CN116054286A - Residential area capacity optimal configuration method considering multiple elastic resources - Google Patents
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Abstract
The invention relates to a residential area capacity optimization configuration method considering multiple elastic resources, which comprises three typical residential area capacity optimization configuration models, an improved non-dominant sorting genetic solving method and a CRITC assignment method. Random fluctuation of uncertainty factors of the residential areas is described by adopting interval numbers, constraint conditions such as power balance, electric vehicle characteristics and equipment working characteristics are considered by taking the power grid fluctuation stabilization and the photovoltaic absorption rate improvement as optimization targets, three typical residential area capacity optimization configuration schemes are solved, and optimized dispatching solution is carried out on the residential areas with one capacity optimized configuration, so that the effectiveness of the provided capacity optimization configuration method is shown.
Description
Technical Field
The invention relates to the field of power systems, in particular to a residential area capacity optimization configuration method considering multi-element elastic resources.
Background
Under the background of the strong development of a novel power system, a power supply area under the management of a transformer is taken into consideration of the access of a large amount of distributed new energy and the participation of flexible load, so that capacity optimization configuration research on the power supply area containing the flexible load and the distributed new energy is particularly urgent.
Because thermal power generation and large-scale new energy power generation are not suitable for residential area loading, and new energy power generation such as photovoltaic has intermittence, randomness and volatility, grid connection of the new energy power generation can generate reverse power transmission, increase of equivalent load peak-valley difference and overload of a transformer, and threat is caused to operation of the residential area, so that research on how the distributed new energy is stably and efficiently connected into the residential area has great significance.
The flexible load is used as an adjustable resource to realize supply-demand interaction in the operation of the platform region and simultaneously coordinate the balance effect of distributed energy power generation, but the response conditions of various resources are uneven, the user behavior characteristics are seriously differentiated, and the capacity optimization configuration research of a typical residential platform region is required to be conducted by integrating adjustable potential of each resource, so that the efficient operation of the platform region is ensured, and the operation of the platform region with higher quality is supported.
On the basis of coordinating new energy power generation and flexible resource participation in the operation of the transformer area, in order to make the optimization result be applicable to actual scenes as much as possible, the influence of uncertainty of basic power utilization of the residential transformer area on the capacity optimization configuration of the transformer area is also required to be considered, and the uncertainty optimization method mainly comprises a random parameter probability method at present, but probability distribution or membership function of the random parameter probability method is difficult to obtain in engineering; based on a time domain rolling optimization or multi-time scale method, although the optimization effect is good, the process is complex and depends on the prediction result; the multi-scene robust optimization method has good robustness, but depends on the establishment of scenes; the combined optimization method integrates the advantages of different methods, but is not easy to realize and is complex. Therefore, a simple and efficient capacity optimization configuration method aiming at uncertainty is required.
The patent with the bulletin number of CN106549395B discloses a capacity configuration method of an urban resident power distribution area comprehensive compensation device, which is used for analyzing the load characteristics of the urban resident power distribution area and establishing an overall external characteristic model of the load group of the urban resident power distribution area; analyzing the basic demand capacity and dynamic change capacity of reactive power in the load model and the change of power factors, and calculating the total reactive power required to be compensated by the load of the transformer area; and according to an optimized compensation scheme of passive part compensation fixed reactive power and active part compensation dynamic reactive power, the capacities of the passive part and the active part of the comprehensive compensation device are configured. The method is applied to the capacity configuration field of the power quality control device, and is especially aimed at the optimal configuration of the comprehensive compensation device of the urban resident power distribution area, but random fluctuation of the area uncertainty factors is not considered.
Disclosure of Invention
The invention provides a residential area capacity optimization configuration method considering multiple elastic resources, which adopts random fluctuation of interval number description area uncertainty factors, aims at photovoltaic digestion and power grid load smoothness, effectively guides a typical residential area to perform capacity optimization configuration, and ensures efficient operation of the residential area.
A residential area capacity optimization configuration method considering multiple elastic resources comprises three typical residential area capacity optimization configuration models, an improved non-dominant sorting genetic solving method and a CRTIC assignment method.
The capacity optimization configuration model of the typical residential area respectively establishes two objective functions of photovoltaic digestion and power grid load smoothness, wherein uncertainty of basic electric loads of the residential area is considered.
The residential area containing distributed photovoltaic, electric automobile and distributed energy storage takes photovoltaic absorption as a target at most, and the objective function is as follows:
P pv (t)＝P pv,fh (t)+P pv,cn (t)+P pv,ev (t)+P pv,grid (t)
wherein: p (P) pv,fh (t) represents the time t distributed photovoltaic providing power to the load; p (P) pv,cn (t) represents the time t distributed photovoltaic providing power to the load; p (P) pv,ev (t) represents that the t-moment distributed type off photovoltaic provides power for the electric automobile; p (P) pv,grid (t) represents the power delivered to the grid by the distributed photovoltaic at time t; p (P) pv And (t) represents the total photovoltaic power generated by the distributed photovoltaic at the moment t.
The residential area containing distributed photovoltaic, electric automobile and distributed energy storage takes the most gentle fluctuation of the power grid as a target, and the objective function is as follows:
wherein: p (P) a (t) represents the average value of the power transmitted to the station area by the power grid at the moment t; p (P) buy,fh (t) represents purchasing power from a power grid to supply load power at the moment t; p (P) ev,u And (t) represents the charging power of the electric automobile at the time t.
According to the distributed photovoltaic output model, typical output characteristics in different seasons are analyzed due to uncertainty of daily irradiation quantity, temperature and humidity, so that the space-time distribution characteristics of the distributed photovoltaic are obtained.
According to the electric automobile model, the randomness of the travel rule of the electric automobile is considered, the Monte Carlo sampling is utilized to obtain the initial SOC of the electric automobile and the time distribution of the access power grid, and the schedulable potential of the electric automobile cluster is obtained based on the initial SOC of the electric automobile and the time distribution.
The distributed energy storage model establishes an aggregation model of distributed energy storage based on a panoramic theory of an operation state, and only considers the influence of distributed energy storage battery equipment to establish adjustable potential of distributed energy storage aggregation.
The uncertainty of the base electrical load of the typical residential block is characterized by intervals, as described in the patent [.]Each represents the number of sections having a certain width. Consider intervals
And->
The widths of the two are respectively denoted as w (a) and w (b), and the interval credibility of a being greater than or equal to b (denoted as a is greater than or equal to b) is defined as the following formula:
constraint g for interval inequality j (x,c)≥a j The confidence that the individual x satisfies the constraint condition is noted as:
δ j ＝P(g j (x,c)≥a j )
accordingly, the degree of reliability that the individual x does not satisfy the constraint condition (degree of violation of the constraint by the individual x) is noted as:
L j ＝1-P(g j (x,c)≥a j )＝P(g j (x,c)≤a j )
the patent uses the credibility of the individual x meeting each constraint condition and the set credibility threshold value
Comparing to determine if it is a viable solution, i.e. for any constraint g j (x,c)≥a j There is->
Then x is referred to as a feasible solution and vice versa.
The invention adopts the technical scheme that: a residential area capacity optimization configuration method considering multiple elastic resources comprises the following steps:
step 1: three typical residential area capacity optimization configuration models are built, and the three typical residential area capacity optimization configuration models are respectively: residential areas containing distributed photovoltaic, electric vehicles and basic loads; residential areas containing distributed photovoltaics, distributed energy storage and basic loads; residential areas containing distributed photovoltaics, electric vehicles, distributed energy storage and basic loads; establishing a distributed photovoltaic power generation, electric automobile and distributed energy storage model;
step 2: collecting parameters of each module;
step 3: determining decision variables: distributed photovoltaic capacity, number of electric vehicles, and distributed energy storage capacity;
step 4: establishing two objective functions of photovoltaic digestion and power grid load smoothness;
(1) Photovoltaic digestion target
P pv (t)＝P pv,fh (t)+P pv,cn (t)+P pv,ev (t)+P pv,grid (t)
Wherein: p (P) pv,fh (t) represents the time t distributed photovoltaic providing power to the load; p (P) pv,cn (t) represents the time t distributed photovoltaic providing power to the load; p (P) pv,ev (t) represents that the t-moment distributed type off photovoltaic provides power for the electric automobile; p (P) pv,grid (t) represents the power delivered to the grid by the distributed photovoltaic at time t; p (P) pv (t) represents the total photovoltaic power generated by the distributed photovoltaic at the moment t;
(2) Grid fluctuation smoothness target
Wherein: p (P) a (t) represents the average value of the power transmitted to the station area by the power grid at the moment t; p (P) buy,fh (t) represents purchasing power from a power grid to supply load power at the moment t; p (P) ev,u (t) represents the charging power of the electric automobile at the moment t;
step 5: determining constraint conditions in aspects of power balance, electric automobile characteristics and equipment working characteristics;
step 6: constructing an improved NSGA-II algorithm suitable for the interval multi-objective optimization problem; judging whether an individual meets constraint conditions or not by introducing interval credibility; defining a dominant relationship of feasible solutions and infeasible solutions; by introducing the interval overlapping degree, the crowding distance of an individual is calculated, and finally an improved NSGA-II algorithm suitable for the interval multi-objective optimization problem is formed.
Step 7: adopting an improved NSGA-II algorithm, calculating each objective function, randomly generating an initial population, merging the populations, and solving each objective function value;
step 8: judging whether iteration times are reached, and outputting a Pareco optimal front;
step 9: performing multi-objective decision by using a CRITC assignment method to obtain three typical residential area capacity optimization configuration schemes;
step 10: and establishing economic and environmental protection targets, and carrying out optimal scheduling verification on the third combination to verify the effectiveness and feasibility of capacity optimal configuration.
The beneficial effects of the invention are as follows:
the invention comprises three typical residential area capacity optimization configuration models, an improved non-dominant sorting genetic solving method and a CRITC assignment method. Random fluctuation of uncertainty factors of the residential areas is described by adopting interval numbers, constraint conditions such as power balance, electric vehicle characteristics and equipment working characteristics are considered by taking the power grid fluctuation stabilization and the photovoltaic absorption rate improvement as optimization targets, three typical residential area capacity optimization configuration schemes are solved, and optimized dispatching solution is carried out on the residential areas with one capacity optimized configuration, so that the effectiveness of the provided capacity optimization configuration method is shown.
Drawings
The invention is further described below with reference to the accompanying drawings:
FIG. 1 is a graph of distributed photovoltaic output;
FIG. 2 is a graph of distributed photovoltaic output from different seasons;
FIG. 3 is an electric vehicle time distribution;
FIG. 4 is an electric vehicle adjustable potential analysis chart;
FIG. 5 is a graph of distributed energy storage adjustable potential analysis;
FIG. 6 is a diagram of a model of a combined representative residential area;
FIG. 7 is a diagram of a combined two-representative residential block model;
FIG. 8 is a diagram of a combined three-representative residential block model;
FIG. 9 is a plot of residential area base electrical load intervals;
FIG. 10 is a graph of the distributed photovoltaic output power after a combined three-capacity optimization configuration;
FIG. 11 is a graph of electricity price fluctuation;
FIG. 12 is a graph of the output power of each subject after a combination of three optimal schedules;
fig. 13 is a graph of power interaction with the grid after combining three optimized schedules.
Detailed description of the preferred embodiments
For a better understanding of the objects, techniques and results of the present invention, it will be apparent that the following description of the invention, taken in conjunction with the accompanying drawings, describes embodiments that are merely some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples
Referring to fig. 4, the electric vehicle is charged with the maximum power immediately after reaching to meet the expected electric quantity, and the process energy variation curve is taken as an energy upper bound; discharging the electric vehicle to the minimum allowable electric quantity with the maximum power after the electric vehicle arrives, charging the electric vehicle with the maximum power before the electric vehicle leaves the network, ensuring that the SOC required value of a user is just reached when the electric vehicle leaves the network, taking the energy change in the process as an energy lower bound, taking all areas between an energy upper bound and a lower bound as feasible energy tracks, wherein the areas of the upper bound and the lower bound are adjustable potentials of a single electric vehicle;
referring to fig. 5, assuming that the maximum power charging is maintained after the distributed energy storage is accessed, a certain time reaches the maximum constraint, then the power is discharged to the original electric quantity at the maximum power, and the adjustable potential of the energy storage can be obtained, before the time, the charging potential is continuously reduced along with the energy storage charging, the discharging potential is continuously increased, the discharging potential is guided to reach the maximum value of the charging potential of 0, then the energy storage starts to discharge, the charging potential is continuously increased, and the discharging potential is reduced;
based on fig. 6, 7 and 8, three typical residential area capacity optimization configuration models are established, and the models are solved by utilizing an improved NSGA-II algorithm, wherein the specific steps include:
step 1: three typical residential area capacity optimization configuration models are built, as shown in fig. 6, and residential area capacity optimization configuration models comprising distributed photovoltaics, electric vehicles and basic loads are built; as shown in fig. 7, establishing a residential area capacity optimization configuration model comprising distributed photovoltaics, distributed energy storage and basic loads; as shown in fig. 8, establishing a residential area capacity optimization configuration model comprising distributed photovoltaics, electric vehicles, distributed energy storage and basic loads;
step 2: collecting residential area basic electricity load data, as shown in fig. 9; collecting charging and discharging parameters and capacity parameters of an electric automobile and distributed energy storage;
step 3: determining decision variables: distributed photovoltaic capacity; the number of electric vehicles; a distributed energy storage capacity;
step 4: establishing an objective function;
(1) Photovoltaic digestion target
P pv (t)＝P pv,fh (t)+P pv,cn (t)+P pv,ev (t)+P pv,grid (t)
Wherein: p (P) pv,fh (t) represents the time t distributed photovoltaic providing power to the load; p (P) pv,cn (t) represents the time t distributed photovoltaic providing power to the load; p (P) pv,ev (t) represents that the t-moment distributed type off photovoltaic provides power for the electric automobile; p (P) pv,grid (t) represents the power delivered to the grid by the distributed photovoltaic at time t; p (P) pv And (t) represents the total photovoltaic power generated by the distributed photovoltaic at the moment t.
(2) Grid fluctuation smoothness target
Wherein: p (P) a (t) represents the average value of the power transmitted to the station area by the power grid at the moment t; p (P) buy,fh (t) represents purchasing power from a power grid to supply load power at the moment t; p (P) ev,u And (t) represents the charging power of the electric automobile at the time t.
Step 5: the virtual power plant operation comprising the electric automobile mainly comprises the following constraint conditions;
1) Power balance constraint
P pv (t)＝P pv,fh (t)+P pv,ev (t)+P pv,cn (t)+P pv,grid (t)
Wherein: p (P) pv,fh (t) represents a distributed photovoltaic providing power to a load; p (P) pv,ev (t) represents a distributed off-photovoltaic providing power to an electric vehicle; p (P) pv (t) represents the total photovoltaic power generated by the distributed photovoltaic; p (P) pv,grid (t) represents distributed photovoltaic power delivery to the grid;
2) Distributed photovoltaic power delivery to a grid
P pv,grid,min ≤P pv,grid ≤P pv,grid,max
Wherein: p (P) pv,grid,min 、P pv,grid,max Representing upper and lower limit power of the distributed photovoltaic to be transmitted to a power grid;
3) Electric automobile charging power constraint per time period
P ev，t，min ≤P ev，t ≤P ev，t，max
Wherein: p (P) ev，t，min 、P ev，t，max Representing the minimum and maximum power of the electric automobile in each moment;
4) Electric automobile electric quantity balance constraint
Wherein: η represents charging efficiency, and the value is 0.9; q (Q) ev Representing the total charge amount of the electric vehicle;
5) Battery power constraint
S(t)＝S(t-1)+α*P pv，cn (t)-P dis (t)/β
Wherein: s (t) represents the charge quantity of the energy storage device in the t period and kWh; p (P) dis (t) represents the energy storage discharge power at the time t; alpha and beta respectively represent the charge and discharge power of the power storage equipment, and 0.9 is taken;
6) Power storage state constraints
U ech +U edis ≤1
Wherein: u (U) ech A charging sign for the electric energy storage system, 1 representing charging and 0 representing no charging; u (U) edis For the discharge sign of the electric energy storage system, 1 represents discharge and 0 generationThe table is not discharged.
7) Energy storage device charge-discharge constraints
Wherein:
representing the rated maximum charge and discharge power of the battery;
8) Device power constraints
Wherein:
es Pthe upper limit and the lower limit of the charge and discharge power of the power storage equipment are respectively set;
step 6: judging whether an individual meets constraint conditions or not by introducing interval credibility; defining a dominant relationship of feasible solutions and infeasible solutions; calculating the crowding distance of an individual by introducing the interval overlapping degree to finally form an improved NSGA-II algorithm suitable for the interval multi-objective optimization problem;
step 7: adopting an improved NSGA-II algorithm, setting algorithm parameters as shown in a table 1, calculating each objective function, inputting basic parameters and other initial parameters of a system, randomly generating an initial population, and calculating objective function values and constraint violation values in the current population;
step 8: according to the objective function values, quick non-dominant sorting is carried out, individuals in each non-dominant layering in the population are selected according to proportion, selection operation is carried out, crossover and mutation calculation is carried out, child population is obtained, the populations are combined, each objective function value is solved, the first N individuals are selected as new parent population, iteration is carried out until the requirements are met, and the Pareco optimal front edge is output;
step 9: obtaining the residential area capacity optimization configuration results under the two conditions of best distributed photovoltaic absorption and most gentle power grid fluctuation;
adopting a CRTIC assignment method to carry out multi-objective decision, and firstly, carrying out data standardization: positive index:
negative index: />
Then calculating a correlation coefficient and a conflict quantization index value, wherein the correlation coefficient between two targets is as follows:
the judgment matrix calculation formula is as follows:
obtaining a target weight (0.7126.0.2874);
obtaining a capacity optimization configuration scheme of multi-objective optimization according to the weight of the previous step, as shown in table 2;
step 10: the optimal scheduling verification of the third combination under the economic and environmental protection targets is analyzed to obtain the photovoltaic output power after capacity optimization configuration, as shown in fig. 10, based on the electricity price fluctuation of fig. 11, an objective function is set:
(1) Economic goal
max[f 1 ]＝[C load ]+C ev +[C grid ]-[C op,pv ]-[C op,es ]
Wherein: [ C load ]Representing the electricity income of the residents; c (C) ev Indicating the electric income of the electric automobile; [ C grid ]Representing the income of selling electricity to a power grid; [ C op,pv ]And [ C ] op,es ]Respectively representing equipment cost of distributed photovoltaic and distributed energy storageAnd (5) a meta.
(2) Environmental protection target
Assuming that the electric quantity interacted with the power grid is coal-fired power generation, the emission amount of carbon dioxide is as follows:
wherein: pgrid, t represents interaction power with a power grid, kW; lambda grid represents the emission coefficient of electricity purchased from the grid, kg/kWh.
The output and interaction with the power grid of each object of the optimized schedule after the combined three-capacity optimized configuration are obtained, as shown in fig. 12 and 13 respectively, and the median value of the optimal objective function is obtained, as shown in table 3: 1607.86 yuan economical efficiency and 1578.13kg environmental protection;
TABLE 1 parameter settings
TABLE 3 median value of optimal objective function
By analyzing the output characteristics of elastic resources of the areas, the time-space distribution characteristics and adjustable potential of the areas are obtained through analysis, and the capacity configuration solution is carried out on the three groups of areas by adopting an improved NSGA-II multi-objective optimization algorithm, so that the following conclusion can be obtained:
the capacity configuration results of the three typical residential area combinations on photovoltaic digestion and power grid load smoothness are characterized, the combination III after capacity optimization configuration achieves excellent scheduling results under the double-objective optimization of economy and environmental protection, the condition of power transmission to the power grid only occurs at 6 points, the problem of reverse power transmission is relieved, and the capacity configuration results are verified to have good feasibility and effectiveness.
The method and the system describe uncertainty factors in the residential areas by using the interval number, and analyze and formulate the capacity optimization configuration scheme to be more realistic because of the fluctuation range of the uncertainty factors, so that the residential area distribution type photovoltaic absorption can be effectively improved and the fluctuation of the power grid is gentle.
It will be apparent to those skilled in the art that the present invention is not limited to the details of the above-described exemplary embodiments, and that while preferred embodiments of the present invention have been described, additional variations and modifications may be made to these embodiments, once the basic inventive concepts are known to those skilled in the art. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
Claims (6)
1. The residential area capacity optimization configuration method considering the multi-element elastic resources is characterized by comprising the steps of establishing three typical residential area capacity optimization configuration models, improving a non-dominant sorting genetic solving method and a CRITC assignment method;
the three typical residential area capacity optimization configuration models describe the uncertainty of the photovoltaic and load in the residential area by using the interval number, and the constraint conditions are considered as follows: and solving three typical residential area capacity optimization configuration schemes according to power balance, electric automobile characteristics and equipment working characteristics.
2. The residential area capacity optimization configuration method considering multi-element elastic resources according to claim 1, wherein three typical residential area capacity optimization configuration models are respectively used for establishing two objective functions of photovoltaic digestion and power grid load smoothness.
3. The residential area capacity optimization configuration method considering multi-element elastic resources according to claim 2, which is characterized by comprising the following steps of;
step 1: three typical residential area capacity optimization configuration models are built, and the three typical residential area capacity optimization configuration models are respectively: a residential area capacity optimization configuration model of distributed photovoltaic, electric vehicles and basic loads; a residential area capacity optimization configuration model of distributed photovoltaic, distributed energy storage and basic load; the residential area capacity optimization configuration model of distributed photovoltaic, electric vehicles, distributed energy storage and basic load;
step 2: collecting parameters of each module;
step 3: determining decision variables: distributed photovoltaic capacity, number of electric vehicles, and distributed energy storage capacity;
step 4: establishing two objective functions of photovoltaic digestion and power grid load smoothness;
step 5: determining constraint conditions in aspects of power balance, electric automobile characteristics and equipment working characteristics;
step 6: constructing an improved NSGA-II algorithm suitable for the interval multi-objective optimization problem;
step 7: adopting an improved NSGA-II algorithm, calculating each objective function, randomly generating an initial population, merging the populations, and solving each objective function value;
step 8: judging whether iteration times are reached, and outputting a Pareco optimal front;
step 9: performing multi-objective decision by using a CRITC assignment method to obtain three typical residential area capacity optimization configuration schemes;
step 10: and establishing economic and environmental protection targets, and carrying out optimal scheduling verification on the combination III after capacity optimal configuration to verify the effectiveness and feasibility of the capacity optimal configuration.
4. The residential area capacity optimization configuration method considering multi-element elastic resources according to claim 3, wherein in the step S2, residential area basic electricity load data, charging and discharging parameters of electric vehicles and distributed energy storage and capacity parameters are collected.
5. The residential area capacity optimization configuration method considering multi-element elastic resources according to claim 4, wherein in the step S4, a photovoltaic digestion target is as follows:
P pv (t)＝P pv,fh (t)+P pv,cn (t)+P pv,ev (t)+P pv,grid (t)
wherein: p (P) pv,fh (t) represents the time t distributed photovoltaic providing power to the load; p (P) pv,cn (t) represents the time t distributed photovoltaic providing power to the load; p (P) pv,ev (t) represents that the t-moment distributed type off photovoltaic provides power for the electric automobile; p (P) pv,grid (t) represents the power delivered to the grid by the distributed photovoltaic at time t; p (P) pv And (t) represents the total photovoltaic power generated by the distributed photovoltaic at the moment t.
6. The residential area capacity optimization configuration method considering multi-element elastic resources according to claim 5, wherein in the step S4, a power grid fluctuation smoothness target is:
wherein: p (P) a (t) represents the average value of the power transmitted to the station area by the power grid at the moment t; p (P) buy,fh (t) represents purchasing power from a power grid to supply load power at the moment t; p (P) ev,u And (t) represents the charging power of the electric automobile at the time t.
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