CN109347151A - A kind of new energy participates in the sending end electric network source structural optimization method of peak regulation - Google Patents
A kind of new energy participates in the sending end electric network source structural optimization method of peak regulation Download PDFInfo
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- CN109347151A CN109347151A CN201811457042.2A CN201811457042A CN109347151A CN 109347151 A CN109347151 A CN 109347151A CN 201811457042 A CN201811457042 A CN 201811457042A CN 109347151 A CN109347151 A CN 109347151A
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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Abstract
The present invention relates to the sending end electric network source structural optimization methods that a kind of new energy participates in peak regulation, combine peak regulation constraint etc. under the conditions of various physical constraints by establishing power supply structure optimization model on the basis of considering wind-light storage peak capacity, to provide the optimum power organization plan under specified conditions, comprising the following steps: obtain unit data, load related data and price information data；Establish the power supply structure optimization model for considering that new energy participates in peak regulation；Unit maintenance scheduling is obtained, considers to install the wind power plant of energy storage and the power output of solar power station additional, carries out the Stochastic Production Simulation that wind, solar energy, water, fire, pumped storage generate electricity；The power supply structure optimization model is solved using Hybrid Particle Swarm, obtains optimum power structure optimization scheme.Compared with prior art, the present invention has many advantages, such as that practical, the feature of environmental protection is good, high-efficient.
Description
Technical field
The present invention relates to electric power system power source planning technology fields, and the sending end of peak regulation is participated in more particularly, to a kind of new energy
Electric network source structural optimization method.
Background technique
Power source planning refers to the electric load demand and part throttle characteristics according to interior prediction project period, the power supply as defined in guaranteeing
Under the premise of reliability index, factory site, the plant condition of each power plant are investigated and implemented, fully considers each power plant's operation characteristic, with
The factors such as system coordination and fuel source and traffic condition carry out simulation calculating, reliable to various possible programmes
Property analysis, technical economic analysis, finally determine most reasonable power supply architecture and optimal power source planning scheme.Wherein it is determined that closing
The power supply architecture of reason is exactly the content of power supply structure optimization.Power supply architecture mentioned here refers to the various hairs in country
The installed capacity (generated energy) of electric energy accounts for the specific gravity of total installation of generating capacity (total power generation), and power supply structure optimization may be summarized to be
According to a certain region within a certain period electricity and load prediction as a result, under conditions of meeting certain reliability level, seek
A most economical power supply architecture is sought, meets the needs of user is to electric energy and the operation etc. of stabilizing the economy of entire electricity generation system requires.
The research that the country plans power supply architecture mainly has following achievement in terms of literature research.Liang Zhihong, Yang Kun etc.
Delivered on " Proceedings of the CSEE " (2010, (16): 74-79) " based on the electricity of Real Option Theory under electricity market
Source trend of investment Study on Decision-Making Models " Real Option Theory (Real Option Approach, ROA) is advised applied to power supply
It draws, and derives a variety of Generation Expansion Planning Models based on option theory.Zhang Qian, Wang Haiqian etc. are in " Automation of Electric Systems "
" the power source planning design that Jiangsu Power Grid dissolves large-scale wind power " delivered on (2011, (22): 60-65) is to the big rule of Jiangsu Power Grid
The power supply structure optimization problem of mould wind-powered electricity generation, applied probability method discuss power supply knot on the basis of analyzing reasonable CAPACITY IN ELECTRICITY
The Reasonable Parameters setting of wind-powered electricity generation peak capacity demand in structure optimization, and wind-powered electricity generation and other power supplys have been obtained by contrast of multiple-schemes
Optimal capacity ratio." the renewable energy that Zhang Xiaohui, Yan Pengda etc. are delivered on " electric power network technique " (2015, (03): 655-662)
Low-carbon economy power source planning under source forcing system " it is dedicated to reducing carbon emission, introducing renewable resource (wind energy, solar energy)
On the basis of, it joined carbon intensity binding character target in power supply structure optimization, by the machine for increasing high energy efficiency, low emission
Group ratio promotes renewable energy construction and optimization power supply architecture.Yuan Jiandang, Yuan Tiejiang etc. are in " electric power system protection and control "
" large-scale wind power integration system power supply project study under the Power Market " building delivered on (2011,39 (5): 22-26)
It is a kind of to meet Power Market constraint, the power supply rule comprising the multiple types set grid-connection such as thermoelectricity, water power, large-scale wind power
Model is drawn, and application enhancements genetic algorithm solves model.Section Tan, Miao Miao etc. are opened in " electric power network technique " (2011,35
(11): 43-49 " the double-deck power source planning containing wind power plant " delivered on), which establishes, considers the net of peak regulation, frequency modulation and environment constraint
Maximum revenue bilayer Generation Expansion Planning Model, and propose plant growth simulation algorithm, minimum accumulative risk degree method, equivalent electric quantity
The method for solving that frequency method combines.In terms of particle swarm algorithm, Liu Jia, Li Dan, Gao Liqun et al. are in " Chinese electrical engineering
Report " (2008,28 (31): 22-28) vector of the multi-objective reactive optimization " evaluation APSO algorithm " delivered be to overcome
The problem of falling into local optimum when particle swarm algorithm higher-dimension challenge optimizing, propose a kind of APSO algorithm and by its
Applied to multi-objective reactive optimization.Lu Jinling, Miao Yuyang et al. are in " electric power system protection and control " (2013,41 (17): 25-
31) " based on the power system optimal dispatch containing wind power plant for improving multi-objective particle swarm algorithm " delivered is by introducing genetic operator
The ability of multi-objective particle swarm algorithm search Unit Combination is improved, the global optimizing ability of the algorithm is improved, and
It applies it in the electric power system dispatching containing wind power plant.Wang Zhidong is sent out in " power construction " (2015,36 (10): 60-66)
" extra-high voltage direct-current wind-powered electricity generation thermoelectricity, which is combined, sends generation expansion planning method outside " of table proposes optimization extra-high voltage direct-current wind fire bundling
It sends the research method of mating power supply scale and the oranon and thinking of mating power supply outside, while establishing extra-high voltage direct-current wind
Electricity, thermoelectricity combine the optimization method for sending mating power supply scale outside, but this method does not consider the practical stochastic production mould of internal system
It is quasi-, therefore practical operation situation is considered insufficient.Document above is directed to renewable energy (wind-powered electricity generation, photovoltaic etc.) access mostly
Corresponding power source planning method afterwards, it is therefore intended that consider the uncertain shadow to power source planning scheme of the power output of renewable energy
It rings, but is not related to actual production simulation and unit maintenance mostly when assessing programme, while connecing to extensive Hydropower Unit
The system entered considers insufficient.In existing patent, the patent of invention of the inventors such as Yu Linlin, Liu Yongmin application " considers extra-high voltage
The receiving end electric network source planing method of direct current access " the analysis extra-high voltage DC transmission system influence master that power construction is planned inside the province
Factor is wanted, and combines traditional power planning method, it is optimal for target with whole social benefit, establish the related extra-high crimping of consideration
The receiving end electric network source plan model entered.The patent of the inventors such as Wang Lihu application " considers that extensive extra-high voltage source adjusts energy
The unit maintenance scheduling optimization system of power " create a kind of unit maintenance scheduling for considering extensive extra-high voltage source regulating power
Optimization system, by database module, input module, Maintenance Schedule Optimization module and output module, the spy that can be inputted
Determine the optimal maintenance plan arrangement and all risk average value evaluation indexes under electric system.The inventors such as Yu Linlin, Huang Jing be intelligent
A kind of patent of invention " power source planning method based on Renewable energy policy control constraint " of application combines renewable energy political affairs
Plan control constraint condition and power source planning constraint condition establish Generation Expansion Planning Model for receiving end power grid, so that system in project period
Net profit of uniting is maximum, which more suits the practical operation situation of direct current receiving end regional power grid.For distributed generation resource
Planning, presently, there are more patents of invention.A kind of patent of invention " distributed electrical of the inventors such as Shi Pu, Ren Hui, Sun Chenjun application
Source planing method and its system " a kind of distributed generation resource planing method and its system are proposed, it has been determined respectively by 4 steps
The best on-position of distributed generation resource and best access capacity improve Voltage Stability of Distribution Networks and reduce the network of system
Loss, but this method mainly pass through calculate voltage stability index VSI determine programme, not in view of scheme economy,
Safety and the feature of environmental protection.The patent of invention of inventors' application such as Li Hong, Zhaoyang, appearance appearance " is based on temporal characteristics and Environmental Effect
The distributed generation resource planing method of benefit " to solve existing distributed generation resource planning technology at high cost, low efficiency, resource utilization poor
The technical issues of, but its planing method pays attention to the processing to load data, takes into consideration only distributed generation resource bring net profit
It is no sufficiently to dissolve renewable energy with net investment.The big thousand inventors application of Lu Jinling, Zhao patent of invention " consider energy storage and
The active distribution network distributed generation resource planing method of reactive compensation " consider energy storage and reactive compensation active distribution network distributed electrical
Source planing method, in the pact of the conditions such as power-balance, node voltage, Node distribution formula power supply capacity, energy storage device output power
Under beam, integrated system variation, route active power loss, the multiple target of average power supply reliability and greenhouse gas emissions are established
Optimal Planning Model does not consider system for the need of peak-load regulating although this method meter and energy storage and reactive compensation effect
It asks, peak modulation capacity provided by possible programme is insufficient in some scenes.Punishment jade brightness, Zhu Guiping, summer inventors such as flood forever
The patent of invention " distributed hybrid power system power source planning method " of application proposes a kind of distributed hybrid power system power supply
Planing method, comprising the following steps: carry out preconsolidation stress, determine installation Wind turbines, photovoltaic array, small hydropower station and energy storage
The quantity of battery generates more optional programmes of set according to the method for permutation and combination；Wind turbines, photovoltaic battle array are established respectively
The output power model of column, small hydropower station and energy storage battery；The system loading for calculating the optional programme of every suit lacks
Electric rate and system capacity excess rate, and judge whether the optional programme of every suit meets system reliability requirement respectively, if symbol
Conjunction then executes subsequent step, gives up if not meeting；The optional programme for meeting system reliability requirement for more covering, according to
Its system loading short of electricity rate and system capacity excess rate calculate corresponding expense discounted value and arrange by ascending order, select expense patch
Programme is recommended in the small conduct of present worth.This method meter and capacity usage ratio and system reliability, but for water power modulability
Energy, the price dynamic change at each moment consider deficiency, so that the total cost finally obtained is less accurate.Wang Wenxi, Liu
A kind of patent of invention " active distribution network distributed generation resource planning of consideration source lotus matching degree of the inventors such as Po Lam, Feng Lei application
Method " propose a kind of active distribution network distributed generation resource planing method of consideration source lotus matching degree, this method consider it is economical at
This and operating index establish bi-level optimal model, and upper layer is planned to plan the minimum target of year comprehensive cost in the time limit, is determined
Distributed generation resource on-position and capacity；Lower layer plans introducing source lotus matching degree index, using source lotus optimal matching degree as target, mould
The operational process of quasi- programme optimizes the timing power output of distributed generation resource.The source lotus matching of this method selective analysis
The problem of spending, but do not contribute at random to renewable energy and load random fluctuation accounts for, one can be generated to final result
Fixing is rung.Meanwhile invention is planned mainly for the receiving end electric network source accessed in extra-high voltage, for sending there are extensive water power
Electric network source planning is held to consider insufficient, and each model does not consider generated abandoning energy problem after Stochastic Production Simulation, will make
At can largely send out waste of energy.
Summary of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide a kind of new energy to participate in adjusting
The sending end electric network source structural optimization method at peak.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of new energy participates in the sending end electric network source structural optimization method of peak regulation, comprising the following steps:
1) unit data, load related data and price information data are obtained；
2) the power supply structure optimization model for considering that new energy participates in peak regulation is established；
3) data obtained based on step 1) obtain unit maintenance scheduling, consider the wind power plant and solar energy that install energy storage additional
The power output in power station carries out the Stochastic Production Simulation that wind, solar energy, water, fire, pumped storage generate electricity；
4) the power supply structure optimization model is solved using Hybrid Particle Swarm based on step 3), obtains optimum power knot
Structure prioritization scheme.
Further, in the step 3), unit maintenance scheduling is obtained by minimum accumulative risk degree method.
Further, in the Stochastic Production Simulation, extra-high voltage, the water power of multizone are considered based on equivalent electric quantity frequency method
The production of unit and new energy unit is simulated.
Further, the power supply structure optimization model is bi-level optimal model, wherein upper layer model is determined for electric generation investment
Question and answer on politics topic, the object of planning is that cost of electricity-generating minimizes, whether decision variable is that unit to be selected is invested to build；Underlying model is Optimizing manufacture
Decision problem, the object of planning are the maintenance period that operating cost minimizes generating set and each generating set on load curve
Running position.
Further, in the bi-level optimal model, the objective function of upper layer model is indicated are as follows:
In formula, B is the total cost of investment present worth of programme, Bft、Bht、Bwt、BvtRespectively indicate t thermal power plant to be selected,
Hydroelectric power plant, wind power plant, the cost of investment of solar power station, T are project period；
The constraint condition of upper layer model includes decision variable Integer constrained characteristic, total installed capacity number of units constrains, power plant is invested to build earliest
Time limit constraint, power balance condition, electric quantity balancing condition and peak modulation capacity constraint；
Consider that wind power plant and solar power station participate in peak regulation in the peak modulation capacity constraint.
Further, the peak modulation capacity constraint representation are as follows:
In formula, αi、αjThe peak regulation depth of respectively newly-built thermal power plant, hydroelectric power plant, αl、αmRespectively built thermal power plant, water power
The peak regulation depth of factory, Pi、PjRespectively newly-built fired power generating unit and Hydropower Unit power output, Pl、PmRespectively built fired power generating unit and water
Electric unit output, NfAnd NhRespectively newly-built fired power generating unit and Hydropower Unit number of units, N0,fAnd N0,hRespectively built fired power generating unit and
Hydropower Unit number of units, NWGFor the number for creating wind power plant, N0,WGFor the number of built wind power plant, ηWGFor the confidence level of wind-power electricity generation
Coefficient,The installed capacity of r-th of wind power plant,U-th of wind energy turbine set installed capacity, N0,SGFor built solar electricity generation field
Number, NSGFor the number for creating solar electricity generation field, ηSGFor the confidence level coefficient of solar power generation,For s-th of sun
The installed capacity of energy electric field,For the installed capacity of v-th of solar energy electric field, WxEnergy storage for x-th of wind power plant configuration is held
Amount includes built wind power plant and newly-built wind power plant, WyIt include the built sun for the stored energy capacitance of y-th of solar power station configuration
Energy power station and newly-built solar power station,For system loading maximum peak-valley difference.
Further, in the bi-level optimal model, the objective function of underlying model is indicated are as follows:
In formula, b is the total operating cost of programme, bft、bht、bwt、bvt、b0tRespectively indicate t thermal power plant to be selected,
Hydroelectric power plant, wind power plant, the operating cost of solar power station and the existing power plant including extra-high voltage, T is project period；
The constraint condition of underlying model includes unit maintenance constraint, system reliability constraint and pollutant discharge amount constraint.
Further, the operating cost includes abandoning energy expense crs, its calculation formula is:
Crs=c (Prs,max-Prs,real)
In formula, c is that unit abandons energy rejection penalty, Prs,maxFor renewable energy generation can practical maximum output, Prs,realFor
Practical power output after renewable energy generation scheduling.
Further, when the solution power supply structure optimization model using Hybrid Particle Swarm, each particle pair
A power supply structure optimization scheme is answered, unit maintenance scheduling and stochastic production are carried out respectively for each power supply structure optimization scheme
The integrated operation cost feedback of acquisition to upper layer model is carried out global optimizing by the optimizing mechanism of particle swarm algorithm by simulation.
Further, the detailed process packet that the power supply structure optimization model is solved using Hybrid Particle Swarm
It includes:
Step1: setting population scale N, particle dimension D, the number of iterations M；
Step2: initialization population space and belief space；
Step3: calculating the fitness value of each particle in population space, by particle position after initialization and fitness value
It is stored as individual optimal value, more all individual optimal values are as global optimum；
Step4: calculating inertia weight w and adjusts policy update w by threshold value, is adjusted to Studying factors；
Step5: belief space, which is based on grading function, which carries out population space, influences operation, calculates the Gauss disturbance factor, root
It makes a variation according to grading classification to population space parent individuality and generates the N number of offspring individual of equivalent；
Step6: processing of crossing the border is carried out to offspring individual position using boundary position processing strategie；
Step7: natural selection is carried out in population space, and replaces population empty with the elite individual stored in situational knowledge
Between in poor individual, the Population Regeneration space optimal and global optimum of individual；
Step8: population space is operated elite individual contributions in space by receiving to belief space, and to elite individual
It is updated using particle swarm algorithm and generates offspring individual, finally update situational knowledge with roulette rule, update belief space individual
Optimal and global optimum；
Step9: comparation and assessment population space and belief space global optimum, use the two compared with the superior as this iteration overall situation most
The figure of merit；
Step10: population's fitness variances sigma is calculated2If σ2≤ ε then carries out Logistic chaos to population global optimum
Variation, ε are TSP question threshold value；
Step11: algorithm is exited if reaching termination and requiring, otherwise returns to Step4.
The present invention is directed to the sending end power grid there are extensive Hydropower Unit, is considering extra-high voltage access and peak regulation demand
On the basis of, it is given birth at random on the basis of Hydropower Unit regulation performance space-time analysis using the multizone of more Hydropower Units power output
It produces simulation and maintenance plan arranges technology addition to abandon and can punish, it will keep programme practical close to sending end power grid, and have more
Good adaptability.
Compared with prior art, the present invention have with following the utility model has the advantages that
One, practical.The present invention carries out Hydropower Unit regulation performance space-time analysis first before power source planning, is uniting
The peak modulation capacity for analyzing each unit on the basis of each unit regulation performance is counted, and controlling policy point is carried out to pumped storage unit price
Analysis, establishes pumped storage price model, can fully consider influence of the price fluctuation to power source planning scheme.In Stochastic Production Simulation
It is added and extra-high voltage submitting, more Hydropower Units collaborations and the modeling of position in Stochastic Production Simulation of new energy unit is examined
Consider, and be generalized to multizone, under this method can fully consider that extensive Hydropower Unit access and new energy are contributed at random
Stochastic Production Simulation.
Two, the feature of environmental protection is good.Carbon emission constraint condition is not only added in the present invention in constraint condition, while in extensive water
Under electricity power generation background, for sufficiently consumption water power and other renewable energy electric energy, examined in power source planning firing floor objective function
Consider generated abandonings water cost, abandonment cost after Stochastic Production Simulation and abandon light cost, can well solve a large amount of abandoning water,
Abandonment, the phenomenon for abandoning light occur, and low-carbon environment-friendly is better achieved.
Three, high-efficient.Belong to extensive, nonlinear mixed integer programming problem in power source planning question essence, directly
Solution will be very time-consuming, according to idea of decomposition and coordination, converts bi-level optimal model for power source planning problem, upper layer is planned to
Electric generation investment decision problem, the object of planning is that cost of electricity-generating minimizes, whether decision variable is that unit to be selected is invested to build；Lower layer's planning
To produce Optimal Decision-making problem, the object of planning is that operating cost minimizes, and is segmented into unit maintenance scheduling and random life again
Produce simulation two sub-problems, their decision variable be respectively generating set the maintenance period and each generating set load song
Running position on line, by Optimizing manufacture decision can obtain the generated energy of each generating set, fuel consumption, environmental protection at
This, to calculate the operating cost of programme.It not only can reduce the dimension of each subproblem, and each subproblem
Model becomes prone to handle.Meanwhile the present invention is based on cultural frame, chaotic maps, Gauss on the basis of conventional particle group's algorithm
Disturbance and natural selection mechanism propose CGPSO algorithm, and combine power source planning practical problem using every planning time as whole
Number decision variable carries out simplifying coding, can quickly search out planning side of global optimum using the optimizing mechanism in CGPSO algorithm
Case greatly improves solution efficiency.
Four, after considering that wind power plant, solar power station (comprising photovoltaic plant, photo-thermal power station etc.) install energy storage additional, electric power is participated in
Peak-load regulating, on the one hand, consider wind power plant, the confidence capacity of solar power station can participate in peak-load regulating, while allowing to exist
A certain range internal cause peak regulation, which needs to generate, abandons energy, on the other hand, after considering that wind power plant, solar power station install energy storage additional, Ke Yiyou
Effect promotes its ability for participating in peak-load regulating.
Five, the present invention according to idea of decomposition and coordination by model decomposition be bi-level optimal model, in combination with equal-risk method,
Equivalent electric quantity frequency method and CGPSO algorithm are embedded in maintenance plan and multizone Stochastic Production Simulation in firing floor and solve double-layer electric
Source plan model obtains optimum programming scheme, and accuracy is high.
Detailed description of the invention
Fig. 1 is flow diagram of the invention；
Fig. 2 is single Hydropower Unit band peak load situation schematic diagram；
Fig. 3 is the determination schematic diagram of single Hydropower Unit on-load position；
Fig. 4 is that all types of units of the year two thousand twenty plan installed capacity schematic diagram in embodiment；
Fig. 5 is all types of unit generation amount schematic diagrames of the year two thousand twenty in embodiment.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.The present embodiment is with technical solution of the present invention
Premised on implemented, the detailed implementation method and specific operation process are given, but protection scope of the present invention is not limited to
Following embodiments.
As shown in Figure 1, the present invention provides a kind of sending end electric network source structural optimization method of new energy participation peak regulation, including
Following steps:
Step S101 obtains input data, provides necessary data for power source planning and supports.The input data includes machine
Group data, load related data and price information data provide necessary data for power source planning and support.Wherein unit dependency number
According to referring mainly to machine set type, single-machine capacity, installation number of units, fired power generating unit coal consumption coefficient, annual utilization hours, the time between overhauls(TBO), strong
Compel outage rate, minimum technology power output, economic life, peak regulation rate, maintenance cost ratio and per unit capacity investment cost etc., bears
Lotus related data refers to annual peak load, year electricity consumption, system maximum peak-valley difference and system loading maximum rate of change etc., pricing information
Refer mainly to rate for incorporation into the power network and coal price.
Step S102, the power supply structure optimization model of foundation consideration new energy participation peak regulation, initialization population space, including
The setting of power source planning amount of projects, scheme number of power sources to be selected and scheme mortality.
Step S103, initializes belief space, and setting constraint condition constitutes feasible zone (standard knowledge), the more excellent planning of storage
Scheme (situational knowledge) divides planning region and evaluates subspace (landform knowledge).
Step S104 carries out the optimization of population space, Population Regeneration space optimum programming scheme and planning side of global optimum
Case.
The optimization process of population space includes: to form unit maintenance scheme of arrangement by unit maintenance, and substitute into random raw
It produces in simulation；Hydropower Unit regulation performance space-time analysis is carried out, each Hydropower Unit is divided by day according to Hydropower Unit regulating power
Adjusting, season are adjusted, endless whole year is adjusted and year adjusts unit, and count the peak modulation capacity of all types of units；To pumped storage unit valence
Lattice carry out controlling policy analysis, establish pumped storage price model；Configure wind power plant, the solar power station generating capacity, peak regulation of energy storage
Ability allows to abandon energy spatial analysis；Stochastic Production Simulation is carried out to wind, light, water, fire, pumped storage unit, and is formed and is thrown comprising power supply
Provide cost, fuel cost, carbon emission cost and the overall merit objective function for abandoning energy cost；It is updated according to cosine decreasing function used
Property weight and carry out Studying factors adjustment, make programme using grading function and grade, generate Gauss if being rated H grades and disturb
Reason simultaneously makes a variation and generates filial generation programme, closed on if being rated L or NE parent programme generate Gauss disturbance because
Son closes on grading H parent variation and generates filial generation programme；Random process is carried out using boundary random process strategy；Naturally it selects
Select operation: outstanding programme replaces programme inferior.
Step S105 carries out the optimization of belief space, updates belief space optimum programming scheme and planning side of global optimum
Case.
The optimization process of belief space includes: that execution receives operation, eliminates poor quality programme；Particle swarm algorithm is become
It is different, generate new power source planning scheme；Situational knowledge is updated using roulette, selects outstanding power source planning scheme.
Step S106 appraises through comparison the global optimum of population space and belief space, uses the two complete as this iteration compared with the superior
Office's optimal value；Calculate population's fitness variances sigma2If σ2≤ ε then carries out Logistic chaotic mutation, ε to population global optimum
For TSP question threshold value.
Step S107 judges difference before and after objective function, exports optimum programming scheme if being less than threshold value, otherwise counts again
Enter population space to be operated, otherwise return to step S104, is included in population space again and is operated.
(1) unit maintenance scheduling arrangement
Unit maintenance scheduling model based on equal-risk principle, the present invention accumulate wind using the minimum of unit maintenance scheduling
Dangerous degree method, objective function are that unit accumulative risk degree within the time between overhauls(TBO) is minimum.Minimum accumulative risk degree method passed through in turn(a)round
It inside searches out in the Awaiting Overhaul unit maintenance period and adds up the risk the smallest period, the maintenance period as the unit.
When formulating unit maintenance scheduling, since the maintenance of unit may continue multiple periods, equal-risk method is usually first
The equivalent load the smallest period is found, has then constantly arranged the turn(a)round of Awaiting Overhaul unit in its left and right.Become in load
Change in biggish situation, equal-risk method is possible to while " valley-fill " but " increasing peak ".Tire out in selection maintenance duration
The product risk the smallest period can overcome this disadvantage of equal-risk method as the maintenance position of unit.Assuming that i-th machine
The maintenance duration of group is diWeek the period of unit maintenance can then be arranged to share 52-d in 1 yeari+ 1.It is constant using half
Amount method can calculate risk LOLP weeklyi, thus it is easy to calculate each Awaiting Overhaul period (duration diWeek)
Accumulative risk angle value, then the accumulative risk degree the smallest period should be selected as the maintenance position of i-th unit.From system etc.
The cumulant for deducting i-th unit outage capacity in the cumulant of lasting load curve is imitated, can calculate and arrange the inspection
Repair the risk in each week after unit maintenance.Take same method that can successively determine the repair time of other units, until whole
Unit arrangement finishes.
(2) Stochastic Production Simulation
After the step considers that wind power plant, solar power station (comprising photovoltaic plant, photo-thermal power station etc.) install energy storage additional, electricity is participated in
Force system peak regulation, and be added on the basis of traditional equivalent electric quantity frequency method and extra-high voltage, Hydropower Unit, new energy unit are built
Mould considers, and method is generalized to multizone.Wherein the Stochastic Production Simulation of separate unit and Duo Tai Hydropower Unit is modeled such as
Under:
1) the case where single Hydropower Unit
Peak load just should be undertaken with water power as far as possible when there is Hydropower Unit in system, to reach the effect for reducing coal consumption.
It is as shown in Figure 2 that Hydropower Unit serves as the case where peak load.Curve cg is to be equivalent to hydroelectric machine from former load curve to left in figure
Pool-size CH?.The area of dash area should be equal to the specified rate E of Hydropower UnitA.Remaining unit should serve as in this case
Load be oacgfh portion enclosed.Away from a point CHThe b point of (Hydropower Unit capacity) makees vertical line be, then the face of figure acg
Long-pending and figure bde area equation.That is the load that remaining unit is served as can be regarded as by Oafh and bde two parts group
At.This is equivalent to the load that Hydropower Unit assumes responsibility for the part abef in figure.
Therefore the treatment principle in the production simulation of single Hydropower Unit is summed up are as follows:
It is found under equivalent load curve and is equivalent to Hydropower Unit capacity CHOne section, area is exactly equal to hydroelectric machine therebetween
The given electricity E of groupA.That is, Hydropower Unit should meet the following conditions in production simulation:
P in formulaHLFor the maximum load power that Hydropower Unit is served as, CHFor Hydropower Unit capacity, ELFor power load, EAFor
Hydropower Unit gives electricity.
Fig. 3 indicates the process that Hydropower Unit running position is determined in production simulation.Make under equivalent load curve first
Characteristic rectangle abb ' a ', bottom C of Hydropower Unit outH, utilization hourage T of a height of Hydropower Unit in simulation cycleH.When
This rectangle is moved right, when area of the load curve in respective bins being made to be equal to the rectangular area, has found hydroelectric machine
Group running position.
The characteristic rectangle of above-mentioned Hydropower Unit actually sequentially arranges the mistake of Hydropower Unit operation to the process for moving to left function
Journey, one Hydropower Unit characteristic rectangle of every arrangement will move right one section of distance corresponding with the fired power generating unit capacity.Due to this
Kind movement is discontinuous.
2) the case where more Hydropower Units.
If there is N in systemHPlatform Hydropower Unit.Their characteristic rectangle by its height (utilizing hourage) size from a left side to
Right arrangement forms Hydropower Unit characteristic rectangle sequence chart.It is moved from left to right when sequence chart, in equivalent load duration curve
When a certain section meets the following conditions:
Preceding n Hydropower Unit can merge into an equivalent Hydropower Unit, and the load with corresponding position.Remaining NH-n
The rectangle sequence chart of platform Hydropower Unit should continue to move right, and merge into another equivalent hydroelectric machine in the section of boundary condition
Group, the load with the section.
It is as follows for the modeling method of multizone Stochastic Production Simulation:
In interacted system production simulation, a unit not only will be in said system on-load, and residual capacity should also be
On-load in another system.If unit i is in state k, (capacity is that k × Δ x) exact method is pi(k).The unit on-load is not
Less than (Ji-1+ m) Δ x probability be F(i-1)(Ji-1+m).Its residual capacity >=l Δ x=(k+1-m) Δ x condition is general
Rate is
1-F(i-1)(Ji-1+ m)=1-F(i-1)(Ji-1+k+1-l)
Definition:
According to total probability formula, it is known that unit i residual capacity >=l Δ x probabilityAre as follows:
ByIt is not difficult to acquire the probability that residual capacity is equal to l Δ x
The transmission capacity of interconnection is stochastic variable, its probability distribution is in production simulation process by two systematic electricity branch
The influence helped and constantly change.WithAndThe forward and reverse transmission capacity of interconnection during expression Stochastic Production Simulation
Probability distribution:
X in formulaABIndicate interconnection from system A to the transmission capacity of system B, XABIndicate the conveying from system B to system A
Capacity.Provide the positive direction from system A to system B for interconnection transmission capacity.
If interconnection is single feedback electric wire, specified transmission capacity is Ct, forced outage rate qt, then do not have on interconnection
Initial delivery capacity distribution when electric power is supported are as follows:
When interconnection is made of more back transmission lines, initial distribution can directly be acquired with formula in parallel.
3) water power regulation performance space-time analysis
Its regulating power is carried out according to Hydropower Unit reapective features, mainly its peak modulation capacity is divided.Storage capacity tune
Section coefficient definition: reservoir utilizable capacity (adjusting storage capacity) averagely carrys out the ratio of water with many years.Generally indicated with β.Power station
Regulating power is determined according to storage capacity adjustment factor.Storage capacity adjustment factor (β) is equal to the same level power station and adjusts storage capacity divided by the same level reservoir
Mean annual runoff；Adjusting storage capacity should be normal pool level to the volume of reservoir between level of dead water；Storage capacity adjustment factor is answered
It is the practical storage capacity adjustment factor in the same level reservoir power station, without upstream power station to the regulating power in the same level power station.It is to adjust below
Ability partitioning standards: 1) β=2% below-without adjusting；2) 2%-8%-season is adjusted；3) 8%-30%-year adjusts (8%--
20% endless annual adjusting, 20-30% perfect year are adjusted)；4) being greater than 30% is many years regulation.Change the irrigation of year water consumption
Reservoir, year adjusting is higher with the cut off value of the storage coefficient β of many years regulation, and about 0.50 or so.
Now dam safety evaluation performance is specifically introduced:
Power station with regulating pondage claims have balancing reservoir power station, claims diameter to the power station of no dam safety evaluation ability
Streaming power station.To there is the power station of dam safety evaluation ability that can be divided into according to the regulation performance of reservoir: adjust day, week is adjusted,
It adjusts the moon, season is adjusted, year is adjusted and the several types such as many years regulation.They are divided by Hydropower Plant Reservoir storage coefficient.
1, run-off river plant: without reservoir, substantially come the hydroelectric power plant how much water generates how many electricity；
2, day adjustable type hydroelectric power plant: the regulating cycle of reservoir very little, reservoir is diel, and diel natural runoff is passed through
The hydroelectric power plant of dam safety evaluation power generation；
3, adjust day, week is adjusted and adjust the moon three types power station reservoir capacity it is small, corresponding water-holding capacity and suitable
Using electrical load requirements regulating power also weaker power station can only be sent out less according to the incoming flow situation of upstream by night water storage, it is white
Its multiple or the first tenday period of a month water storage sends out less, the last ten-days period multiple meet the requirement that electric system adjusts electricity；
4, the power station for adjusting in season and adjusting type year has relatively large reservoir capacity, they can be according to current year river
The incoming flow situation of stream determines in a certain season, such as: flood season generates electricity more water storages less, and the water stored stays in another season (such as withered phase)
Multiple electricity, to reach the adjusting purpose to electric system electricity；
5, year adjustable type hydroelectric power plant: optimizing distribution to the natural runoff of each moon in 1 year, adjust, and the wet season is extra
Water be stored in reservoir, guarantee that dry season discharges water the hydroelectric power plant of power generation；
6, many years regulation formula hydroelectric power plant: non-uniform many years natural water amount is optimized into distribution, is adjusted.Many years regulation
Capacity reservoir it is larger, it can determine the generated energy and storage of current year according to the hydrological data of hydrological data and current year over the years
High flow year institute's reservoir storage can also be left to normal flow year or low flow year to generate electricity, to guarantee the adjustable power output of power plant by water；For many years
Type, which adjusts power station, also has Natural Floods stronger regulating power extra flood can be stored in water in flood period
Ku Li waits until that dry season power generation not only meets the requirement that electric system adjusts electricity in this way, but also passes through rationally in flood period
Reservoir operation, may be implemented flood peak clipping and be staggered flood peak purpose for great river, great river work of flood prevention have it is very heavy
The effect wanted.
Adjust and also divide now between adjusting in year endless annual adjusting season.
(3) power supply structure optimization model
Power supply structure optimization model is to consider that new energy participates in the Generation Expansion Planning Model of peak regulation, using bi-level optimal model.
Upper layer model is upper layer investment decision model, whether decision variable is that unit to be selected is invested to build.
If project period is T；There is N in thermal power plantfIt is a；There is N in hydroelectric power planthIt is a；Wind park has NwIt is a；Solar power station has NwIt is a.
Xf、Xh、Xw、XvThermal power plant respectively to be selected, hydroelectric power plant, wind power plant and solar power station decision variable；Yti、Ytj、Ytk、YtlPoint
Not Wei project period t thermal power plant i, hydroelectric power plant j go into operation, the unit number of units of wind power plant k and solar power station l.The packet established
It is as follows that layer model on the power source planning of target is turned to containing peak regulation constraint and with electric generation investment cost minimization:
In formula, B is the total cost of investment present worth of programme, Bft、Bht、Bwt、BvtRespectively indicate t thermal power plant to be selected,
Hydroelectric power plant, wind power plant, the cost of investment of solar power station, T are project period.
The constraint condition of upper layer model includes decision variable Integer constrained characteristic, total installed capacity number of units constrains, power plant is invested to build earliest
Time limit constraint, power balance condition, electric quantity balancing condition and peak modulation capacity constraint, specifically:
1) decision variable Xf、Xh、XwAnd XvInteger constrained characteristic
2) total installed capacity number of units constrains
In formula, Nfi、Nhj、Nwk、NvlThe respectively maximum construction number of thermal power plant, hydroelectric power plant, wind power plant and solar power station
Amount.
3) time limit constraint is invested to build in power plant earliest
In formula, Ti、Tj、TkAnd TlThe respectively earliest operation time of thermal power plant, hydroelectric power plant, wind power plant and solar power station
Constraint.
4) power balance condition
In formula, P0τTo have the total generated output in power plant；CτFor peak load value required for τ system.Yti、Ytj、
Ytk、YtlThe respectively unit number of units of t thermal power plant i project period, hydroelectric power plant j, wind power plant k and solar power station l.Pi、Pj、
Pk、PlThe single unit generated output of respectively thermal power plant i, hydroelectric power plant j, wind power plant k and solar power station l.
5) electric quantity balancing condition
In formula, Hτi、Hτj、Hτk、HτlRespectively thermal power plant, hydroelectric power plant, wind power plant and solar power station τ average benefit
Use hourage；E0τThe maximum generating watt that can be sent out for the existing power plant τ；EtFor total electricity required for system inside t
Value.
6) peak modulation capacity constrains
In formula, αi、αjThe peak regulation depth of respectively newly-built thermal power plant, hydroelectric power plant, αl、αmRespectively built thermal power plant, water power
The peak regulation depth of factory, Pi、PjRespectively newly-built fired power generating unit and Hydropower Unit power output, Pl、PmRespectively built fired power generating unit and water
Electric unit output, NfAnd NhRespectively newly-built fired power generating unit and Hydropower Unit number of units, N0,fAnd N0,hRespectively built fired power generating unit and
Hydropower Unit number of units, NWGFor the number for creating wind power plant, N0,WGFor the number of built wind power plant, ηWGFor the confidence level of wind-power electricity generation
Coefficient,The installed capacity of r-th of wind power plant,U-th of wind energy turbine set installed capacity, N0,SGFor built solar electricity generation field
Number, NSGFor the number for creating solar electricity generation field, ηSGFor the confidence level coefficient of solar power generation,For s-th of sun
The installed capacity of energy electric field,For the installed capacity of v-th of solar energy electric field, WxEnergy storage for x-th of wind power plant configuration is held
Amount includes built wind power plant and newly-built wind power plant, WyIt include the built sun for the stored energy capacitance of y-th of solar power station configuration
Energy power station and newly-built solar power station,For system loading maximum peak-valley difference.
Lower layer's Optimizing manufacture problem comprising decision variable is established on the basis of investment decision model, the object of planning is operation
Cost minimization is segmented into unit maintenance scheduling and Stochastic Production Simulation two sub-problems again, their decision variable point
Not Wei generating set running position of the maintenance period and each generating set on load curve, can by Optimizing manufacture decision
To obtain the generated energy of each generating set, fuel consumption, environmentally friendly cost, abandon energy cost, to calculate the fortune of programme
Row expense.The lower layer's operating cost least model established is as follows:
In formula, b is the total operating cost of programme, bft、bht、bwt、bvt、b0tRespectively indicate t thermal power plant to be selected,
Hydroelectric power plant, wind power plant, the operating cost of solar power station and the existing power plant including extra-high voltage, T is project period.
Wherein, hydroelectric power plant, wind power plant and solar power station operating cost include abandoning energy expense crs, and calculation is as follows:
Crs=c (Prs,max-Prs,real)
In formula, c is that unit abandons energy rejection penalty；Prs,maxIt can practical maximum output for renewable energy generation；Prs,realFor
Practical power output after renewable energy generation scheduling.
The constraint condition of underlying model includes that unit maintenance constraint, system reliability constraint and pollutant discharge amount constrain,
Specifically:
1) unit maintenance constrains
M (m, t)=0
In formula, m is unit maintenance variable；M (m, t) is unit maintenance constraint function, including unit maintenance time-constrain, inspection
Repair strength constraint etc..
2) system reliability constrains
3) pollutant discharge amount constrains
In formula, RtqBy the gross contamination object amount discharged in each power plant production process of t；Each power plant production of t
Allow the greatest contamination object amount discharged in the process.
(4) Hybrid Particle Swarm
The present invention solves the above-mentioned double-deck Generation Expansion Planning Model, cost minimization using Hybrid Particle Swarm (CGPSO algorithm)
Change the upper layer planning of the double-deck Generation Expansion Planning Model, i.e. electric generation investment decision, belongs to integer programming problem, calculated using hybrid particle swarm
Method solves problems and is very effective.The corresponding electric generation investment decision scheme of each particle, for each electric generation investment
Minimum accumulative risk degree method is respectively adopted in decision scheme and equivalent electric quantity frequency method carries out unit maintenance scheduling and stochastic production mould
It is quasi-, the integrated operation cost feedback of acquisition to upper layer target function value is carried out global by the optimizing mechanism of particle swarm algorithm
Optimizing.
Using the planning time of every unit as integer decision variable, i.e. setting integer variable xn(0≤xn≤ T) indicate n-th
Platform unit invests to build the time, works as xnWhen=0, indicate that the unit is not invested to build.Growing point can state integer sequence as since then
Form, { x1, x2..., xn..., xN, dimension is equal to N, is 1/T, and decision relative to binary coding length reduction
Variable meets automatically invests to build progress constraint, does not need to exclusively carry out constraint detection again.
For conventional particle group's algorithm, the main corrective measure of Hybrid Particle Swarm of the invention is as follows:
1) chaos optimization
Chaos optimization searches solution using global traversal, the pseudorandom feature that Chaos Variable has, due to its tool
There is global convergence, easily jump out local optimum and restrains rapid advantage and be widely used.To improve disadvantage, this hybrid particle swarm
Algorithm is as follows using mapping equation:
X (t+1)=[sin (8 π x (t))+1]2/4
Chaotic maps are introduced into particle swarm algorithm herein, the random number r in standard particle group's algorithm1And r2It is to meet
The random number of equally distributed [0,1], now uses chaotic maps to it, and expression formula is as follows:
viS(t+1)=wviS(t)+c1r1s(t)[pis-xis(t)]+c2r2s(t)[pgs-xis(t)]
r1s(t)=[sin (8 π r1s(t-1))+1]2/4
r2s(t)=[sin (8 π r2s(t-1))+1]2/4
2) landform knowledge evaluation mechanism
It is many sub-spaces that the core concept of landform knowledge, which is exactly by entire search space partition, and in search process
In so that the generation of offspring individual is pursued individual best in subspace.Realization process is as follows: 1) according to dimension by each dimension
Degree is divided into several sub-regions.2) the son sky for constituting existing search space is combined according to the subregion that each dimension divides
Between.3) it is graded according to subspace position where existing population at individual to subspace.4) according to rating result instruct population into
Row variation generates offspring individual.
If being L sub-spaces by original search space partition, gross space can be expressed as being composed of subspace, mathematics
Expression formula is as follows:
CS (t)={ C1(t),C2(t),...,CL(t)}
Every sub-spaces can be expressed as C under landform knowledge in formular(t), mathematic(al) representation is as follows:
Cr(t)={ Lr(t),Ur(t),stater(t),dr(t),ptr(t)}
In formula, Lr (t), Ur (t) --- the lower and upper limit of r-th of subspace variable when the t times iteration；stater
(t) --- the grading classification of the t times iteration, r-th of subspace；dr(t) --- the division time of r-th of subspace when the t times iteration
Number；ptr(t) --- variation division pointer.
stater(t) expression formula is as follows:
In formula, f (Xr,best) --- target function value representated by optimum individual in the r of subspace；f(Xr,avg) --- it is entire
The average value of all individual goal functional values of population space；P (t) --- entire population space；Cr(t) --- r-th of population
Space；H --- this sub-spaces are cited as fine space, and when next iteration preferably scans in this space；
NE --- still exist so far without individual in this sub-spaces, unknown this space quality；L --- it is commented in this space
For space inferior, when next iteration, can avoid this space and scan for.
3) adaptive chaotic mutation
To avoid population precocity and falling into local optimum, introduced in the method based on the mixed of population's fitness variance judgement
Ignorant mutation operation, population's fitness variance formula are as follows:
In formula, fi--- the fitness value of i-th of particle；favg--- the average value of current fitness value；F --- normalizing
Change the factor.
If σ2Too small, algorithm is more restrained, and is more easily trapped into local optimum, therefore adaptive threshold is arranged in the present invention
ε works as σ2It needs to carry out global optimum's particle in population chaotic mutation operation when≤ε.This method develops in view of population
Generally have that stronger optimizing performance is not easy to fall into local optimum an early period and the later period needs to increase variation frequency so that it jumps out part
Optimal situation proposes that a kind of adaptive cosine chaotic mutation changes of threshold method, expression formula are as follows:
In formula, εmin--- chaotic mutation threshold value minimum value；εmax--- chaotic mutation threshold maximum value.
Work as σ2It needs to carry out population chaotic mutation when≤ε, the present invention uses Logistic chaotic maps, utilizes independent variable
Value range carries out chaotic maps to it and reflection is penetrated.Expression formula is as follows:
To yisCarry out chaotic mutation operation.
In formula, μ --- the chaotic maps factor；Y --- the amount after normalization；ys--- the amount after chaotic maps；xs--- it is anti-
Amount after mapping；xmaxAnd xmin--- correspond to practical problem independent variable value.
4) inertia weight coefficient and Studying factors adjustment
Inertia weight stopping threshold values Svalue's is introduced into the calculating time that can effectively reduce inertia weight w in iterative process
Number, the present invention combine the characteristic for the inertia weight that successively decreases to pass through setting using a kind of inertia weight that adaptive cosine function successively decreases
Decrement states are divided into normal and two states of adjustment by one stopping threshold values Svalue, as (w-wmin) value be less than Svalue when into
Enter adjustment state, update inertia weight is wmin, otherwise it is considered as normal condition, is successively decreased inertia weight strategy using cosine, cosine is passed
It is as follows to subtract inertia weight more new-standard cement:
W=[(wmax-wmin)/2]cos(πt/Tmax)+(wmax+wmin)/2
In formula, wmax--- the inertia weight factor maximum value of artificial settings；wmin--- inertia weight factor minimum value；
Tmax--- maximum number of iterations.
Adjustment process is as follows:
This method is using the strategy of asynchronous variation Studying factors to c1、c2It is adjusted, expression formula is as follows:
In formula, c1F、c1l--- Studying factors c1The maximum value and minimum value of adjusting；c2F、c2l--- Studying factors c2It adjusts
Maximum value and minimum value.
5) the more new strategy of Gauss disturbance is incorporated
With the average value for the particle individual optimal value summation that the Gauss disturbance factor is added come generation in speed renewal equation formula
For individual optimal value pis(t).The search capability and efficiency of algorithm not only can be improved in this method, while can effectively help grain
Son jumps out local optimum.Specific mathematic(al) representation is as follows:
In formula, N --- population population；Gaussian --- meet random numbers of Gaussian distribution；μ --- average value；σ——
Standard deviation.
The above-mentioned Gauss disturbance factor is added in location updating formula, it is as follows to obtain expression formula:
xis(t+1)=wxis(t)+Δ+c2r2(pg(t)-xis(t))
6) it crosses the border random variation processing strategie
PSO algorithm directly takes bound in BORDER PROCESSING, will lead to algorithm in this way and is easy to during searching element
Local optimum is fallen at upper and lower extreme position, greatly reduces the global optimizing performance of algorithm.To improve above-mentioned problem,
The variation boundary with random factor is specially taken to cross the border processing method herein, policy expression is as follows:
In formula, ξ --- obey equally distributed pseudo random number.
7) natural selection operates
To improve the case where particle easily falls into local optimum while keeping population diversity, the present invention is in particle swarm algorithm
Natural selection operation is introduced, to make the more global exploring ability of algorithm.This method is based on sequencing selection method, now will be contemporary
Population sorts according to new fitness value, and the particle of worst rear ρ is then replaced using the particle of preceding ρ (mortality) in population,
Deposit excellent rogue.
Hybrid Particle Swarm realizes that steps are as follows:
Step1: setting population scale N, particle dimension D, the number of iterations M；
Step2: initialization population space and belief space；
Step3: calculating the fitness value of each particle in population space, by particle position after initialization and fitness value
It is stored as individual optimal value, more all individual optimal values are as global optimum；
Step4: calculating inertia weight w and adjusts policy update w by threshold values, is adjusted to Studying factors；
Step5: belief space, which is based on grading function, which carries out population space, influences operation, calculates the Gauss disturbance factor, root
It makes a variation according to grading classification to population space parent individuality and generates the N number of offspring individual of equivalent；
Step6: processing of crossing the border is carried out to offspring individual position using boundary position processing strategie；
Step7: natural selection is carried out in population space, and replaces population empty with the elite individual stored in situational knowledge
Between in poor individual, the Population Regeneration space optimal and global optimum of individual；
Step8: population space is operated elite individual contributions in space by receiving to belief space, and to elite individual
It is updated using particle swarm algorithm and generates offspring individual, finally update situational knowledge with roulette rule, update belief space individual
Optimal and global optimum；
Step9: comparation and assessment population space and belief space global optimum, use the two compared with the superior as this iteration overall situation most
The figure of merit；
Step10: population's fitness variances sigma is calculated2.If TSP question threshold value ε is calculated according to the number of iterations, if σ2≤
ε then carries out Logistic chaotic mutation to population global optimum；
Step11: algorithm is exited if reaching termination and requiring；Otherwise Step4 is returned to.
Embodiment
The present embodiment will consider that new energy participates in the sending end electric network source structural optimization method of peak regulation applied to China somewhere
Area A stresses to consider that new energy participates in peak regulation, while considering clean energy resource to realize the power extension demand of this area's the year two thousand twenty
Abandoning can the factor such as cost and peak regulation demand, local load peak modulation capacity, channel constraint, demand response.This area's water power accounts for mostly
Number, all types of machine kludges are as follows within 2017: 85,970,000 kilowatts of water power, 33,610,000 kilowatts of thermoelectricity, and 2,440,000 kilowatts of wind-powered electricity generation, the sun
Can be 2,260,000 kilowatts, 260,000 kilowatts of other type units amount to 124,540,000 kilowatts.The year two thousand twenty plan can increase unit capacity difference
It is as follows: 23,800,000 kilowatts of thermoelectricity, 120,250,000 kilowatts of water power, 48,500,000 kilowatts of wind-powered electricity generation, 19,400,000 kilowatts of solar energy, to amount to 21195
Ten thousand kilowatts.
According to existing power supply capacity and layout, southwest peaking demand of power grid and related specification and the area the year two thousand twenty A
Power supply collection to be selected obtains the area A the year two thousand twenty power source planning scheme.The year two thousand twenty general planning capacity is 15164MW, wherein fired power generating unit
Newly-increased 2161MW, Hydropower Unit increase 9470MW newly, and Wind turbines increase 2422MW newly, and solar energy increases 1111MW newly.To selected works and reality
Programme comparison in border is as shown in table 1.
1 the year two thousand twenty area a-power supply of table waits for selected works and physical planning result
Unit: MW
| Project | To selected works | Physical planning | Ratio |
| Thermoelectricity | 2380 | 2161 | 14.2% |
| Water power | 12025 | 9470 | 62.4% |
| Wind-powered electricity generation | 4850 | 2422 | 15.9% |
| Photovoltaic | 1940 | 1111 | 7.5% |
| It is total | 21195 | 15164 | 100% |
To make full use of water resource abundant, the year two thousand twenty planning power supply in the area A is still based on Hydropower Unit, planned capacity
Up to 9470MW, the 62.4% of general planning capacity is accounted for.Simultaneously because peak regulation pressure increasingly increases, regional A is badly in need of promoting peak modulation capacity,
Therefore fired power generating unit need to plan 2161MW, close to capacity to be selected, account for the 14.2% of general planning capacity, but since coal motor group is deposited
The case where polluting environment, therefore fired power generating unit extension capacity is little compared with Hydropower Unit.Wind-powered electricity generation and photovoltaic power generation have ring
It protects, clean characteristic, although installing higher cost compared with fired power generating unit, due to running almost zero cost and pollution-free,
There is some superiority, installed capacity increases comparatively fast, and the two amounts to planned capacity and reaches 3533MW, accounts for planning total capacity in planning
23.4%.This example will be in terms of power balance, electric quantity balancing, reliability level and peak regulation result four to program analysis.
The area table 2A the year two thousand twenty power source planning scheme power balance table
Unit: MW
| Project | Specific subitem | Capacity |
| Total installation of generating capacity inside and outside area | 129285 | |
| Power supply is planned in area | ||
| Thermoelectricity | 31181 | |
| Water power | 88995 | |
| Wind-powered electricity generation | 5803 | |
| Solar energy | 3306 | |
| Only it send by electric power | 32880 | |
| Entered electric power | 7720 | |
| Send out electric power | 40600 | |
| Maximum load | Feng Qi | 69660 |
| The withered phase | 63420 | |
| Power balance | Feng Qi | +26745 |
| The withered phase | +32985 | |
| Percentage reserve | Feng Qi | 21% |
| The withered phase | 25% |
The area A the year two thousand twenty predicts that rich withered phase peak load is respectively 69660MW and 63420MW, 2020 in this programme
Year area's internal/external power total capacity is 137005MW, and wherein area's interior power is installed 129285MW, and send a telegram here 7720MW outside area.Pass through power supply
Optimization, program results can guarantee the year two thousand twenty power supply and spare requirement, and rich withered phase percentage reserve respectively reaches 21% He
25%, spare abundance can be used as the reference of A regional power planning.Power ratio is for example shown in Fig. 4 in programme.
From fig. 4, it can be seen that the installation of the area the year two thousand twenty A accounts for about 69%, with 2017 years phases still based on water power in programme
Than Hydropower Unit growth is more slow, and average annual growth rate is only 1.15%, this is because A local hydroelectric resources is abundant, especially
Be the wet season meet send requirement outside after still a large amount of water power of surplus, hydropower installed capacity is more to keep the whole network percentage reserve higher, because
This is not especially urgent to increasing demand for installed capacity；Thermoelectricity installation account for the 24% of the whole network total installed capacity, due to thermoelectricity operation at
This height and coal motor group discharge exhaust gas residue contamination environment, therefore to make full use of the clean reproducible energies such as water power, thermoelectricity is deposited
Stop standby long-term 1/4 to 1/2, for gradually limitation thermoelectricity development, reduces thermoelectricity significantly herein and wait for selected works scale, but to meet
Peak-load regulating demand still has some fired power generating units to carry out planning installation, so that average growth rate per annum is 3.6%；Though wind-powered electricity generation and photovoltaic
Right installed capacity is not high, and total accounts for the 7% of total installed capacity altogether, but the two average growth rate per annum respectively reaches 44% and 22.9%.This is
Due to A area scene it is resourceful, B area illumination especially therein is good, intensity of illumination, light application time be better than the whole nation other
Area, while in region C, there are wind resource abundant, annual power generation hourage is due to the other areas in the whole nation.It is abundant
Honourable resource and zero run cost characteristics, although installation higher cost, still have certain economic advantages, future
The investment of Wind turbines and photovoltaic can be increased.
3 area A the year two thousand twenty power source planning scheme electric quantity balancing table of table
Unit: hundred million kWh
| Project | Specific subitem | Electricity |
| Area's interior power generated energy | 5250 | |
| Thermoelectricity | 987 | |
| Water power | 3811 | |
| Wind-powered electricity generation | 280 | |
| Solar energy | 172 | |
| Send electricity outside in area | ||
| By electricity in area | ||
| Electricity demand | 3800 | |
| Load hour | 5226 | |
| Electric quantity balancing | +105 |
The area A the year two thousand twenty power demand is predicted as 380,000,000,000 kWh, the electricity that power source planning scheme area internal/external power is capable of providing
Total 390,500,000,000 kWh are measured, can satisfy the area A the year two thousand twenty electrical demand, and have 10,500,000,000 kilowatt hour of surplus.All types of unit hairs
Electricity is as shown in Figure 5.
In programme, the area A the year two thousand twenty generated energy overwhelming majority is water power, accounts for about 73%, photovoltaic and wind power generation capacity
5% and 7% are accounted for respectively, fired power generating unit stops standby unit due to existing throughout the year, and final thermoelectricity generated energy only accounts for 19% or so,
It can be seen that clean energy resource generated energy summation accounts for about 81%, it is seen that the area A power generation electricity consumption clean and effective.
4 area A the year two thousand twenty power source planning solution technique index of table
As can be seen from Table 4, in the year two thousand twenty southwest power source planning scheme that planing method obtains, power source planning scheme it is total
Cost includes two parts, i.e., the investment and operating cost of power plant yet to be built, and wherein operating cost is mainly based on fuel cost, simultaneously
Also meter and blowdown expense.Although it can be seen from program results Hydropower Unit, Wind turbines and photovoltaic unit specific investment cost
It is higher than fired power generating unit, but the unit cost of electricity-generating of the two is very low, and as clean energy resource forms of electricity generation, they are giving birth at random
Producing can guarantee preferentially to generate electricity in simulation, the fired power generating unit electricity of a part can be substituted, to save a large amount of coal consumption and fortune
Row cost, therefore can be invested to build in the preferential selection of power supply concentration to be selected.
Simultaneously as spare abundance, power source planning result can guarantee that the area A higher reliability, LOLP are 2.81 × 10-4,
EENS is 461MWh, and reliability level is higher, guarantees the feasibility of programme.In addition, this project program results are shown, SO2
With NOXAll within the scope of national regulation, environmental advantage is also relatively obvious for equal discharges of major pollutant.
5 area A the year two thousand twenty peak regulation balance sheet of table
Unit: MW
| Project | Capacity |
| Maximum load | 72000 |
| Minimum load rate (%) | 60 |
| Maximum peak-valley difference | 28800 |
| Stand-by heat | 3667 |
| Wind-powered electricity generation is born spare | 3281 |
| Photovoltaic is born spare | 1890 |
| Need peak capacity | 37638 |
| It can peak capacity | 38165 |
| Peak regulation balance | +527 |
The following peak regulation pressure in the area A is larger, and there is an urgent need to build peaking power source for power grid.In conjunction with the area A energy resource structure and
Future developing trend is analyzed according to the peak-shaving capability of aforementioned all kinds of power supplys, and the possibility measures to regulate rush-hour traffic that future increases newly should be mainly normal
Hydropower Unit, pneumoelectric and coal electricity are advised, and whether thermoelectricity etc. participates in the size of peak regulation and its peak modulation capacity still outside water power and area outside area
With uncertainty.With reference to installation as a result, the present embodiment is optimized in concentration addition intention power supply to be selected, power source planning scheme
Peak modulation capacity reaches 38165MW, and needing peak capacity is 37638MW, can satisfy peak regulation demand and surplus 527MW, therefore can be with
A reference is done for the construction of A regional power.
The preferred embodiment of the present invention has been described in detail above.It should be appreciated that those skilled in the art without
It needs creative work according to the present invention can conceive and makes many modifications and variations.Therefore, all technologies in the art
Personnel are available by logical analysis, reasoning, or a limited experiment on the basis of existing technology under this invention's idea
Technical solution, all should be within the scope of protection determined by the claims.
Claims (10)
1. the sending end electric network source structural optimization method that a kind of new energy participates in peak regulation, which comprises the following steps:
1) unit data, load related data and price information data are obtained；
2) the power supply structure optimization model for considering that new energy participates in peak regulation is established；
3) data obtained based on step 1) obtain unit maintenance scheduling, consider the wind power plant and solar power station that install energy storage additional
Power output, carry out wind, solar energy, water, fire, pumped storage power generation Stochastic Production Simulation；
4) the power supply structure optimization model is solved using Hybrid Particle Swarm based on step 3), it is excellent obtains optimum power structure
Change scheme.
2. the sending end electric network source structural optimization method that new energy according to claim 1 participates in peak regulation, which is characterized in that
In the step 3), unit maintenance scheduling is obtained by minimum accumulative risk degree method.
3. the sending end electric network source structural optimization method that new energy according to claim 1 participates in peak regulation, which is characterized in that
In the Stochastic Production Simulation, extra-high voltage, Hydropower Unit and the new energy unit of multizone are considered based on equivalent electric quantity frequency method
Production simulation.
4. the sending end electric network source structural optimization method that new energy according to claim 1 participates in peak regulation, which is characterized in that
The power supply structure optimization model is bi-level optimal model, wherein upper layer model is electric generation investment decision problem, and the object of planning is
Cost of electricity-generating minimizes, whether decision variable is that unit to be selected is invested to build；Underlying model is production Optimal Decision-making problem, the object of planning
It is that operating cost minimizes running position of the maintenance period and each generating set of generating set on load curve.
5. the sending end electric network source structural optimization method that new energy according to claim 4 participates in peak regulation, which is characterized in that
In the bi-level optimal model, the objective function of upper layer model is indicated are as follows:
In formula, B is the total cost of investment present worth of programme, Bft、Bht、Bwt、BvtRespectively indicate t thermal power plant to be selected, water power
Factory, wind power plant, the cost of investment of solar power station, T are project period；
The constraint condition of upper layer model includes decision variable Integer constrained characteristic, total installed capacity number of units constrains, the time limit is invested to build in power plant earliest
Constraint, power balance condition, electric quantity balancing condition and peak modulation capacity constraint；
Consider that wind power plant and solar power station participate in peak regulation in the peak modulation capacity constraint.
6. the sending end electric network source structural optimization method that new energy according to claim 5 participates in peak regulation, which is characterized in that
The peak modulation capacity constraint representation are as follows:
In formula, αi、αjThe peak regulation depth of respectively newly-built thermal power plant, hydroelectric power plant, αl、αmRespectively built thermal power plant, hydroelectric power plant
Peak regulation depth, Pi、PjRespectively newly-built fired power generating unit and Hydropower Unit power output, Pl、PmRespectively built fired power generating unit and hydroelectric machine
Group power output, NfAnd NhRespectively newly-built fired power generating unit and Hydropower Unit number of units, N0,fAnd N0,hRespectively built fired power generating unit and water power
Unit number of units, NWGFor the number for creating wind power plant, N0,WGFor the number of built wind power plant, ηWGFor the confidence level system of wind-power electricity generation
Number,The installed capacity of r-th of wind power plant,U-th of wind energy turbine set installed capacity, N0,SGFor built solar electricity generation field
Number, NSGFor the number for creating solar electricity generation field, ηSGFor the confidence level coefficient of solar power generation,For s-th of solar energy
The installed capacity of electric field,For the installed capacity of v-th of solar energy electric field, WxFor x-th of wind power plant configuration stored energy capacitance,
Include built wind power plant and newly-built wind power plant, WyIt include built solar-electricity for the stored energy capacitance of y-th of solar power station configuration
It stands and newly-built solar power station,For system loading maximum peak-valley difference.
7. the sending end electric network source structural optimization method that new energy according to claim 4 participates in peak regulation, which is characterized in that
In the bi-level optimal model, the objective function of underlying model is indicated are as follows:
In formula, b is the total operating cost of programme, bft、bht、bwt、bvt、b0tRespectively indicate t thermal power plant to be selected, water power
Factory, wind power plant, the operating cost of solar power station and the existing power plant including extra-high voltage, T is project period；
The constraint condition of underlying model includes unit maintenance constraint, system reliability constraint and pollutant discharge amount constraint.
8. the sending end electric network source structural optimization method that new energy according to claim 7 participates in peak regulation, which is characterized in that
The operating cost includes abandoning energy expense crs, its calculation formula is:
Crs=c (Prs,max-Prs,real)
In formula, c is that unit abandons energy rejection penalty, Prs,maxFor renewable energy generation can practical maximum output, Prs,realFor can be again
Practical power output after raw energy machine unit scheduling.
9. the sending end electric network source structural optimization method that new energy according to claim 4 participates in peak regulation, which is characterized in that
When the solution power supply structure optimization model using Hybrid Particle Swarm, the corresponding power supply structure optimization of each particle
Scheme carries out unit maintenance scheduling and Stochastic Production Simulation for each power supply structure optimization scheme, by the synthesis of acquisition respectively
Operating cost feedback carries out global optimizing to upper layer model, by the optimizing mechanism of particle swarm algorithm.
10. new energy according to claim 1 participates in the sending end electric network source structural optimization method of peak regulation, feature exists
In the detailed process for solving the power supply structure optimization model using Hybrid Particle Swarm includes:
Step1: setting population scale N, particle dimension D, the number of iterations M；
Step2: initialization population space and belief space；
Step3: calculating the fitness value of each particle in population space, by particle position after initialization and fitness value as
Individual optimal value storage, more all individual optimal values are as global optimum；
Step4: calculating inertia weight w and adjusts policy update w by threshold value, is adjusted to Studying factors；
Step5: belief space, which is based on grading function, which carries out population space, influences operation, the Gauss disturbance factor is calculated, according to commenting
Grade classification makes a variation to population space parent individuality and generates the N number of offspring individual of equivalent；
Step6: processing of crossing the border is carried out to offspring individual position using boundary position processing strategie；
Step7: natural selection is carried out in population space, and is replaced in population space with the elite individual stored in situational knowledge
Poor individual, the Population Regeneration space optimal and global optimum of individual；
Step8: population space is utilized by receiving operation for elite individual contributions in space to belief space, and to elite individual
Particle swarm algorithm, which updates, generates offspring individual, finally updates situational knowledge with roulette rule, it is optimal to update belief space individual
And global optimum；
Step9: the global optimum of comparation and assessment population space and belief space uses the two compared with the superior as this iteration global optimum
Value；
Step10: population's fitness variances sigma is calculated2If σ2≤ ε then carries out Logistic chaos to population global optimum and becomes
Different, ε is TSP question threshold value；
Step11: algorithm is exited if reaching termination and requiring, otherwise returns to Step4.
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