CN111144655A - Combined optimization method for site selection, volume fixing and power distribution network frame of distributed power supply - Google Patents
Combined optimization method for site selection, volume fixing and power distribution network frame of distributed power supply Download PDFInfo
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Abstract
The invention relates to a combined optimization method for the site selection and the volume determination of a distributed power supply and the grid structure of a power distribution network, wherein the site selection and the volume determination planning of the distributed power supply and an energy storage device and the grid structure planning of the power distribution network are optimized simultaneously, and an economic optimal planning scheme meeting constraint conditions such as branch capacity, node voltage, radiation operation, reliability requirements and the like is sought by taking the minimum cost-benefit mathematical model function of the power distribution network containing the distributed power supply as a target function; the planning result is closer to the reality, and various economic and technical indexes of the power distribution network are favorably reflected; the multi-energy complementary optimization and the coordinated planning of the distribution network frame reduce the construction cost and the loss cost of the distribution network, delay the upgrading and reconstruction of distribution network equipment and improve the permeability of the distributed power output in the power grid.
Description
Technical Field
The invention relates to a combined optimization method for site selection, volume determination and a power distribution network frame of a distributed power supply.
Background
In the face of the rapid increase of power demand and the increasingly outstanding environment problems, Distributed Generation (DG) which is clean, environment-friendly, high in benefit and flexible to install is gaining more and more favor, wherein the combination of a large power grid and distributed generation is the development direction of the power industry at present. Distributed power generation generally refers to small modular, decentralized, efficient, reliable power generation units arranged near users, generating power in the range of a few kilowatts to hundreds of megawatts. The method mainly comprises the following steps: micro-gas turbines (MT), Photovoltaic (PV), wind power (DWG), biomass power (BG), and the like.
Firstly, because the output of part of distributed power supplies (such as photovoltaic power generation, wind power generation and the like) has random fluctuation [1], the power distribution network is required to provide more spare capacity to make up the uncertainty of the output [2], and the acceptance of the power distribution network to DG is limited in both safety and economic aspects. And a proper amount of energy Storage equipment (BS) is installed, and the fluctuation of DG output can be obviously inhibited by utilizing the time sequence complementary characteristic among the BS, the DG and the load requirement, so that the spare capacity of a distribution network is reduced, and the economy of a planning scheme is improved.
And secondly, the power distribution network expansion planning is to reasonably determine a target grid structure after a plurality of years according to the existing power grid structure on the basis of the known planning horizontal year power supply development and load prediction, so that the optimal power grid economy is achieved on the premise of ensuring safety and reliability. In essence, the power distribution network frame planning is a discrete, nonlinear and multi-constraint integer planning problem, and the access of the DGs and the BSs greatly increases the planning dimension and difficulty. The conventional method is to determine the positions and capacities of DGs and BSs and then plan the grid structure, and is a longitudinal planning method divided artificially; however, in the actual situation, the change of any one of the DG, the BS and the grid structure affects the optimization result of the other, and the method of artificially dividing the DG, the BS and the grid structure cannot achieve the overall optimization.
Aiming at the defects of the existing research, the system research and development of the distributed power supply high-permeability access power distribution network are provided, and the access schemes of DG, BS position and capacity, a line to be modified, a newly-built line and a newly-added load point can be determined at the same time.
Disclosure of Invention
The invention aims to solve the technical problem of providing a distributed power source site selection, volume fixing and power distribution network frame joint optimization method, which can improve the permeability of a distributed power source and effectively improve the comprehensive economic benefit of power distribution network investment.
The technical scheme adopted by the invention is as follows:
a method for jointly optimizing site selection, volume fixing and power distribution network frame of a distributed power supply comprises the following steps:
establishing a power distribution network frame planning mathematical model aiming at improving the permeability of the distributed power supply by taking the minimum cost-benefit mathematical model function of the power distribution network of the distributed power supply as a target function:
fmin＝(Cinvest×PV+Co+CEENS)-(Ce+Cb×PVd+Cdg) (1)
in the formula, CinvestInitial investment cost for the power distribution network; coAnnual operation and maintenance costs of the power distribution network; cEENSAnnual power outage cost of the power distribution network; ceEnvironmental benefits of the distribution network; cbThe energy storage device is paid for the entire life cycle; cdgGenerating revenue for the distributed power; PV ═ r (r +1)n/[(1+r)n-1]The conversion coefficient is equal annual value, and r is conversion rate; PV (photovoltaic)d＝1/(1+r)nAnd expressing the conversion coefficient of the residual value of the power distribution network.
Further, the initial investment cost C of the power distribution networkinvestIncluding distributed power capital cost CDGInvestment cost C of energy storage deviceBSAnd investment cost C of power distribution network framel；
CinvestIs represented by formula (2):
Cinvest＝CDG+CBS+Cl(2)
in the formula:
n is the number of nodes; c. CwgCost per unit volume of wind power generation, cpvThe unit capacity cost of solar power generation is represented; sWGi、SPViRespectively representing the installation capacity of wind power generation and solar power generation at a node i;
Further, the annual operation and maintenance cost of the distribution network CoIncluding line loss, transformer loss and maintenance cost of distribution network line, distributed power supply and energy storage equipment in the whole equipment life cycle, the expression is as shown in formula (3)
Co＝Closs+Cm(3)
In the formula (I), the compound is shown in the specification,
elcharging the wharf with electricity; t iskDays for the cj season; pei(t)、Qei(t) respectively representing the active power and the reactive power of a load node i at the tail end of the branch j; u shapeiThe voltage of a load node i at the tail end of a branch j; r isjIs the resistance value of branch j;
Further, annual power failure cost C of distribution networkEENSIncluding direct power failure loss, social influence loss, user time-interval power failure loss cost calculation method, CEENSIs as shown in formula (4)
In the formula: n is the number of sections of the time sequence characteristic curve of the distributed power supply output and the load time sequence characteristic curve; m is the number of load points of the power distribution network; rjThe power failure loss cost corresponding to the average power failure duration time of each kW load of the node j is obtained; l isijThe load size of the ith section of load point j is obtained; lambda [ alpha ]jIs the failure rate of load point j; t isjThe mean time to failure duration for load point j.
Further, determining the island range according to the capacity and the position of the distributed power supply, so that the fault rate lambda of the load pointjAnd mean time to failure duration TjAs shown in formula (5) and formula (6)
In the formula: lambda [ alpha ]DGAnd gammaDGRespectively indicating the failure rate and the average failure duration of the distributed power supply; lambda [ alpha ]kAnd gammakRespectively indicating the failure rate and the failure average power failure duration time of the kth section of main feeder line; n is a radical ofDGIs the number of main feeder segments before load point j and the distributed power supply; lambda [ alpha ]iAnd gammaiRespectively indicating the fault rate and the average power failure duration time of the section of the load point i; n is a radical ofDThe number of power supply trunk line segments between the distributed power supply and the load point j is determined; omegaDIs a set of load points within an island.
Further, distribution network environment profit CeIncluding alternative coal benefits and pollutant abatement benefits, the expression is as in formula (7)
In the formula, M represents the number of types of pollution gas discharged by the traditional power generation; kkFor generating unit electric quantity of traditional thermal power plantEmission intensity of k pollutants; vkThe price standard is discounted for the environmental value of the kth greenhouse gas; rkA price is levied for the kth greenhouse gas emission.
Further, the energy storage device full life cycle yield CbIs the expression (8)
Cb＝L×DOD×Rout-in×η (8)
Wherein L represents the number of charge and discharge cycles of the full life cycle; dODRepresents the depth of charge and discharge; rout-inη represents the charge-discharge conversion efficiency of the storage battery;
distributed power generation profit CdgIs the expression (9)
CDG＝Edg×eg(9)
Further, the solving method of the power distribution network frame planning mathematical model comprises the following steps:
s01: inputting meteorological data and power distribution network original data of a planning area;
s02: setting algorithm parameters, scene numbers and constraint conditions;
s03: utilizing a discrete binary particle swarm algorithm and multi-particle swarm cooperative optimization, generating the position of an initial population by adopting a breadth-first search method, and randomly generating the speed of the initial population to form an initial planning scheme, namely the position, the capacity and a grid erection line of a distributed power supply, wherein the iteration number is set to 0;
s04: carrying out load flow calculation on the generated power distribution network planning scheme;
s05: according to the result of the load flow calculation, the fitness of each particle of the objective function is further calculated;
s06: judging whether the formed network meets the condition of the radiation network or not, and processing the infeasible solution;
s07: searching individual optimum and global optimum;
s08: updating the particle velocity in each subgroup;
s09: judging a search termination condition, stopping calculation and outputting a result if the maximum iteration number is reached; otherwise, the number of iterations is added to 1, and the process goes to S04.
Further, in S01, a time chart of the wind power generation and solar power generation power output characteristics is plotted by spline difference for several typical days throughout the year.
Further, the formula of the speed and the position of the discrete binary particle swarm algorithm is formula (10):
in the formula, subscript d represents the particle dimension; c1 and c2 are random numbers between 0 and 1; pi represents the best position the particle i experiences; pg is the best position the particles in each cluster experience; rand () is a random number generating function, generating [0,1]]The random numbers are evenly distributed in intervals, and the range of vid, namely v, is limited by using a parameter vmax as a maximum speed valueid∈[-vmax,vmax]。
The invention has the positive effects that: the invention establishes a multi-energy complementation and distribution network frame coordination planning model and a solving method by utilizing the time sequence complementation characteristics of the energy storage device, the distributed power supply and the load on the basis of the obvious time sequence characteristics of the load and the distributed power supply under the specific condition. The planning result is closer to the reality, and various economic and technical indexes of the power distribution network are favorably reflected; the multi-energy complementary optimization and the coordinated planning of the distribution network frame reduce the construction cost and the loss cost of the distribution network, delay the upgrading and reconstruction of distribution network equipment and improve the permeability of the distributed power output in the power grid. And in consideration of comprehensive benefits brought by the characteristics of low investment, flexible power generation mode, environmental protection and the like of the distributed power supply, a planning scheme with higher economic benefits can be obtained by multi-energy complementation and coordinated planning of the power distribution network frame.
Drawings
FIG. 1 is a timing diagram of 4 typical daily power output characteristics of a wind power plant according to the present invention;
FIG. 2 is a timing diagram of 4 exemplary daily power output characteristics of the solar power generation of the present invention;
FIG. 3 is a graph of the time-series characteristics of 4 types of loads according to the present invention;
FIG. 4 is a topology diagram of an initial grid structure of a power distribution network according to the present invention;
fig. 5 is a schematic diagram of the power distribution grid structure expansion optimization results of 3 planning schemes.
Detailed Description
The method optimizes the site selection and volume fixing planning of the distributed power supply and the energy storage device and the grid frame planning of the power distribution network simultaneously, and seeks an economic optimal planning scheme which meets the constraint conditions of branch capacity, node voltage, radiation operation, reliability requirements and the like by taking the minimum cost-benefit mathematical model function of the power distribution network containing the distributed power supply as a target function.
Establishing a power distribution network frame planning mathematical model aiming at improving the permeability of the distributed power supply as a formula (1)
fmin＝(Cinvest×PV+Co+CEENS)-(Ce+Cb×PVd+Cdg) (1)
In the formula: the cost-benefit is the sum of the equal annual values of the total cost and the benefit of each stage in the whole process from planning and designing to decommissioning of the planning scheme; cinvestInitial investment cost for the distribution network; coThe operating cost of the distribution network;
CEENSthe power failure of the distribution network sees the reliability cost; ceEnvironmental benefits of the distribution network; cbThe energy storage device is paid for the entire life cycle; cdgDistributed power generation revenue; PV ═ r (r +1)n/[(1+r)n-1]The conversion coefficient is equal annual value; r is the discount rate, and is taken as 0.08; PV (photovoltaic)d＝1/(1+r)nAnd expressing the conversion coefficient of the residual value of the power distribution network.
Initial investment cost C of power distribution networkinvestAnd assigning the one-time cost of the power grid, including equipment purchase cost, design cost, construction and installation cost and the like, which is paid during planning design and construction. Including distributed power capital cost CDGInvestment cost C of energy storage deviceBSAnd investment cost C of power distribution network framel；
CinvestIs represented by formula (2):
Cinvest＝CDG+CBS+Cl(2)
in the formula:
n is the number of nodes; c. CwgCost per unit volume of wind power generation, cpvThe unit capacity cost of solar power generation is represented; sWGi、SPViRespectively representing the installation capacity of wind power generation and solar power generation at a node i;
Annual operation and maintenance cost C of distribution networkoIncluding line loss, transformer loss and maintenance cost of distribution network line, distributed power supply and energy storage equipment in the whole equipment life cycle, the expression is as shown in formula (3)
Co＝Closs+Cm(3)
In the formula (I), the compound is shown in the specification,
elthe price of the wharf power is 0.5 yuan/kW.h; t iskDays for the cj season; pei(t)、Qei(t) respectively representing the active power and the reactive power of a load node i at the tail end of the branch j (equivalent load obtained according to the time sequence characteristic curve of the load and the output of the distributed power supply); u shapeiThe voltage of a load node i at the tail end of a branch j; r isjIs the resistance value of branch j;
Annual power failure cost C of distribution networkEENSThe method is the economic loss caused by the fault power failure to users and distribution networks, and mainly comprises direct power failure loss, social influence loss and the like. In order to reflect the relation between the power failure occurrence frequency and duration and the power failure loss, the invention adopts a user time-interval power failure loss cost calculation method of' power distribution network multi-stage grid planning optimization based on LCC and improved particle swarm optimization
Including direct power failure loss, social influence loss, user time-interval power failure loss cost calculation method, CEENSIs as shown in formula (4)
In the formula: n is the number of sections of the time sequence characteristic curve of the distributed power supply output and the load time sequence characteristic curve; m is the number of load points of the power distribution network; rjThe power failure loss cost corresponding to the average power failure duration time of each kW load of the node j is obtained; l isijThe load size of the ith section of load point j is obtained; lambda [ alpha ]jIs the failure rate of load point j; t isjThe mean time to failure duration for load point j.
Considering the randomness of the population in the adopted intelligent optimization algorithm, the island range contained by the distributed power supply needs to be determined according to the capacity and the position of the distributed power supply. The invention adopts an island division method of document 'distributed power source site selection and capacity determination based on adaptive variation particle swarm optimization', so that the fault rate lambda of the load pointjAnd mean time to failure duration TjAs shown in formula (5) and formula (6)
In the formula: lambda [ alpha ]DGAnd gammaDGRespectively indicating the failure rate and the average failure duration of the distributed power supply; lambda [ alpha ]kAnd gammakRespectively indicating the failure rate and the failure average power failure duration time of the kth section of main feeder line; n is a radical ofDGIs the number of main feeder segments before load point j and the distributed power supply; lambda [ alpha ]iAnd gammaiRespectively indicating the fault rate and the average power failure duration time of the section of the load point i; n is a radical ofDThe number of power supply trunk line segments between the distributed power supply and the load point j is determined; omegaDIs a set of load points within an island.
Distribution network environmental benefit CeThe gains obtained by adopting the distributed power supply with less pollution comprise alternative coal-fired gains and pollutant emission reduction gains, and the expression formula is shown as (7)
In the formula, M represents the number of types of pollution gas discharged by the traditional power generation; kkThe emission intensity (kg/MW & h) of the kth pollutant is generated for the unit electric quantity of the traditional thermal power plant; vkReducing the value standard (yuan/kg) for the environmental value of the kth greenhouse gas; rkCollecting a price (yuan/kg) for the k-th greenhouse gas emission; edgThe method is used for obtaining the annual available generating capacity of the distributed power supply based on the four-season time sequence characteristics of the distributed power supply.
Energy storage device full life cycle yield CbThe arbitrage obtained by the energy storage system with low storage and high generation under the price of power from the peak valley to the internet is expressed as the formula (8)
Cb＝L×DOD×Rout-in×η (8)
Wherein L represents the number of charge and discharge cycles of the full life cycle; dODRepresents the depth of charge and discharge; rout-inη represents the charge-discharge conversion efficiency of the storage battery;
distributed power generation profit CdgIs the expression (9)
CDG＝Edg×eg(9)
In the formula, egAnd purchasing the electricity unit price for the power grid load of the electricity purchasing distributed power supply.
And a typical scene of a load and DG output time sequence characteristic curve is reasonably found out, time sequence whole-process simulation is accurately carried out on the operation of the power distribution network, and the calculated amount is effectively reduced. By analyzing the statistical data of the climate characteristics of the planned area, the scene days of different scenes can be obtained. The weather statistics of a certain season can be used to obtain about 92 days in spring, about 92 days in summer, about 91 days in autumn and about 90 days in winter.
The solving method of the power distribution network frame planning mathematical model comprises the following steps:
s01: inputting meteorological data and power distribution network original data of a planning area;
s02: setting algorithm parameters, scene numbers and constraint conditions;
s03: utilizing a discrete binary particle swarm algorithm and multi-particle swarm cooperative optimization, generating the position of an initial population by adopting a breadth-first search method, and randomly generating the speed of the initial population to form an initial planning scheme, namely the position, the capacity and a grid erection line of a distributed power supply, wherein the iteration number is set to 0;
s04: carrying out load flow calculation on the generated power distribution network planning scheme;
s05: according to the result of the load flow calculation, the fitness of each particle of the objective function is further calculated;
s06: judging whether the formed network meets the condition of the radiation network or not, and processing the infeasible solution;
s07: searching individual optimum and global optimum;
s08: updating the particle velocity in each subgroup;
s09: judging a search termination condition, stopping calculation and outputting a result if the maximum iteration number is reached; otherwise, the number of iterations is added to 1, and the process goes to S04.
In S02, the constraints include:
1)AP＝D
2)Pi<Pi max
3) DG installation capacity constraints
4) State of charge constrained SOCmin≤SOC≤SOCmax
5) And a radial running mode of the power distribution network.
In the formula: a is a node incidence matrix; p is the network trend; d is the load demand; piIs a branch trend; pi maxMaximum allowable capacity for the branch; sWGi max、SPVi maxRespectively representing the maximum installation capacity of wind power generation and solar power generation at a node i; SOCmin＝0，SOC max1, each represents an upper limit value and a lower limit value of the state of charge; the node voltage deviation is constrained to ± 7%.
Due to the influence of weather conditions such as wind speed, illumination intensity and the like, active power output of wind power generation and solar power generation has obvious volatility and intermittency. Fig. 1 and 2 are meteorological data of a certain place in north of China, and a time chart of power output characteristics of wind power generation and solar power generation of 4 typical days in the whole year is drawn through 3 times of spline interpolation.
The present invention refers to the classification of power load curves in the document "analysis and prediction of power load characteristics in china", and summarizes the load curves of 4 typical users, as shown in fig. 3. Type I: three-shift system users; type II: a one-shift user; type III: municipal life; type IV: agricultural irrigation and drainage (or non-irrigation and drainage).
From the time series characteristic of fig. 3, it can be derived: the daily load rate of the industrial load is high and is hardly influenced by external factors and is more than 0.8; the peak-valley difference of the load curves of commercial load and municipal life is large, and the load rate is low; the agricultural load curve shows obvious seasonality along with irrigation and drainage, autumn harvest and leisure.
The load flow calculation method adopted by the invention is a forward-backward substitution method which is suitable for the power distribution network and comprises the distributed power supply. At present, the grid is required to have the capability of uninterrupted grid-connected operation under the conditions of low voltage ride through and grid voltage fault for a grid-connected distributed power supply. The distributed power supply adopting the power electronic device grid connection is isolated from the power grid, is friendly to the power grid, has good control flexibility, and has excellent capacity in the aspects of low voltage ride through and reactive power control. The distributed power source containing nodes discussed herein are considered solar power generation nodes.
Usually, the particle swarm algorithm is adapted to perform calculation in a continuous search space, and cannot be directly applied in a discrete space. In order to solve the problem of discrete combination optimization by the particle swarm algorithm, the discrete binary particle swarm algorithm is adopted. The velocity update formula of the discrete particle swarm algorithm is the same as the PSO algorithm, but the value of the velocity becomes the probability that the binary bit takes 1, and the value of the velocity is mapped to the interval [0,1 ].
The updating formula of the speed and the position of the binary model of the discrete particle swarm algorithm is as follows:
Example of calculation
According to the method, simulation calculation is carried out on a typical example of 10kV power distribution network frame planning in a document 'distribution network frame planning based on a fuzzy expected value model'. The initial net mount has a total of 5 nodes, 4 branches, as shown by the solid lines in fig. 4. Now to expand the radiating network to 13 nodes, a new line can be created as shown by the dashed line in fig. 4. The node 1 is an 35/10kV transformer substation, and the existing 1 10MVA main transformer can be expanded to 2 x 10 MVA.
The node load values are shown in table 2. The optional types of the lines in the net rack are LGJ-150, LGJ-120 and LGJ-95, and the parameters are shown in Table 1. Other data are as follows: the electricity price is 0.5 yuan/kWh, the failure rate of a unit power (0.1MW) distributed power supply is 0.015 time/year, the average power failure duration time is 2.5 h/time, the failure rate of a unit length (per kilometer) line is 0.075 time/year, and the average power failure duration time is 4 h/time. The rack parameters are shown in tables 3 and 4.
TABLE 1 feeder model and parameters
TABLE 2 node load values
TABLE 3 line parameters
TABLE 4 other parameters
By adopting the model and the algorithm of the invention, 3 schemes are listed to research the power distribution network planning. Scheme 1: the planning of a power distribution network of the distributed power supply is not considered; scheme 2: the planning of the distributed power distribution network frame without considering the energy storage; scheme 3: and (4) planning a multi-energy complementary power distribution network frame. The cost-benefit scenario for power distribution network planning in different situations is shown in table 5.
TABLE 5 distribution network planning cost
Tables 6 and 7 correspondingly show the access positions and access capacities of DGs and BSs of the schemes 2 and 3.
Table 6 access location and capacity of scheme 2 distributed power supply
Table 7 scheme 3 access locations and capacities of distributed power supplies, energy storage devices
The distributed power generation amount of the scheme 2 is 9659.27 kWh; scheme 3 distributed power generation 11303.62 kWh; thus, the permeability of scheme 2 was found to be 19%; the permeability of scheme 3 was 24%.
The branch selection construction scenario with 3 planning schemes obtained simultaneously is shown in table 8. The results of the power distribution grid expansion optimization for the 3 planning schemes are shown in fig. 5.
Table 8 construction branch number ("1" construction branch not extension, "2" construction branch extension)
Analyzing the power distribution network planning cost and the planning net rack result of the 3 schemes:
1) comparing the scheme 1 with the schemes 2 and 3, the scheme 1 does not consider accessing the distributed power supply, 1 line which needs to be upgraded and modified is increased by 1 line and 2 lines which need to be upgraded and modified compared with the scheme 2 and the scheme 3, and the transformer substation at the node 1 needs capacity increase, which indicates that the distributed power supply has a delay effect on the transformation of the distribution network frame and the upgrading of the equipment of the transformer substation.
2) Comparing the scheme 2 with the scheme 3, the two schemes are that the position and the capacity of the distributed power supply (energy storage device), a line to be modified and a newly added load point are determined at the same time. The scheme 3 is additionally provided with the energy storage device, the distributed power supply, the load and the energy storage device are well complemented in time sequence characteristics, the permeability of the distributed power supply output is improved, the permeability of the distributed power supply power generation is increased from 19% in the scheme 2 to 24% in the scheme 3, and therefore the obtained multi-energy complementation planning configuration scheme is more reasonable. Scheme 3 has also improved the economic nature of distribution network planning cost when reducing increase-volume circuit number. Solution 3 saves 37.4 ten thousand dollars per year over the total cost of solution 2.
The invention establishes a multi-energy complementation and distribution network frame coordination planning model and a solving method by utilizing the time sequence complementation characteristics of the energy storage device, the distributed power supply and the load on the basis of the obvious time sequence characteristics of the load and the distributed power supply under the specific condition. The results of the example simulation show that: the time sequence characteristics of the output and the load of the distributed power supply have great influence on the addressing and the positioning of the distributed power supply; the planning result is closer to the reality by considering the time sequence characteristics of the output and the load of the distributed power supply, and various economic and technical indexes of the power distribution network are favorably reflected; the multi-energy complementary optimization and the coordinated planning of the distribution network frame reduce the construction cost and the loss cost of the distribution network, delay the upgrading and reconstruction of distribution network equipment and improve the permeability of the distributed power output in the power grid. And in consideration of comprehensive benefits brought by the characteristics of low investment, flexible power generation mode, environmental protection and the like of the distributed power supply, a planning scheme with higher economic benefits can be obtained by multi-energy complementation and coordinated planning of the power distribution network frame.
Claims (10)
1. A distributed power source location and volume fixing and power distribution network frame joint optimization method is characterized by comprising the following steps:
establishing a power distribution network frame planning mathematical model aiming at improving the permeability of the distributed power supply by taking the minimum cost-benefit mathematical model function of the power distribution network of the distributed power supply as a target function:
fmin＝(Cinvest×PV+Co+CEENS)-(Ce+Cb×PVd+Cdg) (1)
in the formula, CinvestInitial investment cost for the power distribution network; coAnnual operation and maintenance costs of the power distribution network; cEENSAnnual power outage cost of the power distribution network; ceEnvironmental benefits of the distribution network; cbThe energy storage device is paid for the entire life cycle; cdgGenerating revenue for the distributed power; PV ═ r (r +1)n/[(1+r)n-1]The conversion coefficient of the equal annual value and r is the conversion rate.
2. The distributed power supply location determination, sizing and power distribution network rack joint optimization method according to claim 1, characterized in that initial investment cost C of a power distribution networkinvestIncluding distributed power capital cost CDGInvestment cost C of energy storage deviceBSAnd investment cost C of power distribution network framel；
CinvestIs represented by formula (2):
Cinvest＝CDG+CBS+Cl(2)
in the formula:
n is the number of nodes; c. CwgCost per unit volume of wind power generation, cpvThe unit capacity cost of solar power generation is represented; sWGi、SPViRespectively representing the installation capacity of wind power generation and solar power generation at a node i;
3. The distributed power supply location determination, volume determination and power distribution network rack joint optimization method of claim 1, wherein the annual operation and maintenance cost C of a distribution networkoIncluding line loss, transformer loss and maintenance cost of distribution network line, distributed power supply and energy storage equipment in the whole equipment life cycle, the expression is as shown in formula (3)
Co＝Closs+Cm(3)
In the formula (I), the compound is shown in the specification,
elcharging the wharf with electricity; t iskDays for the cj season; pei(t)、Qei(t) respectively representing the active power and the reactive power of a load node i at the tail end of the branch j; u shapeiThe voltage of a load node i at the tail end of a branch j; r isjIs the resistance value of branch j;
4. The distributed power supply location determination, volume determination and power distribution network rack joint optimization method of claim 1, wherein annual power outage cost C of a distribution networkEENSIncluding direct power failure loss, social influence loss, user time-interval power failure loss cost calculation method, CEENSIs as shown in formula (4)
In the formula: n is the number of sections of the time sequence characteristic curve of the distributed power supply output and the load time sequence characteristic curve; m is the number of load points of the power distribution network; rjThe power failure loss cost corresponding to the average power failure duration time of each kW load of the node j is obtained; l isijThe load size of the ith section of load point j is obtained; lambda [ alpha ]jIs the failure rate of load point j; t isjThe mean time to failure duration for load point j.
5. The distributed power supply location determination and distribution network grid joint optimization method of claim 4, wherein an island range is determined according to the capacity and the position of the distributed power supply, so that the fault rate lambda of a load pointjAnd mean time to failure duration TjAs shown in formula (5) and formula (6)
In the formula: lambda [ alpha ]DGAnd gammaDGRespectively indicating the failure rate and the average failure duration of the distributed power supply; lambda [ alpha ]kAnd gammakRespectively indicating the failure rate and the failure average power failure duration time of the kth section of main feeder line; n is a radical ofDGIs the number of main feeder segments before load point j and the distributed power supply; lambda [ alpha ]iAnd gammaiRespectively indicating the fault rate and the average power failure duration time of the section of the load point i; n is a radical ofDThe number of power supply trunk line segments between the distributed power supply and the load point j is determined; omegaDIs a set of load points within an island.
6. The distributed power supply location determination, sizing and power distribution network rack joint optimization method of claim 1, characterized in that distribution network environment profit CeIncluding alternative coal benefits and pollutant abatement benefits, the expression is as in formula (7)
In the formula, M represents the number of types of pollution gas discharged by the traditional power generation; kkThe emission intensity of the kth pollutant is generated for the unit electric quantity of the traditional thermal power plant; vkThe price standard is discounted for the environmental value of the kth greenhouse gas; rkA price is levied for the kth greenhouse gas emission.
7. The distributed power supply location determination, volume determination and power distribution network rack joint optimization method of claim 1, wherein the energy storage device full life cycle profit CbIs the expression (8)
Cb＝L×DOD×Rout-in×η (8)
Wherein L represents the number of charge and discharge cycles of the full life cycle; dODRepresents the depth of charge and discharge; rout-inη represents the charge-discharge conversion efficiency of the storage battery;
distributed power generation profit CdgIs the expression (9)
CDG＝Edg×eg(9)。
8. The distributed power supply site selection, capacity measurement and power distribution network rack joint optimization method according to any one of claims 1 to 7, characterized in that the solving method of the power distribution network rack planning mathematical model comprises the following steps:
s01: inputting meteorological data and power distribution network original data of a planning area;
s02: setting algorithm parameters, scene numbers and constraint conditions;
s03: utilizing a discrete binary particle swarm algorithm and multi-particle swarm cooperative optimization, generating the position of an initial population by adopting a breadth-first search method, and randomly generating the speed of the initial population to form an initial planning scheme, namely the position, the capacity and a grid erection line of a distributed power supply, wherein the iteration number is set to 0;
s04: carrying out load flow calculation on the generated power distribution network planning scheme;
s05: according to the result of the load flow calculation, the fitness of each particle of the objective function is further calculated;
s06: judging whether the formed network meets the condition of the radiation network or not, and processing the infeasible solution;
s07: searching individual optimum and global optimum;
s08: updating the particle velocity in each subgroup;
s09: judging a search termination condition, stopping calculation and outputting a result if the maximum iteration number is reached; otherwise, the number of iterations is added to 1, and the process goes to S04.
9. The distributed power supply siting, sizing and power distribution network grid joint optimization method according to claim 8, characterized in that in S01, a time chart of power output characteristics of wind power generation and solar power generation for a plurality of typical days of the year is drawn by spline difference.
10. The distributed power supply siting, sizing and power distribution network grid joint optimization method according to claim 8, characterized in that the formula of speed and position of the discrete binary particle swarm algorithm is formula (10):
in the formula, subscript d represents the particle dimension; c1 and c2 are random numbers between 0 and 1; pi denotes the path traveled by particle iBest location of the calendar; pg is the best position the particles in each cluster experience; rand () is a random number generating function, generating [0,1]]The random numbers are evenly distributed in intervals, and the range of vid, namely v, is limited by using a parameter vmax as a maximum speed valueid∈[-vmax,vmax]。
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