CN110783950A - Method for determining photovoltaic optimal configuration capacity of power distribution network node - Google Patents
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Abstract
The invention relates to a method for determining photovoltaic optimal configuration capacity of a power distribution network node, which comprises the following steps: establishing an evaluation model of the maximum photovoltaic output allowed to be injected into the power distribution network node at each time of the year, and solving the model by adopting annual time sequence trend simulation of a genetic algorithm to obtain the maximum photovoltaic output allowed to be injected into the power distribution network node at each time of the year; and establishing a photovoltaic power station full life cycle profit evaluation model considering the light abandonment based on the result, solving the model by adopting a genetic algorithm to obtain the photovoltaic optimum capacity which enables the profit of the power station to be maximum, and guaranteeing the safe operation of the power distribution network by the light abandonment if the capacity is higher than the maximum access capacity. Compared with the prior art, the method has the advantages of rapidness, reliability and the like.
Description
Technical Field
The invention relates to a method for configuring photovoltaic capacity in a power distribution network, in particular to a method for determining photovoltaic optimal configuration capacity of a power distribution network node.
Background
With the continuous upgrading of the problems of shortage of fossil energy and environmental pollution in the world, and the support of policies related to renewable energy power generation, renewable energy power generation is widely concerned. The photovoltaic power generation is slightly limited by regions and resource conditions, the power generation process is free of noise and pollution, and the installation and maintenance are simple, so that the photovoltaic power generation system becomes one of hot spots for research and application of various countries. Due to the randomness and intermittency of photovoltaic output, the large-scale photovoltaic access has adverse effects on the operation safety of the power distribution network.
In order to deal with photovoltaic large-scale grid connection, the method has important significance in reasonably evaluating the photovoltaic receiving capacity of the power distribution network. Relevant researches have been carried out by scholars at home and abroad, and the evaluation method of the photovoltaic acceptance of the power distribution network in the existing researches comprises an intelligent optimization algorithm, a random scene simulation method, a Monte Carlo random simulation method and the like. The literature [1] adopts a genetic algorithm to evaluate and obtain the maximum admission capacity of the photovoltaic of the power distribution network when a plurality of photovoltaics are accessed. The literature [2] adopts a random scene analysis method to evaluate the maximum photovoltaic receiving capacity of a given feeder line, and is additionally provided with a lead-acid energy storage device, and the photovoltaic receiving capacity of the feeder line can be greatly improved by obtaining the stored energy through simulation verification. Document [3] adopts a random scene simulation method based on voltage sensitivity to evaluate the photovoltaic receiving capacity of the power distribution network, and improves the evaluation efficiency through quasi-normal probability distribution sampling. The literature [4-6] adopts a Monte Carlo random simulation method, and evaluates and obtains the maximum admission capacity of the photovoltaic of a given power distribution system based on a large amount of operation data of the actual power distribution system. The economic benefit analysis of the existing research on the photovoltaic system is mostly based on the whole life cycle income assessment and development of the photovoltaic system. Document [7] proposes an economic evaluation flow of distributed photovoltaic grid connection, and establishes a corresponding life cycle cost-benefit model for different operation modes of domestic photovoltaic power generation. Document [8] analyzes the electric energy transmission efficiency of a photovoltaic power station in the process of power generation, transmission and distribution, and establishes a photovoltaic system efficiency and cost evaluation model considering photovoltaic system loss and power transmission and distribution cost. According to the research on the photovoltaic receiving capacity of the power distribution network, a specific scene is mostly selected or a certain determined operation section of the power distribution network is developed, the situations that the maximum photovoltaic output and the minimum load are not generated in the same day are ignored, and the accuracy of an evaluation result still needs to pass through annual time sequence tidal current simulation verification.
A photovoltaic investor adopts a traditional photovoltaic capacity planning mode to plan a power station by referring to the maximum photovoltaic access capacity of a node, so that the safe operation of a power distribution network can be ensured when the photovoltaic is fully generated and connected to the grid.
Reference documents:
[1] ding Ming, Liu Sheng, calculating limit power of a plurality of photovoltaic power sources based on a genetic algorithm [ J ]. a power grid technology, 2013, 37 (4): 922-926.
[2] Zhao wave, Weirikun, Xuzhicheng, etc. take into account feeder photovoltaic receptivity random scene analysis of energy storage systems [ J ] power system automation, 2015, 39 (9): 34-40.
[3] Xuzhicheng, Zhao ripples, Dingming, etc. the random scene simulation of the photovoltaic receiving capacity of the power distribution network based on voltage sensitivity and the optimization setting of inverter control parameters [ J ]. Chinese Motor journey report, 2016, 36 (6): 1578-1587.
[5]Anamika D，Surya S.On estimation and sensitivity analysis ofdistribution circuit’s photovoltaic hosting capacity[J].IEEE Transactions onPower Systems，2017，32(4)：2779-2789.
[5]Ricardo T，Diogo S，Caio O P，etal.A comprehensive assessment of PVhosting capacity on low-voltage distribution systems[J].IEEE Transactions onPower Delivery，2018，33(2)：1002-1012.
[6]Fei Ding，Barry M.On distributed PV hosting capacity estimation，sensitivity study and improvement[J].IEEE Transactions on Sustainable Energy，2017，8(3)：1010-1020.
[7] Sujian, zulimei, litmus cost/benefit analysis of distributed photovoltaic grid-connection [ J ]. report on china electrical engineering, 2013, 33 (34): 50-56.
[8] Wangkada, zhangbaoxi, remote centralized versus in-situ distributed photovoltaic power economy comparison [ J ]. power system automation, 2017, 41 (16): 179-186.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method for determining the photovoltaic optimal configuration capacity of a node of a power distribution network.
The purpose of the invention can be realized by the following technical scheme:
a method for determining photovoltaic optimal configuration capacity of a power distribution network node comprises the following steps:
s1: acquiring the maximum photovoltaic output allowed to be injected at each moment of a power distribution network node within one year;
s2: acquiring a one-year time sequence output curve of an existing known photovoltaic power station, and calculating a conversion coefficient matrix between photovoltaic installed capacity and photovoltaic output;
s3: and establishing a photovoltaic power station full life cycle profit evaluation model considering the abandoned light according to the steps S1 and S2, and obtaining the photovoltaic installed capacity which enables the photovoltaic power station full life cycle profit to be maximum, namely the optimal configuration capacity.
Further, the step S1 specifically includes the following steps:
s101: establishing a photovoltaic output evaluation model, wherein the target function of the photovoltaic output evaluation model is as follows:
maxP
PV,t,i
in the formula, P
PV,t,iPhotovoltaic output injected into a node i of the power distribution network at the moment t;
the constraint conditions of the photovoltaic output evaluation model comprise:
and power balance constraint:
limiting the upper and lower limits of the node voltage;
V
i,min≤V
i≤V
i,max
branch transmission capacity constraints;
S
ij≤S
ij,max
wherein, P
DN,t、Q
DN,tActive power and reactive power interacted between the power distribution network and a superior power grid at the moment t; p
load,t、Q
load,tThe active and reactive load values of the power distribution network at the moment t; p
loss,t、Q
loss,tThe active and reactive loss values of the power distribution network at the moment t are obtained; p
PV,t,i、Q
PV,t,iConnecting photovoltaic active and reactive power output to a power distribution network node i at the moment t; v
i,max、V
i,minThe voltage amplitude of the node i is the upper limit and the lower limit; s
ij,maxThe upper limit of the transmission capacity of the branch between the nodes i and j is set;
s102: solving the photovoltaic output evaluation model by utilizing annual time sequence simulation based on genetic algorithm to obtain maximum photovoltaic output P
PV,t,i,maxAnd the individual binary codes and the fitness function in the population represent the photovoltaic output injected by the node i of the power distribution network.
Further, the solving of the photovoltaic output evaluation model by using the annual time series simulation based on the genetic algorithm specifically includes:
a) inputting branch impedance and node load parameters, setting the size of a population, the number of reproduction generations and the length of individual codes, and initializing the population;
b) solving a fitness function, sequentially inputting individual information of the population to perform load flow calculation, circulating to input all the individual information in the population, and solving a corresponding fitness function value;
c) selecting, crossing, mutating and propagating individuals in the population, calculating fitness functions of the individuals in all offspring according to the process, comparing to obtain the maximum value of the fitness functions, wherein the corresponding individual value is the maximum photovoltaic output P allowed to be injected by the power distribution network node i at the time t
PV,t,i,max；
d) Sequentially evaluating each time of the power distribution network node i year to obtain the maximum photovoltaic output which is allowed to be injected at each time of the node 8760h and is obtained at each time of the power distribution network node i year, and generating P
PV,t,i,maxAnd (4) matrix.
Further, in step S2, the method for calculating the conversion coefficient matrix between the photovoltaic installed capacity and the photovoltaic output includes:
sequentially calculating the photovoltaic output P of each moment of the annual time sequence output curve 8760h of the photovoltaic power station according to the annual time sequence output curve of the photovoltaic power station
tInstalled capacity P of power station
0Ratio of K
PV1,tTo obtain a matrix K
PV1The following formula:
P
t＝P
0K
PV1,t
in the formula, K
PV1,tThe conversion coefficient matrix is the conversion coefficient matrix between the photovoltaic installed capacity and the photovoltaic output.
Further, step S3 specifically includes:
1) establishing a photovoltaic power station full life cycle profit evaluation optimization model considering light abandonment, wherein the model takes the maximum photovoltaic power station full life cycle profit as an objective function and takes constraint conditions of safe operation of a power distribution network as constraint conditions, and the objective function is expressed as follows:
maxB＝B
P-C
P
wherein B is the gain obtained by the whole life cycle of the photovoltaic power station, B
PThe current value of the total electricity selling income of the whole life cycle of the photovoltaic power station, C
PThe current value of the total investment of the whole life cycle of the photovoltaic power station is obtained;
B
Pthe calculation method is as follows:
wherein T is the number of years, T is the project year of the photovoltaic power station, i
cTo reduce the current rate, I
nFor annual electricity selling income of a photovoltaic power station, the calculation method comprises the following steps:
I
n＝(W
1-W
2)ω
in the formula, W
1Is the theoretical generating capacity of a photovoltaic power station for one year, W
2The electricity quantity of the photovoltaic power station is abandoned one year, omega is the electricity price of the photovoltaic on-line marker post, W
1And W
2The calculation method comprises the following steps:
conversion coefficient matrix K between photovoltaic installed capacity and photovoltaic output
PV1Converting the photovoltaic installed capacity represented by the genetic algorithm into photovoltaic output at each moment of 8760h a year, drawing an annual photovoltaic output curve, and integrating the curve with time into the annual theoretical power generation W of the photovoltaic power station
1(ii) a Sequentially using lights at each moment of 8760h a yearSubtracting the maximum photovoltaic output allowed to be injected by the node at the corresponding moment from the photovoltaic output to obtain the abandoned light power at each moment of 8760h, drawing an annual abandoned light curve according to the abandoned light power, wherein the integral of the curve to the time is the annual abandoned light quantity W of the photovoltaic power station
2；
C
PThe calculation method of (2) is as follows:
in the formula, C
0Initial investment costs for building photovoltaic power plants, C
opAnnual operating maintenance costs of photovoltaic power stations, C
dAnnual loan cost for photovoltaic power plant project, C
RIs the residual value of the photovoltaic, k is the loan proportion of the photovoltaic power station project, T
1Is the loan age for the project;
2) and solving the photovoltaic power station life cycle yield evaluation optimization model by using a genetic algorithm to obtain the maximum yield of the photovoltaic power station life cycle, wherein the photovoltaic installed capacity represented by the corresponding individual is the optimal configuration capacity of the photovoltaic power station.
Further, in the photovoltaic power station full life cycle profit evaluation optimization model solved by the genetic algorithm, the individual binary code represents the photovoltaic installed capacity of the power station, and the fitness function represents the full life cycle profit of the photovoltaic power station under a certain installed capacity, and the method specifically comprises the following steps:
s301: inputting parameters required for evaluating the full life cycle income of the photovoltaic power station, including a maximum photovoltaic output matrix P
PV,i,maxAnd a conversion coefficient matrix K between the installed photovoltaic capacity and the photovoltaic output
PV1Setting population size, reproduction algebra and individual binary coding length;
s302: initializing a population, and calculating a fitness function:
sequentially inputting the photovoltaic installed capacity of the power station represented by the individual in the population, calculating the whole life cycle income of the individual, and assigning the income to a fitness function;
s303: selecting, crossing and mutating individuals in the population, and reproducing N
2Instead, find the corresponding fitness of all individualsAnd (3) taking values of the response function, comparing and solving the maximum value of the fitness function, namely the maximum gain of the whole life cycle of the photovoltaic power station, wherein the photovoltaic installed capacity represented by the corresponding individual is the photovoltaic installed capacity which enables the maximum gain of the whole life cycle of the photovoltaic power station.
Further, the parameters required for evaluating the full life cycle revenue of the photovoltaic power station in step S301 further include photovoltaic on-line benchmarking electricity price ω, photovoltaic unit installed capacity cost, loan year, loan proportion, annual rate, photovoltaic power generation system project year T, photovoltaic power station operation rate, discount rate, and photovoltaic residual value proportion.
Further, the determination method further includes:
s4: maximum photovoltaic output P of power distribution network node at each moment
PV,t,i,maxBy calculating the coefficient matrix K
PV1And obtaining the maximum admission capacity at the moment, and if the optimal configuration capacity is larger than the maximum admission capacity at the moment, executing the light abandoning operation.
Further, the premise assumption of establishing the photovoltaic power station full-life-cycle profit assessment model considering light abandonment is as follows:
the photovoltaic investor is responsible for investment, construction and operation and maintenance of the power station, enjoys the operating right of photovoltaic power generation, is fully connected with the power grid on the premise of ensuring the safe operation of the power distribution network, and obtains the electricity price subsidy of the power grid enterprise.
Compared with the prior art, the invention has the following beneficial effects:
1) according to the photovoltaic capacity allocation method, the maximum photovoltaic allowable output is obtained through load flow calculation, when the photovoltaic output power exceeds the allowable value, light is abandoned, the photovoltaic power output is reduced, the photovoltaic output is kept at the maximum allowable value, a photovoltaic power station is planned according to the optimal capacity, and the more accurate and most effective photovoltaic capacity allocation can be obtained;
2) according to the photovoltaic maximum allowable output power evaluation method, the photovoltaic maximum allowable output power is obtained through load flow calculation, when the photovoltaic output power exceeds the allowable value, the photovoltaic power output is reduced, the photovoltaic output power is kept at the maximum allowable value, load flow calculation is not required to be carried out again when the photovoltaic capacity changes, and the calculation amount of photovoltaic optimal configuration capacity evaluation is effectively reduced;
3) the method disclosed by the invention is based on the angle of safe operation of the power distribution network, the influences of loads, time sequence fluctuation of photovoltaic all year round and seasonal factors are fully considered, the maximum photovoltaic output allowed to be injected at each moment of a power distribution network node within one year is estimated based on annual time sequence simulation, and the estimation result is accurately determined to be high.
Drawings
FIG. 1 is a flow chart of a photovoltaic power plant full life cycle revenue assessment model solution;
FIG. 2 is a diagram of an IEEE33 node power distribution system;
FIG. 3 is a graph of annual load timing;
FIG. 4 is a graph of annual time series output of a photovoltaic power plant;
fig. 5 is a maximum photovoltaic force diagram of the nodes of the IEEE33 node power distribution system allowed to be injected at 8760h each time in a year.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
In order to avoid the situation that the photovoltaic configuration is too conservative according to the maximum access capacity, from the viewpoint of safe operation of the power distribution network, the influences of loads, time sequence fluctuation of the photovoltaic all year round and seasonal factors are fully considered, and the maximum photovoltaic output allowed to be injected at each moment of the power distribution network node within one year is estimated based on annual time sequence simulation. And then, from the perspective of a photovoltaic investor, calculating installed capacity for maximizing the profit of the photovoltaic system, wherein the installed capacity is called photovoltaic optimal capacity, if the installed capacity is higher than the maximum admissible capacity, the safe operation of the power distribution network is ensured by abandoning light, and based on the maximum photovoltaic output evaluation result allowed to be injected into the power distribution network nodes at all times in one year, a photovoltaic power station full-life cycle profit evaluation model considering the abandoning light is established according to a mode for planning the photovoltaic power station according to the optimal capacity, and the photovoltaic optimal capacity (optimal investment capacity) for maximizing the profit of the power station is finally obtained through evaluation.
The invention provides a method for determining photovoltaic optimal configuration capacity of a power distribution network node, which comprises the following steps:
s1: calculating the maximum photovoltaic output allowed to be injected at each moment of a power distribution network node within one year, specifically as follows:
a) establishing a photovoltaic output evaluation model:
an objective function:
establishing an objective function for evaluating the maximum photovoltaic output allowed to be injected at each moment in the power distribution network node within one year:
maxP
PV,t,i
in the formula, P
PV,t,iPhotovoltaic output injected into a node i of the power distribution network at the moment t;
constraint conditions are as follows:
in order to ensure the safe operation of the power distribution network, the invention mainly considers the network operation constraints, including power balance constraint, node voltage upper and lower limit constraint and branch transmission capacity constraint:
and power balance constraint:
limiting the upper and lower limits of the node voltage;
V
i,min≤V
i≤V
i,max
branch transmission capacity constraints;
S
ij≤S
ij,max
wherein, P
DN,t、Q
DN,tActive power and reactive power interacted between the power distribution network and a superior power grid at the moment t; p
load,t、Q
load,tThe active and reactive load values of the power distribution network at the moment t; p
loss,t、Q
loss,tThe active and reactive loss values of the power distribution network at the moment t are obtained; p
PV,t,i、Q
PV,t,iConnecting photovoltaic active and reactive power output to a power distribution network node i at the moment t; v
i,max、V
i,minThe voltage amplitude of the node i is the upper limit and the lower limit; s
ij,maxThe upper limit of the transmission capacity of the branch between the nodes i and j is set;
b) and solving a photovoltaic output evaluation model by utilizing annual time sequence simulation based on a genetic algorithm, calling time sequence trend calculation, sequentially evaluating the maximum photovoltaic output allowed to be injected at each moment of 8760h in a year at the node i, wherein the photovoltaic output injected by the node i of the power distribution network is represented by the individual binary code and the fitness function in the population. The method comprises the following steps:
s201: inputting branch impedance and node load parameters, setting the size of a population, the number of reproduction generations and the length of individual codes, and initializing the population;
s202: solving the fitness function, comprising the following steps: sequentially inputting individual information of the population to perform load flow calculation, and verifying whether the photovoltaic output injected by the node i represented by the individual can enable the node voltage of the power distribution network and the branch load flow constraint to meet or not; if the constraint condition is met, assigning the individual characterization value to a fitness function; if not, adding a penalty function to the fitness function to enable the corresponding individual to be eliminated in the selection stage, and thus obtaining the fitness function value corresponding to a certain individual in the population; circulating until all individual information in the population is input and calculating the corresponding fitness function value;
s203: selecting, crossing and varying individuals in the population, reproducing for N1 generations, simultaneously calculating fitness functions of the individuals in all the offspring according to the process, comparing to obtain the maximum value of the fitness functions, wherein the corresponding individual value is the maximum photovoltaic output allowed to be injected by the power distribution network node i at the time t;
s204: sequentially evaluating each moment in i years of the node of the power distribution network to obtain a maximum photovoltaic output matrix P allowed to be injected at each moment of 8760h of the node
PV,t,i,max。
S2: and acquiring a one-year time sequence output curve of the existing known photovoltaic power station, and calculating a conversion coefficient matrix between the photovoltaic installed capacity and the photovoltaic output. Conversion coefficient matrix K between photovoltaic installed capacity and photovoltaic output
PV1The calculation process is as follows:
sequentially calculating the photovoltaic output P of each moment of the annual time sequence output curve 8760h of the photovoltaic power station according to the annual time sequence output curve of the photovoltaic power station
tInstalled capacity P of power station
0Ratio of K
PV1,tTo obtain a matrix K
PV1The following formula:
P
t＝P
0K
PV1,t
by means of a matrix K
PV1Can adjust the installed capacity P of any given photovoltaic power station
0The conversion is photovoltaic output P corresponding to 8760h each time in one year
t。
S3: evaluating photovoltaic optimal configuration capacity of power distribution network nodes
The photovoltaic power station built by the photovoltaic investor is assumed to adopt a general purchasing and general marketing operation mode, namely, the photovoltaic investor is responsible for investment, construction and operation and maintenance of the power station and enjoys the operation right of photovoltaic power generation, on the premise of ensuring safe operation of a power distribution network, the generated energy is completely connected to the grid, the electricity price subsidy of a power grid enterprise is obtained, and accordingly, a photovoltaic power station full life cycle income evaluation model is built.
The method comprises the following specific steps:
1) establishing a photovoltaic power station life cycle income assessment model considering light abandonment:
an objective function:
establishing an optimization model with maximum photovoltaic power station life cycle yield as an objective function:
max B＝B
P-C
P
wherein B is the gain obtained by the whole life cycle of the photovoltaic power station, B
PThe current value of the total electricity selling income of the whole life cycle of the photovoltaic power station, C
PThe current value of the total investment of the whole life cycle of the photovoltaic power station is obtained; b is
PAnd C
PThe specific calculation method is as follows:
wherein T is the number of years, T is the project year of the photovoltaic power station, i
cTo reduce the current rate, I
nFor annual electricity selling income of a photovoltaic power station, the calculation method comprises the following steps:
I
n＝(W
1-W
2)ω
in the formula, W
1Is the theoretical generating capacity of a photovoltaic power station for one year, W
2The light and electricity abandoning power of a photovoltaic power station for one year, W
1－W
2For the annual actual power generation of power stationsThe quantity omega is the electricity price of the photovoltaic internet access marker post;
in the formula, C
0Initial investment cost for building a photovoltaic power station comprises equipment expenses and installation and construction cost of a photovoltaic module, an inverter, a bracket, a cable, monitoring protection and the like; c
opThe annual operation maintenance cost of the photovoltaic power station comprises the maintenance labor cost of the power station and the equipment maintenance cost; c
dThe annual loan cost of the project of the photovoltaic power station is calculated, and an equal-amount cost repayment mode is adopted; c
RIs the residual value of the photovoltaic; k is the loan proportion of the photovoltaic power station project; t is
1Is the loan age for the project; c
0、C
op、C
d、C
RThe specific calculation method is as follows:
C
0＝P
capC
1
C
op＝C
0R
op
C
R＝K
RC
0
in the formula, P
capThe total photovoltaic installed capacity; c
1Investment cost for installed capacity of a photovoltaic unit; r
opThe operation and maintenance rates of the photovoltaic power station are obtained; i.e. i
dLoan annual rate for the photovoltaic power plant project; k
RThe ratio of the photovoltaic residual value to the initial investment of the power station is obtained;
the constraint condition is the constraint condition of safe operation of the power distribution network;
theoretical generated energy W of photovoltaic power station for one year
1And the light abandonment electric quantity W of the photovoltaic power station for one year
2The calculation process is as follows:
conversion coefficient matrix K between photovoltaic installed capacity and photovoltaic output
PV1Converting the photovoltaic installed capacity represented by the genetic algorithm individual into photovoltaic output at each moment of 8760h a year, drawing an annual photovoltaic output curve, and integrating the curve with time into photovoltaic outputAnnual theoretical power generation W of power station
1；
Sequentially subtracting the maximum photovoltaic output allowed to be injected by the node at the corresponding moment from the photovoltaic output at each moment 8760h in one year to obtain the abandoned light power at each moment 8760h, drawing an annual abandoned light curve according to the abandoned light power, wherein the integral of the curve to time is the annual abandoned light quantity W of the photovoltaic power station
2。
2) Solving the optimal configuration capacity evaluation model by using a genetic algorithm, as shown in fig. 1, the specific flow is as follows:
a) inputting parameters:
inputting parameters required for evaluating the full life cycle income of the photovoltaic power station, wherein the parameters comprise a maximum photovoltaic output matrix P allowed to be injected at each moment of 8760h in a year by a node i
PV,i,maxConversion coefficient matrix K between photovoltaic installed capacity and photovoltaic output
PV1The method comprises the steps of setting a group size, a reproduction algebra and an individual binary code length, representing the photovoltaic installed capacity of a power station in an algorithm by the binary code, and representing the full life cycle income of the photovoltaic power station under a certain installed capacity in the algorithm by a fitness function;
b) initializing a population, and calculating a fitness function:
sequentially inputting the photovoltaic installed capacity of the power station represented by the individual in the population, calculating the whole life cycle income of the individual, and assigning the income to a fitness function;
c) selecting, crossing and mutating individuals in the population, and reproducing N
2And instead, calculating the fitness function values corresponding to all individuals, and comparing to calculate the maximum value of the fitness function, namely the maximum benefit of the whole life cycle of the photovoltaic power station, wherein the photovoltaic installed capacity represented by the corresponding individual is the optimal capacity of the photovoltaic power station.
Examples of the applications
As shown in fig. 2 for the IEEE33 node power distribution system, the node photovoltaic optimum configuration capacity is evaluated. The simulation program was run on a computer with a processor of Intel Core i5-4590 CPU/3.3GHZ and the simulation software used version MATLAB R2014 b.
The loads in the distribution system shown in fig. 2 are classified into three types of loads, i.e., residential, industrial, and commercial loadsIn the model, the nodes 1 to 17 are residential loads, the nodes 18 to 24 are industrial loads, and the nodes 25 to 32 are commercial loads. The time sequence curves of the total load and various loads are shown in fig. 3, wherein the load is relatively heavy in summer, the loads are relatively average in other seasons, the maximum total active load in the year is 4018.741kW, and the power factor of the loads is 0.9. The voltage at the line head end, node 0, is set to 1.04. The method selects the annual time sequence output curve of the photovoltaic power station shown in figure 4 to obtain a capacity-output conversion matrix K
PV1The installed capacity of the plant is 2 MW. Upper and lower voltage amplitude limits V of node i
i,max、V
i,min1.07, 0.93, respectively, upper limit of branch transmission capacity S
ij,maxThe weight was taken as 6 MW.
The photovoltaic power station life cycle yield assessment parameters are selected as follows: the area belongs to a solar energy second-class resource area, and the electricity price omega of a photovoltaic internet access marker post in 2016 is 0.88 yuan/kWh. According to the 2017 New energy Power Generation analysis report of China issued by the national grid energy research institute, 2016 year China photovoltaic Unit installed Capacity cost C
1Is 7300 yuan/kW. Known from the credit guidance opinions of the new energy industry of the Chinese Industrial and commercial Bank, the construction loan age T1 of the photovoltaic power station is 15 years. According to the requirement of 'notice of national institute on adjusting and perfecting capital fund system of fixed asset project', the loan proportion k of the project of the photovoltaic power station is 80%. Refer to 2016 the loan interest of China Industrial and commercial Bank for more than 5 years, annual interest rate i
dThe yield was 4.9%. The project year T of the photovoltaic power generation system is 25 years according to the service life of the solar panel; photovoltaic power plant operation rate R
opTaking the content to be 0.12%; discount rate i
cTaking the weight percentage to be 5%; photovoltaic residual value ratio K
RThe yield was taken to be 5%.
Analysis of simulation results
The evaluation result of the maximum photovoltaic output allowed to be injected at each moment in one year at the node of the power distribution network is as follows:
based on the annual load time sequence curve shown in fig. 3, the maximum photovoltaic output allowed to be injected at each time of 8760h in a year at each node in the IEEE33 node power distribution system is estimated by applying the estimation model and the solving method, and the simulation result is shown in fig. 5. As can be seen from fig. 5, the maximum photovoltaic output allowed to be injected at each time within one year at the node close to the bus side is relatively high, such as nodes 1 to 4, nodes 18 and 22; and the maximum photovoltaic output allowed by the nodes at the tail ends of the lines is relatively small, such as nodes 14-17 and nodes 31-32. As shown in FIG. 3, the summer load reaches 3700 h-5800 h, which is slightly heavier than the loads in other seasons, so that the maximum photovoltaic output allowed to be injected into each node of the distribution network in summer in the figure is improved to a certain extent.
4.2.2 construction of Power station revenue assessment results based on optimal Capacity
Based on the photovoltaic power station full life cycle income evaluation model considering the abandoned light and the evaluation result of the maximum photovoltaic output allowed to be injected into the node at each moment in one year, the investment capacity which enables the maximum photovoltaic power station income is obtained through evaluation, namely the optimal photovoltaic configuration capacity. Assuming that a photovoltaic investor builds a photovoltaic power station at a node 17, and builds the power station according to the optimal photovoltaic configuration capacity of the node, the evaluation result of the total life cycle income of the photovoltaic power station is shown in table 1.
From table 1, a photovoltaic power station is built at the node 17, the optimal configuration capacity of the photovoltaic power station is 3.632MW, and the annual actual power generation amount of the photovoltaic power station built according to the optimal configuration capacity is 4.3414 × 10
6kWh, light rejection 9.2896X 10
5kWh accounts for 17.63% of theoretically generated energy, and the total life cycle gain of the photovoltaic power station is 2.8782 multiplied by 10
7And (5) Yuan.
Table 1 node 17 photovoltaic power station planning evaluation result according to optimal capacity
In conclusion, the photovoltaic investment capacity of a photovoltaic investor can be properly improved on the basis of the maximum photovoltaic access capacity of the nodes of the power distribution network, the photovoltaic is built according to the optimal photovoltaic capacity, the safe operation of the power distribution network is guaranteed by abandoning light, the difficulty of operation control is increased, and the economic benefit of a photovoltaic power station is obviously improved compared with that of a traditional planning mode on the premise of guaranteeing the safe operation, so that reference is provided for photovoltaic investment.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (8)
1. A method for determining photovoltaic optimal configuration capacity of a power distribution network node is characterized by comprising the following steps:
s1: acquiring the maximum photovoltaic output allowed to be injected at each moment of a power distribution network node within one year;
s2: acquiring a one-year time sequence output curve of an existing known photovoltaic power station, and calculating a conversion coefficient matrix between photovoltaic installed capacity and photovoltaic output;
s3: and establishing a photovoltaic power station full life cycle profit evaluation model considering the abandoned light according to the steps S1 and S2, and obtaining the photovoltaic installed capacity which enables the photovoltaic power station full life cycle profit to be maximum, namely the optimal configuration capacity.
2. The method for determining the optimal photovoltaic configuration capacity of the power distribution network node according to claim 1, wherein the step S1 specifically comprises the following steps:
s101: establishing a photovoltaic output evaluation model, wherein the target function of the photovoltaic output evaluation model is as follows:
max P
PV,t,i
in the formula, P
PV,t,iPhotovoltaic output injected into a node i of the power distribution network at the moment t;
the constraint conditions of the photovoltaic output evaluation model comprise:
and power balance constraint:
limiting the upper and lower limits of the node voltage;
V
i,min≤V
i≤V
i,max
branch transmission capacity constraints;
S
ij≤S
ij,max
wherein, P
DN,t、Q
DN,tActive power and reactive power interacted between the power distribution network and a superior power grid at the moment t; p
load,t、Q
load,tThe active and reactive load values of the power distribution network at the moment t; p
loss,t、Q
loss,tThe active and reactive loss values of the power distribution network at the moment t are obtained; p
PV,t,i、Q
PV,t,iConnecting photovoltaic active and reactive power output to a power distribution network node i at the moment t; v
i,max、V
i,minThe voltage amplitude of the node i is the upper limit and the lower limit; s
ij,maxThe upper limit of the transmission capacity of the branch between the nodes i and j is set;
s102: solving the photovoltaic output evaluation model by utilizing annual time sequence simulation based on genetic algorithm to obtain maximum photovoltaic output P
PV,t,i,maxAnd the individual binary codes and the fitness function in the population represent the photovoltaic output injected by the node i of the power distribution network.
3. The method for determining the optimal photovoltaic configuration capacity of the power distribution network node according to claim 2, wherein the solving of the photovoltaic output evaluation model by means of the annual time series simulation based on the genetic algorithm specifically comprises:
a) inputting branch impedance and node load parameters, setting the size of a population, the number of reproduction generations and the length of individual codes, and initializing the population;
b) solving a fitness function, sequentially inputting individual information of the population to perform load flow calculation, circulating to input all the individual information in the population, and solving a corresponding fitness function value;
c) selecting, crossing, mutating and propagating individuals in the population, calculating fitness functions of the individuals in all offspring according to the process, comparing to obtain the maximum value of the fitness functions, wherein the corresponding individual value is the maximum photovoltaic output P allowed to be injected by the power distribution network node i at the time t
PV,t,i,max；
d) The time of each power distribution network node in i year is evaluated in sequence to obtain the allowable injection of each time of each power distribution network node in one year, wherein the allowable injection of each time of the node 8760h is obtainedMaximum photovoltaic output of the input, generating P
PV,t,i,maxAnd (4) matrix.
4. The method for determining the optimal photovoltaic configuration capacity of the power distribution network node according to claim 1, wherein the calculation method of the conversion coefficient matrix between the installed photovoltaic capacity and the photovoltaic output in step S2 is as follows:
sequentially calculating the photovoltaic output P of each moment of the annual time sequence output curve 8760h of the photovoltaic power station according to the annual time sequence output curve of the photovoltaic power station
tInstalled capacity P of power station
0Ratio of K
PV1,tTo obtain a matrix K
PV1The following formula:
P
t＝P
0K
PV1,t
in the formula, K
PV1,tThe conversion coefficient matrix is the conversion coefficient matrix between the photovoltaic installed capacity and the photovoltaic output.
5. The method for determining the optimal photovoltaic configuration capacity of the distribution network node according to claim 1, wherein the step S3 specifically includes:
1) establishing a photovoltaic power station full life cycle profit evaluation optimization model considering light abandonment, wherein the model takes the maximum photovoltaic power station full life cycle profit as an objective function and takes constraint conditions of safe operation of a power distribution network as constraint conditions, and the objective function is expressed as follows:
max B＝B
P-C
P
wherein B is the gain obtained by the whole life cycle of the photovoltaic power station, B
PThe current value of the total electricity selling income of the whole life cycle of the photovoltaic power station, C
PThe current value of the total investment of the whole life cycle of the photovoltaic power station is obtained;
B
Pthe calculation method is as follows:
wherein T is the number of years, T is the project year of the photovoltaic power station, i
cTo reduce the current rate, I
nFor annual electricity selling income of a photovoltaic power station, the calculation method comprises the following steps:
I
n＝(W
1-W
2)ω
in the formula, W
1Is the theoretical generating capacity of a photovoltaic power station for one year, W
2The electricity quantity of the photovoltaic power station is abandoned one year, omega is the electricity price of the photovoltaic on-line marker post, W
1And W
2The calculation method comprises the following steps:
conversion coefficient matrix K between photovoltaic installed capacity and photovoltaic output
PV1Converting the photovoltaic installed capacity represented by the genetic algorithm into photovoltaic output at each moment of 8760h a year, drawing an annual photovoltaic output curve, and integrating the curve with time into the annual theoretical power generation W of the photovoltaic power station
1(ii) a Sequentially subtracting the maximum photovoltaic output allowed to be injected by the node at the corresponding moment from the photovoltaic output at each moment 8760h in one year to obtain the abandoned light power at each moment 8760h, drawing an annual abandoned light curve according to the abandoned light power, wherein the integral of the curve to time is the annual abandoned light quantity W of the photovoltaic power station
2；
C
PThe calculation method of (2) is as follows:
in the formula, C
0Initial investment costs for building photovoltaic power plants, C
opAnnual operating maintenance costs of photovoltaic power stations, C
dAnnual loan cost for photovoltaic power plant project, C
RIs the residual value of the photovoltaic, k is the loan proportion of the photovoltaic power station project, T
1Is the loan age for the project;
2) and solving the photovoltaic power station life cycle yield evaluation optimization model by using a genetic algorithm to obtain the maximum yield of the photovoltaic power station life cycle, wherein the photovoltaic installed capacity represented by the corresponding individual is the optimal configuration capacity of the photovoltaic power station.
6. The method for determining the optimal photovoltaic configuration capacity of the power distribution network nodes according to claim 5, wherein in the estimation optimization model for solving the full life cycle profit of the photovoltaic power station by using the genetic algorithm, the individual binary code represents the installed photovoltaic capacity of the power station, and the fitness function represents the full life cycle profit of the photovoltaic power station under a certain installed capacity, and the method comprises the following specific steps:
s301: inputting parameters required for evaluating the full life cycle income of the photovoltaic power station, including a maximum photovoltaic output matrix P
PV,i,maxAnd a conversion coefficient matrix K between the installed photovoltaic capacity and the photovoltaic output
PV1Setting population size, reproduction algebra and individual binary coding length;
s302: initializing a population, and calculating a fitness function:
sequentially inputting the photovoltaic installed capacity of the power station represented by the individual in the population, calculating the whole life cycle income of the individual, and assigning the income to a fitness function;
s303: selecting, crossing and mutating individuals in the population, and reproducing N
2And instead, calculating the fitness function values corresponding to all individuals, and comparing to calculate the maximum value of the fitness function, namely the maximum yield of the whole life cycle of the photovoltaic power station, wherein the photovoltaic installed capacity represented by the corresponding individual is the photovoltaic installed capacity which enables the maximum yield of the whole life cycle of the photovoltaic power station.
7. The method for determining the optimal photovoltaic configuration capacity of the power distribution network nodes according to claim 6, wherein the parameters required for evaluating the full-life-cycle income of the photovoltaic power station in step S301 further include photovoltaic on-line benchmarking electricity price ω, photovoltaic unit installed capacity cost, loan year, loan proportion, year rate, photovoltaic power generation system project year T, photovoltaic power station operating rate, discount rate and photovoltaic residue proportion.
8. The method for determining the optimal photovoltaic configuration capacity of the power distribution network node according to claim 1, further comprising:
s4: maximum photovoltaic output P of power distribution network node at each moment
PV,t,i,maxBy calculating the coefficient matrix K
PV1And obtaining the maximum admission capacity at the moment, and if the optimal configuration capacity is larger than the maximum admission capacity at the moment, executing the light abandoning operation.
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