CN107657392B - Particle calculation method for large-scale economic dispatching problem of power grid - Google Patents

Particle calculation method for large-scale economic dispatching problem of power grid Download PDF

Info

Publication number
CN107657392B
CN107657392B CN201711015397.1A CN201711015397A CN107657392B CN 107657392 B CN107657392 B CN 107657392B CN 201711015397 A CN201711015397 A CN 201711015397A CN 107657392 B CN107657392 B CN 107657392B
Authority
CN
China
Prior art keywords
unit
particle
output power
power
particles
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
CN201711015397.1A
Other languages
Chinese (zh)
Other versions
CN107657392A (en
Inventor
李学平
郭东成
方亮星
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.)
Baoding Trillion Micro Software Technology Co ltd
Hebei Kaitong Information Technology Service Co ltd
Original Assignee
Yanshan University
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 Yanshan University filed Critical Yanshan University
Priority to CN201711015397.1A priority Critical patent/CN107657392B/en
Publication of CN107657392A publication Critical patent/CN107657392A/en
Application granted granted Critical
Publication of CN107657392B publication Critical patent/CN107657392B/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
    • G06Q10/06—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
    • 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

Abstract

The invention discloses a particle calculation method aiming at the problem of large-scale economic dispatching of a power grid, which comprises the following steps of: 1. establishing an economic dispatching model comprising an objective function and constraint conditions of the economic dispatching model; 2. carrying out layering granulation on the power grid; 3. equivalence of parameters; 4. dividing the granularity; 5. processing a constraint condition; 6. and optimizing the output of the unit by adopting a particle calculation method. The invention has the beneficial effects that: the factors are considered comprehensively, and the calculation precision is improved; layered granulation is proposed, and the problem is solved by applying an analytic hierarchy process, so that the solving time is greatly reduced, and the solving efficiency is improved; for a large-scale power network, if a proper unit division method is adopted and the parameters of the particles are equivalent, the problem of difficult convergence can be solved, and the calculation speed can be increased.

Description

Particle calculation method for large-scale economic dispatching problem of power grid
Technical Field
The invention relates to the technical field of economic dispatching of a power system, in particular to a particle calculation method aiming at a large-scale economic dispatching problem.
Background
The economic dispatching takes the lowest power supply cost or energy consumption of the whole network as an objective function, carries out dispatching according to an equal micro-increment rate method and a coordination equation, is an important tool for realizing the economic operation of a power system, is a scientific method in an operation link, and is a dispatching principle generally adopted by various countries in the world so far. At present, the problems mainly encountered in the online economic dispatching research of a large power grid are that the data volume is large, the time period of acquisition and operation is long, and the running condition of the power grid is difficult to reflect in real time, so that the economic dispatching is difficult to realize. The economic dispatching of the power system is a high-dimensional, non-convex and non-linear constrained optimization problem, so that the solution of the problem, particularly the treatment of the mutual coupling constraint condition is very difficult. China power system insists on centralized dispatching for a long time. The centralized scheduling makes the solution of the economic scheduling of the power system more difficult, and an effective method for solving the economic scheduling of the large power grid needs to be found urgently. Therefore, the method has important significance for the research on the problem solving of the large power grid economic dispatching.
Disclosure of Invention
The invention aims to provide a particle computing method for solving the large-scale economic scheduling problem of a power grid, which adopts a layering method to decompose the economic scheduling problem into multiple layers so as to reduce the computational complexity, shorten the computation time and improve the accuracy and efficiency of load flow computation.
In order to realize the purpose, the following technical scheme is adopted: the method comprises the following steps:
step 1, establishing an economic dispatching model, including a target function and constraint conditions thereof;
step 2, layering and granulating the power grid;
step 3, equivalence of parameters;
step 4, dividing the granularity;
step 5, processing constraint conditions;
and 6, optimizing the network load flow by adopting a particle calculation method.
Further, in step 1, the specific process of establishing the economic dispatch model is as follows:
step 1-1, establishing an objective function
Under the condition that constraint conditions are met, the lowest total power generation cost of the generator is taken as an objective function, and the mathematical expression is as follows:
Figure GDA0002720968750000021
in the formula PG，iIs the output power of the ith generator; a isi，bi，ciIs the cost factor of the generator set i; n is the number of total generators;
step 1-2, setting constraint conditions of the model, wherein the constraint conditions comprise system power balance constraint and conventional unit output upper and lower limit constraint;
the specific constraint conditions are as follows:
1) system power balance constraints
Figure GDA0002720968750000022
In the formula PDTotal load demand; pLossIs the line loss.
Neglecting line losses, equation (2) is modified to:
Figure GDA0002720968750000031
2) upper and lower limits of output of conventional unit
Figure GDA0002720968750000032
In the formula Pi min，Pi maxIs the minimum and maximum output power of the generator i.
Further, the specific process of step 2 is as follows:
according to a layered quotient space method, granulating a power network, collecting a plurality of fine particles with similar properties to form coarse particles as an equivalent unit, or collecting some coarse particles to form coarse particles as an equivalent unit; all coarse particles are divided into a plurality of layers to form a hierarchical quotient space; the coarse particles are refined into fine particles from the upper layer by layer, the output power of each layer can be obtained after calculation of each layer is completed, the output power is respectively transmitted to the corresponding fine particles to serve as the load requirement of the next layer, and the result of the fine particles of the last layer is the result of economic dispatching.
Further, the specific process of step 3 is as follows:
step 3-1, calculating equivalent parameters
In the economic dispatch model, the cost coefficient ai、bi、ciMinimum output power
Figure GDA0002720968750000033
Maximum output power
Figure GDA0002720968750000037
Calculations are required, in which they are replaced by equivalent parameters;
in the jth particle, the unit number is assumed to be m, and the equivalent principle is as follows:
Figure GDA0002720968750000034
Figure GDA0002720968750000035
(5) in the formula (I), the compound is shown in the specification,
Figure GDA0002720968750000038
is the equivalent cost coefficient for the jth particle; (6) in the formula PG，jIs the output power of the jth particle;
the equivalent parameters can be calculated as follows:
Figure GDA0002720968750000041
Figure GDA0002720968750000042
Figure GDA0002720968750000043
Figure GDA0002720968750000044
Figure GDA0002720968750000045
in the formula, Pj eqmin，Pj eqmaxIs the equivalent minimum and equivalent maximum output power of the jth particle;
however, before the economic scheduling problem is solved, PG，iIs unknown. But equivalent parameters must be prepared prior to particle computation. An approximate method is therefore proposed to initialize PG，i。
Step 3-2, initialization procedure
PG，iThe initialization of (2) is critical to the granularity calculation method, as it determines the equivalent parameters; the closer the initial output power is
Figure GDA0002720968750000049
The closer to the optimal value, the better the result will be shown; there are three steps to PG，iAnd (3) initializing:
first, initializing the output power of each unit
P′G，i＝Pi min+(Pi max-Pi min)/2 (12)
Figure GDA0002720968750000048
P″G，i＝σP′G，i (14)
P′G，iIs the average output power of unit i; σ is the load level coefficient; p ″)G，iIs the output power of unit i;
the process enables the output power of each unit to be close to the average power, and the power balance constraint is met;
second, forward migration
λ′i＝2aiP″G，i+bi (15)
PD′＝αPD (16)
Figure GDA0002720968750000051
λ′iIs the micro-increment rate of the ith unit; α is a positive number; pD' is load compensation; p'G，iIs the output power after the migration of the ith unit;
the process makes the unit have smaller lambda'iObtaining a relatively large positive deviation value;
third, negative migration
Figure GDA0002720968750000052
(18) In the formula (I), the compound is shown in the specification,
Figure GDA0002720968750000053
is the initialized output power of the ith unit;
this process gives the unit a greater lambda'iAnd obtaining a relatively large negative deviation amount; according to the increment principle, the second step and the third step can ensure that the output power is close to the optimal solution;
fourthly, balancing the constraints
The offset adjustment may result in unequal power constraints, so a constraint process is necessary to correct
Figure GDA0002720968750000061
The correction process is in step 5.
Step 3-3, granularity calculation model
After equivalence, the cost function formula of the granularity calculation is as follows:
Figure GDA0002720968750000062
(19) wherein M is the number of subparticles of the host particle;
the power balance constraint of the particle calculation is modified as follows:
Figure GDA0002720968750000063
(20) in the formula, PGDThe output power of the main particle is the load requirement of the next layer of sub-particles;
the power constraints of the particles are as follows:
Pj eqmin≤PG，j≤Pj eqmax (21)
in the particle size calculation method, one particle is considered as an equivalent unit; particles with one unit (M ═ 1) are called fine particles; the equivalent parameters of the fine particles are equal to the parameters of the unit contained in the fine particles; particles with more than one set of units (M > 1) are called coarse particles.
Further, the step 4 is as follows:
particle size is an average measurement of particle size; when describing information, granularity is mainly used for measuring the abstraction degree of data information and knowledge; the particle size is determined by the number of units contained in the particles;
the obvious fluctuation point of the micro-increment rate divides the unit into particles, and the calculation formula of the micro-increment rate is as follows:
Figure GDA0002720968750000065
(22) in the formula, λiIs the micro-increment rate of the ith unit;
after calculation, sorting the micro-increment rates of all the units according to the sequence; separating the unit according to the obvious fluctuation point;
Figure GDA0002720968750000071
(23) in the formula, thetasIs the point of fluctuation of the micro-increment rate,
Figure GDA0002720968750000076
is the rank order fractional increase; s is a sequence number with increasing rate;
θsreact to
Figure GDA0002720968750000077
And
Figure GDA0002720968750000078
the difference in (a); if theta is greater than thetasIs significantly greater than the other values, then θsIs the point of significant fluctuation.
Further, the specific process of step 5 is as follows:
step 5-1, examining each
Figure GDA0002720968750000072
All elements are adjusted to satisfy the inequality constraint as follows:
Figure GDA0002720968750000073
(24) in the formula, if
Figure GDA0002720968750000079
Or
Figure GDA0002720968750000074
Then the transition variable TjSet to 0, otherwise
Figure GDA0002720968750000075
k is the current number of iterations;
step 5-2, by
Figure GDA00027209687500000710
Calculating PRIf | PRIf | is greater than ε, go to step 5-3, if | PRIf | ≦ epsilon, go to step 4, epsilon is the precision requirement;
step 5-3, modification
Figure GDA00027209687500000711
To satisfy the following equation constraint:
Figure GDA0002720968750000081
step 5-4, checking all
Figure GDA0002720968750000086
If the inequality constraint is violated, returning to the step 5-1; if the inequality constraint is not violated, entering step 5-5;
5-5, stopping the constraint processing process;
calculating according to the steps, wherein the initial output power of the equivalent parameter is close to the actual power level, so that the equivalent parameter is more accurate; then hold
Figure GDA0002720968750000087
Put into equations (7) and (8), substitute for PG，jTo calculate
Figure GDA0002720968750000088
And
Figure GDA0002720968750000082
further, the step 6 specifically includes:
and 6-1, parameter preparation. The basic parameters of all units need to be input, namely ai、bi、ci、
Figure GDA0002720968750000083
And
Figure GDA0002720968750000084
calculating initial output power
Figure GDA0002720968750000085
Step 6-2, determining the hierarchical structure of the coarse particles and calculating parameters; determining a proper number of layers according to the scale of the power system; although the hierarchical method can improve the search efficiency and reduce the calculation time, the equivalent process causes deviation; accuracy is reduced if there is too much delamination; first layer M1Number of particles and maximum number of units n of second layermaxSetting is needed before calculation so as to guide the division process of the unit; when the unit division is completed, calculating equivalent parameters in the next part;
6-3, calculating the output power of the particles; the results are transferred accordingly to their sub-particles as the loading requirements for the next layer;
when the bottom layer particle calculation is completed, a final optimized solution is obtained.
Compared with the prior art, the invention has the following advantages:
1. the factors are considered comprehensively, and the calculation precision is improved;
2. providing a layered quotient space, applying an analytic hierarchy process, and solving the problem in a layered granulation manner, so that the solving time can be reduced, and the solving efficiency can be improved;
3. for a large-scale power network, if a reasonable hierarchical particle calculation method is adopted, the problem of difficult convergence can be solved, and the calculation speed can be increased.
Drawings
FIG. 1 is a 3-layer system configuration of an exemplary 10-unit system of the present invention.
FIG. 2 illustrates the micro-augmentation fluctuation of a 10-unit system according to the present invention.
FIG. 3 is a flow chart of particle computation for the method of the present invention.
Fig. 4 is a flow chart of the method of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings:
as shown in fig. 4, the method of the present invention includes the following steps:
step 1, establishing an economic dispatching model, including a target function and constraint conditions thereof;
step 1-1, the specific process of establishing the economic dispatching model is as follows:
under the condition that constraint conditions are met, the lowest total power generation cost of the generator is taken as an objective function, and the mathematical expression is as follows:
Figure GDA0002720968750000091
in the formula PG，iIs the output power of the ith generator; a isi，bi，ciIs the cost factor of the generator set i; n is the number of total generators;
step 1-2, setting constraint conditions of the model, wherein the constraint conditions comprise system power balance constraint and conventional unit output upper and lower limit constraint;
the specific constraint conditions are as follows:
1) system power balance constraints
Figure GDA0002720968750000101
In the formula PDTotal load demand; pLossIs the line loss;
neglecting line losses, equation (2) is modified to:
Figure GDA0002720968750000102
2) upper and lower limits of output of conventional unit
Figure GDA0002720968750000103
In the formula
Figure GDA0002720968750000104
Is the minimum and maximum output power of the generator i.
Step 2, layering and granulating the power grid;
the specific process of layering and granulating the power grid is as follows:
according to an analytic hierarchy process, a method for establishing a hierarchical quotient space is provided for an economic scheduling problem. To clarify the layered structure, a 10-unit power system is taken as an example. The numbers are #1- # 10. Assuming that the system can be divided into three layers, as shown in fig. 1, the respective characteristics of each layer are as follows:
1) a first layer: there are three coarse particles V11，V12，V13In this layer. V11Comprising four units (#1, #3, #6, #7), as shown in FIG. 1, V12(#2, #9) and V13(#4, #5, #8, #10) and V11As such. V11，V12，V13Are three equivalent units. Thus reducing the warpThe dimensionality of the scheduling problem is saved, and the optimization efficiency is improved. After this layer is calculated, V11，V12，V13Their output power can be derived and delivered to their sub-particles separately as the load demand of the next layer.
2) A second layer: the layer has six particles V21，V22，V23，V24，V25，V26。V21And V22Is V11Are considered to be equivalent sets, and V21And V22From V11A load demand is obtained. Host particle V11Is responsible for calculating V21And V22The power output of (1). Calculation of the other two particles and V11The transformation is similar. As shown in fig. 1, at V23，V24，V26There is only one set, so the results for these three particles are exactly the final power outputs of #9, #2, and #5, respectively. However, V21、V22、V25Can still be divided into several sub-particles in the third layer.
3) And a third layer: this layer is the bottom layer, which comprises seven particles, each V31，V32，V33，V34，V35，V36，V37. There is only one unit per particle. The calculation process is similar to the method of the second layer, including V21Is equivalent to V31And V32，V22Is equivalent to V33And V34，V25Is equivalent to V35、V36And V37. When all the calculation processes are completed, V31，V32，V33，V34，V35，V36，V37And V23，V24，V26The result is the final unit output result of 10 units.
In the design of the hierarchical model, a method for finding a reasonable equivalent parameter of the computer set is critical and has a remarkable influence on a final result.
Step 3, equivalence of parameters;
step 3-1, calculating equivalent parameters
In the economic dispatch model, the cost coefficient ai、bi、ciMinimum output power
Figure GDA0002720968750000121
Maximum output power
Figure GDA00027209687500001210
Calculations are required, in which they are replaced by equivalent parameters;
in the jth particle, the unit number is assumed to be m, and the equivalent principle is as follows:
Figure GDA0002720968750000122
Figure GDA0002720968750000123
(5) in the formula (I), the compound is shown in the specification,
Figure GDA00027209687500001211
is the equivalent cost coefficient for the jth particle; (6) in the formula PG，jIs the output power of the jth particle;
the equivalent parameters can be calculated as follows:
Figure GDA0002720968750000124
Figure GDA0002720968750000125
Figure GDA0002720968750000126
Figure GDA0002720968750000127
Figure GDA0002720968750000128
in the formula, Pj eqmin，Pj eqmaxIs the equivalent minimum and equivalent maximum output power of the jth particle;
step 3-2, initialization procedure
PG，iThe initialization of (2) is critical to the granularity calculation method, as it determines the equivalent parameters; the closer the initial output power is
Figure GDA0002720968750000134
The closer to the optimal value, the better the result will be shown; there are three steps to PG，iAnd (3) initializing:
first, initializing the output power of each unit
P′G，i＝Pi min+(Pi max-Pi min)/2 (12)
Figure GDA0002720968750000132
P″G，i＝σP′G，i (14)
P′G，iIs the average output power of unit i; σ is the load level coefficient; p ″)G，iIs the output power of unit i;
the process enables the output power of each unit to be close to the average power, and the power balance constraint is met;
second, forward migration
λ′i＝2aiP″G，i+bi (15)
PD′＝αPD (16)
Figure GDA0002720968750000133
λ′iIs the micro-increment rate of the ith unit; α is a positive number; pD' is load compensation; p'G，iIs the output power after the migration of the ith unit;
the process makes the unit have smaller lambda'iObtaining a relatively large positive deviation value;
third, negative migration
Figure GDA0002720968750000141
(18) In the formula (I), the compound is shown in the specification,
Figure GDA0002720968750000146
is the initialized output power of the ith unit;
this process gives the unit a greater lambda'iAnd obtaining a relatively large negative deviation amount; according to the increment principle, the second step and the third step can ensure that the output power is close to the optimal solution;
fourthly, balancing the constraints
The offset adjustment may result in unequal power constraints, so a constraint process is necessary to correct
Figure GDA0002720968750000142
Step 3-3, granularity calculation model
After equivalence, the cost function formula of the granularity calculation is as follows:
Figure GDA0002720968750000143
(19) wherein M is the number of subparticles of the host particle;
the power balance constraint of the particle calculation is modified as follows:
Figure GDA0002720968750000144
(20) in the formula, PGDThe output power of the main particle is the load requirement of the next layer of sub-particles;
the power constraints of the particles are as follows:
Pj eqmin≤PG，j≤Pj eqmax (21)
in the particle size calculation method, one particle is considered as an equivalent unit; particles with one unit (M ═ 1) are called fine particles; the equivalent parameters of the fine particles are equal to the parameters of the unit contained in the fine particles; particles with more than one set of units (M > 1) are called coarse particles.
Step 4, dividing the granularity;
particle size is an average measurement of particle size. When describing information, granularity is mainly used to measure the abstraction degree of data information and knowledge. In this context, the particle size is determined by the number of units the particle contains.
The obvious fluctuation point of the micro-increment rate divides the unit into particles, and the calculation formula of the micro-increment rate is as follows:
Figure GDA0002720968750000151
(22) in the formula, λiIs the micro-increment rate of the ith unit.
After the calculation is finished, the micro-increment rates of all the units need to be sorted to be small to large. And separating the unit according to the obvious fluctuation point.
Figure GDA0002720968750000152
(23) In the formula, thetasIs the point of fluctuation of the micro-increment rate,
Figure GDA0002720968750000154
is the rank order fractional increase; s is the sequence number of the increasing rate ascending order.
θsReact to
Figure GDA0002720968750000155
And
Figure GDA0002720968750000156
the difference in (a). If theta is greater than thetasIs significantly greater than the other values, then θsIs the point of significant fluctuation.
The granularity division of a 10-unit system is listed in table 1, the power system division scheme of the 10 units is listed, and the fluctuation graph of the ordered growth rate is shown in fig. 2, which shows how the significant fluctuation point fluctuates.
TABLE 1
Figure GDA0002720968750000153
Figure GDA0002720968750000161
1) Dividing a first layer:
in FIG. 2, we observe θ5And theta7Significantly greater than the average, which means that the growth rates of #9 and #4 are significantly different from the units before them, and then these units are divided into three groups to form (V)11，V12，V13) Three particles.
In a practical example, θsNeed to be listed in descending order. Then, according to the number M of particles arranged in the first layer1M before picking1-a point of 1. If there are too many particles, the efficiency of the calculation is reduced. Thus M1The range of (1) is 2 to 9.
2) And second layer division:
at V11In, theta3Is a significant point of fluctuation, and V11Is divided into V21And V22. Likewise, V12And V13Are also divided into V23，V24，V25，V26Which constitute the second layer shown in figure 1.
In practical applications, the division of the layer is implemented independently in each host particle. In the jth main particle of the layer, the number of sub-particles
Figure GDA0002720968750000162
Is set by setting the maximum number n of unitsmaxDetermined, the formula is as follows:
Figure GDA0002720968750000171
in the formula, mjIs the total unit number of the jth main particle.
Then according to the previous
Figure GDA0002720968750000176
The fluctuation point of (a) divides the jth particle into
Figure GDA0002720968750000177
And (4) sub-particles. If the total unit number ratio n of one main particlemaxSmall, this host particle cannot be divided into sub-particles. Such as nmaxIf it is too small, this will have too many sub-particles, which will increase the dimensionality of the GrC method and will reduce the computational efficiency. If n ismaxToo large, this will not reduce the time-efficient sub-particles of the GrC process. Thus n ismaxThe range of (1) is 10 to 30.
In the first-layer and second-layer division, if only one unique unit set exists in the particles, the particles are combined with the previous particles to improve the global search capability of the GrC method.
3) Bottom layer partitioning:
in this layer, all coarse particles must be broken down into fine particles to obtain the final power output of each unit.
Step 5, processing constraint conditions;
step 5-1, examining each
Figure GDA0002720968750000172
Adjust all elementsThe elements satisfy the inequality constraint as follows:
Figure GDA0002720968750000173
(24) in the formula, if
Figure GDA0002720968750000178
Or
Figure GDA0002720968750000174
Then the transition variable TjSet to 0, otherwise
Figure GDA0002720968750000175
k is the current number of iterations;
step 5-2, by
Figure GDA0002720968750000186
Calculating PRIf | PRIf | is greater than ε, go to step 5-3, if | PRIf | ≦ epsilon, go to step 4, epsilon is the precision requirement;
step 5-3, modification
Figure GDA0002720968750000187
To satisfy the following equation constraint:
Figure GDA0002720968750000181
step 5-4, checking all
Figure GDA0002720968750000188
If the inequality constraint is violated, returning to the step 5-1; if the inequality constraint is not violated, entering step 5-5;
5-5, stopping the constraint processing process;
calculating according to the steps, wherein the initial output power of the equivalent parameter is close to the actual power level, so that the equivalent parameter is more accurate; then hold
Figure GDA0002720968750000189
Put into equations (7) and (8), substitute for PG，jTo calculate
Figure GDA00027209687500001810
And
Figure GDA0002720968750000182
and 6, optimizing the network load flow by adopting a particle calculation method, wherein a flow chart of a particle calculation process is shown in fig. 3.
And 6-1, parameter preparation. The basic parameters of all units need to be input, namely ai、bi、ci、
Figure GDA0002720968750000183
And
Figure GDA0002720968750000184
calculating initial output power
Figure GDA0002720968750000185
And 6-2, determining the hierarchical structure of the coarse particles and calculating parameters. The appropriate number of tiers is determined based on the size of the power system. Although the hierarchical approach can improve search efficiency and reduce computation time, the equivalent process can cause bias. Accuracy is reduced if there is too much delamination. First layer M1Number of particles and maximum number of units n of second layermaxBefore calculation, setting is needed to guide the division process of the unit. When the crew division is complete, the next part is to calculate the equivalent parameters.
And 6-3, calculating the output power of the particles. A new intelligent optimization algorithm can be applied to the output power of the optimized particles and the results are transferred to their sub-particles accordingly as the load demand of the next layer.
When the bottom layer particle calculation is completed, a final optimized solution is obtained.
In order to verify the effectiveness of the invention more completely, the comparison between the particle swarm algorithm and the mean variance mapping method shows that the invention can provide a satisfactory global optimal solution and has better time benefit. The two results are compared as follows:
TABLE 2 comparison of Power Generation cost versus time results for two algorithms
Figure GDA0002720968750000191
Obviously, the advantages of the power generation cost of the invention are not obvious, but the efficiency is greatly improved for the calculation time, and the superiority of the particle calculation is more obvious as the scale of the power grid is enlarged. The above results demonstrate the superiority of the present invention.
The above-mentioned embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solution of the present invention by those skilled in the art should fall within the protection scope defined by the claims of the present invention without departing from the spirit of the present invention.

Claims (6)

1. A particle calculation method for the problem of large-scale economic dispatching of a power grid is characterized by comprising the following steps:
step 1, establishing an economic dispatching model, including a target function and constraint conditions thereof;
step 2, layering and granulating the power grid;
according to a layered quotient space method, granulating a power network, collecting a plurality of fine particles with similar properties to form coarse particles as an equivalent unit, or collecting some coarse particles to form coarse particles as an equivalent unit; all coarse particles are divided into a plurality of layers to form a hierarchical quotient space; the coarse particles are refined into fine particles from the upper layer by layer, the output power of each layer can be obtained after calculation of each layer is finished, and the output power is respectively transmitted to the corresponding fine particles to serve as the load requirement of the next layer, and the result of the fine particles of the last layer is the result of economic dispatching;
step 3, equivalence of parameters;
step 4, dividing the granularity;
step 5, processing constraint conditions;
and 6, optimizing the network load flow by adopting a particle calculation method.
2. The particle computation method for the large-scale economic dispatching problem of the power grid according to claim 1, wherein in the step 1, the specific process of establishing the economic dispatching model is as follows:
step 1-1, establishing an objective function
Under the condition that constraint conditions are met, the lowest total power generation cost of the generator is taken as an objective function, and the mathematical expression is as follows:
Figure FDA0002720968740000011
in the formula PG，iIs the output power of the ith generator; a isi，bi，ciIs the cost factor of the generator set i; n is the number of total generators;
step 1-2, setting constraint conditions of the model, wherein the constraint conditions comprise system power balance constraint and conventional unit output upper and lower limit constraint;
the specific constraint conditions are as follows:
1) system power balance constraints
Figure FDA0002720968740000021
In the formula PDTotal load demand; pLossIs the line loss;
neglecting line losses, equation (2) is modified to:
Figure FDA0002720968740000022
2) upper and lower limits of output of conventional unit
Figure FDA0002720968740000023
In the formula
Figure FDA0002720968740000024
Is the minimum and maximum output power of the generator i.
3. The particle computation method for the grid large-scale economic dispatching problem according to claim 1, wherein the specific process of the step 3 is as follows:
step 3-1, calculating equivalent parameters
In the economic dispatch model, the cost coefficient ai、bi、ciMinimum output power
Figure FDA0002720968740000025
Maximum output power
Figure FDA0002720968740000026
Calculations are required, in which they are replaced by equivalent parameters;
in the jth particle, the unit number is assumed to be m, and the equivalent principle is as follows:
Figure FDA0002720968740000031
Figure FDA0002720968740000032
(5) in the formula (I), the compound is shown in the specification,
Figure FDA0002720968740000033
is the equivalent cost coefficient for the jth particle; (6) in the formula PG，jIs the output power of the jth particle;
the equivalent parameters were calculated as follows:
Figure FDA0002720968740000034
Figure FDA0002720968740000035
Figure FDA0002720968740000036
Figure FDA0002720968740000037
Figure FDA0002720968740000038
in the formula, Pj eqmin，Pj eqmaxIs the equivalent minimum and equivalent maximum output power of the jth particle;
step 3-2, initialization procedure
PG，iThe initialization of (2) is critical to the granularity calculation method, as it determines the equivalent parameters; the closer the initial output power is
Figure FDA0002720968740000039
The closer to the optimal value, the better the result will be shown; there are three steps to PG，iAnd (3) initializing:
first, initializing the output power of each unit
Figure FDA00027209687400000310
Figure FDA0002720968740000041
P″G，i＝σP′G，i (14)
P′G，iIs the average output power of unit i; σ is the load level coefficient; p ″)G，iIs the output power of unit i;
the process enables the output power of each unit to be close to the average power, and the power balance constraint is met;
second, forward migration
λ′i＝2aiP″G，i+bi (15)
PD′＝αPD (16)
Figure FDA0002720968740000042
λ′iIs the micro-increment rate of the ith unit; α is a positive number; pD' is load compensation; p'G，iIs the output power after the migration of the ith unit;
the process makes the unit have smaller lambda'iObtaining a relatively large positive deviation value;
third, negative migration
Figure FDA0002720968740000043
(18) In the formula (I), the compound is shown in the specification,
Figure FDA0002720968740000044
is the initialized output power of the ith unit;
this process gives the unit a greater lambda'iAnd obtaining a relatively large negative deviation amount; according to the principle of increment, the second step and the third step can ensureThe output power is close to the optimal solution;
fourthly, balancing the constraints
The offset adjustment results in unequal power constraints that are corrected by a constraint process
Figure FDA0002720968740000051
Step 3-3, granularity calculation model
After equivalence, the cost function formula of the granularity calculation is as follows:
Figure FDA0002720968740000052
(19) wherein M is the number of subparticles of the host particle;
the power balance constraint of the particle calculation is modified as follows:
Figure FDA0002720968740000053
(20) in the formula, PGDThe output power of the main particle is the load requirement of the next layer of sub-particles;
the power constraints of the particles are as follows:
Pj eqmin≤PG，j≤Pj eqmax (21)
in the particle size calculation method, one particle is considered as an equivalent unit; a particle M with one unit is called a fine particle 1; the equivalent parameters of the fine particles are equal to the parameters of the unit contained in the fine particles; particles M > 1 with more than one unit are called coarse particles.
4. The particle computation method for the grid large-scale economic dispatching problem according to claim 1, wherein the step 4 is as follows:
particle size is an average measurement of particle size; when describing information, granularity is used to measure the abstraction degree of data information and knowledge; the particle size is determined by the number of units contained in the particles;
the obvious fluctuation point of the micro-increment rate divides the unit into particles, and the calculation formula of the micro-increment rate is as follows:
Figure FDA0002720968740000061
(22) in the formula, λiIs the micro-increment rate of the ith unit;
after calculation, sorting the micro-increment rates of all the units according to the sequence; separating the unit according to the obvious fluctuation point;
Figure FDA0002720968740000062
(23) in the formula, thetasIs the point of fluctuation of the micro-increment rate,
Figure FDA0002720968740000063
is the rank order fractional increase; s is a sequence number with increasing rate;
θsreact to
Figure FDA0002720968740000064
And
Figure FDA0002720968740000065
the difference in (a); if theta is greater than thetasIs significantly greater than the other values, then θsIs the point of significant fluctuation.
5. The particle computation method for the grid large-scale economic dispatching problem according to claim 1, wherein the specific process of the step 5 is as follows:
step 5-1, examining each
Figure FDA0002720968740000066
All elements are adjusted to satisfy the inequality constraint as follows:
Figure FDA0002720968740000067
(24) in the formula, if
Figure FDA0002720968740000068
Or
Figure FDA0002720968740000069
Then the transition variable TjSet to 0, otherwise
Figure FDA00027209687400000610
k is the current number of iterations;
step 5-2, by
Figure FDA00027209687400000611
Calculating PRIf | PRIf | is greater than ε, go to step 5-3, if | PRIf | ≦ epsilon, go to step 4, epsilon is the precision requirement;
step 5-3, modification
Figure FDA0002720968740000071
To satisfy the following equation constraint:
Figure FDA0002720968740000072
step 5-4, checking all
Figure FDA0002720968740000073
If the inequality constraint is violated, returning to the step 5-1; if the inequality constraint is not violated, entering step 5-5;
5-5, stopping the constraint processing process;
calculating according to the steps, wherein the initial output power of the equivalent parameter is close to the actual power level, so that the equivalent parameter is enabled to be equivalentThe number is more accurate; then hold
Figure FDA0002720968740000074
Put into equations (7) and (8), substitute for PG，jTo calculate
Figure FDA0002720968740000075
And
Figure FDA0002720968740000076
6. the particle computation method for the grid large-scale economic dispatching problem according to claim 1, wherein the step 6 is as follows:
step 6-1, parameter preparation; the basic parameters of all units need to be input, namely ai、bi、ci、
Figure FDA0002720968740000077
And
Figure FDA0002720968740000078
calculating initial output power
Figure FDA0002720968740000079
Step 6-2, determining the hierarchical structure of the coarse particles and calculating parameters; determining a proper number of layers according to the scale of the power system; although the hierarchical method can improve the search efficiency and reduce the calculation time, the equivalent process causes deviation; accuracy is reduced if there is too much delamination; first layer M1Number of particles and maximum number of units n of second layermaxSetting is needed before calculation so as to guide the division process of the unit; when the unit division is completed, calculating equivalent parameters in the next part;
6-3, calculating the output power of the particles; the results are transferred accordingly to their sub-particles as the loading requirements for the next layer; when the bottom layer particle calculation is completed, a final optimized solution is obtained.
CN201711015397.1A 2017-10-26 2017-10-26 Particle calculation method for large-scale economic dispatching problem of power grid Active CN107657392B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201711015397.1A CN107657392B (en) 2017-10-26 2017-10-26 Particle calculation method for large-scale economic dispatching problem of power grid

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201711015397.1A CN107657392B (en) 2017-10-26 2017-10-26 Particle calculation method for large-scale economic dispatching problem of power grid

Publications (2)

Publication Number Publication Date
CN107657392A CN107657392A (en) 2018-02-02
CN107657392B true CN107657392B (en) 2021-01-08

Family

ID=61119543

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201711015397.1A Active CN107657392B (en) 2017-10-26 2017-10-26 Particle calculation method for large-scale economic dispatching problem of power grid

Country Status (1)

Country Link
CN (1) CN107657392B (en)

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108400617B (en) * 2018-03-19 2021-05-28 燕山大学 Constraint processing method for economic dispatch
CN108565900B (en) * 2018-05-14 2021-09-14 南京邮电大学 Distributed energy optimization operation method based on game theory
CN109193807B (en) * 2018-11-13 2020-07-28 燕山大学 Economic dispatching method and system for power system
CN112052322B (en) * 2020-09-03 2023-03-21 哈尔滨工业大学 Intelligent robot conversation strategy generation method based on particle calculation
CN112231926A (en) * 2020-10-29 2021-01-15 高小翎 Node-optimized harmonic impedance measurement method for large power system
CN113469566B (en) * 2021-07-21 2022-11-11 燕山大学 Method and system for determining initial distribution scheme of generator

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103678889A (en) * 2013-12-02 2014-03-26 国家电网公司 Photovoltaic power generation accommodating capacity calculation method taking static voltage stability into consideration
CN106505575A (en) * 2016-12-09 2017-03-15 燕山大学 A kind of Line Flow economic load dispatching method based on Granule Computing
CN107171365A (en) * 2017-06-23 2017-09-15 国网山东省电力公司经济技术研究院 Multiple target stochastic and dynamic economic load dispatching method with asynchronous iteration is decoupled based on scene

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7290152B2 (en) * 2002-12-30 2007-10-30 International Business Machines Corporation Method and system for managing power within a compute component of a multiprocessor system

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103678889A (en) * 2013-12-02 2014-03-26 国家电网公司 Photovoltaic power generation accommodating capacity calculation method taking static voltage stability into consideration
CN106505575A (en) * 2016-12-09 2017-03-15 燕山大学 A kind of Line Flow economic load dispatching method based on Granule Computing
CN107171365A (en) * 2017-06-23 2017-09-15 国网山东省电力公司经济技术研究院 Multiple target stochastic and dynamic economic load dispatching method with asynchronous iteration is decoupled based on scene

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
"粒计算在电力系统优化调度中的应用探索";张鹏 等;《东北电力技术》;20160131;全文 *

Also Published As

Publication number Publication date
CN107657392A (en) 2018-02-02

Similar Documents

Publication Publication Date Title
CN107657392B (en) Particle calculation method for large-scale economic dispatching problem of power grid
CN107316107B (en) Warp knitting machine assembly line balancing method oriented to multi-objective optimization
CN108694467B (en) Method and system for predicting line loss rate of power distribution network
Singh et al. An efficient technique for reliability analysis of power systems including time dependent sources
CN109345019B (en) Improved particle swarm algorithm-based micro-grid economic dispatching optimization strategy
CN106155791B (en) A kind of workflow task dispatching method under distributed environment
CN112907057B (en) Production scheduling optimization method and system based on improved MOPSO algorithm
CN114488988A (en) Industrial Internet of things for production line balance rate regulation and control method
CN110097267B (en) Multi-target multi-robot task allocation method based on improved firework algorithm
CN110489200A (en) A kind of method for scheduling task suitable for embedded container cluster
CN105487496A (en) Optimization method for heat-engine plant thermal on-line process identification and control algorithm based on dual-objective parallel ISLAND-HFC mixed model genetic programming algorithm
CN109193807A (en) A kind of Economic Dispatch method and system
CN108599172A (en) A kind of transmission ＆ distribution net overall situation tidal current computing method based on artificial neural network
CN109445906A (en) A kind of virtual machine quantity required prediction technique
CN103345555A (en) Thermal power unit coal consumption characteristic curve modeling method based on genetic programming
CN115940267A (en) Distributed photovoltaic cluster division method for regional power distribution network
CN107341567A (en) The storage capacity displacement computational methods of Cascade Reservoirs
Yang et al. The algorithm and simulation of multi-objective sequence and balancing problem for mixed mode assembly line
CN113364058A (en) Reactive power control method and system for power distribution network
CN102254245A (en) Method for checking safety and stability of scheduling daily plans of power system in time-interval sharing parallel mode
CN108875961A (en) A kind of online weighting extreme learning machine method based on pre- boundary's mechanism
CN113762646A (en) Photovoltaic short-term power intelligent prediction method and system
CN109409594B (en) Short-term wind power prediction method
CN115145301A (en) Multi-unmanned aerial vehicle task allocation method based on improved particle swarm optimization
CN112396318A (en) Time robustness considered cluster support personnel and equipment distribution method

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
TR01 Transfer of patent right
TR01 Transfer of patent right

Effective date of registration: 20221114

Address after: 073099 The intersection of Business Street and Xingding Road in Dingzhou City, Baoding City, Hebei Province, runs 200 meters north to the west (No. 1910, 19/F, Building 3, Jueshishan Community)

Patentee after: Hebei Kaitong Information Technology Service Co.,Ltd.

Address before: 066004 No. 438 west section of Hebei Avenue, seaport District, Hebei, Qinhuangdao

Patentee before: Yanshan University

Effective date of registration: 20221114

Address after: 071000 the power park of Fang Shun Qiao Town, Mancheng District, Baoding, Hebei

Patentee after: Baoding trillion Micro Software Technology Co.,Ltd.

Address before: 073099 The intersection of Business Street and Xingding Road in Dingzhou City, Baoding City, Hebei Province, runs 200 meters north to the west (No. 1910, 19/F, Building 3, Jueshishan Community)

Patentee before: Hebei Kaitong Information Technology Service Co.,Ltd.