CN114400712A - Improved second-order particle swarm algorithm-based micro-grid cluster optimization scheduling method - Google Patents

Improved second-order particle swarm algorithm-based micro-grid cluster optimization scheduling method Download PDF

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CN114400712A
CN114400712A CN202210031104.3A CN202210031104A CN114400712A CN 114400712 A CN114400712 A CN 114400712A CN 202210031104 A CN202210031104 A CN 202210031104A CN 114400712 A CN114400712 A CN 114400712A
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power
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microgrid
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方仍存
雷何
严道波
杨东俊
黄志强
孙建军
查晓明
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Wuhan University WHU
Economic and Technological Research Institute of State Grid Hubei Electric Power Co Ltd
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Economic and Technological Research Institute of State Grid Hubei Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/10—The dispersed energy generation being of fossil origin, e.g. diesel generators
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    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
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    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
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    • 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
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
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    • 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
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects
    • 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
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin

Abstract

A micro-grid group optimal scheduling method based on an improved second-order particle swarm algorithm is characterized by firstly respectively building mathematical models of various devices in a micro-grid group, building a micro-grid group optimal scheduling model with the aims of minimum comprehensive operation cost, maximum wind-light absorption rate and minimum power fluctuation of a connecting line of the micro-grid group as targets, then taking the output of wind power, photoelectricity, energy storage and a diesel engine set as the positions of particle swarms and a fitness function as a target function, and solving the optimal scheduling model by adopting the improved second-order particle swarm algorithm to obtain an optimal scheduling scheme of the micro-grid group. The method not only minimizes the overall operation cost of the micro-grid group, but also reduces the impact of the micro-grid connection on the power distribution network, and improves the accuracy and the calculation speed of the algorithm.

Description

Improved second-order particle swarm algorithm-based micro-grid cluster optimization scheduling method
Technical Field
The invention belongs to the field of power grid optimized dispatching, and particularly relates to a micro-grid cluster optimized dispatching method based on an improved second-order particle swarm algorithm.
Background
In recent years, with the use of a large amount of fossil energy, the carbon dioxide content in the atmosphere is continuously rising, the problem of environmental protection is increasingly highlighted, the rapid development of clean energy has become a future trend, especially Wind Turbine (WT) and Photovoltaic (PV), but how to effectively absorb Wind and light with intermittency and volatility becomes a problem to be solved urgently. The Microgrid (Microgrid, MG) is a small-sized power generation and distribution System which connects wind, light and power nearby, and a large amount of wind and light can be effectively promoted to be accessed and consumed by configuring an Energy Storage System (ESS) and a diesel engine set with certain capacity, so that how to properly manage the optimal scheduling between the Microgrid and the power distribution network becomes a popular research.
The microgrid group interaction optimization scheduling model mainly relates to an optimization model and an optimization algorithm. The optimization model mainly comprises an optimization target and constraint conditions, the constraint conditions are usually operation constraints and electric energy quality index constraints which are arranged inside the microgrid, and the optimization target usually comprises the operation cost of the microgrid, power fluctuation of a connecting line, an electric energy quality index, environmental protection and the like. The optimization algorithm is mainly a heuristic algorithm, such as a genetic algorithm, an ant colony algorithm, a particle swarm algorithm and the like, the main focus is the convergence, the calculation time and the global optimization capability of the optimization algorithm, and how to use the high-performance optimization algorithm in the microgrid colony optimization scheduling model becomes a key problem.
The particle swarm algorithm is derived from a complex adaptive system, is a model for simulating the behavior of a bird swarm, and finds an optimal value through iteration after an initial solution is randomly obtained. Each particle has a fitness function value, a position coordinate, a speed size and a speed direction of the particle, and the particle is searched in a feasible domain space according to the position of the global optimal particle and the historical optimal position of the particle. However, the traditional particle swarm algorithm has the defects of insufficient optimizing capability and convergence capability, and needs to be improved so as to be better used for solving the microgrid cluster interaction optimization scheduling model.
Disclosure of Invention
The invention aims to overcome the problems in the prior art and provides a microgrid cluster optimization scheduling method based on an improved second-order particle swarm algorithm.
In order to achieve the above purpose, the invention provides the following technical scheme:
a microgrid cluster optimization scheduling method based on an improved second-order particle swarm algorithm sequentially comprises the following steps:
a, respectively constructing mathematical models of various devices in a microgrid group, wherein the various devices comprise a wind generating set, a photovoltaic generating set, a load, an energy storage device and a diesel engine set;
b, establishing a micro-grid group optimized dispatching model with the aims of minimum comprehensive operation cost of the micro-grid group, maximum wind and light absorption rate and minimum power fluctuation of a tie line;
and step C, solving the optimized scheduling model by adopting an improved second-order particle swarm algorithm by taking wind power, photoelectricity, energy storage and diesel set output as the positions of the particle swarm and a fitness function as a target function to obtain an optimized scheduling scheme of the micro-grid group.
The step C comprises the following steps in sequence:
step C1, acquiring various equipment parameters and wind, light and load operation data in the microgrid group, and initializing basic parameters of the improved second-order particle swarm algorithm;
step C2, generating a chaotic sequence by utilizing chaotic mapping, selecting a Logistic mapping model to generate an initial population, performing inverse normalization on a variable to a search space to obtain the initial positions of particles, and enabling the iteration number k to be 1, wherein each particle corresponds to each optimized scheduling scheme;
step C3, calculating the fitness value of each particle, and updating the historical optimal value of each particle and the historical optimal value of the population;
step C4, calculating the current inertia coefficient, learning factor and oscillation factor according to the iteration times and the fitness value, updating the speed and the position of the particle, and simultaneously carrying out-of-limit processing on the speed and the position of the particle;
step C5, calculating the distance between any particle and the current optimal particle, if the calculated distance is less than the reference value, keeping the optimal particle unchanged, making other particles perform chaotic motion, performing chaotic search in a given step number, and replacing the original particle with a new particle obtained by the chaotic search;
step C6, judging whether convergence is achieved, if yes, exiting the iteration process, decoding the particles, and obtaining an optimized scheduling scheme of the microgrid group; if not, let k be k +1 and return to step C2 for the next iteration.
In step C4, the inertia coefficient, learning factor, oscillation factor, velocity and position are updated by the following formula:
Figure BDA0003466482460000031
Figure BDA0003466482460000032
Figure BDA0003466482460000033
Figure BDA0003466482460000034
Figure BDA0003466482460000035
λ1＜λ2-1,λ3＜λ4-1,0＜λ2＜1,0＜λ4＜1 k≥kmax/2
in the above formula, the first and second carbon atoms are,
Figure BDA0003466482460000036
respectively, the inertia coefficient, velocity and position, ω, of the ith particle at the kth iterationmin、ωmaxRespectively, the minimum and maximum values of the inertia coefficient, f () is a fitness function,
Figure BDA0003466482460000037
is the mean fitness value, p, of the population at the kth iterationg、piRespectively the historical optimal positions of the particle swarm and the ith particle,
Figure BDA0003466482460000038
respectively the learning factor c at the k-th iteration1、c2，r1、r2Is [0,1 ]]Random numbers distributed uniformly within, c1b、c1e、c2b、c2eAre respectively c1、c2Iterating the initial and final values, λ1、λ3The oscillation factors, lambda, of the ith particle optimal and global optimal positions in the current iterative process respectively2、λ4The oscillation factors of the ith particle optimal and global optimal positions in the last iteration process, d1、d2Is c1Control factor of d3、d4Is c2Control factor of, kmaxIs the maximum number of iterations.
In step C2, the Logistic mapping model is:
Figure BDA0003466482460000039
Figure BDA00034664824600000310
in the above formula, the first and second carbon atoms are,
Figure BDA00034664824600000311
is the d-dimensional component of the chaotic variable at the kth iteration.
In step C5, the distance between the arbitrary particle and the current best particle is calculated according to the following formula:
Figure BDA0003466482460000041
in the above formula, the first and second carbon atoms are,
Figure BDA0003466482460000042
the distance between the ith particle and the current best particle at the kth iteration,
Figure BDA0003466482460000043
for the d-dimensional component, p, of the ith particle during the kth iterationgdFor the d-dimensional component of the current best particle,n is the dimension of the particle, i.e. the number of optimization variables.
In the step B, an objective function f of the microgrid group optimization scheduling model is as follows:
minf＝ω1f1+ω2f2+ω3f3
Figure BDA0003466482460000044
Figure BDA0003466482460000045
Figure BDA0003466482460000046
Figure BDA0003466482460000047
Figure BDA0003466482460000048
in the above formula, f1、f2、f3Respectively the comprehensive operation cost, the wind-light absorption rate, the power fluctuation of the connecting line, omega of the micro-grid group1、ω2、ω3Respectively the weight of the integrated running cost, the wind-solar absorption rate and the power fluctuation of the tie line, NMGT is the number of micro-grids and the number of calculation time segments respectively, ct,buy、ct,sellThe electricity purchasing price and the electricity selling price of the micro-grid group to the power distribution network at the t moment are respectively,
Figure BDA0003466482460000049
the electricity purchasing power and the electricity selling power of the micro-grid group to the power distribution network at the t moment are respectively
Figure BDA00034664824600000410
cfuel,i、cM,i、cE,iRespectively equivalent unit fuel cost, maintenance cost and environmental cost of the diesel engine set,
Figure BDA00034664824600000411
the output of the diesel engine set and the energy storage equipment, cDS,i、cd,iMaintenance cost and depreciation cost of the energy storage device respectively, delta t is the duration of a single time interval, ai、bi、ciRespectively a quadratic term, a primary term and a constant term coefficient of the cost function of the diesel engine unit,
Figure BDA0003466482460000051
respectively active power output of the ith micro-grid after wind power and photovoltaic reduction at the t moment,
Figure BDA0003466482460000052
respectively the maximum active power output, P, of wind power and photovoltaic at the t moment of the ith microgridt LineThe power of the interaction between the microgrid group and the power distribution network at the t moment,
Figure BDA0003466482460000053
and the average value of the power of the interaction of the micro-grid group and the power distribution network is obtained.
The constraints of the objective function include:
and (3) power flow balance constraint:
Figure BDA0003466482460000054
Figure BDA0003466482460000055
in the above formula, the first and second carbon atoms are,
Figure BDA0003466482460000056
the load size of the ith micro-grid at the t moment,
Figure BDA0003466482460000057
the power interacted with the power distribution network at the t moment of the ith microgrid is obtained;
wind-solar output constraint:
Figure BDA0003466482460000058
Figure BDA0003466482460000059
tie line power capacity constraint:
Figure BDA00034664824600000510
Figure BDA00034664824600000511
in the above formula, the first and second carbon atoms are,
Figure BDA00034664824600000512
the upper limit of the interaction power of the ith micro-grid,
Figure BDA00034664824600000513
the upper limit value of the total interaction power;
ESS operation constraints:
Figure BDA00034664824600000514
Figure BDA0003466482460000061
in the above formula, the first and second carbon atoms are,
Figure BDA0003466482460000062
respectively the discharging power and the charging power of the ith energy storage device at the tth moment,
Figure BDA0003466482460000063
respectively is the upper limit value of the discharge power and the charging power of the ith energy storage device, Et,i、Ei,maxRated values of energy and storage capacity, SOC, respectively, stored for the ith energy storage device at the tth timet,iThe electric charge quantity and SOC of the ith energy storage equipment at the t momenti,min、SOCi,maxRespectively is the lower limit value and the upper limit value eta of the charge quantity in the charging and discharging processes of the ith energy storage deviced、ηcRespectively the discharging efficiency and the charging efficiency of the energy storage equipment;
output restraint of the diesel engine set:
Figure BDA0003466482460000064
Figure BDA0003466482460000065
in the above formula, the first and second carbon atoms are,
Figure BDA0003466482460000066
respectively a lower limit value and an upper limit value of the output of the diesel engine unit,
Figure BDA0003466482460000067
the maximum downward climbing speed and the maximum upward climbing speed of the ith diesel engine unit are respectively.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention discloses a micro-grid group optimal scheduling method based on an improved second-order particle swarm algorithm, which comprises the steps of respectively constructing mathematical models of various devices in a micro-grid group, establishing a micro-grid group optimal scheduling model aiming at the minimum comprehensive operation cost, the maximum wind-light absorption rate and the minimum power fluctuation of a tie line of the micro-grid group, then taking the output of wind power, photoelectricity, energy storage and a diesel engine set as the positions of particle swarms and a fitness function as an objective function, solving the optimal scheduling model by adopting the improved second-order particle swarm algorithm, and obtaining an optimal scheduling scheme of the micro-grid group. Therefore, the method provided by the invention can be used for minimizing the overall operation cost of the micro-grid group and reducing the impact of micro-grid connection on the power distribution network.
2. The invention relates to a micro-grid group optimal scheduling method based on an improved second-order particle swarm algorithm, which adopts the improved second-order particle swarm algorithm, firstly, chaotic mapping is utilized to generate a chaotic sequence, a Logistic mapping model is selected to generate an initial population, variables are reversely normalized to a search space to obtain the initial positions of particles, the distance between any particle and the current optimal particle can be calculated after the inertia coefficient, the learning factor, the oscillation factor, the speed and the positions of the particles are calculated and updated, if the calculated distance is smaller than a reference value, the optimal particle is kept unchanged, other particles are subjected to chaotic motion, chaotic search is carried out in a given step number, the new particles obtained by chaotic search are used for replacing the original particles, the flow can ensure that the initial value distribution of the population is more uniform, the searchable space is increased, and on the other hand, the distance between any particle and the current optimal particle is calculated, and chaotic search is carried out when the distance is too small, certain variability can be ensured, the algorithm can jump out of local optimum to a certain degree, and the global optimization capability is improved. Therefore, the improved second-order particle swarm algorithm not only enables the initial value distribution of the swarm to be more uniform, but also can increase the global optimization capability.
3. The second-order particle swarm optimization adopted by the improved second-order particle swarm optimization-based micro-grid swarm optimization scheduling method improves the inertia coefficient, the learning factor and the speed updating formula, the calculation of the inertia coefficient and the learning factor is changed from conventional linear decrement into nonlinear decrement, the searching capability in the early stage can be increased, the convergence capability in the later stage can be enhanced, and therefore the accuracy and the calculation speed of the algorithm are improved. Therefore, the accuracy and the calculation speed of the algorithm are improved by improving the second-order particle swarm optimization.
Drawings
FIG. 1 is a flow chart of an improved second-order particle swarm algorithm in the present invention.
Fig. 2 is a structural diagram of the microgrid group in embodiment 1.
Fig. 3 is a diagram showing an internal structure of the microgrid in embodiment 1.
Detailed Description
The present invention will be further described with reference to the following detailed drawings and embodiments.
Referring to fig. 1, a microgrid cluster optimization scheduling method based on an improved second-order particle swarm algorithm sequentially includes the following steps:
a, respectively constructing mathematical models of various devices in a microgrid group, wherein the various devices comprise a wind generating set, a photovoltaic generating set, a load, an energy storage device and a diesel engine set;
b, establishing a micro-grid group optimized dispatching model with the aims of minimum comprehensive operation cost of the micro-grid group, maximum wind and light absorption rate and minimum power fluctuation of a tie line;
and step C, solving the optimized scheduling model by adopting an improved second-order particle swarm algorithm by taking wind power, photoelectricity, energy storage and diesel set output as the positions of the particle swarm and a fitness function as a target function to obtain an optimized scheduling scheme of the micro-grid group.
The step C comprises the following steps in sequence:
step C1, acquiring various equipment parameters and wind, light and load operation data in the microgrid group, and initializing basic parameters of the improved second-order particle swarm algorithm;
step C2, generating a chaotic sequence by utilizing chaotic mapping, selecting a Logistic mapping model to generate an initial population, performing inverse normalization on a variable to a search space to obtain the initial positions of particles, and enabling the iteration number k to be 1, wherein each particle corresponds to each optimized scheduling scheme;
step C3, calculating the fitness value of each particle, and updating the historical optimal value of each particle and the historical optimal value of the population;
step C4, calculating the current inertia coefficient, learning factor and oscillation factor according to the iteration times and the fitness value, updating the speed and the position of the particle, and simultaneously carrying out-of-limit processing on the speed and the position of the particle;
step C5, calculating the distance between any particle and the current optimal particle, if the calculated distance is less than the reference value, keeping the optimal particle unchanged, making other particles perform chaotic motion, performing chaotic search in a given step number, and replacing the original particle with a new particle obtained by the chaotic search;
step C6, judging whether convergence is achieved, if yes, exiting the iteration process, decoding the particles, and obtaining an optimized scheduling scheme of the microgrid group; if not, let k be k +1 and return to step C2 for the next iteration.
In step C4, the inertia coefficient, learning factor, oscillation factor, velocity and position are updated by the following formula:
Figure BDA0003466482460000081
Figure BDA0003466482460000082
Figure BDA0003466482460000083
Figure BDA0003466482460000084
Figure BDA0003466482460000085
λ1＜λ2-1,λ3＜λ4-1,0＜λ2＜1,0＜λ4＜1 k≥kmax/2
in the above formula, the first and second carbon atoms are,
Figure BDA0003466482460000091
respectively, the inertia coefficient, velocity and position, ω, of the ith particle at the kth iterationmin、ωmaxRespectively, the minimum and maximum values of the inertia coefficient, f () is a fitness function,
Figure BDA0003466482460000092
is the mean fitness value, p, of the population at the kth iterationg、piRespectively the historical optimal positions of the particle swarm and the ith particle,
Figure BDA0003466482460000093
respectively the learning factor c at the k-th iteration1、c2，r1、r2Is [0,1 ]]Random numbers distributed uniformly within, c1b、c1e、c2b、c2eAre respectively c1、c2Iterating the initial and final values, λ1、λ3The oscillation factors, lambda, of the ith particle optimal and global optimal positions in the current iterative process respectively2、λ4The oscillation factors of the ith particle optimal and global optimal positions in the last iteration process, d1、d2Is c1Control factor of d3、d4Is c2Control factor of, kmaxIs the maximum number of iterations.
In step C2, the Logistic mapping model is:
Figure BDA0003466482460000094
Figure BDA0003466482460000095
in the above formula, the first and second carbon atoms are,
Figure BDA0003466482460000096
for the k-th iteration of the chaotic variableThe d-dimensional component of time.
In step C5, the distance between the arbitrary particle and the current best particle is calculated according to the following formula:
Figure BDA0003466482460000097
in the above formula, the first and second carbon atoms are,
Figure BDA0003466482460000098
the distance between the ith particle and the current best particle at the kth iteration,
Figure BDA0003466482460000099
for the d-dimensional component, p, of the ith particle during the kth iterationgdFor the d-dimensional component of the current best particle, n is the dimension of the particle, i.e. the number of optimization variables.
In the step B, an objective function f of the microgrid group optimization scheduling model is as follows:
minf＝ω1f1+ω2f2+ω3f3
Figure BDA00034664824600000910
Figure BDA0003466482460000101
Figure BDA0003466482460000102
Figure BDA0003466482460000103
Figure BDA0003466482460000104
in the above formula, f1、f2、f3Respectively the comprehensive operation cost, the wind-light absorption rate, the power fluctuation of the connecting line, omega of the micro-grid group1、ω2、ω3Respectively the weight of the integrated running cost, the wind-solar absorption rate and the power fluctuation of the tie line, NMGT is the number of micro-grids and the number of calculation time segments respectively, ct,buy、ct,sellThe electricity purchasing price and the electricity selling price of the micro-grid group to the power distribution network at the t moment are respectively,
Figure BDA0003466482460000105
the electricity purchasing power and the electricity selling power of the micro-grid group to the power distribution network at the t moment are respectively
Figure BDA0003466482460000106
cfuel,i、cM,i、cE,iRespectively equivalent unit fuel cost, maintenance cost and environmental cost of the diesel engine set,
Figure BDA0003466482460000107
the output of the diesel engine set and the energy storage equipment, cDS,i、cd,iMaintenance cost and depreciation cost of the energy storage device respectively, delta t is the duration of a single time interval, ai、bi、ciRespectively a quadratic term, a primary term and a constant term coefficient of the cost function of the diesel engine unit,
Figure BDA0003466482460000108
respectively active power output of the ith micro-grid after wind power and photovoltaic reduction at the t moment,
Figure BDA0003466482460000109
respectively the maximum active power output, P, of wind power and photovoltaic at the t moment of the ith microgridt LineThe power of the interaction between the microgrid group and the power distribution network at the t moment,
Figure BDA00034664824600001010
and the average value of the power of the interaction of the micro-grid group and the power distribution network is obtained.
The constraints of the objective function include:
and (3) power flow balance constraint:
Figure BDA00034664824600001011
Figure BDA00034664824600001012
in the above formula, the first and second carbon atoms are,
Figure BDA0003466482460000111
the load size of the ith micro-grid at the t moment,
Figure BDA0003466482460000112
the power interacted with the power distribution network at the t moment of the ith microgrid is obtained;
wind-solar output constraint:
Figure BDA0003466482460000113
Figure BDA0003466482460000114
tie line power capacity constraint:
Figure BDA0003466482460000115
Figure BDA0003466482460000116
in the above formula, the first and second carbon atoms are,
Figure BDA0003466482460000117
the upper limit of the interaction power of the ith micro-grid,
Figure BDA0003466482460000118
the upper limit value of the total interaction power;
ESS operation constraints:
Figure BDA0003466482460000119
Figure BDA00034664824600001110
in the above formula, the first and second carbon atoms are,
Figure BDA00034664824600001111
respectively the discharging power and the charging power of the ith energy storage device at the tth moment,
Figure BDA00034664824600001112
respectively is the upper limit value of the discharge power and the charging power of the ith energy storage device, Et,i、Ei,maxRated values of energy and storage capacity, SOC, respectively, stored for the ith energy storage device at the tth timet,iThe electric charge quantity and SOC of the ith energy storage equipment at the t momenti,min、SOCi,maxRespectively is the lower limit value and the upper limit value eta of the charge quantity in the charging and discharging processes of the ith energy storage deviced、ηcRespectively the discharging efficiency and the charging efficiency of the energy storage equipment;
output restraint of the diesel engine set:
Figure BDA0003466482460000121
Figure BDA0003466482460000122
in the above formula, the first and second carbon atoms are,
Figure BDA0003466482460000123
respectively a lower limit value and an upper limit value of the output of the diesel engine unit,
Figure BDA0003466482460000124
the maximum downward climbing speed and the maximum upward climbing speed of the ith diesel engine unit are respectively.
The parameters used in the present invention are described below:
the control factor is as follows: the invention adopts a control factor d1、d2、d3、d4To control the learning factor c1、c2The shape of the function curve varies with the number of iterations.
Oscillation factor: oscillation factor lambda in the invention1、λ2、λ3、λ4Representing the magnitude of the weight, λ, of the particle velocity update affected by the particle position1And λ3Influenced by the position of the particle at the current stage, λ2And λ4Influenced by the position of the particles in the previous stage.
Example 1:
a microgrid cluster optimization scheduling method based on an improved second-order particle swarm algorithm takes a certain microgrid cluster as a research object (the structure of the microgrid cluster is shown in figure 2), and the method is sequentially carried out according to the following steps:
1. respectively constructing mathematical models of various devices in a microgrid group, wherein the internal structure of the microgrid is shown in figure 3, and the devices comprise a wind generating set, a photovoltaic generating set, a load, an energy storage device and a diesel engine set;
2. based on the various devices, establishing a microgrid cluster optimization scheduling model with the goals of minimum comprehensive operation cost, maximum wind-solar energy consumption rate and minimum power fluctuation of a connecting line of the microgrid cluster as follows:
minf＝ω1f1+ω2f2+ω3f3
Figure BDA0003466482460000125
Figure BDA0003466482460000126
Figure BDA0003466482460000127
Figure BDA0003466482460000131
Figure BDA0003466482460000132
in the above formula, f is the objective function of the model, f1、f2、f3Respectively the comprehensive operation cost, the wind-light absorption rate, the power fluctuation of the connecting line, omega of the micro-grid group1、ω2、ω3Respectively the weight of the integrated running cost, the wind-solar absorption rate and the power fluctuation of the tie line, NMGT is the number of micro-grids and the number of calculation time segments respectively, ct,buy、ct,sellThe electricity purchasing price and the electricity selling price of the micro-grid group to the power distribution network at the t moment are respectively,
Figure BDA0003466482460000133
Figure BDA0003466482460000134
the electricity purchasing power and the electricity selling power of the micro-grid group to the power distribution network at the t moment are respectively
Figure BDA0003466482460000135
cfuel,i、cM,i、cE,iRespectively equivalent unit fuel cost, maintenance cost and environmental cost of the diesel engine set,
Figure BDA0003466482460000136
respectively a diesel engine set and an energy storage deviceA force of (c)DS,i、cd,iMaintenance cost and depreciation cost of the energy storage device respectively, delta t is the duration of a single time interval, ai、bi、ciRespectively a quadratic term, a primary term and a constant term coefficient of the cost function of the diesel engine unit,
Figure BDA0003466482460000137
respectively active power output of the ith micro-grid after wind power and photovoltaic reduction at the t moment,
Figure BDA0003466482460000138
respectively the maximum active power output, P, of wind power and photovoltaic at the t moment of the ith microgridt LineThe power of the interaction between the microgrid group and the power distribution network at the t moment,
Figure BDA0003466482460000139
the average value of the power of interaction of the micro-grid group and the power distribution network is obtained;
the constraints of the objective function include:
and (3) power flow balance constraint:
Figure BDA00034664824600001310
Figure BDA00034664824600001311
in the above formula, the first and second carbon atoms are,
Figure BDA00034664824600001312
the load size of the ith micro-grid at the t moment,
Figure BDA00034664824600001313
the power interacted with the power distribution network at the t moment of the ith microgrid is obtained;
wind-solar output constraint:
Figure BDA0003466482460000141
Figure BDA0003466482460000142
tie line power capacity constraint:
Figure BDA0003466482460000143
Figure BDA0003466482460000144
in the above formula, the first and second carbon atoms are,
Figure BDA0003466482460000145
the upper limit of the interaction power of the ith micro-grid,
Figure BDA0003466482460000146
the upper limit value of the total interaction power;
ESS operation constraints:
Figure BDA0003466482460000147
Figure BDA0003466482460000148
in the above formula, the first and second carbon atoms are,
Figure BDA0003466482460000149
respectively the discharging power and the charging power of the ith energy storage device at the tth moment,
Figure BDA00034664824600001410
respectively is the upper limit value of the discharge power and the charging power of the ith energy storage device, Et,i、Ei,maxAre respectively the ith energy storage equipmentStored energy and storage Capacity ratings at time t, SOCt,iThe electric charge quantity and SOC of the ith energy storage equipment at the t momenti,min、SOCi,maxRespectively is the lower limit value and the upper limit value eta of the charge quantity in the charging and discharging processes of the ith energy storage deviced、ηcRespectively the discharging efficiency and the charging efficiency of the energy storage equipment;
output restraint of the diesel engine set:
Figure BDA00034664824600001411
Figure BDA0003466482460000151
in the above formula, the first and second carbon atoms are,
Figure BDA0003466482460000152
respectively a lower limit value and an upper limit value of the output of the diesel engine unit,
Figure BDA0003466482460000153
the maximum downward climbing speed and the maximum upward climbing speed of the ith diesel engine unit are respectively;
3. acquiring installed capacities of wind driven generators and photovoltaic generators in different micro-grids, acquiring running data of wind and light loads, boundary parameters of equipment and the like, and initializing basic parameters of a second-order particle swarm algorithm;
4. the method comprises the steps of taking wind power, photoelectricity, energy storage and diesel set output as positions of particle swarms, generating a chaotic sequence by utilizing chaotic mapping, selecting a Logistic mapping model to generate an initial population, carrying out inverse normalization on variables to a search space to obtain initial positions of particles, enabling iteration times k to be 1, and enabling each particle to correspond to each optimized scheduling scheme, wherein the Logistic mapping model is as follows:
Figure BDA0003466482460000154
Figure BDA0003466482460000155
in the above formula, the first and second carbon atoms are,
Figure BDA0003466482460000156
d-dimensional components of the chaotic variables in the k iteration are obtained;
5. calculating the fitness value of each particle, and updating the historical optimal value of each particle and the historical optimal value of the population;
6. calculating a current inertia coefficient, a learning factor and an oscillation factor according to the iteration times and the fitness value, updating the speed and the position of the particle, and simultaneously performing out-of-limit processing on the speed and the position of the particle, wherein the inertia coefficient, the learning factor, the oscillation factor, the speed and the position are calculated and updated through the following formulas:
Figure BDA0003466482460000157
Figure BDA0003466482460000158
Figure BDA0003466482460000159
Figure BDA00034664824600001510
Figure BDA0003466482460000161
λ1＜λ2-1,λ3＜λ4-1,0＜λ2＜1,0＜λ4＜1k≥kmax/2
in the above formula, the first and second carbon atoms are,
Figure BDA0003466482460000162
respectively, the inertia coefficient, velocity and position, ω, of the ith particle at the kth iterationmin、ωmaxRespectively, the minimum and maximum values of the inertia coefficient, f () is a fitness function,
Figure BDA0003466482460000163
is the mean fitness value, p, of the population at the kth iterationg、piRespectively the historical optimal positions of the particle swarm and the ith particle,
Figure BDA0003466482460000164
is the learning factor at the k-th iteration, r1、r2Is [0,1 ]]Random numbers distributed uniformly within, c1b、c1e、c2b、c2eAre respectively c1、c2Iterating the initial and final values, λ1、λ3The oscillation factors, lambda, of the ith particle optimal and global optimal positions in the current iterative process respectively2、λ4The oscillation factors of the ith particle optimal and global optimal positions in the last iteration process, d1、d2Is c1Control factor of d3、d4Is c2Control factor of, kmaxIs the maximum iteration number;
7. calculating the distance between any particle and the current optimal particle according to the following formula, if the calculated distance is smaller than a reference value, keeping the optimal particle unchanged, enabling other particles to perform chaotic motion, performing chaotic search within a given step number, and replacing the original particle with a new particle obtained by the chaotic search:
Figure BDA0003466482460000165
in the above formula, the first and second carbon atoms are,
Figure BDA0003466482460000166
is the k-thThe distance between the ith particle and the current best particle at the time of the sub-iteration,
Figure BDA0003466482460000167
for the d-dimensional component, p, of the ith particle during the kth iterationgdD-dimensional components of the current optimal particles, wherein n is the dimension of the particles, namely the number of the optimization variables;
8. judging whether convergence occurs or not, if yes, exiting the iteration process, decoding the particles, and obtaining an optimized scheduling scheme of the microgrid group; if not, let k be k +1 and return to step 4 for the next iteration.

Claims (7)

1. A microgrid cluster optimization scheduling method based on an improved second-order particle swarm algorithm is characterized in that:
the optimized scheduling method sequentially comprises the following steps:
a, respectively constructing mathematical models of various devices in a microgrid group, wherein the various devices comprise a wind generating set, a photovoltaic generating set, a load, an energy storage device and a diesel engine set;
b, establishing a micro-grid group optimized dispatching model with the aims of minimum comprehensive operation cost of the micro-grid group, maximum wind and light absorption rate and minimum power fluctuation of a tie line;
and step C, solving the optimized scheduling model by adopting an improved second-order particle swarm algorithm by taking wind power, photoelectricity, energy storage and diesel set output as the positions of the particle swarm and a fitness function as a target function to obtain an optimized scheduling scheme of the micro-grid group.
2. The improved second-order particle swarm optimization scheduling method based on the microgrid cluster of claim 1 is characterized in that:
the step C comprises the following steps in sequence:
step C1, acquiring various equipment parameters and wind, light and load operation data in the microgrid group, and initializing basic parameters of the improved second-order particle swarm algorithm;
step C2, generating a chaotic sequence by utilizing chaotic mapping, selecting a Logistic mapping model to generate an initial population, performing inverse normalization on a variable to a search space to obtain the initial positions of particles, and enabling the iteration number k to be 1, wherein each particle corresponds to each optimized scheduling scheme;
step C3, calculating the fitness value of each particle, and updating the historical optimal value of each particle and the historical optimal value of the population;
step C4, calculating the current inertia coefficient, learning factor and oscillation factor according to the iteration times and the fitness value, updating the speed and the position of the particle, and simultaneously carrying out-of-limit processing on the speed and the position of the particle;
step C5, calculating the distance between any particle and the current optimal particle, if the calculated distance is less than the reference value, keeping the optimal particle unchanged, making other particles perform chaotic motion, performing chaotic search in a given step number, and replacing the original particle with a new particle obtained by the chaotic search;
step C6, judging whether convergence is achieved, if yes, exiting the iteration process, decoding the particles, and obtaining an optimized scheduling scheme of the microgrid group; if not, let k be k +1 and return to step C2 for the next iteration.
3. The improved second-order particle swarm optimization scheduling method based on the microgrid cluster of claim 2 is characterized in that:
in step C4, the inertia coefficient, learning factor, oscillation factor, velocity and position are updated by the following formula:
Figure FDA0003466482450000021
Figure FDA0003466482450000022
Figure FDA0003466482450000023
Figure FDA0003466482450000024
Figure FDA0003466482450000025
λ1＜λ2-1,λ3＜λ4-1,0＜λ2＜1,0＜λ4＜1 k≥kmax/2
in the above formula, the first and second carbon atoms are,
Figure FDA0003466482450000026
respectively, the inertia coefficient, velocity and position, ω, of the ith particle at the kth iterationmin、ωmaxRespectively, the minimum and maximum values of the inertia coefficient, f () is a fitness function,
Figure FDA0003466482450000027
is the mean fitness value, p, of the population at the kth iterationg、piRespectively the historical optimal positions of the particle swarm and the ith particle,
Figure FDA0003466482450000028
as a learning factor c at the k-th iteration1，
Figure FDA0003466482450000029
As a learning factor c at the k-th iteration2，r1、r2Is [0,1 ]]Random numbers distributed uniformly within, c1b、c1e、c2b、c2eAre respectively c1、c2Iterating the initial and final values, λ1、λ3The oscillation factors, lambda, of the ith particle optimal and global optimal positions in the current iterative process respectively2、λ4Are respectively asOscillation factor of the ith particle optimum and global optimum position in the last iteration, d1、d2Is c1Control factor of d3、d4Is c2Control factor of, kmaxIs the maximum number of iterations.
4. The improved second-order particle swarm optimization scheduling method based on the microgrid cluster of claim 2 is characterized in that:
in step C2, the Logistic mapping model is:
Figure FDA0003466482450000031
Figure FDA0003466482450000032
in the above formula, the first and second carbon atoms are,
Figure FDA0003466482450000033
is the d-dimensional component of the chaotic variable at the kth iteration.
5. The improved second-order particle swarm optimization scheduling method based on the microgrid cluster of claim 2 is characterized in that:
in step C5, the distance between the arbitrary particle and the current best particle is calculated according to the following formula:
Figure FDA0003466482450000034
in the above formula, the first and second carbon atoms are,
Figure FDA0003466482450000035
the distance between the ith particle and the current best particle at the kth iteration,
Figure FDA0003466482450000036
for the d-dimensional component, p, of the ith particle during the kth iterationgdFor the d-dimensional component of the current best particle, n is the dimension of the particle, i.e. the number of optimization variables.
6. The optimized dispatching method for the microgrid cluster based on the improved second-order particle swarm optimization algorithm, as claimed in claim 1 or 2, is characterized in that:
in the step B, an objective function f of the microgrid group optimization scheduling model is as follows:
minf＝ω1f1+ω2f2+ω3f3
Figure FDA0003466482450000037
Figure FDA0003466482450000038
Figure FDA0003466482450000039
Figure FDA00034664824500000310
Figure FDA0003466482450000041
in the above formula, f1、f2、f3Respectively the comprehensive operation cost, the wind-light absorption rate, the power fluctuation of the connecting line, omega of the micro-grid group1、ω2、ω3Respectively the weight of the integrated running cost, the wind-solar absorption rate and the power fluctuation of the tie line, NMGT is the number of micro-grids and the number of calculation time segments respectively, ct,buy、ct,sellThe electricity purchasing price and the electricity selling price of the micro-grid group to the power distribution network at the t moment are respectively,
Figure FDA0003466482450000042
the electricity purchasing power and the electricity selling power of the micro-grid group to the power distribution network at the t moment are respectively
Figure FDA0003466482450000043
cfuel,i、cM,i、cE,iRespectively equivalent unit fuel cost, maintenance cost and environmental cost of the diesel engine set,
Figure FDA0003466482450000044
the output of the diesel engine set and the energy storage equipment, cDS,i、cd,iMaintenance cost and depreciation cost of the energy storage device respectively, delta t is the duration of a single time interval, ai、bi、ciRespectively a quadratic term, a primary term and a constant term coefficient of the cost function of the diesel engine unit,
Figure FDA0003466482450000045
respectively active power output of the ith micro-grid after wind power and photovoltaic reduction at the t moment,
Figure FDA0003466482450000046
respectively the maximum active power output, P, of wind power and photovoltaic at the t moment of the ith microgridt LineThe power of the interaction between the microgrid group and the power distribution network at the t moment,
Figure FDA0003466482450000047
and the average value of the power of the interaction of the micro-grid group and the power distribution network is obtained.
7. The optimized dispatching method for the microgrid cluster based on the improved second-order particle swarm optimization algorithm, as claimed in claim 1 or 2, is characterized in that:
the constraints of the objective function include:
and (3) power flow balance constraint:
Figure FDA0003466482450000048
Figure FDA0003466482450000049
in the above formula, the first and second carbon atoms are,
Figure FDA00034664824500000410
the load size of the ith micro-grid at the t moment,
Figure FDA00034664824500000411
the power interacted with the power distribution network at the t moment of the ith microgrid is obtained;
wind-solar output constraint:
Figure FDA00034664824500000412
Figure FDA0003466482450000051
tie line power capacity constraint:
Figure FDA0003466482450000052
Figure FDA0003466482450000053
in the above formula, the first and second carbon atoms are,
Figure FDA0003466482450000054
the upper limit of the interaction power of the ith micro-grid,
Figure FDA0003466482450000055
the upper limit value of the total interaction power;
ESS operation constraints:
Figure FDA0003466482450000056
Figure FDA0003466482450000057
in the above formula, the first and second carbon atoms are,
Figure FDA0003466482450000058
respectively the discharging power and the charging power of the ith energy storage device at the tth moment,
Figure FDA0003466482450000059
respectively is the upper limit value of the discharge power and the charging power of the ith energy storage device, Et,i、Ei,maxRated values of energy and storage capacity, SOC, respectively, stored for the ith energy storage device at the tth timet,iThe electric charge quantity and SOC of the ith energy storage equipment at the t momenti,min、SOCi,maxRespectively is the lower limit value and the upper limit value eta of the charge quantity in the charging and discharging processes of the ith energy storage deviced、ηcRespectively the discharging efficiency and the charging efficiency of the energy storage equipment;
output restraint of the diesel engine set:
Figure FDA00034664824500000510
Figure FDA00034664824500000511
in the above formula, the first and second carbon atoms are,
Figure FDA0003466482450000061
respectively a lower limit value and an upper limit value of the output of the diesel engine unit,
Figure FDA0003466482450000062
the maximum downward climbing speed and the maximum upward climbing speed of the ith diesel engine unit are respectively.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115296347A (en) * 2022-07-07 2022-11-04 国网甘肃省电力公司电力科学研究院 Rural power distribution network three-party game optimization scheduling method and system based on edge control
CN115577864A (en) * 2022-12-07 2023-01-06 国网浙江省电力有限公司金华供电公司 Distribution network operation optimization scheduling method based on multi-model combined operation
CN115796393A (en) * 2023-01-31 2023-03-14 深圳市三和电力科技有限公司 Energy network management optimization method, system and storage medium based on multi-energy interaction

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115296347A (en) * 2022-07-07 2022-11-04 国网甘肃省电力公司电力科学研究院 Rural power distribution network three-party game optimization scheduling method and system based on edge control
CN115577864A (en) * 2022-12-07 2023-01-06 国网浙江省电力有限公司金华供电公司 Distribution network operation optimization scheduling method based on multi-model combined operation
CN115796393A (en) * 2023-01-31 2023-03-14 深圳市三和电力科技有限公司 Energy network management optimization method, system and storage medium based on multi-energy interaction

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