions of the fuzzy reactive power optimization model; the constraint condition at least comprises any one of the following: the power flow equation constraint, the inequality constraint of the control variable and the inequality constraint of the node voltage of the distributed power generation and distribution network are adopted;
and the solving module is used for solving the control variable of the fuzzy reactive power optimization model according to the constraint condition and the objective function, and taking the solving result meeting the condition as a reactive power optimization result of the power distribution network.
The present application provides a computer device comprising a memory storing a computer program and a processor executing the method described above.
The present application provides a computer readable storage medium having stored thereon a computer program for execution by a processor of the above method.
The present application provides a computer program product having a computer program stored thereon, the computer program being executed by a processor to perform the above method.
The reactive power optimization method, the reactive power optimization device, the computer equipment, the storage medium and the computer program product of the power distribution network take reactive power output, the on-load tap-changer ratio and the parallel capacitor switching capacity of the distributed power generation power distribution network as control variables; acquiring an objective function; the objective function minimizes the sum of the expected value of active power loss of the distributed generation power distribution network and the expected value of node voltage deviation; obtaining a fuzzy reactive power optimization model of the distributed power generation and distribution network according to the variable and the objective function; obtaining constraint conditions of a fuzzy reactive power optimization model; the constraint condition at least comprises any one of the following: the power flow equation constraint, the inequality constraint of the control variable and the inequality constraint of the node voltage of the distributed power generation and distribution network are adopted; and solving the control variable of the fuzzy reactive power optimization model according to the constraint condition and the objective function, and taking the solving result meeting the condition as a reactive power optimization result of the power distribution network. According to the application, the reactive power of distributed power generation, the running number of the parallel capacitors and the tap of the load regulating transformer are taken as optimization variables, the expected value of active loss and the minimization of node voltage deviation are selected as objective functions, and the obtained reactive power optimization result of the power distribution network not only effectively reduces the network loss of the power distribution network, but also improves the voltage quality.
Drawings
FIG. 1 is a flow chart of a reactive power optimization method for a power distribution network in one embodiment;
FIG. 2 is a schematic flow chart of a reactive power optimization method for a power distribution network in another embodiment;
FIG. 3 is a flow chart of a reactive power optimization method for a power distribution network in yet another embodiment;
FIG. 4 is a schematic diagram of voltage magnitudes for each node before and after distributed power generation, under an embodiment;
FIG. 5 is a graph of membership in one embodiment taking into account the expected voltage of each node before and after distributed power generation;
FIG. 6 is a schematic diagram of membership functions considering nodes before and after distributed power generation in one embodiment;
FIG. 7 is a schematic diagram of convergence curves for three algorithms in one embodiment;
FIG. 8 is a block diagram of a reactive power optimization device of a power distribution network in one embodiment;
fig. 9 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the described embodiments of the application may be combined with other embodiments.
The application is based on the fact that most of power distribution networks are in a radial structure, distributed power sources are reasonably used, and the current network structure is changed from a single power source to a plurality of power sources for combination. The capacity of the power distribution network is flexibly and fully utilized to control the output of active power and reactive power on the basis of distributed power generation access, and the power distribution network is involved in voltage regulation on the basis of grid-connected power generation, so that the on-site compensation of the reactive power can be realized, and the problem of voltage out-of-limit can be effectively solved.
The reactive power optimization method of the power distribution network provided by the application comprises the steps shown in fig. 1:
step S101, reactive power output, on-load tap-changer ratio and parallel capacitor switching capacity of a distributed power generation and distribution network are used as control variables;
step S102, obtaining an objective function; the objective function minimizes the sum of the expected value of active power loss of the distributed generation power distribution network and the expected value of node voltage deviation;
step S103, obtaining a fuzzy reactive power optimization model of the distributed generation power distribution network according to the variable and the objective function;
step S104, obtaining constraint conditions of a fuzzy reactive power optimization model; the constraint condition at least comprises any one of the following: the power flow equation constraint, the inequality constraint of the control variable and the inequality constraint of the node voltage of the distributed power generation and distribution network are adopted;
the reactive power adjustment capability of the distributed power generation and distribution network can be analyzed, and the reactive power adjustment capability of the distributed power generation and distribution network comprises reactive power adjustment capability of a photovoltaic power station and reactive power adjustment capability of a doubly-fed induction wind driven generator; and then, establishing a fuzzy reactive power optimization model of the distributed generation power distribution network. Establishing an objective function of a fuzzy reactive power optimization model, wherein the objective function minimizes the sum of an expected value of active power loss of the distributed generation power distribution network and an expected value of node voltage deviation; and then, establishing constraint conditions of the fuzzy reactive power optimization model, wherein the constraint conditions comprise a tide equation constraint of the distributed generation and distribution network, an inequality constraint of a control variable and an inequality constraint of a node voltage.
And step 105, solving the control variable of the fuzzy reactive power optimization model according to the constraint condition and the objective function, and taking the solving result meeting the condition as a reactive power optimization result of the power distribution network.
After the fuzzy reactive power optimization model is constructed, as shown in fig. 2, an improved firefly algorithm can be adopted for solving, and chaotic motion is introduced into an iterative process of the firefly algorithm, so that random, traversal and rule characteristics of chaotic variables are utilized for searching, the firefly algorithm is prevented from being trapped into local optimum, a solving result meeting the condition is obtained, and the solving result is used as a reactive power optimization result of the power distribution network.
According to the application, the reactive power of distributed power generation, the running number of the parallel capacitors and the tap of the load regulating transformer are taken as optimization variables, the expected value of active loss and the minimization of node voltage deviation are selected as objective functions, and the obtained reactive power optimization result of the power distribution network not only effectively reduces the network loss of the power distribution network, but also improves the voltage quality.
In one embodiment, in the case where the photovoltaic power station passes through the inverter and to the distributed generation power distribution network, the method provided by the present application further comprises: analyzing the data according to the reactive power regulation capacity of the photovoltaic power station to obtain an upper limit and a lower limit of the reactive power regulation capacity of the photovoltaic power station; and obtaining inequality constraint on reactive power output of the distributed generation power distribution network according to the upper limit and the lower limit of reactive power regulation capacity of the photovoltaic power station.
The data used for analyzing the reactive power regulation capacity of the photovoltaic power station comprises the following steps: the photovoltaic array comprises active power and reactive power output by the photovoltaic array, voltage of a connecting point of the inverter and the distributed generation power distribution network, maximum working current amplitude of the distributed generation power distribution network, intermediate direct current voltage of the inverter, inductance and angular frequency of the distributed generation power distribution network.
When a photovoltaic power station passes through an inverter and to a distributed generation and distribution network, the reactive power regulation capability of the photovoltaic power station is closely related to the stable operation of the inverter. The relation between the active power and the reactive power emitted by a single photovoltaic is limited by the maximum working current and the reversible voltage of the inverter:
in the model, P pv 、Q pv Respectively outputting active power and reactive power to the photovoltaic array; u (U) pcc The voltage of the connection point of the inverter and the power grid, namely the rated voltage of the power grid; i max Is the maximum operating current amplitude, which can be expressed asI N Is the rated current of the power grid; u (U) dc Is the intermediate dc voltage of the inverter; l=l 1 +L 2 ，L 1 And L 2 The inductance of the inverter and the inductance of the grid side respectively; ω is the angular frequency of the mesh side.
According to formulas (1) and (2), reactive power regulation capability (Q) of the photovoltaic power station is obtained respectively pvmin ，Q pvmax ) Lower and upper limits of (2):
in one embodiment, in the case that the doubly-fed induction wind generator is connected to the distributed generation power distribution network through an inverter, the method provided by the present application further comprises: analyzing the data used according to the reactive power regulation capacity of the doubly-fed induction wind power generator to obtain the upper limit and the lower limit of the reactive power regulation capacity of the doubly-fed induction wind power generator; and obtaining inequality constraint on reactive power output of the distributed generation power distribution network according to the upper limit and the lower limit of reactive power regulation capacity of the doubly-fed induction wind generator.
The data used for analyzing the reactive power regulation capacity of the doubly-fed induction wind power generator comprises the following steps: active power and reactive power output by the doubly-fed induction wind driven generator, voltage at the stator side of the doubly-fed induction wind driven generator, maximum current of a converter at the rotor side of the doubly-fed induction wind driven generator, maximum current of a winding at the stator side of the doubly-fed induction wind driven generator, stator leakage reactance, excitation reactance and slip ratio of the doubly-fed induction wind driven generator.
The doubly-fed induction wind generator is connected to the grid through an inverter, and thus the reactive power regulation capability of the wind power generation is affected by the operation of the inverter. The relation between the active power and the reactive power generated by a single wind driven generator is as follows, limited by the maximum current of the doubly-fed induction wind driven generator stator and the doubly-fed induction wind driven generator rotor side converter:
wherein: p (P) w 、Q w The active power and the reactive power are output by the doubly-fed induction wind driven generator; u (U) s Is the stator side voltage; i s,max And I R,max Maximum currents for the rotor-side current transformer and the stator winding, respectively; x is X S Is stator leakage reactance; x is X M Is an excitation reactance; s is slip.
Meanwhile, the wind driven generator is limited by static stability, as shown in formula (6):
according to equations (4), (5) and (6), the reactive power of the wind turbine adjusts the lower and upper limits (Q wmin 、Q wmax ) Can be obtained as follows:
the objective function of the application minimizes the sum of the expected value of active power loss and the expected value of node voltage deviation of the distributed generation power distribution network, the optimal control variables are reactive power output, on-load tap-changer ratio and parallel capacitor switching capacity of the distributed generation, and a fuzzy expected value model of reactive optimization is established, and the objective function is as follows:
minf(x)＝λ 1 E(P loss )+λ 2 E(V offset ) (8)
wherein: e () is the expected value; lambda (lambda) 1 And lambda (lambda) 2 Is a weight coefficient; x is the control variable x= [ T ] ap ,Q C ,Q DG ]；T ap Is an on-load tap-changer ratio vector; q (Q) C Is a parallel capacitor switching capacity vector; q (Q) DG Is a reactive power output vector of distributed power generation, meets the requirement of Q DG ＝[Q pv ,Q w ]；P loss The total active power loss of the power distribution network; v (V) offset Is the voltage deviation of the distribution network.
Wherein P is loss And V offset The method is a trapezoidal fuzzy number, and is calculated by fuzzy power flow, and the specific formula is as follows:
in the formula, U i 、U j Is the voltage amplitude of node i and node j; g ij ，B ij ，δ ij Is the conductivity of each branch i-j and the phase angle difference between nodes i and j, N L Is the set of transmission lines and N is the number of system nodes.
In one embodiment, the step of establishing the fuzzy reactive power optimization model constraint includes:
the equality constraint in the fuzzy reactive optimization model is the power flow equation of the power distribution network, which is expressed as:
wherein P is i And Q i The method comprises the steps of respectively predicting active power and reactive power of an input node of the power distribution network; p (P) DGi And Q DGi The active power and the reactive power of the injection node of the distributed power generation unit are predicted respectively; q (Q) Ci Is the reactive power injected into the capacitor compensation node; p (P) Di And Q Di Is the predicted active power and reactive power, respectively, for the load prediction at the node.
The inequality constraint of the control variable is expressed as:
Q DGimin ≤Q DGi ≤Q DGimax (13)
Q Cimin ≤Q Ci ≤Q Cimax (14)
T apimin ≤T api ≤T apimax (15)
in which Q DGimax 、Q DGimin Is the upper limit and the lower limit of reactive power regulation capability of a distributed power generation unit in a distributed power generation distribution network, Q Cimax 、Q Cimin Is the upper limit and upper limit of the capacitance of the switched capacitor, T apimax 、T apimin The upper and lower limits of the conversion ratio of the on-load tap-changer are respectively.
The voltage amplitude of the constrained node of the state variable is determined by the trapezoidal blur number U i ＝(U i1 ,U i2 ,U i3 ,U i4 ) And (3) representing. To ensure that the voltage magnitude of the load node does not exceed the limit in all cases, a node voltage constraint inequality is employed.
U imin ≤U i1 ≤U i2 ≤U i3 ≤U i4 ≤U imax (16)
Wherein: u (U) imax And U imin The upper and lower limits of the node voltage, respectively.
In one embodiment, solving the control variable of the fuzzy reactive optimization model according to the constraint condition and the objective function comprises: acquiring a firefly algorithm based on chaos sequence improvement; and solving the control variable of the fuzzy reactive power optimization model according to the constraint condition and the objective function by utilizing a firefly algorithm based on chaos sequence improvement.
The firefly algorithm achieves the optimization goal by mutual attraction among firefly individuals, and the main concept of the firefly algorithm is brightness and attraction, and the basic concept in the optimization process is as follows:
1) Relative brightness; the light intensity of firefly I at the position of firefly J is the relative brightness of firefly I and firefly J, recorded as I ij The calculation formula is as follows:
wherein the absolute brightness of the firefly I is equal to the objective function of the firefly I, namely the light absorption quantity expressed by gamma; r is (r) ij Is the Cartesian distance between firefly I and firefly J.
2) Attractive force; the attraction of firefly I to firefly J is proportional to the absolute brightness of firefly I in firefly J, which is expressed as:
wherein beta is 0 For maximum attraction, i.e. the attraction of fireflies to light sources, beta is desirable 0 ＝1。
In the standard firefly algorithm, firefly J moves to it and updates its position as attracted by firefly I, the formula for updating the position of firefly J is as follows:
x j ＝x j +β ij (x i -x j )+α(rand-0.5) (19)
wherein x is i And x j Is the spatial position of firefly I and firefly J; alpha is a constant; the random distribution is a uniform distribution. The second term of the location update formula depends onThe third term is a random term with a specific coefficient.
Aiming at the problem of premature stagnation of a basic firefly algorithm, chaotic motion is introduced into algorithm iteration, and random, traversal and rule characteristics of chaotic variables are utilized for searching, so that the algorithm is prevented from being trapped into local optimum. A typical Logistic equation is chosen herein to generate the chaotic sequence. Meanwhile, the firefly algorithm has weak attraction at a long distance, and the position update is difficult to influence. The application provides a method for adjusting a position updating formula according to the distance between fireflies so as to improve convergence speed and search accuracy. The location update formula of the improved firefly algorithm is as follows:
where α (t) and γ (t) are chaotic sequences. In order to increase the diversity of the population, the iterative formula is as follows:
γ(t)＝μ 1 ·γ(t-1)·[1-γ(t-1)] (21)
α(t)＝μ 2 ·γ(t-1)·[1-α(t-1)] (22)
wherein: mu (mu) 1 Sum mu 2 Is a control parameter and satisfies 1 mu or less 1 ，μ 2 ≤4。
In one embodiment, the step of solving using the modified firefly algorithm model, as shown in FIG. 3, comprises:
s1: initializing system parameters: and inputting related parameters of the power distribution network and the distributed power generation, a predicted value of the distributed power generation and the load and a fuzzy parameter, and calculating a reactive power adjustment range of the distributed power generation.
S2: algorithm initialization: setting a parameter k=0 of a firefly algorithm, and setting a maximum iteration number K, a population size n and chaotic sequence parameters alpha (t) and gamma (t).
S3: the initial firefly position is randomly generated from the range of control variables. And obtaining an objective function of a single firefly by using the fuzzy power flow, and processing unequal constraint of the firefly, namely absolute brightness of the firefly by using a punishment term.
S4: the position of the firefly is updated according to formula (21) to generate a new individual while retaining the old position of the firefly.
S5: calculating the absolute brightness of the new individual, combining the new and old firefly positions, and selecting the best individual to enter the next iteration.
S6: k=k+1; updating parameters of the chaotic sequence according to formulas (22) and (23), and ending and outputting the optimal individual if K > K is satisfied; otherwise go to step S4.
Referring to fig. 4, 5 and 6, on-line simulation is performed by Matlab, the maximum iteration number is 200, the total number is 50, and the optimization operation is repeated 20 times.
The optimization results of the first stage are analyzed first, and then overall analysis is performed for the five stages.
For phase 1, the optimization results before and after considering the reactive power regulation capability of distributed generation are as follows: without consideration of the non-power regulation capability of the distributed generation, the expected value of the active power loss is 116.7869kW, and the expected value of the voltage deviation is 0.5932pu; when the non-power regulation capability of the distributed power generation is considered, the expected value of the active power loss is 103.1953kW, and the loss is reduced by 11.64%; the expected voltage deviation is 0.4409pu, 16.67% less. Fig. 4 and 5 consider the membership of the expected voltage and reactive power loss of each node before and after distributed power generation, respectively. In fig. 4, it can be seen that node 32 has the lowest voltage. Thus, FIG. 6 shows membership functions of nodes 32 before and after consideration of distributed power generation. In fig. 6, the node 32 voltage may exceed the limit without regard to the distributed generation regulation capability. This possibility is eliminated and the voltage level is increased after distributed generation is considered.
Table 1 compares the results of reactive power optimization of the distribution network in five periods to better understand the impact of reactive power adjustment capability of distributed generation on the power-free optimization of the distribution network. At each cycle, distributed generation coordinates with capacitors and transformers to optimize network losses and voltage deviations. Meanwhile, the wind and light are complementary in the aspect of active power output and reactive power regulation capacity, and reactive power optimization of the power distribution network is facilitated.
TABLE 1
Referring to fig. 7, in order to verify the effectiveness of the proposed improved firefly algorithm (MFA), the convergence curves of the Particle Swarm Optimization (PSO) algorithm and the basic Firefly Algorithm (FA) are further compared. Compared with a basic firefly algorithm and a particle swarm optimization algorithm, the improved firefly algorithm has more sufficient information exchange, so that the convergence speed and the optimal solution of the firefly algorithm are faster than those of the particle swarm optimization algorithm. The improved firefly algorithm provided by the document increases the diversity of the population and enhances the global search and the local search of the algorithm. Therefore, the optimal solution is superior to the basic firefly algorithm.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
In one embodiment, as shown in fig. 8, there is provided a reactive power optimization device for a power distribution network, including:
the control variable acquisition module 801 is configured to take reactive power output, an on-load tap-changer ratio and a parallel capacitor switching capacity of the distributed power generation and distribution network as control variables;
an objective function obtaining module 802, configured to obtain an objective function; the objective function minimizes the sum of the expected value of active power loss of the distributed generation power distribution network and the expected value of node voltage deviation;
the model obtaining module 803 is configured to obtain a fuzzy reactive power optimization model of the distributed power generation and distribution network according to the variable and the objective function;
a constraint condition acquisition module 804, configured to acquire constraint conditions of the fuzzy reactive power optimization model; the constraint condition at least comprises any one of the following: the power flow equation constraint, the inequality constraint of the control variable and the inequality constraint of the node voltage of the distributed power generation and distribution network are adopted;
and the solving module 805 is configured to solve the control variable of the fuzzy reactive power optimization model according to the constraint condition and the objective function, and take a solving result satisfying the condition as a reactive power optimization result of the power distribution network.
In one embodiment, in the case that the photovoltaic power station passes through the inverter and reaches the distributed generation and distribution network, the device further comprises an inequality constraint acquisition module, which is used for analyzing the used data according to the reactive power regulation capacity of the photovoltaic power station to obtain an upper limit and a lower limit of the reactive power regulation capacity of the photovoltaic power station; and obtaining inequality constraint on reactive power output of the distributed generation power distribution network according to the upper limit and the lower limit of reactive power regulation capacity of the photovoltaic power station.
In one embodiment, the data used for reactive power regulation capability analysis of the photovoltaic power plant includes: the photovoltaic array comprises active power and reactive power output by the photovoltaic array, voltage of a connecting point of the inverter and the distributed generation power distribution network, maximum working current amplitude of the distributed generation power distribution network, intermediate direct current voltage of the inverter, inductance and angular frequency of the distributed generation power distribution network.
In one embodiment, in the case that the doubly-fed induction wind generator is connected to the distributed generation power distribution network through an inverter, the device further comprises an inequality constraint acquisition module, configured to obtain an upper limit and a lower limit of reactive power regulation capacity of the doubly-fed induction wind generator according to data used for reactive power regulation capacity analysis of the doubly-fed induction wind generator; and obtaining inequality constraint on reactive power output of the distributed generation power distribution network according to the upper limit and the lower limit of reactive power regulation capacity of the doubly-fed induction wind generator.
In one embodiment, the data used for reactive power regulation capability analysis of the doubly-fed induction wind generator includes: active power and reactive power output by the doubly-fed induction wind driven generator, voltage at the stator side of the doubly-fed induction wind driven generator, maximum current of a converter at the rotor side of the doubly-fed induction wind driven generator, maximum current of a winding at the stator side of the doubly-fed induction wind driven generator, stator leakage reactance, excitation reactance and slip ratio of the doubly-fed induction wind driven generator.
In one embodiment, the solving module 805 is further configured to obtain a firefly algorithm that is improved based on a chaotic sequence; and solving the control variable of the fuzzy reactive power optimization model according to the constraint condition and the objective function by utilizing a firefly algorithm based on chaos sequence improvement.
For specific limitations of the reactive power optimization device of the power distribution network, reference may be made to the above limitation of the reactive power optimization method of the power distribution network, and no further description is given here. All or part of each module in the reactive power optimization device of the power distribution network can be realized by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, the internal structure of which may be as shown in FIG. 9. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing reactive power optimization data of the power distribution network. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer equipment also comprises an input/output interface, wherein the input/output interface is a connecting circuit for exchanging information between the processor and the external equipment, and the input/output interface is connected with the processor through a bus and is called as an I/O interface for short. The computer program when executed by the processor is configured to implement a reactive power optimization method for a power distribution network.
It will be appreciated by persons skilled in the art that the architecture shown in fig. 9 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory storing a computer program and a processor implementing the steps of the method embodiments described above when the processor executes the computer program.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, carries out the steps of the respective method embodiments described above.
In one embodiment, a computer program product is provided, on which a computer program is stored, which computer program is executed by a processor for performing the steps of the various method embodiments described above.
The user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or sufficiently authorized by each party.
Those skilled in the art will appreciate that implementing all or part of the above-described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (10)
1. A method for reactive power optimization of a power distribution network, the method comprising:
the reactive power output, the on-load tap-changer ratio and the parallel capacitor switching capacity of the distributed power generation and distribution network are used as control variables;
acquiring an objective function; the objective function minimizes the sum of the expected value of active power loss of the distributed generation power distribution network and the expected value of node voltage deviation;
obtaining a fuzzy reactive power optimization model of the distributed power generation and distribution network according to the variable and the objective function;
obtaining constraint conditions of a fuzzy reactive power optimization model; the constraint condition at least comprises any one of the following: the power flow equation constraint, the inequality constraint of the control variable and the inequality constraint of the node voltage of the distributed power generation and distribution network are adopted;
and solving the control variable of the fuzzy reactive power optimization model according to the constraint condition and the objective function, and taking the solving result meeting the condition as a reactive power optimization result of the power distribution network.
2. The method of claim 1, wherein in the case of a photovoltaic power plant passing through an inverter and to a distributed generation power grid, the method further comprises:
analyzing the data according to the reactive power regulation capacity of the photovoltaic power station to obtain an upper limit and a lower limit of the reactive power regulation capacity of the photovoltaic power station;
and obtaining inequality constraint on reactive power output of the distributed generation power distribution network according to the upper limit and the lower limit of reactive power regulation capacity of the photovoltaic power station.
3. The method according to claim 2, characterized in that the data used for the reactive power regulation capability analysis of the photovoltaic power plant comprises: the photovoltaic array comprises active power and reactive power output by the photovoltaic array, voltage of a connecting point of the inverter and the distributed generation power distribution network, maximum working current amplitude of the distributed generation power distribution network, intermediate direct current voltage of the inverter, inductance and angular frequency of the distributed generation power distribution network.
4. The method according to claim 1, wherein in case the doubly-fed induction wind generator is connected to the distributed generation power distribution network via an inverter, the method further comprises:
analyzing the data used according to the reactive power regulation capacity of the doubly-fed induction wind power generator to obtain the upper limit and the lower limit of the reactive power regulation capacity of the doubly-fed induction wind power generator;
and obtaining inequality constraint on reactive power output of the distributed generation power distribution network according to the upper limit and the lower limit of reactive power regulation capacity of the doubly-fed induction wind generator.
5. The method of claim 4, wherein the data for reactive regulation capability analysis of the doubly-fed induction wind generator comprises: active power and reactive power output by the doubly-fed induction wind driven generator, voltage at the stator side of the doubly-fed induction wind driven generator, maximum current of a converter at the rotor side of the doubly-fed induction wind driven generator, maximum current of a winding at the stator side of the doubly-fed induction wind driven generator, stator leakage reactance, excitation reactance and slip ratio of the doubly-fed induction wind driven generator.
6. A method according to any one of claims 1 to 5, characterized in that solving the control variables of the fuzzy reactive optimization model according to the constraints and the objective function comprises:
acquiring a firefly algorithm based on chaos sequence improvement;
and solving the control variable of the fuzzy reactive power optimization model according to the constraint condition and the objective function by utilizing a firefly algorithm based on chaos sequence improvement.
7. A power distribution network reactive power optimization device, characterized in that the device comprises:
the control variable acquisition module is used for taking reactive power output, an on-load tap-changer ratio and a parallel capacitor switching capacity of the distributed power generation and distribution network as control variables;
the objective function acquisition module is used for acquiring an objective function; the objective function minimizes the sum of the expected value of active power loss of the distributed generation power distribution network and the expected value of node voltage deviation;
the model acquisition module is used for obtaining a fuzzy reactive power optimization model of the distributed generation power distribution network according to the variable and the objective function;
the constraint condition acquisition module is used for acquiring constraint conditions of the fuzzy reactive power optimization model; the constraint condition at least comprises any one of the following: the power flow equation constraint, the inequality constraint of the control variable and the inequality constraint of the node voltage of the distributed power generation and distribution network are adopted;
and the solving module is used for solving the control variable of the fuzzy reactive power optimization model according to the constraint condition and the objective function, and taking the solving result meeting the condition as a reactive power optimization result of the power distribution network.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the method of any one of claims 1 to 6 when executing the computer program.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the method of any one of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor, implements the method of any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310741012.9A CN116805178A (en) | 2023-06-21 | 2023-06-21 | Reactive power optimization method, device, equipment and storage medium for power distribution network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310741012.9A CN116805178A (en) | 2023-06-21 | 2023-06-21 | Reactive power optimization method, device, equipment and storage medium for power distribution network |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116805178A true CN116805178A (en) | 2023-09-26 |
Family
ID=88080411
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310741012.9A Pending CN116805178A (en) | 2023-06-21 | 2023-06-21 | Reactive power optimization method, device, equipment and storage medium for power distribution network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116805178A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117856264A (en) * | 2023-10-20 | 2024-04-09 | 三峡电能有限公司 | Power grid line loss optimization method, equipment and medium |
-
2023
- 2023-06-21 CN CN202310741012.9A patent/CN116805178A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117856264A (en) * | 2023-10-20 | 2024-04-09 | 三峡电能有限公司 | Power grid line loss optimization method, equipment and medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Muthukumar et al. | Optimal placement and sizing of distributed generators and shunt capacitors for power loss minimization in radial distribution networks using hybrid heuristic search optimization technique | |
| Zhang et al. | Multi-objective optimal reactive power dispatch of power systems by combining classification-based multi-objective evolutionary algorithm and integrated decision making | |
| Reddy et al. | Optimal capacitor placement for loss reduction in distribution systems using fuzzy and harmony search algorithm | |
| Abbasi et al. | Single and multi-objective optimal power flow using a new differential-based harmony search algorithm | |
| Shaheen et al. | An improved sunflower optimization algorithm-based Monte Carlo simulation for efficiency improvement of radial distribution systems considering wind power uncertainty | |
| CN108631328A (en) | It is a kind of to consider that DG reactive power supports and the active distribution network of switch reconstruct are distributed robust idle work optimization method | |
| CN109904877B (en) | Distributed wind power plant optimization operation method based on variable power factor | |
| CN116805178A (en) | Reactive power optimization method, device, equipment and storage medium for power distribution network | |
| Al-Ammar et al. | Optimal capacitor placement in radial distribution systems using a fuzzy-dragonfly method | |
| Thakur et al. | Reliability analysis and power quality improvement model using enthalpy based grey wolf optimizer | |
| Safari et al. | Optimal setting and placement of FACTS devices using strength Pareto multi-objective evolutionary algorithm | |
| Ahmadi et al. | Optimal allocation of multi-type distributed generators for minimization of power losses in distribution systems | |
| CN115833228A (en) | Alternating current-direct current distribution network photovoltaic absorption capacity prediction method based on Harris eagle algorithm | |
| Shi et al. | A gradient based decentralized volt/var optimization scheme for distribution systems with high DER penetration | |
| PADMA et al. | Application of fuzzy and ABC algorithm for DG placement for minimum loss in radial distribution system | |
| Kumar et al. | Optimisation of voltage and frequency regulation in an isolated wind-driven six-phase self-excited induction generator | |
| CN116345467A (en) | Power distribution network power flow calculation method and system considering node power correlation | |
| Zakka et al. | Optimal capacitor placement using fuzzy and artificial bee colony algorithm for maximum loss reduction | |
| CN112734590B (en) | Two-stage regional power grid reactive power optimization method based on interior point method and dynamic programming method of variable domain search | |
| Guliyev | Reactive power adaptive control system in networks with distributed generation based on fuzzy set theory | |
| Priyanka et al. | Performance Analysis of Distribution System with Optimal Allocation of Unified Power Quality Conditioner Considering Distribution Network Reconfiguration. | |
| Dsouza et al. | A hybrid approach for enhancing and optimizing the power quality and power flow in Smart Grid Connected System | |
| Hussaini et al. | Optimal Droop Control Design Using Artificial Intelligent Techniques for Electric Power Systems of More-Electric Aircraft | |
| Rao | Improved Adaptive Network-Based Fuzzy Inference System Approach for Power Flow Analysis in Interconnected DC Microgrid | |
| Lenin | Real power loss reduction by hybridization of tree–seed algorithm with sine–cosine algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |