CN116451875A - Optical storage and filling integrated station capacity optimization configuration method - Google Patents
Optical storage and filling integrated station capacity optimization configuration method Download PDFInfo
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Abstract
The invention relates to a capacity optimization configuration method of an optical storage and charging integrated station, which aims at optimizing and configuring the capacity of the optical storage and charging integrated station with the minimum construction and operation comprehensive cost, and obtains the capacity of an energy storage system of the optical storage and charging integrated station, wherein constraint conditions comprise power balance constraint based on opportunity constraint, and the opportunity constraint considers deviation factors of photovoltaic output and charging load prediction. The photovoltaic power generation system and the photovoltaic power generation method can fully consider randomness of photovoltaic output and charging load power of the electric automobile, and realize optimal configuration of the capacity of the optical storage and charging integrated station.
Description
Technical Field
The invention relates to an optimal configuration method for capacity of an optical storage and filling integrated station.
Background
Currently, the power of electric automobiles is actually dependent on the conversion of traditional coal energy sources (about 75% -80%), and the carbon emission is not reduced fundamentally. Under such circumstances, the development of new energy power generation has been advanced in recent years by countries, and the local consumption of renewable energy has been trended by directly establishing a charging device linked to a renewable energy power generation system. Therefore, the photovoltaic and energy storage matched charging station with certain capacity is reasonably configured, so that the power purchase cost and the self-use of the charging station can be reduced, greater economic benefits are obtained, the randomness of the charging load can be restrained, and the near consumption of renewable energy sources to generate electricity and the development of low-carbon electric vehicles can be promoted. However, the energy storage cost is high, and how to reasonably allocate the optical storage capacity of the optical storage and charging integrated charging station is a technical problem to be solved urgently. The current research on energy storage capacity allocation is mainly divided into the following two aspects.
The first is a capacity configuration in which the energy storage device smoothes load fluctuations. A grading method for accurately distributing capacity of an energy storage system by aiming at a distributed photovoltaic power distribution system of a large-scale centralized photovoltaic power station comprises the steps of deducing an energy storage capacity distribution function by utilizing an interval estimation method through analyzing a probability statistical rule and a random process of a photovoltaic power generation short-term prediction error and a load short-term prediction error. Another energy storage method adopting concentrated solar power generation. The method aims at improving the stability of photovoltaic output and determining the energy capacity by adopting a statistical method in consideration of the influence of various weather conditions.
The second category is energy storage capacity allocation for economic purposes. There are domestic scholars who use entropy weight and similar ordering preference techniques to evaluate optimal capacity configurations taking into account economic indicators such as energy costs, capital costs, etc. Several uk students have proposed an optimal configuration method aimed at minimizing the cost of energy storage system construction and operation, and maximizing photovoltaic and wind power consumption. There are also domestic scholars who have proposed two iterative search algorithms based on constraint conditions to determine the optimal capacity of the energy storage system with the goal of system reliability and economy.
In summary, although the light storage and charging integrated station has been studied more recently, the randomness of the photovoltaic output and the charging load power of the electric vehicle is not considered, and a problem model for accurately modeling the photovoltaic output and the charging load based on the opportunity constraint is not available.
Disclosure of Invention
The invention aims to provide an optimal configuration method for the capacity of an optical storage and charging integrated station, which can fully consider the randomness of photovoltaic output and charging load power of an electric automobile and realize the optimal configuration of the capacity of the optical storage and charging integrated station.
The technical scheme for realizing the purpose of the invention comprises the following steps:
the capacity optimization configuration method of the optical storage and charging integrated station aims at the minimum construction and operation comprehensive cost to optimize and configure the capacity of the optical storage and charging integrated station, the capacity of an energy storage system of the optical storage and charging integrated station is obtained, and constraint conditions comprise power balance constraint based on opportunity constraint, and the opportunity constraint considers deviation factors of photovoltaic output and charging load prediction.
Further, the objective function of the capacity optimization configuration of the optical storage and filling integrated station is as follows:
，
wherein:is an objective function; />，/>The construction and maintenance costs of the photovoltaic power generation system and the energy storage system are respectively;the cost of purchasing electricity from an upper power grid for the charging station; />And->Maintenance cost coefficients of the photovoltaic power generation system and the energy storage system are respectively; />，/>，/>The unit cost of the rated power of the photovoltaic power generation system, the capacity of the energy storage system and the rated power of charge and discharge are respectively set; />，/>Respectively photovoltaic output rated power and energy storage charging and discharging rated power; />Rated capacity of the energy storage system; />The equipment depreciation rate; />，/>The service lives of the photovoltaic system and the energy storage system are respectively; />The power purchased by the charging station to the upper power grid; />The power selling to the upper power grid for the charging station;qthe time-sharing electricity price is obtained.
Further, the power balance constraint based on the opportunity constraint is specifically expressed as the following formula,
，
in the method, in the process of the invention,，/>error coefficients of photovoltaic output and charge load prediction are respectively adopted; />，/>Respectively the energy storage systems are intCharging and discharging power at moment; />Charging power for the electric automobile; />Is a smaller constant; />Confidence level for the establishment of the formula, +.>Power purchased from charging station to upper grid, +.>For the rated power of the photovoltaic output, pr is a probability function of the opportunity constraint.
Further, the constraint condition comprises the rated power of the photovoltaic power generation system and the capacity constraint of the energy storage system, and the constraint condition is specifically represented by the following formula,
，
，
in the method, in the process of the invention,，/>the upper limit of the rated power of the photovoltaic power generation system and the capacity of the energy storage system are respectively set.
Further, the constraint conditions comprise capacity constraint of the energy storage system at each moment, and the constraint conditions are specifically expressed by the following formula,
。
further, the constraint conditions comprise energy storage system power constraint at each moment, and the constraint conditions are specifically expressed as the following formula,
。
further, the constraints include charging station operational constraints, specifically as shown in the following formulas,
，
。
further, based on the capacity optimization configuration result of the optical storage and filling integrated station obtained by the method, carrying out admission capacity assessment on the power distribution network according to 3 primary indexes of safety rationality, economic rationality and environmental protection rationality; the method is characterized in that 4 secondary indexes including green income, network loss rate, power distribution network running cost and power distribution network investment acquisition cost are arranged under the economic rationality primary index, and 3 secondary indexes including distributed power source permeability, the maximum residual power generation ratio of the distributed power source and coal saving amount are arranged under the environmental rationality primary index.
Further, based on 3 primary indexes of safety rationality, economic rationality and environmental protection rationality, a multi-objective optimization result is obtained through calculation by using a pareto front face method, a set of relatively better solutions of the power distribution network is obtained, and the optimal solution of the power distribution network is obtained through comprehensive evaluation aiming at the set of relatively better solutions of the power distribution network.
Further, the following steps are executed to obtain an optimal solution of the power distribution network through comprehensive evaluation:
step 1: calculating subjective weight values of all index layers by adopting an analytic hierarchy process, selecting a judgment matrix of each index layer according to expert experience and a scale principle, and solving subjective assignment of weights of all index layers; the index layer consists of a second-level index arranged under the first-level index and comprises a safety rationality index layer, an economic rationality index layer and an environment-friendly rationality index layer;
step 2: calculating subjective and objective comprehensive weight values by adopting an entropy weight correction analytic hierarchy process, normalizing the judgment matrix to obtain a standard matrix, and further obtaining an entropy weight matrix with a correction function by adopting an entropy weight method; multiplying the entropy weight matrix with the subjective weight to obtain objective weight; multiplying the objective weight by the subjective weight to obtain a comprehensive weight;
step 3: calculating each index value according to a specific charging station access scheme, and obtaining the membership degree of each index according to a calculated value corresponding interval so as to obtain a fuzzy scoring matrix;
step 4: calculating the comprehensive score according to the following formula, wherein the highest-scoring solution is the optimal solution,
，
in the method, in the process of the invention,Fcalculating the capacity of the power distribution network for charging the electric automobile by combining an analytic hierarchy process modified by an entropy weight process with a fuzzy scoring process to obtain a comprehensive score;P 1 the weight matrix is synthesized for the safety rationality index layer;M 1 a fuzzy scoring matrix for environmental protection rationality;P 2 the weight matrix is synthesized for the economic rationality index layer;M 2 fuzzy scoring matrix for security rationality;P 3 the comprehensive weight matrix is an environmental protection rationality index layer;M 3 the scoring matrix is fuzzy for running economic rationality.
The invention has the beneficial effects that:
the invention aims at optimizing and configuring the capacity of the photovoltaic storage and charging integrated station by taking the minimum construction and operation comprehensive cost as a target, and the constraint conditions comprise power balance constraint based on opportunity constraint, wherein the opportunity constraint considers deviation factors of photovoltaic output and charging load prediction. According to the invention, the randomness of the photovoltaic output and the charging load power of the electric vehicle is considered, the capacity configuration model of the charging and storage integrated charging station of the electric vehicle is established with the aim of minimum construction and operation comprehensive cost, the power balance constraint based on opportunistic constraint of deviation factors of the photovoltaic output and the charging load prediction is considered, the uncertainty of the photovoltaic output and the charging load prediction is considered to be an optimal configuration problem, the optimal configuration effect of the capacity of the charging and storage integrated station of the electric vehicle is effectively improved, and the cost is effectively saved.
The objective function of the capacity optimization configuration of the optical storage and filling integrated station is as follows:
，
wherein:is an objective function; />，/>Respectively a photovoltaic power generation system and an energy storage systemConstruction maintenance costs of (2);the cost of purchasing electricity from an upper power grid for the charging station; />And->Maintenance cost coefficients of the photovoltaic power generation system and the energy storage system are respectively; />，/>，/>The unit cost of the rated power of the photovoltaic power generation system, the capacity of the energy storage system and the rated power of charge and discharge are respectively set; />，/>Respectively photovoltaic output rated power and energy storage charging and discharging rated power; />Rated capacity of the energy storage system; />The equipment depreciation rate; />，/>The service lives of the photovoltaic system and the energy storage system are respectively; />The power purchased by the charging station to the upper power grid; />The power selling to the upper power grid for the charging station;qthe time-sharing electricity price is obtained.
The power balance constraint based on the opportunity constraint is specifically expressed as follows,
，
in the method, in the process of the invention,，/>error coefficients of photovoltaic output and charge load prediction are respectively adopted; />，/>Respectively the energy storage systems are intCharging and discharging power at moment; />Charging power for the electric automobile; />Is a smaller constant; />Confidence level for the establishment of the formula, +.>Power purchased from charging station to upper grid, +.>For the rated power of the photovoltaic output, pr is a probability function of the opportunity constraint.
According to the invention, the optimal configuration effect of improving the capacity of the optical storage and filling integrated station is further ensured through the objective function and the power balance constraint formula based on the opportunity constraint, and the cost is effectively saved.
Based on the capacity optimization configuration result of the optical storage and filling integrated station, the invention evaluates the admitting capability of the power distribution network according to 3 primary indexes of safety rationality, economic rationality and environmental protection rationality; the method is characterized in that 4 secondary indexes including green income, network loss rate, power distribution network running cost and power distribution network investment acquisition cost are arranged under the economic rationality primary index, and 3 secondary indexes including distributed power source permeability, the maximum residual power generation ratio of the distributed power source and coal saving amount are arranged under the environmental rationality primary index. The existing power distribution network admission capacity evaluation does not comprehensively consider the photovoltaic access influence, the economic rationality and the environmental rationality, and the power distribution network admission capacity evaluation comprehensively considers the photovoltaic access influence, the safety rationality, the economic rationality and the environmental rationality, so that the optimal solution of the power distribution network can be ensured to be obtained through comprehensive evaluation.
The method is based on 3 primary indexes of safety rationality, economic rationality and environmental protection rationality, a multi-objective optimization result is obtained through calculation by using a pareto front surface method, a set of relatively good solutions of the power distribution network is obtained, and the optimal solution of the power distribution network is obtained through comprehensive evaluation aiming at the set of relatively good solutions of the power distribution network.
The optimal solution of the power distribution network is obtained through comprehensive evaluation, and the following steps are executed:
step 1: calculating subjective weight values of all index layers by adopting an analytic hierarchy process, selecting a judgment matrix of each index layer according to expert experience and a scale principle, and solving subjective assignment of weights of all index layers; the index layer consists of a second-level index arranged under the first-level index and comprises a safety rationality index layer, an economic rationality index layer and an environment-friendly rationality index layer;
step 2: calculating subjective and objective comprehensive weight values by adopting an entropy weight correction analytic hierarchy process, normalizing the judgment matrix to obtain a standard matrix, and further obtaining an entropy weight matrix with a correction function by adopting an entropy weight method; multiplying the entropy weight matrix with the subjective weight to obtain objective weight; multiplying the objective weight by the subjective weight to obtain a comprehensive weight;
step 3: calculating each index value according to a specific charging station access scheme, and obtaining the membership degree of each index according to a calculated value corresponding interval so as to obtain a fuzzy scoring matrix;
step 4: calculating the comprehensive score according to the following formula, wherein the highest-scoring solution is the optimal solution,
，
in the method, in the process of the invention,Fcalculating the capacity of the power distribution network for charging the electric automobile by combining an analytic hierarchy process modified by an entropy weight process with a fuzzy scoring process to obtain a comprehensive score;P 1 the weight matrix is synthesized for the safety rationality index layer;M 1 a fuzzy scoring matrix for environmental protection rationality;P 2 the weight matrix is synthesized for the economic rationality index layer;M 2 fuzzy scoring matrix for security rationality;P 3 the comprehensive weight matrix is an environmental protection rationality index layer;M 3 the scoring matrix is fuzzy for running economic rationality.
According to the method, the steps of the method for obtaining the optimal solution of the power distribution network through the comprehensive evaluation are performed, and the rapidity and the reliability of obtaining the optimal solution of the power distribution network through the comprehensive evaluation are ensured.
Drawings
FIG. 1 is a schematic diagram of a load capacity assessment index system of a power distribution network receiving charging station;
FIG. 2 is a schematic diagram of a power prediction curve;
FIG. 3 is a graph showing the comparison of the power consumption curves;
FIG. 4 is a graph showing the comparison of power curves of the energy storage system.
Detailed Description
The present invention will be described in detail below with reference to the embodiments shown in the drawings, but it should be understood that the embodiments are not limited to the present invention, and functional, method, or structural equivalents and alternatives according to the embodiments are within the scope of protection of the present invention by those skilled in the art.
Example 1
Optical storage and filling integrated station capacity optimization configuration method
And optimizing and configuring the capacity of the optical storage and charging integrated station by taking the minimum construction and operation comprehensive cost as a target, wherein constraint conditions comprise power balance constraint based on opportunity constraint, and obtaining the capacity of an energy storage system of the optical storage and charging integrated station, and the opportunity constraint considers deviation factors of photovoltaic output and charge load prediction.
The objective function of the capacity optimization configuration of the optical storage and filling integrated station is as follows:
，
wherein:is an objective function; />，/>The construction and maintenance costs of the photovoltaic power generation system and the energy storage system are respectively;the cost of purchasing electricity from an upper power grid for the charging station; />And->Maintenance cost coefficients of the photovoltaic power generation system and the energy storage system are respectively; />，/>，/>The unit cost of the rated power of the photovoltaic power generation system, the capacity of the energy storage system and the rated power of charge and discharge are respectively set; />，/>Respectively photovoltaic output rated power and energy storage charging and discharging rated power; />Rated capacity of the energy storage system; />The equipment depreciation rate; />，/>The service lives of the photovoltaic system and the energy storage system are respectively; />The power purchased by the charging station to the upper power grid; />The power selling to the upper power grid for the charging station;qthe time-sharing electricity price is obtained.
The power balance constraint based on the opportunity constraint is specifically expressed as follows,
，
in the method, in the process of the invention,，/>error coefficients of photovoltaic output and charge load prediction are respectively adopted; />，/>Respectively the energy storage systems are intPower of charge and discharge at moment； />Charging power for the electric automobile; />Is a smaller constant; />Confidence level for the establishment of the formula, +.>Power purchased from charging station to upper grid, +.>For the rated power of the photovoltaic output, pr is a probability function of the opportunity constraint.
The constraint condition comprises the rated power of the photovoltaic power generation system and the capacity constraint of the energy storage system, and the specific formula is shown as follows,
，
，
in the method, in the process of the invention,，/>the upper limit of the rated power of the photovoltaic power generation system and the capacity of the energy storage system are respectively set.
The constraint conditions comprise capacity constraint of the energy storage system at each moment, and the constraint conditions are specifically expressed by the following formula,
。
the constraint conditions comprise the power constraint of the energy storage system at each moment, and the constraint conditions are specifically expressed by the following formula,
。
the constraints include charging station operational constraints, specifically as shown in the following formulas,
，
。
configuration problem calculation verification:
the total number of electric vehicles served by the electric vehicle optical storage and charging integrated charging station is 120, the battery capacity of each electric vehicle is 40 kWh, the charging power is 60 kW, and the time distribution of the charging load of the electric vehicle is sampled and simulated by adopting a Monte Carlo method. The power uncertainty of the photovoltaic output and the charging load of the electric automobile can be approximately described by adopting normal distribution, and the error is 0, and the variance is 10% of the predicted value, and the normal distribution is 5%. Taking a photovoltaic system with rated power of 100 kW as an example, the predicted curves of photovoltaic output and charging load are shown in fig. 2.
In the power balance constraint, σ is taken to be 1 and the confidence level α is taken to be 0.95. Other parameters of the electric vehicle charging and storage integrated charging station capacity optimization model are shown in table 1. The operation mode is mainly to purchase electricity from the power grid according to peak-valley electricity prices, charge the charging fee of the user according to the set price, and the operator earns the price profit from the middle. Peak-to-valley electricity prices are set as shown in tables 1 and 2 with reference to large industrial electricity prices in Jiangsu province, peak Gu Dianjia and charging station charge.
TABLE 1
TABLE 2
Comparing the capacity optimizing configuration method (model 1) of the traditional optical storage and filling integrated station with the capacity optimizing configuration method (model 2) of the optical storage and filling integrated station:
model 1: neglecting the photovoltaic output and the prediction error of the charging load of the electric vehicle, the model determines the equality power balance constraint condition above, and the charging load amount is 1.1 times of the predicted value in order to cope with the uncertainty factor.
Model 2: and taking the prediction errors of the photovoltaic output and the electric steam charging load into consideration, and adopting a power balance constraint condition based on opportunistic constraint. In the process of carrying out deterministic equivalent transformation on power balance constraint by adopting a sequence operation theory, generating 100 random variable sample values; the sampling period is set to 1 when discretizing the probability distribution.
The simulation configuration results of the conventional optical storage and filling integrated station capacity optimization configuration method (model 1) and the simulation configuration results of the optical storage and filling integrated station capacity optimization configuration method (model 2) are shown in table 3.
TABLE 3 Table 3
From an analysis of the data in table 3, model 2 has lower construction and maintenance costs, annual electricity purchase costs, and overall costs than model 1's photovoltaic system. By analyzing the joint probability density of the model 2, the charging load is about 1.09 times of the predicted value in the model 2 under the condition that the confidence level is 0.95, the photovoltaic output is about 0.8 times of the predicted value, the charging load is only 1.1 times of the predicted value in the model 1, and the model 2 has better economical efficiency compared with the model 1 under the condition that the charging loads are similar.
Fig. 3 and 4 are power curves of the energy storage system and the power curves of the electricity purchasing power curves of the 2 models respectively. From the analysis of the curves, model 2 has a higher charge capacity at the valley and the period of higher photovoltaic output and a higher discharge capacity at the peak than model 1.
Example two
Power distribution network acceptance assessment method
As shown in fig. 1, based on the capacity optimization configuration result of the optical storage and filling integrated station obtained by the embodiment method, carrying out admission capacity evaluation on the power distribution network according to 3 primary indexes (criterion layers) of safety rationality, economic rationality and environmental protection rationality; the method comprises the steps of setting up 6 secondary indexes of total network voltage deviation rate, voltage fluctuation rate, reverse load rate, harmonic content rate, total harmonic distortion rate and short-circuit current under the safety rationality primary index, setting up 4 secondary indexes of green evidence income, network loss rate, power distribution network running cost and power distribution network investment acquisition cost under the economic rationality primary index, setting up 3 secondary indexes of distributed power permeability, maximum residual power generation ratio of distributed power and coal saving under the environment-friendly rationality primary index, and forming an index layer by the secondary indexes.
The network total voltage deviation rate, the voltage fluctuation rate, the reverse load rate, the harmonic content, the total harmonic distortion rate and the short-circuit current 6 secondary index values which are set under the safety rationality primary index can be calculated by the existing power flow calculation method. The method for calculating the secondary index under the primary index of economic rationality and environmental rationality is described below.
First-level index of economic rationality:
(1) Benefit of green syndrome
Index of green evidence incomeI Green The method is divided into two types of economic benefit I1 and environmental benefit I2:
economic benefit:
，
wherein:-net side power generation change production total value implementing load admission scheme;
-the production cost corresponding to the power plant.
Environmental benefit:
，
wherein:-implementing a carbon dioxide emission reduction contributed by the distributed power supply acceptance scheme;
-the corresponding area of the power plant.
(2) Network loss rate
，
Wherein:P kloss is the firstkNetwork loss of the segment line. Wherein:
，
wherein:、/>-firstkActive and reactive load at node i on the segment line;
-firstkResistance value of the section line;
-nodeiBus voltage at.
(3) Charging station operating costs
Running costC rc Mainly comprises the annual running cost and annual fuel purchasing cost of important equipment, namely:
，
wherein:，
，
wherein:-annual running cost of important equipment of the active distribution network;
-the annual purchase fuel consumption in the active distribution network;
-operating costs of the equipment in the active distribution network;
-time-by-time loading of equipment in the active distribution network;
-time-by-time gas usage of equipment in the active distribution network;
-gas price.
(4) Charging station investment acquisition cost
The active distribution network system can adopt a plurality of devices, different devices can further form distribution network structures in different forms, and the investment and acquisition cost is highC total The total investment cost of each device of the power distribution network is reflected, and the calculation formula is as follows:
，
wherein:-the cost of each device of the active distribution network;
-optimal capacity of the optimal design of the distribution network equipment in the active distribution network;
rate of discount
-the service life of distribution network equipment in the active distribution network.
Environmental protection rationality first level index:
(1) Distributed power permeability
Defined as the percentage of the ratio of the installed capacity of the distributed energy source to the maximum load of the grid. The calculation formula is as follows:
，
in the method, in the process of the invention,-distributed energy installation capacity; />-active distribution network maximum load.
The numerator and denominator of the above formula may be larger or smaller with time variation, thusMay rise or fall.
(2) Maximum residual power generation ratio of distributed power supply
Distributed energy sourceThere is time-varying property, and the load also has time-varying property depending on the working environment. Maximum residual power generation ratio index using distributed power supplyAnd analyzing the capacity of the active power distribution network to absorb the distributed energy in a time-varying period, and calculating the maximum power flow which can be born by equipment such as a circuit, a transformer and the like. The calculation formula is as follows:
，
in the method, in the process of the invention,-distributed energy generation instantaneous power in an active distribution network;
-instantaneous power of the load in the active distribution network;
-the sum of the limit transmission power of each line in the active distribution network.
(3) Saving coal quantity
Assume that the annual Internet surfing electric quantity of the distributed power supply isαMW.h, compared with the thermal power of the fire coal, the unit degree electric standard coal consumption is 350g/kW.h, and the standard coal can be saved for the country each year 350α kg。
Based on 3 primary indexes of safety rationality, economic rationality and environmental protection rationality, a pareto front surface method is utilized to calculate and obtain a multi-objective optimization result, and a set of relatively better solutions of the power distribution network is obtained.
The multi-objective optimization problem model is as follows:
，
f (x) is a dependent variable of the multi-objective optimization problem, and V-min is a prefix indicating that the objective function is optimal with the minimum objective.
Charging station capacity constraint, safety constraint, economical constraint and environmental protection constraint are respectively adopted. And calculating according to the constraint conditions to obtain a multi-objective optimization result, and obtaining a relatively optimal solution set, wherein the relatively optimal solution is required to be obtained in the set or the pareto front surface method is required to be used for the optimal solution.
The dominance, predominance, between specific solutions is described below.
1: solution A is superior to solution B (solution A Jiang Palei Torr dominates solution B)
Assuming that there are two objective functions, the objective function value corresponding to the solution a is better than the objective function value corresponding to the solution B, the solution a is said to be superior to the solution B, and may also be called the solution a Jiang Palei torr dominant solution B.
2: solution A is indistinguishable from solution B (solution A can Pa Lituo dominant solution B)
Also assuming that two objective functions, one objective function value corresponding to solution a is better than one objective function value corresponding to solution B, but the other objective function value corresponding to solution a is worse than one objective function value corresponding to solution B, then it is called that solution a is not different from solution B, also called that solution a can pareto dominate solution B.
3 pareto optimal solution
Also assuming two objective functions, for solution a, no other solution can be found in the variable space that is better than solution a (note that here the preference must be given to both objective function values being better than the value corresponding to a), then solution a is the pareto optimal solution.
4 Pareto optimal front edge
All Pareto optimal solutions form a Pareto optimal solution set, and the Pareto optimal front or Pareto front of the problem is formed by mapping the solutions through an objective function, namely, the objective function value corresponding to the Pareto optimal solution is the Pareto optimal front.
And aiming at the set of relatively better solutions of the power distribution network, obtaining the optimal solution of the power distribution network through comprehensive evaluation.
The optimal solution of the power distribution network is obtained through comprehensive evaluation, and the following steps are executed:
step 1: calculating subjective weight values of all index layers by adopting an analytic hierarchy process, selecting a judgment matrix of each index layer according to expert experience and a scale principle, and solving subjective assignment of weights of all index layers; the index layer consists of a second-level index arranged under the first-level index and comprises a safety rationality index layer, an economic rationality index layer and an environment-friendly rationality index layer;
step 2: calculating subjective and objective comprehensive weight values by adopting an entropy weight correction analytic hierarchy process, normalizing the judgment matrix to obtain a standard matrix, and further obtaining an entropy weight matrix with a correction function by adopting an entropy weight method; multiplying the entropy weight matrix with the subjective weight to obtain objective weight; multiplying the objective weight by the subjective weight to obtain a comprehensive weight;
step 3: calculating each index value according to a specific charging station access scheme, and obtaining the membership degree of each index according to a calculated value corresponding interval so as to obtain a fuzzy scoring matrix;
step 4: calculating the comprehensive score according to the following formula, wherein the highest-scoring solution is the optimal solution,
，
in the method, in the process of the invention,Fcalculating the capacity of the power distribution network for charging the electric automobile by combining an analytic hierarchy process modified by an entropy weight process with a fuzzy scoring process to obtain a comprehensive score;P 1 the weight matrix is synthesized for the safety rationality index layer;M 1 a fuzzy scoring matrix for environmental protection rationality;P 2 the weight matrix is synthesized for the economic rationality index layer;M 2 fuzzy scoring matrix for security rationality;P 3 the comprehensive weight matrix is an environmental protection rationality index layer;M 3 the scoring matrix is fuzzy for running economic rationality.
The above list of detailed descriptions is only specific to practical embodiments of the present invention, and they are not intended to limit the scope of the present invention, and all equivalent embodiments or modifications that do not depart from the spirit of the present invention should be included in the scope of the present invention.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Claims (10)
1. An optical storage and filling integrated station capacity optimization configuration method is characterized by comprising the following steps of: optimizing and configuring the capacity of the optical storage and charging integrated station by taking the minimum construction and operation comprehensive cost as a target to obtain the capacity of an energy storage system of the optical storage and charging integrated station, wherein the constraint conditions comprise power balance constraint based on opportunity constraint, and the opportunity constraint considers deviation factors of photovoltaic output and charge load prediction;
the objective function of the capacity optimization configuration of the optical storage and filling integrated station is as follows:
，
wherein:is an objective function; />，/>The construction and maintenance costs of the photovoltaic power generation system and the energy storage system are respectively; />The cost of purchasing electricity from an upper power grid for the charging station; />And->Maintenance cost coefficients of the photovoltaic power generation system and the energy storage system are respectively;，/>，/>the unit cost of the rated power of the photovoltaic power generation system, the capacity of the energy storage system and the rated power of charge and discharge are respectively set;，/>respectively photovoltaic output rated power and energy storage charging and discharging rated power; />Rated capacity of the energy storage system; />The equipment depreciation rate; />，/>The service lives of the photovoltaic system and the energy storage system are respectively; />The power purchased by the charging station to the upper power grid; />The power selling to the upper power grid for the charging station;qthe time-sharing electricity price is obtained.
2. The optical storage and filling integrated station capacity optimization configuration method according to claim 1, wherein the method comprises the following steps: the power balance constraint based on the opportunity constraint is specifically expressed as follows:
，
in the method, in the process of the invention,，/>error coefficients of photovoltaic output and charge load prediction are respectively adopted; />，/>Respectively the energy storage systems are intCharging and discharging power at moment; />Charging power for the electric automobile; />Is a smaller constant; />Confidence level for the establishment of the formula, +.>Power purchased from charging station to upper grid, +.>For the rated power of the photovoltaic output, pr is a probability function of the opportunity constraint.
3. The optical storage and filling integrated station capacity optimization configuration method according to claim 2, wherein the method is characterized in that: the constraint condition comprises the rated power of the photovoltaic power generation system and the capacity constraint of the energy storage system, and the specific formula is shown as follows,
，
，
in the method, in the process of the invention,，/>the upper limit of the rated power of the photovoltaic power generation system and the capacity of the energy storage system are respectively set.
4. The optical storage and filling integrated station capacity optimization configuration method according to claim 2, wherein the method is characterized in that: the constraint conditions comprise capacity constraint of the energy storage system at each moment, and the constraint conditions are specifically expressed by the following formula,
。
5. the optical storage and filling integrated station capacity optimization configuration method according to claim 2, wherein the method is characterized in that: the constraint conditions comprise the power constraint of the energy storage system at each moment, and the constraint conditions are specifically expressed by the following formula,
。
6. the optical storage and filling integrated station capacity optimization configuration method according to claim 2, wherein the method is characterized in that: the constraints include charging station operational constraints, specifically as shown in the following formulas,
，
。
7. the optimal configuration method for capacity of an optical storage and inflation integrated station according to any one of claims 1 to 6, further comprising evaluating a configuration result by: and carrying out admission capacity assessment on the power distribution network according to 3 primary indexes of safety rationality, economic rationality and environmental protection rationality.
8. The method of claim 7, wherein 4 secondary indexes of green income, network loss rate, power distribution network running cost and power distribution network investment acquisition cost are set under the economic rationality primary index, and 3 secondary indexes of distributed power source permeability, maximum residual power generation ratio of the distributed power source and coal saving amount are set under the environmental rationality primary index.
9. The method of claim 8, wherein evaluating the configuration result further comprises: based on 3 primary indexes of safety rationality, economic rationality and environmental protection rationality, calculating by using a pareto front surface method to obtain a multi-objective optimization result, obtaining a set of relatively better solutions of the power distribution network, and obtaining the optimal solution of the power distribution network by comprehensive evaluation aiming at the set of relatively better solutions of the power distribution network.
10. The method of claim 9, wherein evaluating the configuration result further comprises obtaining an optimal solution for the power distribution network by comprehensive evaluation by:
step 1: calculating subjective weight values of all index layers by adopting an analytic hierarchy process, selecting a judgment matrix of each index layer according to expert experience and a scale principle, and solving subjective assignment of weights of all index layers; the index layer consists of a second-level index arranged under the first-level index and comprises a safety rationality index layer, an economic rationality index layer and an environment-friendly rationality index layer;
step 2: calculating subjective and objective comprehensive weight values by adopting an entropy weight correction analytic hierarchy process, normalizing the judgment matrix to obtain a standard matrix, and further obtaining an entropy weight matrix with a correction function by adopting an entropy weight method; multiplying the entropy weight matrix with the subjective weight to obtain objective weight; multiplying the objective weight by the subjective weight to obtain a comprehensive weight;
step 3: calculating each index value according to a specific charging station access scheme, and obtaining the membership degree of each index according to a calculated value corresponding interval so as to obtain a fuzzy scoring matrix;
step 4: calculating the comprehensive score according to the following formula, wherein the highest-scoring solution is the optimal solution,
，
in the method, in the process of the invention,Fcalculating the capacity of the power distribution network for charging the electric automobile by combining an analytic hierarchy process modified by an entropy weight process with a fuzzy scoring process to obtain a comprehensive score;P 1 the weight matrix is synthesized for the safety rationality index layer;M 1 a fuzzy scoring matrix for environmental protection rationality;P 2 the weight matrix is synthesized for the economic rationality index layer;M 2 fuzzy scoring matrix for security rationality;P 3 the comprehensive weight matrix is an environmental protection rationality index layer;M 3 the scoring matrix is fuzzy for running economic rationality.
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