CN108594658A - A kind of electric-gas coupled system maximum probability load margin Model for Multi-Objective Optimization and its method for solving - Google Patents
A kind of electric-gas coupled system maximum probability load margin Model for Multi-Objective Optimization and its method for solving Download PDFInfo
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- CN108594658A CN108594658A CN201810372258.2A CN201810372258A CN108594658A CN 108594658 A CN108594658 A CN 108594658A CN 201810372258 A CN201810372258 A CN 201810372258A CN 108594658 A CN108594658 A CN 108594658A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
Abstract
The present invention discloses a kind of electrical couplings system maximum probability load margin Model for Multi-Objective Optimization and its method for solving, is related to multipotency streaming system operation control field, includes the following steps：Using electric system peak load nargin in electrical couplings system and natural gas system peak load nargin as multiple target, meter and the uncertainty of wind-powered electricity generation input, consider electric power networks, the constraints of tri- parts of natural gas network and EH establishes the mathematical model of electrical couplings system maximum probability load margin multiple-objection optimization；Based on SRSM, Newton method and NSGA II and maximum satisfaction decision hybrid algorithm solve Model for Multi-Objective Optimization, obtain the data informations such as optimal compromise scheduling strategy and its distribution of corresponding trend.The present invention is adapted to energy source interconnection development trend, it is ensured that the efficient and environmentally friendly operation of multipotency streaming system.
Description
Technical field
The present invention relates to a kind of electric-gas coupled system maximum probability load margin Model for Multi-Objective Optimization and its solution sides
Method belongs to multipotency streaming system operation control field.
Background technology
With energy crisis getting worse, the demand for energy such as electric power, natural gas are big, the continuous hair of current multipotency stream coupling
Exhibition causes the method for traditional single energy streaming system to be difficult directly to apply.There is interval along with wind power output is influenced by wind speed
The uncertain features such as property, fluctuation, in order to ensure the Effec-tive Function of multipotency streaming system, in the electrical couplings of the electric system containing wind-powered electricity generation
In the optimization problem of system, analysis probabilistic to wind-powered electricity generation and electrical couplings system maximum probability load margin multiple target are excellent
Change modeling urgently to study.Based on this, propose a kind of electric-gas coupled system maximum probability load margin Model for Multi-Objective Optimization and its
Method for solving has definite meaning to the energy management of multipotency streaming system.
Document《The Calculation of Available Transfer Capability of meter and electricity-gas interconnection energy resource system security constraint》Consider electrical couplings
System security constraint proposes that a kind of Continuation Method based on linear prediction is solved, is recognized with linear prediction method and restrict electricity
Force system can ability to transmit electricity crucial constraint, to electric system can ability to transmit electricity calculated；Document《Containing Large Scale Wind Farm Integration
Probabilistic total transfer capability quickly calculates》Pass through the available transmission capacity progress to the electric system containing Wind turbines
Research carries out correlative study to the maximum loadability of the electric system containing Wind turbines；Document《The maximum
injection power analysis of grid-connected wind farms》Construct the electric heating based on energy stream
Integrated energy system (integrated energy systems, IES) abandons the all-round flow model of wind consumption Optimal Operation Model, and
Management study is optimized to the energy；Document《Electricity-gas series-parallel connection integrated energy system likelihood energy flow point analysis》Have studied load
And the randomness of output of wind electric field, the analysis of likelihood energy flow point has been carried out to the IES of electric-gas series-parallel connection network.Although these researchs exist
Certain achievement is achieved in the modeling of multipotency flow network and its derivation algorithm, but there has been no abundant from multipotency stream maximum probability load
Spend foundation and its method for solving expansion research of Model for Multi-Objective Optimization.
In order to ensure the efficient and environmentally friendly operation of multipotency streaming system, electric-gas coupled system maximum probability load margin is studied
Model for Multi-Objective Optimization and its method for solving are very necessary, the important in inhibiting under energy Background of Internet.
Invention content
In view of the deficiencies of the prior art, " a kind of electric-gas coupled system maximum probability load margin multiple-objection optimization of the invention
Model and its method for solving " establishes electric-gas coupled system maximum probability load margin Model for Multi-Objective Optimization, proposes to be based on
The electrical probability multipotency stream load nargin mixing that SRSM, Newton method and NSGA-II and maximum satisfaction decision hybrid algorithm combine
Algorithm, which solves, obtains the data informations such as optimal compromise scheduling strategy and its distribution of corresponding trend.
The present invention adopts the following technical scheme that：A kind of electric-gas coupled system maximum probability load margin multiple-objection optimization mould
Type and its method for solving, this method comprises the following steps：
Step 1：It proposes to be based on electric-gas coupled system maximum probability load margin Model for Multi-Objective Optimization；
Step 2：Based on SRSM, Newton method and NSGA-II and maximum satisfaction decision hybrid algorithm solve multiple-objection optimization
Model obtains the data informations such as optimal compromise scheduling strategy and its distribution of corresponding trend.
Description of the drawings
Fig. 1 is the logical architecture figure of the present invention.
Fig. 2 is the solution flow diagram of the present invention.
Specific implementation mode
The present invention includes the following steps：
Step 1：It proposes to be based on electric-gas coupled system maximum probability load margin Model for Multi-Objective Optimization；
(1) object function
max{λe,λg}
In formula, λ is load parameter, wherein λe, λgThe respectively load parameter of electric system and natural gas system.λ=0 is
For the load level of original operating point, λe, λgMaximum to be respectively electric system and natural gas system join in the load of critical point
Number.PLeiFor the load level of the original operating point of electric system, flgiFor the load level of the original operating point of natural gas network system.
PLeimaxFor the maximum loadability of power system load node, flgimaxIt is negative for the maximum of load bus in natural gas network system
Lotus ability.
(2) constraints
The constraints packet of electrical couplings system maximum probability load margin Model for Multi-Objective Optimization proposed by the invention
Include electric power networks, three parts of natural gas network and energy hub (energyhub, EH).
1) electric power networks constrain
The uncertainty of present invention research output of wind electric field describes wind speed v using the Weibull distribution model of two parameters
Random changing rule, the output power P of wind-driven generatorW.Electric power networks constraints include electric power networks power-balance about
Beam, the constraint of unit active power output, unit is idle units limits, node voltage constraint, spinning reserve constraint and branch power transmission
Constraint, has been widely studied, the present invention will be answered directly about the modeling of Wind turbines and the constraints of electric power network system
With details repeats no more.
2) natural gas network constraint
Natural gas network constraint has certain similarity with electric power networks constraint, and trend is solved in electric system
When equation, node voltage amplitude V and phase angle theta are state variables, are analogous to electric system, natural gas network power flow equation it is main
State variable is then state variable nodes pressure p.
1. natural gas network Constraints of Equilibrium
General stable state depends on being pipe ends pressure and pipeline attribute by the flow of pipeline, in natural gas system
Gas pipeline k (i, j are the both ends of natural gas network pipeline), flow fkI.e.：
In formula, fkFor the flow of kth natural gas line；MkFor natural gas line constant；SijIt is flowed for air in pipeline
Direction；P is node pressure.
The flow equation of compressor consumption can be approximated to be：
Qc=kc×Qr×Δp
In formula, QcTo pass through the flow of compressor；kcFor compressor constant；QrThe efficiency constant for passing through flow for compressor；
Δ p is the pressure difference of pipe ends.
Similar to electric system, the flux balance equations of each node are in natural gas network system：
(B+U)f+ω-Tτ=0
In formula, B is the branch node incidence matrix for removing Compressor Pipes；U is compressor node incidence matrix；ω is gas
Body input quantity；TτFor compressor gas demand.
2. compressor pressure ratios constrain：
1≤Ri≤RimaxJ=1 ..., Nc
In formula, RimaxFor the compressor pressure ratios upper limit；NcFor the set of natural gas network compressor.
3. node pressure constrains：
pimin≤p≤pimaxI=1 ..., Nn
In formula, pimin, pimaxFor the lower and upper limit value of natural gas network node pressure；NnFor natural gas network node pressure
By force.
3) coupling constraint of electric power-natural gas system
1. electric-gas system coupled relation constrains
In formula：ηTrans, ηCHP.e, ηCHP.Th, ηFur, ηEx, respectively transformer, CHP power generation parts, CHP heat supplies part, heat
The transformation efficiency of boiler and heat exchanger；PWIt contributes for wind turbine.
2. energy input constraint
Pemin≤Pe≤Pemax
Pgmin≤Pg≤Pgmax
In formula, Pmin, PmaxThe lower and upper limit value inputted for the energy in EH.
Step 2：Based on SRSM, Newton method and NSGA-II and maximum satisfaction decision hybrid algorithm solve multiple-objection optimization
Model obtains the data informations such as optimal compromise scheduling strategy and its distribution of corresponding trend.
First, the randomness that wind speed is considered using SRSM, using wind speed as stochastic variable, i.e. the input of system is uncertain
Parameter is responded the active power output of wind power generating set as the output of system, then solves electric-gas coupled system likelihood energy
Stream and peak load nargin, finally can be obtained the probability distribution of each state variable of multifunctional system.Newton method is solving multipotency system
Steady-state load flow equation in system determines suitable quantity of state initial value, and the correction amount that state variable is calculated by Newton method is made
For the initial value calculated next time, iteration reaches system Critical operating point always.
Pareto optimal solution sets are sought using NSGA-II algorithms, optimal compromise solution is screened with maximum satisfaction degree method, is optimizing
Penalty function is added in object function.
Using type fuzzy satisfactory degree calculation formula less than normal in maximum satisfaction degree method.For the non-branch of each of Pareto solution concentrations
With solution, the satisfaction of each of which desired value and the comprehensive satisfaction of each non-domination solution are calculated, it is maximum to choose comprehensive satisfaction
Non-domination solution, to obtain multiobjective optimization compromise solution.
A kind of electric-gas coupled system maximum probability load margin Model for Multi-Objective Optimization and its solution side are just obtained accordingly
Method.
Embodiments above is merely to illustrate the present invention, and not limitation of the present invention, in relation to the common of technical field
Technical staff can also make a variety of changes and modification without departing from the spirit and scope of the present invention, therefore all
Equivalent technical solution also belongs to the protection category of the present invention.
Claims (3)
1. a kind of electric-gas coupled system maximum probability load margin Model for Multi-Objective Optimization and its method for solving, it is characterised in that：
(1) it proposes to be based on electric-gas coupled system maximum probability load margin Model for Multi-Objective Optimization；
(2) SRSM, Newton method and NSGA-II are based on and maximum satisfaction decision hybrid algorithm solves Model for Multi-Objective Optimization, is obtained
To the data informations such as optimal compromise scheduling strategy and its distribution of corresponding trend.
2. a kind of electric-gas coupled system maximum probability load margin Model for Multi-Objective Optimization according to claim 1 and its
Method for solving, which is characterized in that proposed in the step 1) excellent based on electric-gas coupled system maximum probability load margin multiple target
Change model, the present invention is more with electric system peak load nargin in electrical couplings system and natural gas system peak load nargin
The uncertainty of target, meter and wind-powered electricity generation input, considers electric power networks, the constraints of tri- parts of natural gas network and EH, structure
Build the mathematical model of electrical couplings system maximum probability load margin multiple-objection optimization.
3. a kind of electric-gas coupled system maximum probability load margin Model for Multi-Objective Optimization according to claim 1 and its
Method for solving, which is characterized in that SRSM, Newton method and NSGA-II and maximum satisfaction decision mixing are based in the step 2)
Algorithm solves Model for Multi-Objective Optimization, obtains the data informations such as optimal compromise scheduling strategy and its distribution of corresponding trend.First,
Consider that the randomness of wind speed, Newton method are then based on to solve the steady-state load flow equation in multifunctional system using SRSM
NSGA-II algorithms seek Pareto disaggregation, are concentrated from Pareto solutions using Analysis of Satisfaction method and obtain optimal compromise scheduling strategy
And its data informations such as corresponding trend distribution.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109918795A (en) * | 2019-03-11 | 2019-06-21 | 长沙理工大学 | A kind of energy hinge canonical matrix Steady state modeling method for taking into account physical property and reliability |
| CN110277785A (en) * | 2019-06-26 | 2019-09-24 | 国网浙江省电力有限公司电力科学研究院 | Electrical couplings system loading margin calculation method and system based on continuous multipotency stream |
| CN110417062A (en) * | 2019-07-31 | 2019-11-05 | 广东电网有限责任公司 | A kind of electrical integrated energy system Optimization Scheduling |
| CN113128894A (en) * | 2021-04-28 | 2021-07-16 | 东北大学 | Multi-energy flow dynamic coupling optimization regulation and control method |
-
2018
- 2018-04-24 CN CN201810372258.2A patent/CN108594658A/en active Pending
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109918795A (en) * | 2019-03-11 | 2019-06-21 | 长沙理工大学 | A kind of energy hinge canonical matrix Steady state modeling method for taking into account physical property and reliability |
| CN110277785A (en) * | 2019-06-26 | 2019-09-24 | 国网浙江省电力有限公司电力科学研究院 | Electrical couplings system loading margin calculation method and system based on continuous multipotency stream |
| CN110277785B (en) * | 2019-06-26 | 2021-08-03 | 国网浙江省电力有限公司电力科学研究院 | Electrical coupling system load margin calculation method and system based on continuous multi-energy flow |
| CN110417062A (en) * | 2019-07-31 | 2019-11-05 | 广东电网有限责任公司 | A kind of electrical integrated energy system Optimization Scheduling |
| CN110417062B (en) * | 2019-07-31 | 2022-11-29 | 广东电网有限责任公司 | Optimized dispatching method for electrical comprehensive energy system |
| CN113128894A (en) * | 2021-04-28 | 2021-07-16 | 东北大学 | Multi-energy flow dynamic coupling optimization regulation and control method |
| CN113128894B (en) * | 2021-04-28 | 2023-10-31 | 东北大学 | Multi-energy flow dynamic coupling optimization regulation and control method |
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Application publication date: 20180928 |