CN110635486B - Load frequency modulation optimal scheduling method considering constraint conditions of power distribution network - Google Patents

Load frequency modulation optimal scheduling method considering constraint conditions of power distribution network Download PDF

Info

Publication number
CN110635486B
CN110635486B CN201911097139.1A CN201911097139A CN110635486B CN 110635486 B CN110635486 B CN 110635486B CN 201911097139 A CN201911097139 A CN 201911097139A CN 110635486 B CN110635486 B CN 110635486B
Authority
CN
China
Prior art keywords
frequency modulation
distribution network
solution
load
node
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.)
Active
Application number
CN201911097139.1A
Other languages
Chinese (zh)
Other versions
CN110635486A (en
Inventor
李中伟
张杨柳
白子扬
裴碧莹
金显吉
佟为明
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Harbin Institute of Technology
Original Assignee
Harbin Institute of Technology
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Harbin Institute of Technology filed Critical Harbin Institute of Technology
Priority to CN201911097139.1A priority Critical patent/CN110635486B/en
Publication of CN110635486A publication Critical patent/CN110635486A/en
Application granted granted Critical
Publication of CN110635486B publication Critical patent/CN110635486B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • G—PHYSICS
    • G06—COMPUTING; 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas 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
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

Abstract

A load frequency modulation optimal scheduling method considering constraint conditions of a power distribution network belongs to the technical field of load side frequency modulation. The method determines the power grid nodes participating in frequency modulation and the capacity of each node participating in frequency modulation through a genetic tabu search algorithm, and firstly, the total capacity of temperature control load participating in auxiliary frequency modulation provided by a scheduling center and the number of the power grid nodes participating in frequency modulation are used as basic data to calculate by using the genetic algorithm to obtain a local optimal solution based on the genetic algorithm; and then the optimal solution is used as the initial solution of the tabu search algorithm, and the optimal solution of the genetic tabu algorithm based on basic data is obtained through calculation. The optimal solution is the optimal solution of the participating nodes and the participating capacity when the load participates in the frequency modulation optimal scheduling of the power distribution network. The invention comprehensively uses the genetic algorithm and the tabu search algorithm, and fully utilizes the advantages of good convergence performance of the genetic algorithm and low requirement on the initial value.

Description

Load frequency modulation optimal scheduling method considering constraint conditions of power distribution network
Technical Field
The invention belongs to the technical field of load side frequency modulation, and particularly relates to a load frequency modulation optimal scheduling method considering constraint conditions of a power distribution network.
Background
The frequency of the power system is one of the key parameters of the power system, and the quality of the frequency directly affects the working efficiency and the service life of the power-sending and power-supplying equipment and the normal use of the electric equipment related to the frequency of the system. When the frequency of the power system has larger deviation and does not meet the frequency quality requirement of the power system, a series of adverse consequences and serious faults can be caused. The frequency of the power system can be adjusted by balancing the power deviation between power generation and load by changing the load size besides the power generation side frequency modulation by constantly changing the generated energy, and the frequency modulation mode is a load side frequency modulation mode based on 'follow-up power supply'. The demand side frequency modulation mode has the advantages of high response speed, low cost, zero pollution and the like, and the normal use and the comfort degree of a user cannot be obviously influenced when certain loads with energy storage characteristics are controlled. When the temperature control load is used as a demand side response source and a power generation side generator set to cooperatively participate in frequency modulation of the power system, the requirement for balance of supply and demand of active power is met, power constraint conditions are met, the problems of heavy load of a certain branch (node) of a power distribution network, even power out-of-limit and the like are prevented, and the operation safety of the power system is further protected. The temperature control load is widely distributed in each node of a power distribution network of the power system. When the temperature control load aggregate power is larger than the reserve capacity required by frequency modulation, the temperature control loads on which nodes are selected to participate in the frequency modulation of the power system can influence the power flow distribution of the power distribution network, and further influence the line loss of the power distribution network and the voltage deviation of each node. If the temperature control load participates in the frequency modulation of the power system and improves the frequency quality of the system, the line loss of the power distribution network and the voltage deviation of each node can be reduced as much as possible, and the operation safety, stability and economy of the power system can be greatly improved.
Disclosure of Invention
The invention aims to provide a load frequency modulation optimal scheduling method considering constraint conditions of a power distribution network for preventing the problems of heavy load of a branch (node) of a power distribution network, even power out-of-limit and the like and improving the operation safety of a power system, and comprehensively evaluating the load nodes and capacity participating in frequency modulation.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a load frequency modulation optimal scheduling method considering constraint conditions of a power distribution network comprises the following steps:
(1) Obtaining the total capacity of temperature control load participating in frequency modulation and the number of nodes of the power distribution network from a dispatching center;
(2) The dispatching center determines the total capacity P of the needed temperature control load participating in frequency modulation according to the constraint condition cut And the number N of nodes of the power distribution network which actually need to participate in frequency modulation;
(3) Setting a load reduction step length, enumerating N allowed load capacity combinations of nodes by using a depth search algorithm, and numbering the combinations;
(4) Applying a genetic algorithm, wherein an initial individual of the genetic algorithm is formed by a power distribution network node number and a reduced capacity combination number to generate an initial population;
(5) According to population individuals, network parameters are modified, load flow calculation is carried out by applying a Newton-Raphson method, an adaptive value function is obtained according to the load flow calculation result, and the adaptive value function is obtained by the following formula of MaxF = alpha 1 *(P sum0 -P sum )-α 2 *(1-min(V i∈N )/V N ) Where MaxF is the objective function, α 1 Optimizing a weight coefficient, P, for a line loss target sum0 Is per unit value, P, of line loss of the distribution network before frequency modulation sum Is the per unit value alpha of the line loss of the distribution network after frequency modulation 2 Optimizing a weight coefficient, V, for a voltage offset target i∈n Is the voltage amplitude, V, of the ith node of the N nodes N Is a rated voltage;
(6) Determining individual adaptive value of population by roulette method according to formula
Figure BDA0002268677880000021
And
Figure BDA0002268677880000022
obtaining an individual fitness value, wherein P (x) i ) Probability of inheritance of an individual i into the next generation population, f (x) i ) Is the adaptive value of the ith individual, i and j are individual numbers, f (x) j ) Is the fitness value of the jth individual, q i Accumulating probabilities x for individuals i For the ith individual, x j For the jth individual, P (x) j ) Probability of inheritance for individual j into the next generation population;
(7) Carrying out binary coding on the population individuals, and carrying out random cross operation;
(8) Selecting a population which accords with constraint conditions after cross operation, and performing binary decoding, wherein the constraint conditions are divided into equality constraint and inequality constraint;
the equation is constrained to:
Figure BDA0002268677880000023
and
Figure BDA0002268677880000024
wherein, P i Active power of the ith branch, V i Is the voltage of node i, V j Is the voltage of node j, G ij Is the conductance of branch ij, θ ij Phase difference of branch ij, B ij Susceptance, Q, for branch ij i The reactive power of the ith branch is N, and the N is the node number of the power distribution network;
the inequality constraints are: u shape imin ≤U i ≤U imax And I i ＜I imax And P j 2 +Q j 2 ≤0.8·S jmax ；
Wherein, U imin Is the magnitude of the lower voltage limit, U, of node i i Is the voltage of node i, U imax Is the magnitude of the upper voltage limit, I, of node I i To flow through branch b i Current of (I) imax Is a branch b i Maximum allowable current value, P j Active power, Q, for the jth branch j Is the reactive power of the jth branch, S jmax Is the rated transmission capacity of the line; n is a radical of n Number of nodes of distribution network, N b The number of branches of the power distribution network;
(9) Calculating the difference rate of the populations meeting the constraint conditions;
(10) Judging whether the difference rate is smaller than a set value, if so, executing the step (11), otherwise, repeating the steps (5) to (9) until the difference rate is smaller than the set value;
(11) Outputting the individual with the maximum fitness obtained in the evolution process as the local optimal solution of the genetic algorithm;
(12) Taking the local optimal solution obtained by the genetic algorithm as an initial solution of a tabu search algorithm;
(13) Inputting a power distribution network node parameter, setting a forbidden table to be empty, and performing initial load flow calculation by applying a Newton-Raphson method;
(14) Judging whether the current solution meets a termination principle, if so, directly jumping to the step (22), and if not, executing the step (15);
(15) Judging whether the neighborhood needs to be modified, if so, modifying, searching the neighborhood of the node and the capacity number by two parts of concentration and diversity, dividing the neighborhood into two parts, wherein the elements of the first half part are concentration elements and comprise upstream nodes and downstream nodes of a solution, the upstream nodes and the downstream nodes of the solution are used as the neighborhood after non-repeated arrangement, the elements of the second half part are called diversity elements, namely, the neighborhood solution of the first half part is removed, and the neighborhood is randomly generated; if the field does not need to be modified, directly executing the step (16);
(16) Neighborhood searching of the frequency modulation load position to generate a candidate solution;
(17) Modifying network parameters to perform load flow calculation, and calculating an individual adaptation value function with a formula of MaxF = alpha 1 *(P sum0 -P sum )-α 2 *(1-min(V i∈N )/V N )；
(18) Judging whether the adaptation value meets the constraint condition, if so, performing the step (19), and if not, repeating the steps (14) to (17); the constraint conditions are as follows: equality constraint and inequality constraint;
the equation constraints are:
Figure BDA0002268677880000031
and
Figure BDA0002268677880000032
wherein, P i Active power of the ith branch, V i Is the voltage of node i, V j Is the voltage of node j, G ij Is the conductance of branch ij, θ ij Phase difference of branch ij, B ij Susceptance, Q, of branch ij i For the reactive power of the ith branch, N isThe number of nodes of the power distribution network;
the inequality constraints are: u shape imin ≤U i ≤U imax And I i ＜I imax And P j 2 +Q j 2 ≤0.8·S jmax ；
Wherein, U imin Is the magnitude of the lower voltage limit of node i, U i Is the node i voltage, U imax Is the magnitude of the upper voltage limit, I, of node I i To flow through branch b i Current of (I) imax Is a branch b i Maximum allowable current value, P j Active power of the jth branch, Q j Is the reactive power of the jth branch, S jmax Is the rated transmission capacity of the line;
(19) Judging whether scofflaw criteria are met, if yes, taking the solution meeting scofflaw rules as the current solution, updating the taboo list, updating the optimal state, and returning to the step (14), if not, executing the step (20), adopting the global scofflaw criteria, updating the global optimal solution record, and only recording the optimal solution so far;
(20) Judging whether the candidate solution is in a tabu table;
(21) Taking the optimal candidate solution of the non-taboo object as the current optimal solution, updating the taboo table and returning to the step (14);
(22) And outputting the optimal solution and the corresponding objective function value, and terminating the loop exit program.
Compared with the prior art, the invention has the beneficial effects that: the invention comprehensively uses the genetic algorithm and the tabu search algorithm, and fully utilizes the advantages of good convergence performance of the genetic algorithm and low requirement on the initial value. And the early maturing phenomenon which is possibly generated when the genetic algorithm is singly used is eliminated by combining the genetic algorithm and the tabu search algorithm. Meanwhile, the optimal solution of the genetic algorithm is used as the initial solution of the tabu search algorithm, and the requirement of the tabu search algorithm on the accuracy of the initial value is effectively met. Moreover, the method is easy to implement, and can also be used for calculating the optimal solution of other types of objects.
Drawings
Fig. 1 is a flowchart of a load frequency modulation optimization scheduling method considering constraint conditions of a power distribution network.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings, but not limited thereto, and modifications or equivalent substitutions may be made to the technical solution of the present invention without departing from the spirit of the technical solution of the present invention, and are intended to be covered by the scope of the present invention.
The method determines the power grid nodes participating in frequency modulation and the capacity of each node participating in frequency modulation through a genetic tabu search algorithm, and firstly, the total capacity of temperature control load participating in auxiliary frequency modulation provided by a scheduling center and the number of the power grid nodes participating in frequency modulation are used as basic data to calculate by using the genetic algorithm to obtain a local optimal solution based on the genetic algorithm; and then the optimal solution is used as the initial solution of the tabu search algorithm, and the optimal solution of the genetic tabu algorithm based on basic data is obtained through calculation. The optimal solution is the optimal solution of the participating nodes and the participating capacity when the load participates in the frequency modulation optimal scheduling of the power distribution network.
The first embodiment is as follows: as shown in fig. 1, the present embodiment describes a load frequency modulation optimization scheduling method considering constraint conditions of a power distribution network, the method includes:
(1) Obtaining the total capacity of temperature control load participating in frequency modulation and the number of nodes of the power distribution network from a dispatching center;
(2) The dispatching center determines the total capacity P of the needed temperature control load participating in frequency modulation according to the constraint condition cut And the number N of nodes of the power distribution network which actually need to participate in frequency modulation;
(3) Setting a load reduction step length, enumerating N allowed load capacity combinations of nodes by using a depth search algorithm, and numbering the combinations;
(4) Applying a genetic algorithm, wherein an initial individual of the genetic algorithm is formed by a power distribution network node number and a reduced capacity combination number to generate an initial population;
(5) According to population individuals, network parameters are modified, load flow calculation is carried out by applying a Newton-Raphson method, an adaptive value function is obtained according to the load flow calculation result, and MaxF =is obtained by the adaptive value function according to the following formulaα 1 *(P sum0 -P sum )-α 2 *(1-min(V i∈N )/V N ) Wherein MaxF is an objective function, α 1 Optimizing a weight coefficient, P, for a line loss target sum0 Is per unit value, P, of line loss of the distribution network before frequency modulation sum Is the per unit value alpha of the line loss of the distribution network after frequency modulation 2 Optimizing a weight coefficient, V, for a voltage offset target i∈n Is the voltage amplitude, V, of the ith node of the N nodes N Is a rated voltage;
(6) Determining individual adaptive value of population by roulette method according to formula
Figure BDA0002268677880000051
And
Figure BDA0002268677880000052
obtaining an individual fitness value, wherein P (x) i ) Probability of inheritance of an individual i into the next generation population, f (x) i ) Is the adaptive value of the ith individual, i and j are individual numbers, f (x) j ) Is the fitness value of the jth individual, q i Accumulating probabilities x for individuals i For the ith individual, x j Is the jth individual, P (x) j ) Probability of inheritance for individual j into the next generation population;
(7) Carrying out binary coding on the population individuals, and carrying out random cross operation;
(8) Selecting a population which accords with constraint conditions after cross operation, and performing binary decoding, wherein the constraint conditions are divided into equality constraint and inequality constraint;
the equation is constrained to:
Figure BDA0002268677880000053
and
Figure BDA0002268677880000054
wherein, P i Active power of the ith branch, V i Is the voltage of node i, V j Is the voltage of node j, G ij Is the conductance of branch ij, θ ij Phase difference of branch ij, B ij Susceptance, Q, for branch ij i The reactive power of the ith branch is N, and the N is the node number of the power distribution network;
the inequality constraints are: u shape imin ≤U i ≤U imax And I i ＜I imax And P j 2 +Q j 2 ≤0.8·S jmax ；
Wherein, U imin Is the magnitude of the lower voltage limit, U, of node i i Is the voltage of node i, U imax Is the magnitude of the upper voltage limit, I, of node I i To flow through branch b i Current of (I) imax Is a branch b i Maximum allowable current value, P j Active power, Q, for the jth branch j Is the reactive power of the jth branch, S jmax The rated transmission capacity of the line is multiplied by 0.8 because the line is heavily loaded when the load rate of radiation and atypical wiring exceeds 80%, which is not favorable for the stability of a power grid; n is a radical of n Number of nodes of distribution network, N b The number of branches of the power distribution network;
(9) Calculating the difference rate of the population meeting the constraint condition, wherein the difference rate is the ratio of the number of individual species in the population to the size of the population and is a mark for judging whether the genetic algorithm falls into the premature condition;
(10) Judging whether the difference rate is smaller than a set value, if so, executing the step (11), otherwise, repeating the steps (5) to (9) until the difference rate is smaller than the set value;
(11) Outputting the individuals with the maximum fitness obtained in the evolution process as the local optimal solution of the genetic algorithm;
(12) Taking the local optimal solution obtained by the genetic algorithm as an initial solution of a tabu search algorithm;
(13) Inputting power distribution network node parameters, forbidding a table to be empty, and performing initial power flow calculation by applying a Newton-Raphson method;
(14) Judging whether the current solution meets a termination principle, if so, directly jumping to the step (22), and if not, executing the step (15);
(15) Judging whether the neighborhood needs to be modified, if so, modifying, wherein the neighborhood search of the node and the capacity number is composed of two parts of concentration and diversity, the neighborhood is divided into two parts, the elements of the first half part are concentration elements and are composed of upstream nodes and downstream nodes of the solution, the upstream nodes and the downstream nodes of the solution are used as the neighborhood after non-repeated arrangement, the elements of the second half part are called diversity elements, namely, the neighborhood solution of the first half part is removed, and the neighborhood is generated randomly; if the field does not need to be modified, directly executing the step (16);
(16) Neighborhood searching of the frequency modulation load position to generate a candidate solution;
(17) Modifying network parameters to perform load flow calculation, and calculating an individual adaptation value function with a formula of MaxF = alpha 1 *(P sum0 -P sum )-α 2 *(1-min(V i∈N )/V N )；
(18) Judging whether the adaptation value meets the constraint condition, if so, performing the step (19), and if not, repeating the steps (14) to (17); the constraint conditions are as follows: equality constraint and inequality constraint;
the equation is constrained to:
Figure BDA0002268677880000061
and
Figure BDA0002268677880000062
wherein, P i Active power of the ith branch, V i Is the voltage of node i, V j Is the voltage of node j, G ij To the conductance of branch ij, θ ij Phase difference of branch ij, B ij Susceptance, Q, for branch ij i The reactive power of the ith branch is N, and the N is the node number of the power distribution network;
the inequality constraints are: u shape imin ≤U i ≤U imax And I i ＜I imax And P j 2 +Q j 2 ≤0.8·S jmax ；
Wherein, U imin Is the magnitude of the lower voltage limit, U, of node i i Is the voltage of node i, U imax Is the magnitude of the upper voltage limit, I, of node I i To flow through branch b i Current of (I) imax Is a branch b i Maximum allowable current value, P j Active power of the jth branch, Q j Is the reactive power of the jth branch, S jmax The factor of 0.8 is the rated transmission capacity of the line, because the line is heavily loaded when the load rate of the radiation and atypical connection exceeds 80%, which is not favorable for the stability of the power grid;
(19) Judging whether scofflaw criteria are met, if yes, taking the solution meeting scofflaw rules as the current solution, updating the taboo list, updating the optimal state, and returning to the step (14), if not, executing the step (20), adopting the global scofflaw criteria, updating the global optimal solution record, and only recording the optimal solution so far;
(20) Judging whether the candidate solution is in a tabu table;
(21) Taking the optimal candidate solution of the non-tabu object as the current optimal solution, updating the tabu table and returning to the step (14);
(22) And outputting the optimal solution and the corresponding objective function value, and terminating the loop exit program.
The second embodiment is as follows: in the first specific embodiment, the temperature control load frequency modulation optimization scheduling strategy considering the constraint conditions of the power distribution network based on the genetic tabu algorithm includes, in step (2), the constraint conditions including a generator set climbing rate, a required frequency modulation reserve capacity and a minimum stable output.
The third concrete implementation mode: in a first specific embodiment, the temperature control load frequency modulation optimization scheduling strategy taking into account constraint conditions of the power distribution network based on the genetic tabu algorithm includes, in step (14), the termination principle: the optimal value remains unchanged for 20 iterations.
The fourth concrete implementation mode: in a first specific embodiment, the temperature control load frequency modulation optimization scheduling strategy taking into account constraint conditions of the power distribution network based on the genetic tabu algorithm includes, in step (15), the judging conditions are: the current optimal solution has been taboo in the taboo table, but does not satisfy the scofflaw criteria superior to all candidate solutions.
The fifth concrete implementation mode: in the first specific embodiment, the temperature control load frequency modulation optimization scheduling strategy taking the constraint conditions of the power distribution network into account based on the genetic tabu algorithm is that in the step (19), the scofflaw criterion is that the current solution is superior to all candidate solutions.

Claims (5)

1. A load frequency modulation optimal scheduling method considering constraint conditions of a power distribution network is characterized by comprising the following steps: the method comprises the following steps:
(1) Obtaining the total capacity of temperature control load participating in frequency modulation and the number of nodes of the power distribution network from a dispatching center;
(2) The scheduling center determines the total capacity P of the needed temperature control load participating in frequency modulation according to the first constraint condition cut And the number N of nodes of the power distribution network which actually need to participate in frequency modulation;
(3) Setting a load reduction step length, enumerating N load capacity combinations allowed to be reduced by nodes by using a depth search algorithm, and numbering the combinations;
(4) Applying a genetic algorithm, wherein an initial individual of the genetic algorithm is formed by a power distribution network node number and a reduced capacity combination number to generate an initial population;
(5) According to population individuals, network parameters are modified, load flow calculation is carried out by applying a Newton-Raphson method, an adaptive value function is obtained according to the load flow calculation result, and the adaptive value function is obtained by the following formula
Figure FDA0003939598600000011
Wherein MaxF is an adaptive value function, alpha 1 Optimizing a weight coefficient, P, for a line loss target sum0 Is per unit value, P, of line loss of the distribution network before frequency modulation sum Is the per unit value alpha of the line loss of the distribution network after frequency modulation 2 Optimizing a weight coefficient, V, for a voltage offset target i∈N Is the voltage of the ith node of the N nodes,
Figure FDA0003939598600000012
is a rated voltage;
(6) Determining individual adaptive value of population by roulette method according to formula
Figure FDA0003939598600000013
And
Figure FDA0003939598600000014
obtaining an individual fitness value, wherein,
Figure FDA0003939598600000015
is the ith b The probability of an individual being inherited into the next generation population,
Figure FDA0003939598600000016
is the ith b Adaptation value of individual, i b 、j b The number of the individual is given to the individual,
Figure FDA0003939598600000017
is jth b The fitness value of the individual is determined,
Figure FDA0003939598600000018
is the ith b The probability is accumulated for each individual, and,
Figure FDA0003939598600000019
is jth b The number of the individuals is small,
Figure FDA00039395986000000110
is jth b Probability of individual inheritance into next generation populations;
(7) Carrying out binary coding on the population individuals, and carrying out random cross operation;
(8) Selecting a population which accords with a second constraint condition after cross operation, and performing binary decoding, wherein the second constraint condition is divided into equality constraint and inequality constraint;
the equation is constrained to:
Figure FDA00039395986000000111
and
Figure FDA00039395986000000112
wherein, P i Injected for node iActive power, V i Is the voltage of node i, V j Is the voltage of node j, G ij Is the conductance of branch ij, θ ij Phase difference of branch ij, B ij Susceptance, Q, of branch ij i Reactive power injected for node i;
the inequality constraints are: v imin ≤V i ≤V imax And I ij ＜I ijmax And
Figure FDA00039395986000000113
wherein, V imin Is the magnitude of the lower voltage limit, V, of node i i Is the voltage of node i, V imax Is the magnitude of the upper voltage limit, I, of node I ij For the current flowing through branch ij, I ijmax Maximum current value, P, allowed for branch ij ij Active power, Q, for branch ij ij Reactive power, S, for branch ij ijmax The rated transmission capacity of the branch circuit;
(9) Calculating the difference rate of the population meeting the second constraint condition;
(10) Judging whether the difference rate is smaller than a set value, if so, executing the step (11), otherwise, repeating the steps (5) to (9) until the difference rate is smaller than the set value;
(11) Outputting the individual with the maximum adaptive value obtained in the evolution process as the local optimal solution of the genetic algorithm;
(12) Taking the local optimal solution obtained by the genetic algorithm as an initial solution of a tabu search algorithm;
(13) Inputting a power distribution network node parameter, setting a forbidden table to be empty, and performing initial load flow calculation by applying a Newton-Raphson method;
(14) Judging whether the current solution meets a termination principle, if so, directly jumping to the step (22), and if not, executing the step (15);
(15) Judging whether the neighborhood needs to be modified, if so, modifying, searching the neighborhood of the node and the reduced capacity combination number by two parts of concentration and diversity, dividing the neighborhood into two parts, wherein the elements of the first half part are concentration elements and are composed of upstream nodes and downstream nodes of the solution, the solution itself, the upstream nodes and the downstream nodes of the solution are used as the neighborhood after non-repeated arrangement, the elements of the second half part are called diversity elements, namely, the neighborhood solution of the first half part is removed, and the neighborhood is generated randomly; if the field does not need to be modified, directly executing the step (16);
(16) Neighborhood search of the temperature control load position to generate a candidate solution;
(17) Modifying network parameters to perform load flow calculation, and calculating an individual adaptive value function with the formula of
Figure FDA0003939598600000021
(18) Judging whether the adaptive value meets a second constraint condition, if so, performing a step (19), and if not, repeating the steps (14) to (17); the second constraint is: equality constraint and inequality constraint;
the equation is constrained to:
Figure FDA0003939598600000022
and
Figure FDA0003939598600000023
the inequality constraints are: v imin ≤V i ≤V imax And I ij ＜I ijmax And
Figure FDA0003939598600000024
(19) Judging whether scofflaw criteria are met, if yes, taking the scofflaw rule-meeting solutions as current solutions, updating the taboo list, updating the optimal state and returning to the step (14), if not, executing the step (20), adopting the global scofflaw criteria, updating the global optimal solution record, and only recording the optimal solution so far;
(20) Judging whether the candidate solution is in a tabu table;
(21) Taking the optimal candidate solution of the non-tabu object as the current solution, updating the tabu table and returning to the step (14);
(22) And outputting the optimal solution and the function value of the corresponding adaptive value, and ending the loop to exit the program.
2. The load frequency modulation optimal scheduling method considering the constraint conditions of the power distribution network, according to claim 1, is characterized in that: in the step (2), the first constraint conditions are the climbing rate of the generator set, the required frequency modulation reserve capacity and the minimum stable output.
3. The load frequency modulation optimal scheduling method considering the constraint conditions of the power distribution network, according to claim 1, is characterized in that: in the step (14), the termination principle is as follows: the optimal solution iterates 20 times and remains unchanged.
4. The load frequency modulation optimal scheduling method considering the constraint conditions of the power distribution network, according to claim 1, is characterized in that: in the step (15), the judging conditions are as follows: the current solution has been tabbed in the tabu table, but does not satisfy scofflaw criteria superior to all candidate solutions.
5. The load frequency modulation optimal scheduling method considering the constraint conditions of the power distribution network, according to claim 1, is characterized in that: in step (19), the scofflaw criterion is that the current solution is superior to all candidate solutions.
CN201911097139.1A 2019-11-11 2019-11-11 Load frequency modulation optimal scheduling method considering constraint conditions of power distribution network Active CN110635486B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201911097139.1A CN110635486B (en) 2019-11-11 2019-11-11 Load frequency modulation optimal scheduling method considering constraint conditions of power distribution network

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201911097139.1A CN110635486B (en) 2019-11-11 2019-11-11 Load frequency modulation optimal scheduling method considering constraint conditions of power distribution network

Publications (2)

Publication Number Publication Date
CN110635486A CN110635486A (en) 2019-12-31
CN110635486B true CN110635486B (en) 2023-01-06

Family

ID=68979442

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201911097139.1A Active CN110635486B (en) 2019-11-11 2019-11-11 Load frequency modulation optimal scheduling method considering constraint conditions of power distribution network

Country Status (1)

Country Link
CN (1) CN110635486B (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111049151A (en) * 2020-01-03 2020-04-21 云南电网有限责任公司电力科学研究院 NSGA 2-based two-stage optimization algorithm for power distribution network voltage
CN113326581B (en) * 2021-05-28 2022-07-19 江麓机电集团有限公司 Genetic scheduling method based on combined production and equipment fault constraint

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102256369A (en) * 2011-06-28 2011-11-23 武汉理工大学 Task scheduling method for wireless sensor grid based on energy and communication overhead
CN104112237A (en) * 2014-06-30 2014-10-22 国家电网公司 WAMS-based genetic algorithm-improved power grid reactive capacity optimization configuration method
CN104682405A (en) * 2015-03-31 2015-06-03 福州大学 Tabu particle swarm algorithm based reactive power optimization method of power distribution network

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103280817B (en) * 2013-05-24 2015-04-15 武汉大学 Reactive balance area determining method based on tabu search
CN107392402B (en) * 2017-09-11 2018-08-31 合肥工业大学 Production and transport coordinated dispatching method based on modified Tabu search algorithm and system

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102256369A (en) * 2011-06-28 2011-11-23 武汉理工大学 Task scheduling method for wireless sensor grid based on energy and communication overhead
CN104112237A (en) * 2014-06-30 2014-10-22 国家电网公司 WAMS-based genetic algorithm-improved power grid reactive capacity optimization configuration method
CN104682405A (en) * 2015-03-31 2015-06-03 福州大学 Tabu particle swarm algorithm based reactive power optimization method of power distribution network

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
基于禁忌遗传算法的油田配电网无功优化方案研究;胡文嵩;《中国优秀博硕士学位论文全文数据库（硕士） 工程科技II辑》;20090615;第30-58页 *
空调负荷参与电力系统需求响应的建模及控制策略研究;胡晓青;《中国优秀博硕士学位论文全文数据库（博士） 工程科技II辑》;20180215;第47-66页 *

Also Published As

Publication number Publication date
CN110635486A (en) 2019-12-31

Similar Documents

Publication Publication Date Title
Yu et al. A chance constrained transmission network expansion planning method with consideration of load and wind farm uncertainties
CN108683186B (en) Power supply capacity improvement-oriented power distribution network double-layer expansion planning method
CN108365608B (en) Uncertain optimization scheduling method and system for regional energy Internet
CN108880886B (en) Method for planning protection communication network of cross-regional power system
CN112541626B (en) Multi-target power distribution network fault reconstruction method based on improved genetic algorithm
CN110635486B (en) Load frequency modulation optimal scheduling method considering constraint conditions of power distribution network
CN113935603B (en) Reservoir group multi-target prediction pre-discharge scheduling rule optimization method, system and medium
CN112994099B (en) High-proportion distributed photovoltaic grid-connected digestion capacity analysis method
CN113962159A (en) Method for evaluating maximum photovoltaic access capacity of power distribution network based on reasonable light abandonment
CN114977152A (en) Control method and device of energy management system, storage medium and equipment
CN113162055B (en) Coordination control method and system for reactive voltage of energy storage and new energy power station
CN109038546B (en) AC/DC series-parallel power grid load recovery method and system based on VSC-HVDC system
CN112531735A (en) Power distribution method and device of automatic power generation control system based on machine learning
CN109390971B (en) Power distribution network multi-target active reconstruction method based on doorman pair genetic algorithm
CN116865358A (en) Multi-time long-scale power system wind power waste and load fluctuation tracking method and equipment
CN116645089A (en) Energy storage system double-layer optimal configuration method considering capacity degradation of retired battery
CN110322092B (en) Source-network-load multi-layer game economic dispatching method
JP6327498B2 (en) Solution search device, solution search method and program, schedule generation device, schedule generation method and program, and charging control system
Alawode Kehinde et al. Multiobjective optimal power flow using hybrid evolutionary algorithm
Salama et al. Short term optimal generation scheduling of fixed head hydrothermal system using genetic algorithm and constriction factor based particle swarm optimization technique
CN114204613A (en) Reactive compensation method and system for offshore wind farm access power system
Mohamed et al. Intelligent optimization techniques for optimal power flow using Interline Power Flow Controller
Wu et al. Research on Low Carbon Dispatching of Hybrid Power Generation System with Wind Power and Pumped Storage Station
Liu et al. Load Restoration Multi-Objective Optimization Strategy Based on Time Step Dynamic Contraction Mechanism
CN114156915B (en) Multi-objective optimization method for three-phase unbalance of transformer area

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
GR01 Patent grant
GR01 Patent grant