CN109800927A - Power distribution network distributed optimization method under bilateral Power Market - Google Patents
Power distribution network distributed optimization method under bilateral Power Market Download PDFInfo
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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
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
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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
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S50/00—Market activities related to the operation of systems integrating technologies related to power network operation or related to communication or information technologies
- Y04S50/10—Energy trading, including energy flowing from end-user application to grid
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Abstract
The invention discloses a kind of power distribution network distributed optimization methods under bilateral Power Market, this method comprises: the distribution network system under (1) building two day market environment, it including distribution network system operator DSO, buys electric user and sells electric user, each user is equipped with controllable and uncontrollable unit；(2) marketing users Optimized model and DSO Security Checking model are constructed respectively；(3) Optimization Solution market clearing solves marketing users Optimized model in expectation transaction power correction stage marketing users, uses ADMM algorithm iteration to update expectation transaction power between user, DSO calls Security Checking model to check iteration result；Pricing is corrected by modifying coefficient of bidding in the desired pricing amendment stage.The present invention solves the power distribution network optimization problem under market condition using distributed optimization, has taken into account the interests and security of system of different subjects, and only need to exchange a small amount of information can be realized resource distribution between marketing users, simplifies network communication difficulty.
Description
Technical field
The present invention relates to a kind of power distribution network distributed optimization methods under bilateral Power Market, belong to distribution network energy
Management domain.
Background technique
Along with the rapid development of a large amount of accesses and intelligent power grid technology of distributed generation resource, with micro-capacitance sensor, Load aggregation
Quotient and sale of electricity Shang Wei represent for can member because having independent decision-making ability can pass through energy management and effectively manage distribution
Power supply and flexible load change the structure and the method for operation of traditional power grid.It often include multiple benefits in a distribution network system
Beneficial main body both can carry out energetic interaction with power distribution network, and can also carry out electricity transaction, the distribution under market condition each other
Network optimizationization becomes increasingly complex, and that there are information collecting amounts is big, modeling accuracy is low, solves difficulty for traditional centralized optimization method
The problems such as high.
Summary of the invention
Goal of the invention: income in order to improve each user realizes that power distribution network resource is distributed rationally under market condition, simultaneously
The security and stability of guarantee system, object of the present invention is to propose the power distribution network distributed optimization under a kind of bilateral Power Market
Method, synthetic user market power index establish Optimized model, improve the win bit rate and income of user.Using alternating direction multiplier
(Alternating Direction Method of Multipliers, ADMM) algorithm realizes that distribution solves, between user only
A small amount of information, which need to be transmitted, can be realized market clearing.
Technical solution: for achieving the above object, the present invention adopts the following technical scheme:
A kind of power distribution network distributed optimization method under bilateral Power Market, includes the following steps:
(1) distribution network system under two day market environment is constructed, is equipped in distribution network system and is sold electric user, buys electric user
With distribution network system operator (Distribution System Operator, DSO), sell electric user include micro-capacitance sensor user and
Sale of electricity quotient, buying electric user is Load aggregation quotient；Photovoltaic generating system, blower fan power generation system, combustion gas are equipped in micro-grid system
Turbine and energy storage device, wherein gas turbine and energy storage device are controllable micro battery, and load in the micro-capacitance sensor is divided into can
Control load and uncontrollable load；Photovoltaic generating system, blower fan power generation system, gas turbine and storage are equipped in electric business system on sale
It can equipment；It include controllable burden and uncontrollable load in Load aggregation quotient system system；
(2) marketing users Optimized model and DSO Security Checking model are constructed respectively, and wherein marketing users Optimized model is with most
Smallization operating cost is target, considers that system power Constraints of Equilibrium, dominant eigenvalues constrain, controllable burden electricity consumption constrains and can
Control micro battery units limits optimize, and the power output and electricity consumption situation of the internal each controllable of distribution, DSO Security Checking model are examined
Consider the out-of-limit constraint of Branch Power Flow and system load flow and voltage are checked in the out-of-limit constraint of node voltage；
(3) market clearing process is divided into the desired pricing amendment stage and expectation is traded by Optimization Solution market clearing
Power correction stage two parts, between expectation transaction power correction stage marketing users solution marketing users Optimized model, user
Expectation transaction power is updated using ADMM algorithm iteration, subsequent DSO calls Security Checking model to check iteration result；?
It is expected that the pricing amendment stage corrects pricing by modifying coefficient of bidding.
In preferred embodiments, marketing users Optimized model is expressed as follows in the step (2):
In formula, CcommonFor user's operating cost；CuseFor power unit operating cost, CsellFor sale of electricity unit operation at
This, CpccTo exchange power cost between DSO；Power is exchanged for expectation,Electricity is bought in the expression that is positive from other users,Being negative indicates to sell electricity to other users；It is expected electricity price；δ is loss factor；It is got in touch with for marketing users and bulk power grid
Linear heat generation rate,It is to buy electricity to DSO for timing, sells electricity to DSO when being negative；It contributes for controllable micro battery,For can not
Micro battery power output is controlled,For uncontrollable load t moment power demand,For the electricity consumption of controllable burden t moment；Ppcc_minWith
Ppcc_maxRespectively interconnection minimum, maximum power；Pcl_minAnd Pcl_maxRespectively controllable burden minimum, maximum electricity consumption；
Pcms_minAnd Pcms_maxRespectively controllable micro battery minimum, maximum power.
In preferred embodiments, marketing users expectation electricity price is expressed as follows with the relationship that expectation exchanges power:
In formula: α and β is coefficient of bidding；
Marketing users power unit cost is expressed as follows:
In formula: QtIndicate the users'comfort of t moment, QsetIndicate user setting ideal comfort value, μ indicate punishment because
Son；
Marketing users sale of electricity unit operating cost is expressed as follows:
In formula: a, b, c are cost coefficient.
The power cost that exchanges between marketing users and DSO is expressed as follows:
In formula: λtFor time-of-use tariffs.
In preferred embodiments, DSO Security Checking model in the step (2) are as follows:
In formula: Pi tFor the power of t moment node i, inflow is positive, and outflow is negative, and node i assigns in power grid user i and big
The intersection point of the public interconnection of power grid；N indicates node total number；Vi tFor the voltage of t moment node i；Subscript j:i~j indicates all and i
The connected j node of node；yijIndicate the admittance of node i and the connection branch ij of node j；PminAnd PmaxRespectively Branch Power Flow is most
Small, maximum value；VminAnd VmaxRespectively node voltage minimum, maximum value.
In preferred embodiments, the coefficient calculation method of bidding of marketing users bidding curve are as follows: be based on marketing users
The marginal cost of controllable micro battery and the use of controllable burden can constrain, and establish its basic Competitive Bidding Model respectively, and synthesis user is whole
Basic Competitive Bidding Model then calculates user's items market power index, carries out quantitative evaluation and generate to change to user comprehensive market power
Into Competitive Bidding Model；Wherein, user's mass-type foundation Competitive Bidding Model indicates are as follows:
In formula, p, q are multiple scattered according to obtaining to the marketing users acceptance of the bid power simulated under different market clearing prices
The coefficient of bidding that point linear fit obtains；
Improved Competitive Bidding Model are as follows:
K is to generate equal proportion coefficient according to user's items market power index:
K=s1·IHHI+s2·IMRR+s3·ILerner+s4
s1、s2、s3It is the constant between 0~1 for the weighting coefficient of three norms；s4For the constant between [1,1.5]；
Wherein IHHICalculation method it is as follows:
In formula: QiFor the installed capacity of user i；QjFor the installed capacity of user j；N is number of users total in system；
IMRRCalculation method it is as follows:
In formula: DPFor system loading,For the sum of remaining user's active volume in addition to user i,For user i can
Use capacity；
ILernerCalculation method it is as follows:
In formula: λe'xGo out clear electricity price, mc for market predictioniThe marginal cost of unit when for user's i profit maximization.
In preferred embodiments, according to following formula by enumeration method obtain user integrally get the bid power with go out clear electricity
Multiple scatterplots of valence:
In formula:Power is exchanged for the whole expectation of marketing users；WithRespectively indicate out clear electricity
Valence isWhen controllable micro battery i expectation exchange power and the expectation of controllable burden j exchanges power；ψ cmsIndicate controllable micro battery
Unit set；ψ clIndicate controllable burden unit set；WithThe respectively purchase of the sale of electricity price of t moment PCC point and PCC point
Electricity price lattice；Indicate electricity price interval；The expectation electricity price of controllable micro batteryPower is exchanged with the expectation of controllable micro battery
Relationship are as follows:
The expectation electricity price of controllable burdenPower is exchanged with controllable burden expectationRelationship are as follows:
In formula: Tt-1、TmaxRespectively t-1 moment room temperature, market clearing when room temperature, user setting most
High-temperature；For t moment outdoor temperature；C, R and COP is respectively the thermal capacity in room, room thermal resistance and air-conditioning Energy Efficiency Ratio；Δt
For time interval.
In preferred embodiments, the market clearing of the step (3) solves process specifically:
(3-1) each user establishes respective market Optimized model according to step (2), and DSO establishes Security Checking model；
(3-2) initializes the coefficient of bidding of bid round n, marketing users iWith
(3-3) initializes the Lagrangian penalty coefficient initial value of ADMM the number of iterations k, penalty coefficient ρ and marketing users i
(3-4) user i solves market Optimized model, obtains expectation transaction powerControllable micro battery power output is controllably born
Lotus electricity consumption and PCC point work order initial value, transmitting expectation exchanges power between user；
(3-5) user carries out ADMM iteration, and optimization calculates expectation transaction power, controllable micro battery power output, controllable burden and uses
Electricity and PCC point power, each user update expectation and exchange power
(3-6) iteration convergence criterion are as follows:
In formula: ε is convergence threshold value；N indicates total number of users；
Each user judges whether iteration restrains, if convergence, stops iteration, executes step (3-7)；Otherwise the number of iterations k adds
1, update Lagrange multiplierReturn step (3-5) carries out next iteration；
(3-7) DSO calls trend to check model testing ADMM iteration result, judges whether node power, voltage meet about
Beam, if meeting constraint thens follow the steps (3-8), otherwise the number of iterations k adds 1, and updatesReturn step (3-5)；
(3-8) judges that buyer it is expected whether electricity price and seller's expectation electricity price are consistent, and round of bidding if inconsistent n adds 1, and
Update is bidded coefficient, return step (3-3)；Operation is exited if consistent, this, which goes out, settles accounts beam；Each user coefficient amendment of bidding is public
Formula are as follows:
In formula: μαAnd μβFor weight coefficient；It bids the expectation electricity price of the n-th wheel user i for t moment；It is competing for t moment
The expectation electricity price of the wheel of valence n-th user j；N indicates total number of users, if user i is to buy electricity side, user j to sell electricity side, if it is on the contrary with
Family i is to sell electricity side, then user j is to buy electricity side.
In preferred embodiments, in the step (3-2) marketing users i coefficient of biddingWithInitial value be
Improving the improved Competitive Bidding Model of Competitive Bidding Model isIn kp and kq；Wherein p, q be according to simulation not
With the coefficient of bidding that the obtained multiple scatterplot linear fits of marketing users acceptance of the bid power under market clearing price obtain, k is root
The equal proportion coefficient generated according to user HHI index, MRR index and Lerner index.
In preferred embodiments, in the step (3-5), the ADMM iterative formula of user i is as follows:
In formula: CcommoniFor the operating cost of user i.
The utility model has the advantages that compared with prior art, the present invention having the advantage that
The present invention considers that the different characteristics of user controllable micro battery and controllable burden establish basic Competitive Bidding Model, and according to
Family comprehensive market power index corrects Competitive Bidding Model, sufficiently excavates itself and adjusts potentiality, highlights user personality, can be improved user's
Income and win bit rate.
The present invention realizes the power distribution network Optimization Solution under market condition using ADMM algorithm, need to only transmit between market member few
Amount information can reach market equilibrium by parallel computation, alleviate centralization and go out clear mode to the pressure of systematic communication network.
DSO will check system load flow as market surpervision person during iteration goes out clearly, coordinate in the present invention
The interests of single user and the security and stability of whole system, it is ensured that the stable operation of power grid.
Detailed description of the invention
Fig. 1 is 9 standard nodes system diagram of IEEE；
Fig. 2 is air conditioner load basis bidding curve figure, wherein air-conditioning basis bidding curve figure when (a) is cooling in summer,
(b) be winter refrigeration when air-conditioning basis bidding curve figure；
Fig. 3 is market clearing flow chart；
Fig. 4 is ADMM iterative solution procedure chart, wherein (a) is operating cost with the number of iterations change curve, it is (b) pair
Even residual error is with the number of iterations change curve.
Specific embodiment
Technical solution of the present invention is described in further detail below with reference to specific embodiment, so that the skill of this field
Art personnel can better understand the present invention and can be practiced, but illustrated embodiment is not as a limitation of the invention.
Present embodiments provide a kind of power distribution network distributed optimization method under bilateral Power Market, including following step
It is rapid:
Step (1): the distribution network system under building two day market environment is equipped in distribution network system and sells electric user, buys
Electric user and distribution network system operator (Distribution System Operator, DSO), selling electric user includes micro-capacitance sensor
User and sale of electricity quotient, buying electric user is Load aggregation quotient: photovoltaic generating system, wind turbine power generation system are equipped in micro-grid system
System, gas turbine and energy storage device, wherein gas turbine and energy storage device are controllable micro battery, by load in the micro-capacitance sensor
It is divided into two class of controllable burden and uncontrollable load, controllable burden is by taking air conditioner load as an example；Photovoltaic hair is equipped in electric business system on sale
Electric system, blower fan power generation system, gas turbine and energy storage device；It include controllable burden and can not in Load aggregation quotient system system
Load is controlled, wherein controllable burden is by taking air conditioner load as an example.
Step (2): constructing marketing users Optimized model and DSO Security Checking model respectively, buys electric user and sells electric user
It calls marketing users Optimized model allotment controllable and formulates bidding strategies, DSO calls Security Checking model to system load flow
It is checked with voltage.
Step (3): Optimization Solution market clearing determines each controllable power output and electricity consumption in power distribution network under market condition
Transaction power and pricing between situation, dominant eigenvalues, different user.Market clearing process is divided into expectation transaction electricity
Valence corrects stage and expectation transaction power correction stage two parts.Market is solved in expectation transaction power correction stage marketing users
User optimization model, updates expectation using ADMM algorithm iteration between user and trades power, and subsequent DSO calls Security Checking model pair
Iteration result is checked；Pricing is corrected by modifying coefficient of bidding in the desired pricing amendment stage.
It is specifically introduced below:
Firstly, the present embodiment is by taking improved IEEE9 standard nodes as an example according to step (1) constitution and implementation example simulating scenes
It is analyzed, electrical structure is as shown in Figure 1, power distribution network is connected by node 1 with major network.In system ordinary user include MG,
LA and RES, concrete configuration are as shown in table 1.
1 ordinary user's configuration parameter of table
Building marketing users Optimized model and DSO Security Checking model in step (2), specifically include:
(2-1) establishes marketing users Optimized model:
In formula, CcommonFor user's operating cost；CuseFor power unit operating cost, CsellFor sale of electricity unit operation at
This, CpccTo exchange power cost between DSO；Power is exchanged for expectation,Electricity is bought in the expression that is positive from other users,Being negative indicates to sell electricity to other users；It is expected electricity price；δ is loss factor, is constant；For marketing users and big electricity
Net dominant eigenvalues,It is to buy electricity to DSO for timing, sells electricity to DSO when being negative；It contributes for controllable micro battery,For
Uncontrollable micro battery power output,For uncontrollable load t moment power demand,For the electricity consumption of controllable burden t moment；
Ppcc_minAnd Ppcc_maxRespectively interconnection minimum, maximum power；Pcl_minAnd Pcl_maxRespectively controllable burden minimum, maximum use
Electricity；Pcms_minAnd Pcms_maxRespectively controllable micro battery minimum, maximum power.
(2-1-1) establishes marketing users Competitive Bidding Model:
In formula: α and β is to bid coefficient, can synthetic user operating cost, with capable of constrain and market power index calculating acquisition.
(2-1-2) establishes the power unit cost model of meter and users'comfort:
In formula, QtIndicate the users'comfort of t moment, QsetIndicate user setting ideal comfort value, μ indicate punishment because
Son.
(2-1-3) establishes sale of electricity unit operating cost model:
In formula, it is known constant that a, b, c, which are cost coefficient,.
(2-1-4) establishes marketing users and DSO switching cost model:
In formula, λtIt is normal number for time-of-use tariffs.
(2-2) establishes DSO Security Checking model:
Using DC power flow, consider the out-of-limit constraint of Branch Power Flow and the out-of-limit constraint of node voltage, DSO to security of system into
Row is checked, DSO Security Checking model are as follows:
In formula: Pi tFor the power of t moment node i, inflow is positive, and outflow is negative；Vi tFor the voltage of t moment node i；Under
Mark j:i~j indicates all j nodes being connected with i-node；yijIndicate the admittance of node i and the connection branch ij of node j；PminWith
PmaxRespectively Branch Power Flow minimum, maximum value；VminAnd VmaxRespectively node voltage minimum, maximum value.
Wherein, the use of marginal cost and controllable burden of the step (2-1-1) based on the controllable micro battery of marketing users can constrain,
Its basic Competitive Bidding Model is established respectively, synthesizes user's mass-type foundation Competitive Bidding Model, then calculates user's items market power index, it is right
User comprehensive market power carries out quantitative evaluation and generates improvement Competitive Bidding Model.It specifically includes:
(a) to cost function derivation in (2-1-3), i.e., controllable micro battery basis Competitive Bidding Model is established based on marginal cost:
In formula,For the expectation electricity price of controllable micro battery,It is expected to exchange power for controllable micro battery.
(b) based on establishing controllable burden basis Competitive Bidding Model with capable of constraining, by time-of-use tariffs and room temperature direct correlation,
Thus air conditioner load basis bidding curve is as shown in Fig. 2, be divided into two kinds of situations of cooling in summer and winter heating, by taking summer as an example in detail
It describes in detail bright.
T in figuret-1、TmaxAnd TminRespectively t-1 moment room temperature, market clearing when room temperature, user setting
Maximum temperature and user setting minimum temperature,WithRespectively t moment user and bulk power grid points of common connection
Sale of electricity price, the power purchase price of the expectation electricity price of controllable burden and PCC point of (Point of Common Coupling, PCC).
Air-conditioning basis bidding curve when being cooling in summer in (a) of Fig. 2.WithFor starting point, withFor end
Point, connection two o'clock is air conditioner load basis bidding curve, mathematical model are as follows:
By air conditioner heat mechanical model, the mathematical relationship between room temperature and air-conditioning power is established:
In formula:For t moment outdoor temperature；C, R and COP is respectively the thermal capacity in room, room thermal resistance and air-conditioning efficiency
Than being constant；Δ t is time interval；It is expected to exchange power for controllable burden.
(c) marketing users mass-type foundation Competitive Bidding Model is established:
In formula, p, q are coefficient of bidding, and are obtained according to linear fit.
Power is exchanged using the expectation that enumeration method simulates each controllable of the marketing users under different market clearing prices,
It is cumulative that marketing users integrally it is expected to exchange power, it can thus be concluded that multiple scatterplots, pass through the marketing users entirety base of linear fit
Plinth bidding curve.
In formula:Power is exchanged for the whole expectation of marketing users；WithIt respectively indicates out clear
Electricity price isWhen controllable micro battery i expectation exchange power and the expectation of controllable burden j exchanges power；ψ cmsIndicate controllable micro- electricity
Source unit set；ψ clIndicate controllable burden unit set；Indicate electricity price interval.
(d) marketing users HHI index I is calculated separatelyHHI, MRR index IMRRWith Lerner index ILerner。
Wherein IHHICalculation method it is as follows:
In formula: QiFor the installed capacity of user i；N is number of users total in system.
IMRRCalculation method it is as follows:
In formula: DPFor system loading,For the sum of remaining user's active volume in addition to i,For the active volume of i.
The value of user MRR is limited between [0,1], if IMRR< 0, the MRR value that user is arranged is 0；If IMRR> 1, the MRR of user is set
Value is 1.
ILernerCalculation method it is as follows:
In formula: λe'xGo out clear electricity price, mc for market predictioniThe marginal cost of unit when for user's i profit maximization.
(e) equal proportion coefficient k is generated according to user's items market power index:
K=s1·IHHI+s2·IMRR+s3·ILerner+s4
In formula: s1、s2、s3It is the constant between 0~1 for the weighting coefficient of three norms；s4For the constant between [1,1.5],
In s4On the basis of be superimposed each index coefficient, it is ensured that k >=1 to guarantee the income of user, while preventing k value excessive, guarantee
The win bit rate of user.
(f) in (d) in user base Competitive Bidding Model multiplied by equal proportion coefficient k, generate and improve Competitive Bidding Model:
In formula: kp is α in (2-1-1), and kq is β in (2-1-1).
Step (3) is as shown in figure 3, specifically include:
(3-1) each user establishes respective market Optimized model according to step (2), and DSO establishes Security Checking model.
(3-2) initializes the coefficient of bidding of bid round n, marketing users iWithInitial coefficient of bidding, which can be used, to be changed
Into Competitive Bidding Model in kp and kq.
(3-3) initializes the Lagrangian penalty coefficient initial value of ADMM the number of iterations k, penalty coefficient ρ and marketing users i
(3-4) user i solves market Optimized model using the tool box yalmip, obtains expectation transaction powerIt is controllable micro-
Power supply power output, controllable burden electricity consumption and PCC point work order initial value, transmitting expectation exchanges power between user.
(3-5) user carries out ADMM iteration, and optimization calculates expectation transaction power, controllable micro battery power output, controllable burden and uses
Electricity and PCC point power, each user update expectation and exchange powerAccording to marketing users Optimized model and ADMM in (2-1)
Basic principle can derive that the ADMM iterative formula of user i is as follows:
It is superimposed marketing users expectation in the optimization aim of market Optimized model and exchanges the penalty term that power is zero, and adopts
Market Optimized model constraint condition is solved using the tool box yalmip, thus acquires the expectation exchange power under the targetAnd controllable micro battery power output, controllable burden electricity consumption and PCC point work order.
(3-6) iteration convergence criterion are as follows:
In formula: ε is convergence threshold value.
Each user judges whether iteration restrains, if convergence, stops iteration, executes step (3-7)；Otherwise the number of iterations k adds
1, update Lagrange multiplierReturn step (3-5) carries out next iteration.
(3-7) DSO calls trend to check model testing ADMM iteration result, judges whether node power, voltage meet about
Beam, if meeting constraint thens follow the steps (3-8), otherwise the number of iterations k adds 1, and updatesReturn step (3-5).
(3-8) judges that buyer it is expected whether electricity price and seller's expectation electricity price are consistent, and round of bidding if inconsistent n adds 1, and
Update is bidded coefficient, return step (3-3)；Operation is exited if consistent, this, which goes out, settles accounts beam.Each user coefficient amendment of bidding is public
Formula are as follows:
In formula: μαAnd μβIt is constant for weight coefficient；It bids the expectation electricity price of the n-th wheel user i for t moment.
Distributed iterative is carried out with ADMM algorithm, convergence process is as shown in figure 4, (a) is user's operating cost in Fig. 4
Convergence curve, it can be seen that in alternating iteration solution procedure, each ordinary user's operating cost gradually converges to minimum value.Fig. 4
In (b) be ADMM iterative solution during antithesis residual error change curve, in limited the number of iterations antithesis residual error convergence tend to
0, show gradually to meet various constraint conditions during Optimization Solution, as a result tends to optimal solution.In complex chart 4 (a) and (b), repeatedly
Calculated result reaches convergence after generation 20 times, illustrates that Algorithm Convergence is preferable.
Claims (9)
1. a kind of power distribution network distributed optimization method under bilateral Power Market, which comprises the steps of:
(1) distribution network system under two day market environment is constructed, is equipped in distribution network system and is sold electric user, buys electric user and match
Network system operator DSO, selling electric user includes micro-capacitance sensor user and sale of electricity quotient, and buying electric user is Load aggregation quotient；In micro- electricity
Photovoltaic generating system, blower fan power generation system, gas turbine and energy storage device, wherein gas turbine and energy storage are equipped in net system
Equipment is controllable micro battery, and load in the micro-capacitance sensor is divided into controllable burden and uncontrollable load；Match in electric business system on sale
Standby photovoltaic generating system, blower fan power generation system, gas turbine and energy storage device；It include controllable negative in Load aggregation quotient system system
Lotus and uncontrollable load；
(2) marketing users Optimized model and DSO Security Checking model are constructed respectively, and wherein marketing users Optimized model is to minimize
Operating cost is target, considers that system power Constraints of Equilibrium, dominant eigenvalues constrain, controllable burden electricity consumption constrains and controllable micro-
Power supply units limits optimize, and the power output and electricity consumption situation of the internal each controllable of distribution, DSO Security Checking model consider branch
System load flow and voltage are checked in the out-of-limit constraint of road trend and the out-of-limit constraint of node voltage；
(3) market clearing process is divided into desired pricing amendment stage and expectation transaction power by Optimization Solution market clearing
Amendment stage two parts solve marketing users Optimized model in expectation transaction power correction stage marketing users, use between user
ADMM algorithm iteration updates expectation transaction power, and subsequent DSO calls Security Checking model to check iteration result；It is expected
The pricing amendment stage corrects pricing by modifying coefficient of bidding.
2. the power distribution network distributed optimization method under bilateral Power Market according to claim 1, which is characterized in that
Marketing users Optimized model is expressed as follows in the step (2):
In formula, CcommonFor user's operating cost；CuseFor power unit operating cost, CsellFor sale of electricity unit operating cost, Cpcc
To exchange power cost between DSO；Power is exchanged for expectation,Electricity is bought in the expression that is positive from other users,It is negative
It indicates to sell electricity to other users；It is expected electricity price；δ is loss factor；For marketing users and bulk power grid dominant eigenvalues,It is to buy electricity to DSO for timing, sells electricity to DSO when being negative；It contributes for controllable micro battery,For uncontrollable micro battery
Power output,For uncontrollable load t moment power demand,For the electricity consumption of controllable burden t moment；Ppcc_minAnd Ppcc_maxPoint
It Wei not interconnection minimum, maximum power；Pcl_minAnd Pcl_maxRespectively controllable burden minimum, maximum electricity consumption；Pcms_minWith
Pcms_maxRespectively controllable micro battery minimum, maximum power.
3. the power distribution network distributed optimization method under bilateral Power Market according to claim 2, which is characterized in that
Marketing users it is expected that electricity price is expressed as follows with the relationship that expectation exchanges power:
In formula: α and β is coefficient of bidding；
Marketing users power unit cost is expressed as follows:
In formula: QtIndicate the users'comfort of t moment, QsetIndicate that the ideal comfort value of user setting, μ indicate penalty factor；
Marketing users sale of electricity unit operating cost is expressed as follows:
In formula: a, b, c are cost coefficient.
The power cost that exchanges between marketing users and DSO is expressed as follows:
In formula: λtFor time-of-use tariffs.
4. the power distribution network distributed optimization method under bilateral Power Market according to claim 1, which is characterized in that
DSO Security Checking model in the step (2) are as follows:
In formula: Pi tFor the power of t moment node i, inflow is positive, and outflow is negative, and node i assigns user i and bulk power grid in power grid
The intersection point of public interconnection；N indicates node total number；Vi tFor the voltage of t moment node i；Subscript j:i~j indicates all and i-node
Connected j node；yijIndicate the admittance of node i and the connection branch ij of node j；PminAnd PmaxRespectively Branch Power Flow it is minimum,
Maximum value；VminAnd VmaxRespectively node voltage minimum, maximum value.
5. the power distribution network distributed optimization method under bilateral Power Market according to claim 3, which is characterized in that
The coefficient calculation method of bidding of marketing users bidding curve are as follows: the marginal cost based on the controllable micro battery of marketing users is born with controllable
The use of lotus can constrain, and establish its basic Competitive Bidding Model respectively, synthesize user's mass-type foundation Competitive Bidding Model, it is every then to calculate user
Market power index carries out quantitative evaluation to user comprehensive market power and generates improvement Competitive Bidding Model；Wherein, user's mass-type foundation is competing
Valence model is expressed as:
In formula, p, q are according to the multiple scatterplot lines obtained to the marketing users acceptance of the bid power simulated under different market clearing prices
The coefficient of bidding that property is fitted；
Improved Competitive Bidding Model are as follows:
K is to generate equal proportion coefficient according to user's items market power index:
K=s1·IHHI+s2·IMRR+s3·ILerner+s4
s1、s2、s3It is the constant between 0~1 for the weighting coefficient of three norms；s4For the constant between [1,1.5]；
Wherein IHHICalculation method it is as follows:
In formula: QiFor the installed capacity of user i；QjFor the installed capacity of user j；N is number of users total in system；
IMRRCalculation method it is as follows:
In formula: DPFor system loading,For the sum of remaining user's active volume in addition to user i,For the available appearance of user i
Amount；
ILernerCalculation method it is as follows:
In formula: λ 'exGo out clear electricity price, mc for market predictioniThe marginal cost of unit when for user's i profit maximization.
6. the power distribution network distributed optimization method under bilateral Power Market according to claim 5, which is characterized in that
User is obtained by enumeration method according to following formula integrally to get the bid multiple scatterplots of power and clear electricity price out:
In formula:Power is exchanged for the whole expectation of marketing users；WithRespectively indicating out clear electricity price isWhen controllable micro battery i expectation exchange power and the expectation of controllable burden j exchanges power；ψcmsIndicate controllable micro battery unit
Set；ψclIndicate controllable burden unit set；WithThe respectively power purchase valence of the sale of electricity price of t moment PCC point and PCC point
Lattice；Indicate electricity price interval；The expectation electricity price of controllable micro batteryPower is exchanged with the expectation of controllable micro batteryPass
System are as follows:
The expectation electricity price of controllable burdenPower is exchanged with controllable burden expectationRelationship are as follows:
In formula: Tt-1、TmaxRespectively t-1 moment room temperature, market clearing when room temperature, the highest temperature of user setting
Degree；For t moment outdoor temperature；C, R and COP is respectively the thermal capacity in room, room thermal resistance and air-conditioning Energy Efficiency Ratio；When Δ t is
Between be spaced.
7. the power distribution network distributed optimization method under bilateral Power Market according to claim 2, which is characterized in that
The market clearing of the step (3) solves process specifically:
(3-1) each user establishes respective market Optimized model according to step (2), and DSO establishes Security Checking model；
(3-2) initializes the coefficient of bidding of bid round n, marketing users iWith
(3-3) initializes the Lagrangian penalty coefficient initial value of ADMM the number of iterations k, penalty coefficient ρ and marketing users i
(3-4) user i solves market Optimized model, obtains expectation transaction powerControllable micro battery power output, controllable burden electricity consumption
With PCC point work order initial value, transmitting expectation exchanges power between user；
(3-5) user carry out ADMM iteration, optimization calculate expectation trade power, controllable micro battery power output, controllable burden electricity consumption with
And PCC point power, each user update expectation and exchange power
(3-6) iteration convergence criterion are as follows:
In formula: ε is convergence threshold value；N indicates total number of users；
Each user judges whether iteration restrains, if convergence, stops iteration, executes step (3-7)；Otherwise the number of iterations k adds 1, more
New Lagrange multiplierReturn step (3-5) carries out next iteration；
(3-7) DSO calls trend to check model testing ADMM iteration result, judges whether node power, voltage meet constraint, if
Meet constraint and then follow the steps (3-8), otherwise the number of iterations k adds 1, and updatesReturn step (3-5)；
(3-8) judges that buyer it is expected electricity price it is expected whether electricity price is consistent, and round of bidding if inconsistent n adds 1, and updates with the seller
It bids coefficient, return step (3-3)；Operation is exited if consistent, this, which goes out, settles accounts beam；Each user bids coefficient correction formula
Are as follows:
In formula: μαAnd μβFor weight coefficient；It bids the expectation electricity price of the n-th wheel user i for t moment；N-th is bidded for t moment
Take turns the expectation electricity price of user j；N indicates total number of users, if user i is to buy electricity side, user j is to sell electricity side, if user i on the contrary is
Electricity side is sold, then user j is to buy electricity side.
8. the power distribution network distributed optimization method under bilateral Power Market according to claim 7, which is characterized in that
The coefficient of bidding of marketing users i in the step (3-2)WithInitial value be improve the improved Competitive Bidding Model of Competitive Bidding Model
ForIn kp and kq；Wherein p, q are according to the marketing users simulated under different market clearing prices
The coefficient of bidding that the obtained multiple scatterplot linear fits of acceptance of the bid power obtain, k be according to user HHI index, MRR index and
The equal proportion coefficient that Lerner index generates.
9. the power distribution network distributed optimization method under bilateral Power Market according to claim 7, which is characterized in that
In the step (3-5), the ADMM iterative formula of user i is as follows:
In formula: CcommoniFor the operating cost of user i.
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