CN113988471A - Multi-objective optimization method for micro-grid operation - Google Patents

Multi-objective optimization method for micro-grid operation Download PDF

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CN113988471A
CN113988471A CN202111389057.1A CN202111389057A CN113988471A CN 113988471 A CN113988471 A CN 113988471A CN 202111389057 A CN202111389057 A CN 202111389057A CN 113988471 A CN113988471 A CN 113988471A
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孙检
廖凯
何正友
赵倩林
茹行
李红训
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Southwest Jiaotong University
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Abstract

The invention discloses a multi-objective optimization method for micro-grid operation, which comprises the following steps: s1: acquiring running data of a load management object on a demand side; s2: classifying the load conditions according to the operation data to obtain a classification result; s3: establishing an equivalent mathematical model corresponding to the category according to the classification result; s4: establishing a microgrid model based on the equivalent mathematical model; s5: establishing a user comfort level model participating in demand side load response according to the microgrid model; s6: establishing a target optimization model of the microgrid according to the microgrid model and the user comfort level model; s7: and solving the target optimization model by adopting a differential evolution algorithm to obtain a target optimization result.

Description

Multi-objective optimization method for micro-grid operation
Technical Field
The invention relates to the technical field of micro-grids, in particular to a multi-objective optimization method for micro-grid operation.
Background
Disclosure of Invention
The invention aims to provide a multi-objective optimization method for micro-grid operation, which can realize the maximum operation benefit of a micro-grid and simultaneously maximize the operation income and the user comfort of the micro-grid.
The technical scheme for solving the technical problems is as follows:
the invention provides a multi-objective optimization method for micro-grid operation, which comprises the following steps:
s1: acquiring running data of a load management object on a demand side;
s2: classifying the load conditions according to the operation data to obtain a classification result;
s3: establishing an equivalent mathematical model corresponding to the category according to the classification result;
s4: establishing a microgrid model based on the equivalent mathematical model;
s5: establishing a user comfort level model participating in demand side load response according to the microgrid model;
s6: establishing a target optimization model of the microgrid according to the microgrid model and the user comfort level model;
s7: and solving the target optimization model by adopting a differential evolution algorithm to obtain a target optimization result.
Alternatively, in step S1, the demand side load management object is household power load management; the operation data comprises the electricity utilization habits of the household users, the service time of the household appliances and the operation characteristics of the household appliances.
Optionally, in step S2, the classification result includes: a vital load comprising all basic electrical devices, an interruptible load comprising all alternative electrical devices, and a transferable load comprising all electrical devices with flexibility.
Optionally, in the step S3, the equivalent mathematical models of the corresponding categories include a mathematical model of an interruptible load and a mathematical model of a transferable load.
Optionally, the mathematical model of the interruptible load comprises: the total power of each interruptible load during the working period and the total user power consumption of all interruptible loads;
total power of said interruptible load i during operation
Figure BDA0003368043000000021
Comprises the following steps:
Figure BDA0003368043000000022
wherein,
Figure BDA0003368043000000023
representing the operational deadline of the interruptible load i,
Figure BDA0003368043000000024
representing the state of use of the interruptible load i at time t, aIL,iIndicating the start time of the operation of the interruptible load i.
Optionally, the mathematical model of the transferable loads comprises a total power of each transferable load;
total user power consumption of all interruptible loads
Figure BDA0003368043000000025
Comprises the following steps:
Figure BDA0003368043000000026
wherein,
Figure BDA0003368043000000027
indicating transferable loadsi the cut-off time of the operation,
Figure BDA0003368043000000028
representing the state of use of the interruptible load i at time t, aTL,iIndicating the start time of the operation of the interruptible load i.
Alternatively, the step S4 includes: acquiring micro-grid power supplies of different power generation types; respectively building a mathematical model according to the micro-grid power supply of each power generation type and the equivalent mathematical model to obtain a micro-grid model; the micro-grid power supplies of different power generation types comprise wind power generation, photovoltaic power generation and gas turbine power generation; the mathematical models comprise a first mathematical model of the output power and the wind speed of the fan, a random model of the output power of the photovoltaic power generation and a second mathematical model of the power generation cost and the power generation capacity of the gas turbine.
Optionally, the first mathematical model is:
Figure BDA0003368043000000031
wherein, PwRepresenting the output power of the fan, v representing the wind speed,
Figure BDA0003368043000000032
indicating rated output power, v, of the faninIndicating cut-in wind speed, vEIndicating a specific wind speed, voutRepresenting the cut-out wind speed;
the stochastic model of the output power of the photovoltaic power generation is as follows:
Figure BDA0003368043000000033
wherein f ispv(Ppv) Probability density function representing output power of photovoltaic power generation, Beta (x, y) representing Beta function, PpvRepresents the photovoltaic power generation amount in a specific time period,
Figure BDA0003368043000000034
representing the maximum output power of the photovoltaic system, and x and y represent the shape parameters of a Beta function;
the second mathematical model is stable generated power
Figure BDA0003368043000000035
Optionally, in step S5, the user comfort model is:
Figure BDA0003368043000000036
wherein,
Figure BDA0003368043000000037
and
Figure BDA0003368043000000038
is the power before and after optimization of load i at time t;
Figure BDA0003368043000000039
and
Figure BDA00033680430000000310
respectively planning the electricity consumption before and after optimizing the load i at the time t; dcIs the electricity comfort of the user.
Optionally, the objective optimization model is:
Figure BDA00033680430000000311
wherein F is the target optimization model, C is the benefit of each time period and
Figure BDA0003368043000000041
wherein, CGridIs the price of electricity sold by the micro-grid,
Figure BDA0003368043000000042
is the actual output power at time t of the microgrid,
Figure BDA0003368043000000043
is the operation management coefficient of the fan at the moment t,
Figure BDA0003368043000000044
is the output power of the fan at the moment t,
Figure BDA0003368043000000045
is an operation management coefficient at the moment t of photovoltaic power generation,
Figure BDA0003368043000000046
is the output power at the moment t of photovoltaic power generation,
Figure BDA0003368043000000047
is the operation management coefficient of the gas turbine at the moment t,
Figure BDA0003368043000000048
is the output power of the gas turbine at time t,
Figure BDA0003368043000000049
is the unit fuel cost at time t of the gas turbine,
Figure BDA00033680430000000410
is the electricity purchasing cost of the micro-grid to the large grid at the time t,
Figure BDA00033680430000000411
is the penalty cost coefficient at time t,
Figure BDA00033680430000000412
is the deviation value of the output of the micro-grid, T is the hours of the operation cycle of the micro-grid, dcIs the electricity comfort of the user.
The invention has the following beneficial effects:
1. the maximum operation benefit of the micro-grid is realized by adjusting the use of household load to absorb wind energy and solar energy for power generation and reducing output deviation;
2. the comfort level of the user is ensured, and the demand side load response management is carried out according to the habit of the user.
3. And (3) considering the relevance of the response load of the demand side, and establishing a micro-grid operation multi-objective optimization model to maximize the operation income and the user comfort of the micro-grid.
Drawings
Fig. 1 is a flowchart of a microgrid operation target optimization method provided by the invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
Examples
The technical scheme for solving the technical problems is as follows:
the invention provides a multi-objective optimization method for micro-grid operation, which is shown in a reference figure 1 and comprises the following steps:
s1: acquiring running data of a load management object on a demand side;
s2: classifying the load conditions according to the operation data to obtain a classification result;
s3: establishing an equivalent mathematical model corresponding to the category according to the classification result;
s4: establishing a microgrid model based on the equivalent mathematical model;
s5: establishing a user comfort level model participating in demand side load response according to the microgrid model;
s6: establishing a target optimization model of the microgrid according to the microgrid model and the user comfort level model;
s7: and solving the target optimization model by adopting a differential evolution algorithm to obtain a target optimization result.
Alternatively, in step S1, the demand side load management object is household power load management; the operation data comprises the electricity utilization habits of the household users, the service time of the household appliances and the operation characteristics of the household appliances.
Optionally, in step S2, the classification result includes: a vital load comprising all basic electrical devices, an interruptible load comprising all alternative electrical devices, and a transferable load comprising all electrical devices with flexibility.
Optionally, in the step S3, the equivalent mathematical models of the corresponding categories include a mathematical model of an interruptible load and a mathematical model of a transferable load.
Optionally, the mathematical model of the interruptible load comprises: the total power of each interruptible load during the working period and the total user power consumption of all interruptible loads;
wherein the total power of the interruptible load i during operation
Figure BDA0003368043000000051
Comprises the following steps:
Figure BDA0003368043000000052
wherein,
Figure BDA0003368043000000053
representing the operational deadline of the interruptible load i,
Figure BDA0003368043000000054
representing the state of use of the interruptible load i at time t, aIL,iIndicating the start time of the operation of the interruptible load i.
Optionally, the mathematical model of the transferable loads comprises a total power of each transferable load;
total user power consumption of all transferable loads
Figure BDA0003368043000000055
Comprises the following steps:
Figure BDA0003368043000000061
wherein,
Figure BDA0003368043000000062
indicating the transferable load i work cutoff time,
Figure BDA0003368043000000063
representing the state of use of the interruptible load i at time t, aTL,iIndicating the start time of the operation of the interruptible load i.
Alternatively, the step S4 includes: acquiring micro-grid power supplies of different power generation types; respectively building a mathematical model according to the micro-grid power supply of each power generation type and the equivalent mathematical model to obtain a micro-grid model; the micro-grid power supplies of different power generation types comprise wind power generation, photovoltaic power generation and gas turbine power generation; the mathematical models comprise a first mathematical model of the output power and the wind speed of the fan, a random model of the output power of the photovoltaic power generation and a second mathematical model of the power generation cost and the power generation capacity of the gas turbine.
Optionally, the first mathematical model is:
Figure BDA0003368043000000064
wherein, PwRepresenting the output power of the fan, v representing the wind speed,
Figure BDA0003368043000000065
indicating rated output power, v, of the faninIndicating cut-in wind speed, vEIndicating a specific wind speed, voutRepresenting the cut-out wind speed;
the stochastic model of the output power of the photovoltaic power generation is as follows:
Figure BDA0003368043000000066
wherein f ispv(Ppv) Probability density function representing output power of photovoltaic power generation, Beta (x, y) representing Beta function, PpvRepresents the photovoltaic power generation amount in a specific time period,
Figure BDA0003368043000000067
representing the maximum output power of the photovoltaic system, and x and y represent the shape parameters of a Beta function;
the second mathematical model is stable generated power
Figure BDA0003368043000000068
Optionally, in step S5, the user comfort model is:
Figure BDA0003368043000000071
wherein,
Figure BDA0003368043000000072
and
Figure BDA0003368043000000073
is the power before and after optimization of load i at time t;
Figure BDA0003368043000000074
and
Figure BDA0003368043000000075
respectively planning the electricity consumption before and after optimizing the load i at the time t; dcIs the electricity comfort of the user.
Optionally, the objective optimization model is:
Figure BDA0003368043000000076
wherein F is the target optimization model, C is the benefit of each time period and
Figure BDA0003368043000000077
wherein, CGridIs the price of electricity sold by the micro-grid,
Figure BDA0003368043000000078
is the actual output power at time t of the microgrid,
Figure BDA0003368043000000079
is the operation management coefficient of the fan at the moment t,
Figure BDA00033680430000000710
is the output power of the fan at the moment t,
Figure BDA00033680430000000711
is an operation management coefficient at the moment t of photovoltaic power generation,
Figure BDA00033680430000000712
is the output power at the moment t of photovoltaic power generation,
Figure BDA00033680430000000713
is the operation management coefficient of the gas turbine at the moment t,
Figure BDA00033680430000000714
is the output power of the gas turbine at time t,
Figure BDA00033680430000000715
is the unit fuel cost at time t of the gas turbine,
Figure BDA00033680430000000716
is the electricity purchasing cost of the micro-grid to the large grid at the time t,
Figure BDA00033680430000000717
is the penalty cost coefficient at time t,
Figure BDA00033680430000000718
is the deviation value of the output of the micro-grid, T is the hours of the operation cycle of the micro-grid, dcIs the electricity comfort of the user.
And solving the target optimization model by adopting a differential evolution algorithm, thereby realizing the multi-target optimization of the micro-grid operation by considering the load response relevance of the demand side. Initializing a population according to the information (output constraint, wind speed and the like) of a power generation unit of the microgrid and load information, calculating individual fitness through population division and population variation cross solving, then selecting and reserving an optimal individual to enter a next population, judging whether constraint conditions are met or not through population evaluation, stopping iteration and outputting an optimal scheme if the constraint conditions are met for the maximum number of times, and otherwise, continuing iteration.
The specific algorithm flow is as follows:
1) according to the number of computer kernels, dividing the total with the scale p into n sub-populations with the scale q, and dividing the population into computing units lab1-labn for optimization.
2) And each computing unit calculates the fitness value of the individuals in the sub-population and sorts the fitness values according to the fitness values.
3) Dividing the classified groups into dominant groups CgAnd disadvantaged group CbAnd different variation strategies are adopted for carrying out differential evolution updating.
4) And combining the dominant community and the disadvantaged community into C, and calculating the fitness value of the new species, wherein the iteration number is g + 1. If g is<gen(genMaximum iteration times), returning to the step (2); if g is equal to genThe loop is stopped and the optimized sub-populations are combined to obtain the optimal solution.
The micro-grid operation multi-objective optimization model considering the load response relevance of the demand side can be combined with micro-grid and load operation data to solve and obtain an operation strategy considering both the comfort of users and the maximization of the benefits of the micro-grid.
The invention has the following beneficial effects:
1. the maximum operation benefit of the micro-grid is realized by adjusting the use of household load to absorb wind energy and solar energy for power generation and reducing output deviation;
2. the comfort level of the user is ensured, and the demand side load response management is carried out according to the habit of the user.
3. And (3) considering the relevance of the response load of the demand side, and establishing a micro-grid operation multi-objective optimization model to maximize the operation income and the user comfort of the micro-grid.
Example 2
The household electrical load is selected as an object of demand side load management in the patent, and of course, a person skilled in the art can select the household electrical load according to actual conditions, for example, in some embodiments, the household electrical load is selected.
The following description will be given taking a household electrical load as an example:
according to the habit of electricity utilization of users and the difference of operating characteristics such as service time, power of household appliances, the household load is divided into 3 types from the angle of operation scheduling: important load, interruptible load and transferable load, and respectively establishing equivalent mathematical models
Important loads are some basic electrical devices. Interruptible loads are replacement electrical devices. A transferable load is an electrical device with some flexibility. Since the power usage behavior optimization does not affect the power usage tasks of important loads, the power usage behavior optimization scheduling is mainly directed to interruptible loads and transferable loads.
Interruptible loads are some alternative electrical devices, such as washing machines, exercise equipment, and the like. The power consumption of an interruptible load depends on whether the load is running, running time and power.
Total power of said interruptible load i during operation
Figure BDA0003368043000000091
Comprises the following steps:
Figure BDA0003368043000000092
wherein,
Figure BDA0003368043000000093
representing the operational deadline of the interruptible load i,
Figure BDA0003368043000000094
representing the state of use of the interruptible load i at time t, aIL,iIndicating the start time of the operation of the interruptible load i.
The transferable load can be an electrical device with certain flexibility in a certain period of time, such as an air conditioner, a water heater, an electric bicycle and the like. The transferable load realizes reasonable power utilization by adjusting the power utilization time and mode. The period during which the load can be transferred is
Figure BDA0003368043000000095
During this time, the mathematical model of the transferable loads includes the total power of each transferable load;
total user power consumption of all transferable loads
Figure BDA0003368043000000096
Comprises the following steps:
Figure BDA0003368043000000097
wherein,
Figure BDA0003368043000000098
indicating the transferable load i work cutoff time,
Figure BDA0003368043000000099
representing the state of use of the interruptible load i at time t, aTL,iIndicating the start time of the operation of the interruptible load i.
The power supply in the micro-grid mainly considers three types of power generation, namely wind power generation, photovoltaic power generation and gas turbine power generation, and mathematical models are respectively built according to the relation between the output power of a fan and the wind speed, the randomness characteristic of the output power of the photovoltaic power generation and the relation between the power generation cost and the power generation capacity of the gas turbine.
The wind output power is mainly related to the wind speed, and the relationship between the two can be expressed as:
Figure BDA0003368043000000101
wherein, PwRepresenting the output power of the fan, v representing the wind speed,
Figure BDA0003368043000000102
indicating rated output power, v, of the faninIndicating cut-in wind speed, vEIndicating a specific wind speed, voutRepresenting the cut-out wind speed;
the change in solar radiation intensity can be described approximately in terms of a beta distribution over a period of time, so the output power of the photovoltaic power generation also follows the beta distribution with a probability density function of:
Figure BDA0003368043000000103
wherein f ispv(Ppv) Probability density function representing output power of photovoltaic power generation, Beta (x, y) representing Beta function, PpvRepresents the photovoltaic power generation amount in a specific time period,
Figure BDA0003368043000000104
representing the maximum output power of the photovoltaic system, and x and y represent the shape parameters of a Beta function;
the gas turbine can be regarded as a stable output power source, and the generated power of the gas turbine is
Figure BDA0003368043000000105
Starting with consideration of two aspects of economy of microgrid operation and relevance during load response, establishing a user comfort level model participating in demand side load response, establishing an intelligent microgrid multi-objective optimization model considering operation income and load response relevance, and evaluating the influence of demand side load response on user living comfort level.
The microgrid system aims to maximize the benefit of each period, taking into account the microgrid electricity price, the operation and management costs, the home load response costs, the gas turbine operation management and fuel costs and the penalty costs of deviating from the planned output. The target functions are:
Figure BDA0003368043000000106
wherein, CGridIs the price of electricity sold by the micro-grid,
Figure BDA0003368043000000107
is the actual output power at time t of the microgrid,
Figure BDA0003368043000000108
is the operation management coefficient of the fan at the moment t,
Figure BDA0003368043000000109
is the output power of the fan at the moment t,
Figure BDA00033680430000001010
is an operation management coefficient at the moment t of photovoltaic power generation,
Figure BDA0003368043000000111
is the output power at the moment t of photovoltaic power generation,
Figure BDA0003368043000000112
is the operation management coefficient of the gas turbine at the moment t,
Figure BDA0003368043000000113
is the output power of the gas turbine at time t,
Figure BDA0003368043000000114
is the unit fuel cost at time t of the gas turbine,
Figure BDA0003368043000000115
is t atThe micro-grid needs the electricity purchasing cost of the large power grid,
Figure BDA0003368043000000116
is the penalty cost coefficient at time t,
Figure BDA0003368043000000117
is the deviation value of the output of the micro-grid, T is the hours of the operation cycle of the micro-grid, dcIs the electricity comfort of the user.
The micro-grid can make an output plan according to the predicted renewable energy power generation amount and the household load operation condition. The micro-grid declaration plan output is as follows:
Figure BDA0003368043000000118
wherein, PtPlanning the output power for the microgrid at time t,
Figure BDA0003368043000000119
a force value is predicted for the moment t of the fan,
Figure BDA00033680430000001110
predicting a force value, eta, for the photovoltaic power generation time ttThe planned output coefficients for the gas turbine at time t,
Figure BDA00033680430000001111
the maximum force output value at the moment t of the gas turbine.
When a user uses a certain household appliance, the user can simultaneously use another household appliance or a plurality of household appliances matched with the user, and the correlation matrix is utilized to obtain the common use coefficient of the corresponding household appliances, so that the profit maximization model is perfected. When r isijWhen 1, it indicates that the home appliances i and j are used simultaneously. When r isijWhen 0, it means that the home appliances i and j do not cross when used. r isij∈[0,1]The higher the load association usage, the closer the association is to 1, and vice versa, the closer to 0. Establishing a cooperative use matrix between the household appliances according to the use time of each household appliance to obtain the association relationship between the household appliancesIs described. The expression using the matrix is:
Figure BDA00033680430000001112
the household appliance cooperative use coefficient is determined according to the starting time and the working time of each household appliance, namely:
Figure BDA00033680430000001113
given r of loads i and jijThe relationship between the start times can be expressed as:
Figure BDA0003368043000000121
wherein, aeiIndicates the operation start time of the load i, aejIndicating the work start time, a, of a load j used in association with a load iliIndicates the operation stop time of the load i, aljThe operation stop time of the load j used in association with the load i is shown.
User comfort refers to the impact of changes in electricity usage plans or habits on the user. When the electricity utilization behavior is optimized, more adjustment is carried out on the original behavior, and the running time of the household appliance is changed, the comfort level of a user is low; when the electricity consumption is not adjusted, the electricity consumption habit does not need to be changed, and the use comfort level is highest at the moment. The user comfort model is:
Figure BDA0003368043000000122
wherein,
Figure BDA0003368043000000123
and
Figure BDA0003368043000000124
is the work before and after the optimization of the load i at time tRate;
Figure BDA0003368043000000125
and
Figure BDA0003368043000000126
respectively carrying out power consumption planning before and after optimizing the load i at time t, wherein the power consumption planning is 1 when the load i works and is 0 when the load i does not work; dcIs the electricity comfort of the user. When the power plan for the load does not change before and after optimization,
Figure BDA0003368043000000127
is equal to
Figure BDA0003368043000000128
Namely, it is
Figure BDA0003368043000000129
Is 0, at this time dcIs 1.
Two factors are considered comprehensively: 1) the maximum operation benefit of the micro-grid is realized by adjusting the use of household load to absorb wind energy and solar energy for power generation and reducing output deviation; 2) the comfort level of the user is ensured, and the demand side load response management is carried out according to the habit of the user. Considering the relevance of the response load of the demand side, establishing a micro-grid operation multi-objective optimization model to maximize the operation income and the user comfort of the micro-grid, namely the objective optimization model is as follows:
Figure BDA00033680430000001210
wherein F is the target optimization model, C is the benefit of each time period and
Figure BDA0003368043000000131
wherein, CGridIs the price of electricity sold by the micro-grid,
Figure BDA0003368043000000132
is the actual output power at time t of the microgrid,
Figure BDA0003368043000000133
is the operation management coefficient of the fan at the moment t,
Figure BDA0003368043000000134
is the output power of the fan at the moment t,
Figure BDA0003368043000000135
is an operation management coefficient at the moment t of photovoltaic power generation,
Figure BDA0003368043000000136
is the output power at the moment t of photovoltaic power generation,
Figure BDA0003368043000000137
is the operation management coefficient of the gas turbine at the moment t,
Figure BDA0003368043000000138
is the output power of the gas turbine at time t,
Figure BDA0003368043000000139
is the unit fuel cost at time t of the gas turbine,
Figure BDA00033680430000001310
is the electricity purchasing cost of the micro-grid to the large grid at the time t,
Figure BDA00033680430000001311
is the penalty cost coefficient at time t,
Figure BDA00033680430000001312
is the deviation value of the output of the micro-grid, T is the hours of the operation cycle of the micro-grid, dcIs the electricity comfort of the user.
The multi-objective optimization of the micro-grid has the characteristics of multiple clusters, multiple constraints and multiple types of source loads, and the model calculation amount is large. And solving by adopting a differential evolution algorithm to improve the calculation efficiency.
And solving the target optimization model by adopting a differential evolution algorithm, thereby realizing the multi-target optimization of the micro-grid operation by considering the load response relevance of the demand side. The specific algorithm flow is as follows:
1) according to the number of computer kernels, dividing the total with the scale p into n sub-populations with the scale q, and dividing the population into computing units lab1-labn for optimization.
2) And each computing unit calculates the fitness value of the individuals in the sub-population and sorts the fitness values according to the fitness values.
3) Dividing the classified groups into dominant groups CgAnd disadvantaged group CbAnd different variation strategies are adopted for carrying out differential evolution updating.
4) And combining the dominant community and the disadvantaged community into C, and calculating the fitness value of the new species, wherein the iteration number is g + 1. If g is<gen(genMaximum iteration times), returning to the step (2); if g is equal to genThe loop is stopped and the optimized sub-populations are combined to obtain the optimal solution.
The micro-grid operation multi-objective optimization model considering the load response relevance of the demand side can be combined with micro-grid and load operation data to solve and obtain an operation strategy considering both the comfort of users and the maximization of the benefits of the micro-grid.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (10)

1. A multi-objective optimization method for micro-grid operation is characterized by comprising the following steps:
s1: acquiring running data of a load management object on a demand side;
s2: classifying the load conditions according to the operation data to obtain a classification result;
s3: establishing an equivalent mathematical model corresponding to the category according to the classification result;
s4: establishing a microgrid model based on the equivalent mathematical model;
s5: establishing a user comfort level model participating in demand side load response according to the microgrid model;
s6: establishing a target optimization model of the microgrid according to the microgrid model and the user comfort level model;
s7: and solving the target optimization model by adopting a differential evolution algorithm to obtain a target optimization result.
2. The microgrid operation multi-objective optimization method according to claim 1, characterized in that in the step S1, the demand side load management object is household power load management;
the operation data comprises the electricity utilization habits of the household users, the service time of the household appliances and the operation characteristics of the household appliances.
3. The microgrid operation multi-objective optimization method according to claim 2, wherein in the step S2, the classification result includes: a vital load comprising all basic electrical devices, an interruptible load comprising all alternative electrical devices, and a transferable load comprising all electrical devices with flexibility.
4. The microgrid operation multi-objective optimization method of claim 3, wherein in the step S3, the equivalent mathematical models of the corresponding categories include a mathematical model of interruptible loads and a mathematical model of transferable loads.
5. The microgrid operation multiobjective optimization method of claim 4, wherein the mathematical model of interruptible loads comprises: the total power of each interruptible load during the working period and the total user power consumption of all interruptible loads;
total power of said interruptible load i during operation
Figure FDA0003368042990000021
Comprises the following steps:
Figure FDA0003368042990000022
wherein,
Figure FDA0003368042990000023
representing the operational deadline of the interruptible load i,
Figure FDA0003368042990000024
representing the state of use of the interruptible load i at time t, aIL,iIndicating the start time of the operation of the interruptible load i.
6. The microgrid operation multiobjective optimization method of claim 4, wherein the mathematical model of the transferable loads comprises a total power of each transferable load;
total user power consumption of all transferable loads
Figure FDA0003368042990000025
Comprises the following steps:
Figure FDA0003368042990000026
wherein,
Figure FDA0003368042990000027
indicating the transferable load i work cutoff time,
Figure FDA0003368042990000028
representing the state of use of the interruptible load i at time t, aTL,iIndicating the start time of the operation of the interruptible load i.
7. The microgrid operation multi-objective optimization method according to claim 1, wherein the step S4 includes:
acquiring micro-grid power supplies of different power generation types;
respectively building a mathematical model according to the micro-grid power supply of each power generation type and the equivalent mathematical model to obtain a micro-grid model;
the micro-grid power supplies of different power generation types comprise wind power generation, photovoltaic power generation and gas turbine power generation;
the mathematical models comprise a first mathematical model of the output power and the wind speed of the fan, a random model of the output power of the photovoltaic power generation and a second mathematical model of the power generation cost and the power generation capacity of the gas turbine.
8. The microgrid operation multiobjective optimization method of claim 7, wherein the first mathematical model is:
Figure FDA0003368042990000031
wherein, PwRepresenting the output power of the fan, v representing the wind speed,
Figure FDA0003368042990000032
indicating rated output power, v, of the faninIndicating cut-in wind speed, vEIndicating a specific wind speed, voutRepresenting the cut-out wind speed;
the stochastic model of the output power of the photovoltaic power generation is as follows:
Figure FDA0003368042990000033
wherein f ispv(Ppv) Probability density function representing output power of photovoltaic power generation, Beta (x, y) representing Beta function, PpvRepresents the photovoltaic power generation amount in a specific time period,
Figure FDA0003368042990000034
representing the maximum output power of the photovoltaic system, and x and y represent the shape parameters of a Beta function;
the second mathematical model is stable generated power
Figure FDA0003368042990000035
9. The microgrid operation multi-objective optimization method according to claim 1, wherein in the step S5, the user comfort model is:
Figure FDA0003368042990000036
wherein,
Figure FDA0003368042990000037
and
Figure FDA0003368042990000038
is the power before and after optimization of load i at time t;
Figure FDA0003368042990000039
and
Figure FDA00033680429900000310
respectively planning the electricity consumption before and after optimizing the load i at the time t; dcIs the electricity comfort of the user.
10. The microgrid operation multiobjective optimization method of claim 1, wherein the objective optimization model is:
Figure FDA00033680429900000311
wherein F is the target optimization model, C is the benefit of each time period and
Figure FDA0003368042990000041
wherein, CGridIs the price of electricity sold by the micro-grid,
Figure FDA0003368042990000042
is the actual output power at time t of the microgrid,
Figure FDA0003368042990000043
is the operation management coefficient of the fan at the moment t,
Figure FDA0003368042990000044
is the output power of the fan at the moment t,
Figure FDA0003368042990000045
is an operation management coefficient at the moment t of photovoltaic power generation,
Figure FDA0003368042990000046
is the output power at the moment t of photovoltaic power generation,
Figure FDA0003368042990000047
is the operation management coefficient of the gas turbine at the moment t,
Figure FDA0003368042990000048
is the output power of the gas turbine at time t,
Figure FDA0003368042990000049
is the unit fuel cost at time t of the gas turbine,
Figure FDA00033680429900000410
is tThe micro-grid wants the electricity purchasing cost of the large grid,
Figure FDA00033680429900000411
is the penalty cost coefficient at time t,
Figure FDA00033680429900000412
is the deviation value of the output of the micro-grid, T is the hours of the operation cycle of the micro-grid, dcIs the electricity comfort of the user.
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