CN115241923A - Micro-grid multi-objective optimization configuration method based on snake optimization algorithm - Google Patents

Micro-grid multi-objective optimization configuration method based on snake optimization algorithm Download PDF

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CN115241923A
CN115241923A CN202211005049.7A CN202211005049A CN115241923A CN 115241923 A CN115241923 A CN 115241923A CN 202211005049 A CN202211005049 A CN 202211005049A CN 115241923 A CN115241923 A CN 115241923A
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snake
microgrid
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李金璐
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Shanghai Dianji University
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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
    • H02J3/381—Dispersed generators
    • 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
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • 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
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • 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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously

Abstract

The invention provides a micro-grid multi-objective optimization configuration method based on a snake optimization algorithm, which comprises the following steps: constructing a double-layer energy optimization management model; constructing a mathematical model of each target in the micro-grid system; introducing a snake optimization algorithm and adding a levy flight strategy; and performing optimal configuration on multiple targets of the micro-grid based on a snake optimization algorithm. The method for optimizing and configuring the multiple targets of the microgrid based on the snake optimization algorithm has the advantages of wider search range, improved later convergence precision of population and capability of effectively solving the problem of optimizing and configuring the units in the microgrid in each time period.

Description

Micro-grid multi-objective optimization configuration method based on snake optimization algorithm
Technical Field
The invention relates to the technical field of micro-grid multi-objective optimization configuration, in particular to a micro-grid multi-objective optimization configuration method based on a snake optimization algorithm.
Background
At present, solving the output condition of each power generation unit by using a given objective function and constraint conditions is taken as an important research direction for optimizing energy distribution management of a microgrid, energy in three forms of cold, heat and electricity is taken as an objective function, a whale algorithm is used for carrying out the most output solving, a wind-solar energy storage combined power supply system is taken as a research object, the cost required by each energy storage is taken as a minimum objective function, and the constraint conditions are selected as the system load power shortage rate and the storage battery output power, and an energy storage optimization model is established. Such algorithms do not take into account the operational and maintenance costs of the plant and the environmental costs generated by thermal power generation. An objective function selected by a recently proposed sparrow optimization algorithm is the economic cost and the environmental cost of the microgrid, and a self-adaptive weight coefficient is introduced into the objective function. Although improvement is provided, only the operation cost of the microgrid is considered, the operation benefit generated by the microgrid when the power consumption demand is low is ignored, and the economical efficiency of the operation of the microgrid is not considered comprehensively.
In addition, the traditional genetic algorithm has strong global search capability and weak local search capability, and can only obtain suboptimal solution rather than optimal solution; the particle swarm algorithm generates premature convergence and is proved not to be global convergence, and unweighted heavy convergence is fast but is easy to fall into a local optimal solution; the parameter setting of the ant colony algorithm and the fish colony algorithm is complex, and if the parameter setting is improper, the parameters are easy to deviate from a high-quality solution; the whale optimization algorithm has the problems that the algorithm is trapped in a local extreme value and the convergence rate is high.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a micro-grid multi-objective optimization configuration method based on a snake optimization algorithm, which can effectively solve the problem of optimal configuration of a unit in the micro-grid in each time period.
In order to solve the problems, the technical scheme of the invention is as follows:
a micro-grid multi-objective optimization configuration method based on a snake optimization algorithm comprises the following steps:
constructing a double-layer energy optimization management model;
constructing a mathematical model of each target in the micro-grid system;
introducing a snake optimization algorithm and adding a levy flight strategy;
and performing optimization configuration on multiple targets of the microgrid based on a snake optimization algorithm.
Optionally, in the step of constructing the double-layer energy optimization management model, an upper layer of the double-layer energy optimization management model is a fuzzy management system, a lower layer of the double-layer energy optimization management model is a multi-objective function optimization layer, the upper layer fuzzy management system determines a working mode of the hybrid micro-grid according to the photovoltaic output, the wind turbine output and the residential load demand, and the lower layer receives upper layer data and performs optimization management on energy.
Optionally, the operating mode includes:
in a first mode: renewable energy and an energy storage battery jointly generate electricity to meet load requirements;
and a second mode: when the load is increased to the mode that the load demand cannot be met, the participation of the power distribution network and each thermal generator set is needed.
Optionally, the step of constructing the two-layer energy optimization management model specifically includes:
output P of wind driven generator WT And photovoltaic output P PV Maximum output P of energy storage battery SBmax Inputting a fuzzy management control layer, and the hourly load demand P of the microgrid L ；
And calculating the integral generating capacity by the formula: p ALL ＝P PV +P WT +P SBmax ；
Judging renewable energy output P ALL Whether or not it is greater than the load demand P L If P is ALL Greater than P L Then the microgrid operates in a mode one;
calculating the maximum output sum P of each thermal generator set TGmax ＝P MTmax +P FCmax The sum of the maximum output of all units and the maximum exchange power of the distribution network is compared with the load demand P L By comparison, if P ALL +P TGmax +P PDNmax Greater than P L If so, the micro-grid works in a mode II;
each thermal power generating unit P MT And P FC Energy storage battery P SB And a distribution network P PDN And outputting the real-time output.
Optionally, in the step of constructing mathematical models of targets in the microgrid system, the step of constructing mathematical models of targets in the microgrid system is as follows:
Figure BDA0003808723790000021
wherein f (t) is an optimized objective function; c m (t)、f c (t)、P loss (t) cost-benefit, carbon emission, active power loss of microgrid operation,h i (t) and g i And (t) is an equality and inequality constraint.
Optionally, the microgrid operating cost-operating profit C m The expression of (t) is:
Figure BDA0003808723790000031
in the formula: c m1 (t) investment cost required for the microgrid, C m2 (t) earnings of battery energy storage device, renewable energy and thermal generator set, C fuel (t) represents the fuel cost of the microgrid; c sud (t) represents the start-up or shut-down cost of each unit; c on (t) represents the operation and maintenance cost of the microgrid; c SB (t)、C RES (t)、C TG (t) respectively representing the benefits generated by the energy storage device, the renewable energy source and the thermal generator set to the micro-grid load electricity selling; c PDN (t) represents the revenue generated by the hybrid microgrid selling electrical energy to the distribution grid.
Optionally, the carbon emission f of the microgrid c (t) the expression is:
Figure BDA0003808723790000032
in the formula: l i,j The quantity of the j-th polluted gas generated by the distributed power generation unit; lambda [ alpha ] j The cost required for treating the corresponding pollution gas of the unit.
Optionally, the active power loss expression of the microgrid is as follows:
Figure BDA0003808723790000033
in the formula: p ij 、Q ij Respectively active power and reactive power, U i Is the voltage amplitude, R, of node i ij Is the line equivalent resistance.
Optionally, the step of introducing a snake optimization algorithm and adding a levy flight strategy specifically includes:
the snake optimization algorithm first generates a uniformly distributed random population to be able to start the process of optimizing the algorithm;
introducing a levy flight optimization strategy.
Optionally, the step of optimally configuring multiple targets of the microgrid based on a snake optimization algorithm includes the following steps:
selecting a micro-grid low-voltage test network;
and optimizing the three objective functions to obtain an optimal solution.
Compared with the prior art, the invention provides a micro-grid multi-objective optimization configuration method based on a snake optimization algorithm, a double-layer energy optimization management model is established, an upper layer utilizes a fuzzy management system to determine a micro-grid operation mode, a lower layer adopts an improved snake optimization algorithm to carry out optimization management on energy, aiming at different electricity consumption demands of users in different periods, the micro-grid operation economy, carbon emission and power loss in a network are considered, three variables are used as target functions to establish a mathematical model, an optimization comparison experiment is carried out aiming at the micro-grid target function model, on the basis of the original snake optimization algorithm, the target functions further refer to a Levy flight strategy to improve the later convergence precision of population, the problem of local optimization is solved, and an optimal solution is obtained through algorithm optimization.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a flow chart of a micro-grid multi-objective optimization configuration method based on a snake optimization algorithm according to an embodiment of the present invention;
fig. 2 is a specific flowchart for constructing a two-layer energy optimization management model according to an embodiment of the present invention;
FIG. 3 is a diagram of a low voltage test network according to an embodiment of the present invention;
fig. 4 is an optimal force diagram of each unit according to an embodiment of the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
Specifically, fig. 1 is a micro-grid multi-objective optimization configuration method based on a snake optimization algorithm, as shown in fig. 1, the method includes the following steps:
s1: constructing a double-layer energy optimization management model;
specifically, the upper layer of the double-layer energy optimization management model is a fuzzy management system, the lower layer of the double-layer energy optimization management model is a multi-objective function optimization layer, the upper layer fuzzy management system determines the working mode of the hybrid micro-grid according to photovoltaic output, wind driven generator output and resident load requirements, and the lower layer receives upper layer data and performs optimization management on energy. The working modes are divided into the following two cases:
the first mode is as follows: renewable energy and an energy storage battery jointly generate electricity to meet load requirements. In order to reduce carbon emission and power loss in the network as much as possible, the output of each thermal generator set, and the exchange power between the power distribution network and the microgrid are 0.
And a second mode: when the load is increased to the mode I and the load demand cannot be met, the participation of the power distribution network and each thermal generator set is needed. At the moment, the upper-layer fuzzy management system transmits information to the lower-layer multi-objective function optimization layer to perform energy optimization management.
As shown in fig. 2, the specific process of establishing the two-layer energy optimization management model is as follows:
step 11: output P of wind driven generator WT And photovoltaic output P PV Maximum output P of energy storage battery SBmax Inputting a fuzzy management control layer, and the hourly load demand P of the microgrid L ；
Step 12: and calculating the integral generating capacity by the formula: p is ALL ＝P PV +P WT +P SBmax ；
Step 13: judging renewable energy output P ALL Whether or not it is greater than the load demand P L If P is ALL Greater than P L The microgrid then operates in mode.
Step 14: calculating the sum P of the maximum output of each thermal generator set TGmax ＝P MTmax +P FCmax The sum of the maximum output of all units and the maximum exchange power of the distribution network is compared with the load demand P L By comparison, if P ALL +P TGmax +P PDNmax Greater than P L And the microgrid is operated in a mode two.
Step 15: each thermal power generating unit P MT And P FC Energy storage battery P SB And a distribution network P PDN And outputting the real-time output.
And the energy management system transmits the information of the upper layer to the multi-objective function optimization layer of the lower layer, and solves the capacity configuration of each distributed power supply in the micro-grid.
S2: constructing a mathematical model of each target in the micro-grid system;
specifically, the construction of each target mathematical model in the microgrid system is as follows:
Figure BDA0003808723790000051
in the formula: f (t) is an optimized objective function; c m (t)、f c (t)、P loss (t) cost-benefit, carbon emission, active power loss, h, respectively, for microgrid operation i (t) and g i And (t) is an equality and inequality constraint.
Operating cost-operating profit C of the microgrid m The expression of (t) is:
Figure BDA0003808723790000052
in the formula: c m1 (t) investment cost required for microgrid, C m2 (t) is a battery energy storage device, a rechargeable batteryProfits from renewable energy and thermal power generation units, C fuel (t) represents the fuel cost of the microgrid; c sud (t) represents the start-up or shut-down cost of each unit; c on (t) represents the operation and maintenance cost of the microgrid; c SB (t)、C RES (t)、C TG (t) respectively representing the benefits generated by the energy storage device, the renewable energy source and the thermal generator set to the micro-grid load electricity selling; c PDN (t) represents the revenue generated by the hybrid microgrid selling electrical energy to the distribution grid.
Wherein the investment cost C m1 (t) consists essentially of the following three parts:
1) Fuel cost C of the microgrid fuel (t)
The fuel cost expression of the thermal power generation with MT and FC as main power generation units is as follows:
Figure BDA0003808723790000061
Figure BDA0003808723790000062
in the formula: alpha is alpha MT 、β MT 、γ MT Cost factor for MT; alpha is alpha FC 、β FC 、γ FC Cost factor for FC: p MT (t) and P FC (t) represents the output power at time t for MT and FC, respectively.
The total fuel cost of the microgrid is:
Figure BDA0003808723790000063
in the formula: i is the number of each thermal generator set; t is the total discrete time interval, T =24h; n is a radical of MT 、N FC The total number of MT generator sets and the total number of FC generator sets are respectively. T is p And (t) represents the working state of the P unit, wherein P is MT or FC.
Figure BDA0003808723790000064
2) Cost C for shutting down and starting up each power generation unit sud (t)
The starting and stopping cost of each power generation unit is related to the working state of the unit at the moment and the previous moment, and the expression is as follows:
Figure BDA0003808723790000065
in the formula: c sd ，C su Which is the turn-on and turn-off cost of the power generation unit.
Operating maintenance cost C of micro-grid on (t)：
Figure BDA0003808723790000066
In the formula: k is Mi The operation and maintenance cost coefficient of the ith distributed power supply in the micro-grid is obtained; p i And (t) is the output power of the ith power supply at the time t.
Revenue generated by the microgrid C m2 (t) is composed of the following four parts:
energy storage battery power supply income C SB (t)：
Figure BDA0003808723790000067
In the formula: price SB (t) represents the real-time electricity price of the energy storage battery at the moment t;
Figure BDA0003808723790000071
the working state of SB at the time t.
Income C of exchanging electric energy with the electric network PDN (t)：
Figure BDA0003808723790000072
In the formula: p PDN (t) isAt the time t, power exchange between the power distribution network and the micro power grid is carried out, when the power exchange is positive, the micro power grid purchases electric energy from the power distribution network, and when the power exchange is negative, the micro power grid sells electric energy from the power distribution network; price PDN And (t) is the electricity price when the t moment is in transaction with the power distribution network.
Figure BDA0003808723790000073
And the working state of the power distribution network is shown.
Carbon emission f of microgrid c (t)：
Each thermal power generating set can generate CO2, SO2, NOx and other pollution gases during operation, and the environmental cost of the microgrid is mainly the treatment cost of the pollutants. Because wind power and photovoltaic power generation are both carbon emission-free clean energy power generation, the environmental cost is as follows:
Figure BDA0003808723790000074
in the formula: l i,j The quantity of the j-th polluted gas generated by the distributed power generation unit; lambda [ alpha ] j The cost required for treating the corresponding pollution gas of the unit.
Active power loss of the microgrid:
Figure BDA0003808723790000075
in the formula: p ij 、Q ij Respectively active power and reactive power, U i Is the voltage amplitude, R, of node i ij Is the line equivalent resistance.
Distributed power source and distribution network constraints:
the constraint of equation:
1) Power balance constraint expression:
∑P L +∑P loss ＝∑P RES +∑P SB +∑P TG +P PDN
in the formula: sigma P L The total load generated for the microgrid; sigma P loss Active and reactive power losses for the microgrid;ΣP RES total active power output for renewable energy; sigma P SB The total active output of the energy storage battery is obtained; sigma P TG The total active power output of each thermal generator set.
2) The power balance constraint expression of the single machine capacity and the total generated energy is as follows:
Figure BDA0003808723790000081
in the formula: p i PV Power generated for node i solar energy; p j WT The power generated by the wind energy generator for node j. P p SB Storing the power sent out by the battery for the node p.
Inequality constraint conditions:
1) Output power constraint of each thermal power generator
The electric energy generated by the MT and the FC in unit time is related to the performance of each unit, and the inequality constraint mathematical expression is as follows:
Figure BDA0003808723790000082
in the formula: i, j =1,2 MTmin,i And P MTmax,i Respectively, i node MT power limit values; p FCmin,j And P FCmax,j Respectively j-node FC power limits.
2) Renewable energy generated energy constraint
Output power P of wind power and photovoltaic generator WT And P PV The inequality constraint condition satisfies the formula:
Figure BDA0003808723790000083
in the formula: p is WT,MPPT And P PV,MPPT The maximum output power of the wind driven generator and the maximum output power of the photovoltaic generator are respectively.
3) Energy storage battery state of charge and power constraints thereof
Energy storage batteryIs determined by the state of charge (SOC) of the battery p (t) represents the constraint conditions:
SOC min,p ≤SOC p (t)≤SOC max,p
in the formula: SOC (system on chip) min,p And SOC max,p Respectively the minimum and maximum values of the stored charge of the energy storage battery p.
Due to the state of charge SOC of the energy storage battery p (t) magnitude of output P from the battery SB (t) is in a linear relationship, so the constraint relationship of the charging and discharging power of the energy storage battery is as follows:
-P SB,max,p ≤P SB (t)≤P SB,max,p
in the formula: p SB,max,p The maximum output power of the energy storage battery pack p.
4) The electric energy exchange constraint expression of the micro-grid and the power distribution network is as follows:
-P PDN,max ≤P PDN (t)≤P PDN,max
in the formula: p is PDN,max The maximum power exchanged between the micro-grid and the power distribution network.
S3: introducing a snake optimization algorithm and adding a levy flight strategy;
specifically, a snake optimization algorithm is introduced, a levy flight strategy is added, and after the algorithm is updated, the individual positions are updated by the levy flight again, so that the algorithm is not easy to fall into local optimum while the global convergence of the algorithm is improved.
The step S3 specifically includes the following steps:
step 31: the snake optimization algorithm first generates a uniformly distributed random population to be able to start the process of optimizing the algorithm;
the initial population can be obtained by the following equation:
X i ＝X min +r×(X max -X min )
X i is the position of the ith individual, r is a random number between 0 and 1, X max 、X min The lower and upper bounds of the problem, respectively.
It is assumed that the number of males is 50% and the number of females is 50%. The population is divided, using the following two formulas:
N M ≈N/2
N f ＝N-N m
wherein N is the number of individuals, N m Is the number of males, N f Refers to the number of females, and finds the best individual in each group, resulting in the best male (f) best,m ) And optimal female (f) best,f ) And food position (f) food )。
The temperature can be defined by the following equation:
Figure BDA0003808723790000091
where T is the current iteration and T is the maximum number of iterations.
Defining the food quantity (Q) can be expressed by the following formula:
Figure BDA0003808723790000092
where C1 equals a constant of 0.5.
If Q < Threshold (Threshold = 0.25), the snake looks for food by selecting any random location and updates their location.
X i,m (t+1)＝X rand,m (t)±c 2 ×A m ×((X max -X min )×rand+X min )
X i,m Is the ith male position, X rand,m Random number m between rand 0 for random male positions, which is the ability to find food, can be calculated as follows:
Figure BDA0003808723790000093
f rand,m is X rand,m Fitness f i,m Individual fitness in the male population. C 2 Is a value of 0.05 is constant.
x i,f ＝X rand,f (t+1)±c 2 ×A f ×((X max -X min )×rand+X min )
X i,f Is the ith female position, X rand,f For random female locations, the random number f between rand and 1 a is the ability to find food, and can be calculated as follows:
Figure BDA0003808723790000101
f rand,f is X rand,f Fitness f i,f Individual fitness in the female population.
If the temperature is greater than or equal to Threshold (hot), the snake will only be close to the food.
X i,j (t+1)＝X food ±c 3 X temperature X volume (X) food -X i,j (t))
X i,j Is the position of the individual, X food As a food location, C 3 Is a constant with a value of 2.
If the temperature is less than or equal to Threshold (cold), the snake will be in a fighting or mating mode.
X i,m (t+1)＝X i,m (t)+c 3 ×FM×rand×(Q×X best,f -X i,m (t))
X best,f FM is the male's fighting ability, the location of the best individual in the female population.
X i,f (t+1)＝X i,f (t+1)+c 3 ×FF×rand×(Q×X best,m -X i,F (t+1))
X best,m Represents the location of the best individual in the male population and FF represents the fighting capabilities of the female.
FF and FM are expressed by the following equations:
Figure BDA0003808723790000102
Figure BDA0003808723790000103
f best,f optimal proxy for female population f best,m Best surrogate for male population f i Is the proxy fitness.
Mating mode:
X i,m (t+1)＝X i,m (t)+c 3 ×M m ×rand×(Q×X i,f (t)-X i,m (t))
X i,f (t+1)＝X i,f (t)+c 3 ×M f ×rand×(Q×X i,m (t)-X i,f (t))
X i,f location of agent in female population, X i,m Location of agent in Male population, M m 、M f The mating ability of male and female is expressed by the following calculation formula:
Figure BDA0003808723790000104
Figure BDA0003808723790000105
if the egg is hatched, the worst males and females are selected and replaced
X worst,m ＝X min +rand×(X max -X min )
X worst,f ＝X min +rand×(X max -X min )
Wherein X worst,m Is worst in the male group, X worst,f Is the worst in the female group. The marker direction operator, also known as a diversity factor, provides the possibility to increase or decrease the position solution, so that there is a great chance to change the direction of the agent, so that a good scan of a given search space is made in all possible directions.
Step 32: introducing a levy flight optimization strategy.
After the algorithm is updated, the individual positions are updated by levy flight again, so that the algorithm is not easy to fall into local optimum while the global convergence of the algorithm is improved. The location update formula is:
Figure BDA0003808723790000111
in the formula: alpha is a random number subject to normal distribution, and Levy (lambda) is a path function randomly searched by Levy.
S4: and performing optimization configuration on multiple targets of the microgrid based on a snake optimization algorithm.
Specifically, the step of performing optimal configuration on multiple targets of the microgrid based on the snake optimization algorithm comprises the following steps:
step 41: selecting a micro-grid low-voltage test network;
specifically, as shown in fig. 3, the microgrid low-voltage test network comprises a 0.4kV microgrid and a 20kV power distribution network, and the microgrid is composed of renewable energy sources WT and PV, thermal power generators MT and FC and an energy storage device SB. The aluminum stranded wires are used as a microgrid line, R =1.97 omega/km, X =0.35 omega/km, and the power factor is 0.85.
Step 42: optimizing the three objective functions to obtain an optimal solution
Specifically, the three objective functions are optimized to obtain an optimal solution as shown in fig. 4.
Compared with the prior art, the invention provides a micro-grid multi-objective optimization configuration method based on a snake optimization algorithm, a double-layer energy optimization management model is established, an upper layer utilizes a fuzzy management system to determine a micro-grid operation mode, a lower layer adopts an improved snake optimization algorithm to carry out optimization management on energy, aiming at different electricity consumption demands of users in different periods, the micro-grid operation economy, carbon emission and power loss in a network are considered, three variables are used as target functions to establish a mathematical model, an optimization comparison experiment is carried out aiming at the micro-grid target function model, on the basis of the original snake optimization algorithm, the target functions further refer to a Levy flight strategy to improve the later convergence precision of population, the problem of local optimization is solved, and an optimal solution is obtained through algorithm optimization.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.

Claims (10)

1. A micro-grid multi-objective optimization configuration method based on a snake optimization algorithm is characterized by comprising the following steps:
constructing a double-layer energy optimization management model;
constructing a mathematical model of each target in the micro-grid system;
introducing a snake optimization algorithm and adding a levy flight strategy;
and performing optimal configuration on multiple targets of the micro-grid based on a snake optimization algorithm.
2. The micro-grid multi-objective optimization configuration method based on the snake optimization algorithm as claimed in claim 1, wherein in the step of constructing the double-layer energy optimization management model, an upper layer of the double-layer energy optimization management model is a fuzzy management system, a lower layer of the double-layer energy optimization management model is a multi-objective function optimization layer, the fuzzy management system of the upper layer determines an operation mode of the hybrid micro-grid according to photovoltaic output, wind generator output and residential load demand, and the lower layer receives upper layer data and performs optimization management on energy.
3. The micro-grid multi-objective optimization configuration method based on the snake optimization algorithm, as claimed in claim 2, wherein the operation mode comprises:
in a first mode: renewable energy and an energy storage battery jointly generate electricity to meet load requirements;
and a second mode: when the load is increased to the mode that the load demand cannot be met, the participation of the power distribution network and each thermal generator set is needed.
4. The micro-grid multi-objective optimization configuration method based on the snake optimization algorithm as claimed in claim 3, wherein the step of constructing the two-layer energy optimization management model specifically comprises:
output P of wind driven generator WT And photovoltaic output P PV Maximum output P of energy storage battery SBmax Inputting a fuzzy management control layer, and the hourly load demand P of the microgrid L ；
And calculating the integral generating capacity by the formula: p is ALL ＝P PV +P WT +P SBmax ；
Judging renewable energy output P ALL Whether or not it is greater than the load demand P L If P is ALL Greater than P L If so, the microgrid operates in a first mode;
calculating the sum P of the maximum output of each thermal generator set TGmax ＝P MTmax +P FCmax The sum of the maximum output of all units and the maximum exchange power of the distribution network is compared with the load demand P L By comparison, if P ALL +P TGmax +P PDNmax Greater than P L If so, the micro-grid works in a mode II;
each thermal power generating unit P MT And P FC Energy storage battery P SB And a distribution network P PDN And outputting the real-time output.
5. The micro-grid multi-objective optimization configuration method based on the snake optimization algorithm as claimed in claim 1, wherein in the step of constructing each objective mathematical model in the micro-grid system, the step of constructing each objective mathematical model in the micro-grid system comprises the following steps:
Figure FDA0003808723780000021
wherein f (t) is an optimized objective function; c m (t)、f c (t)、P loss (t) cost-benefit, carbon emissions for microgrid operation, respectivelyAmount, active power loss, h i (t) and g i (t) is the equality and inequality constraints.
6. The micro-grid multi-objective optimization configuration method based on snake optimization algorithm, as claimed in claim 5, wherein the micro-grid operation cost-operation profit C m The expression of (t) is:
Figure FDA0003808723780000022
in the formula: c m1 (t) investment cost required for the microgrid, C m2 (t) earnings of battery energy storage device, renewable energy and thermal generator set, C fuel (t) represents the fuel cost of the microgrid; c sud (t) represents the start-up or shut-down cost of each unit; c on (t) represents the operation and maintenance cost of the microgrid; c SB (t)、C RES (t)、C TG (t) respectively representing the profits generated by selling electricity to the microgrid load by the energy storage device, the renewable energy source and the thermal generator set; c PDN (t) represents the revenue generated by the hybrid microgrid selling electrical energy to the distribution grid.
7. The micro-grid multi-objective optimization configuration method based on snake optimization algorithm, as claimed in claim 5, wherein the carbon emission f of the micro-grid c (t) the expression is:
Figure FDA0003808723780000023
in the formula: l i,j The quantity of the j-th polluted gas generated by the distributed power generation unit; lambda [ alpha ] j The cost required for treating the corresponding pollution gas of the unit.
8. The micro-grid multi-objective optimization configuration method based on the snake optimization algorithm as claimed in claim 5, wherein the active power loss expression of the micro-grid is as follows:
Figure FDA0003808723780000024
in the formula: p ij 、Q ij Respectively active power and reactive power, U i Is the voltage amplitude, R, of node i ij Is the line equivalent resistance.
9. The microgrid multi-objective optimization configuration method based on a snake optimization algorithm, as claimed in claim 1, wherein the step of introducing the snake optimization algorithm and adding a levy flight strategy specifically comprises:
the snake optimization algorithm first generates a uniformly distributed random population so that the process of optimizing the algorithm can begin;
introducing a levy flight optimization strategy.
10. The micro-grid multi-objective optimization configuration method based on the snake optimization algorithm, as claimed in claim 1, wherein the step of performing optimization configuration on micro-grid multi-objectives based on the snake optimization algorithm comprises the following steps:
selecting a micro-grid low-voltage test network;
and optimizing the three objective functions to obtain an optimal solution.
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CN116522580A (en) * 2023-02-22 2023-08-01 广东轻工职业技术学院 Buck-Boost intermediate frequency inversion main circuit parameter optimization method

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116522580A (en) * 2023-02-22 2023-08-01 广东轻工职业技术学院 Buck-Boost intermediate frequency inversion main circuit parameter optimization method
CN116522580B (en) * 2023-02-22 2023-12-29 广东轻工职业技术学院 Buck-Boost intermediate frequency inversion main circuit parameter optimization method

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