CN106549378A - It is a kind of to exert oneself probabilistic distribution coordinated dispatching method for distributed power source - Google Patents
It is a kind of to exert oneself probabilistic distribution coordinated dispatching method for distributed power source Download PDFInfo
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- CN106549378A CN106549378A CN201611129816.XA CN201611129816A CN106549378A CN 106549378 A CN106549378 A CN 106549378A CN 201611129816 A CN201611129816 A CN 201611129816A CN 106549378 A CN106549378 A CN 106549378A
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Classifications
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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
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- H02J3/005—
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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
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
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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
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
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Abstract
The invention discloses one kind is exerted oneself probabilistic distribution coordinated dispatching method for distributed power source, comprise the following steps：Step 1：By Power Flow Tracing Method, analysis, the interactive connection in structure distribution between distributed power source, distribution network, power load are tracked to the distributed power source trend flow direction in distribution control area；Step 2：By building chance constrained programming, that what is set up under given level of confidence exerts oneself probabilistic source net lotus collaboration Optimal Operation Model for distributed power source, is optimized by target of economic benefit；Step 3：Combined with particle swarm intelligence algorithm solving-optimizing model using Monte Carlo simulation approach, complete collaboration optimization.The present invention considers distributed power source and goes out the uncertain factor that fluctuation and generating, the uncertainty of load prediction error are brought to distribution optimization operation, by the chance constrained programming method for introducing uncertain factor, optimization operation, reduce risk.
Description
Technical field
The present invention relates to intelligent distribution network accesses the Optimum Scheduling Technology field of distributed power source on a large scale, and in particular to one
Kind exert oneself probabilistic distribution coordinated dispatching method for distributed power source.
Background technology
Active distribution network is the introducing active control mechanism in power distribution network, is that following intelligent distribution network is realized to a large amount of accesses
Distributed energy carry out the effective solution of active management；What but the intermittence distributed power source such as photovoltaic, blower fan was exerted oneself
Randomness brings greatly challenge for its participation distribution scheduling operation.
At present, distribution is not taken into full account in the net lotus control of distribution source point for the extensive access of distributed energy
The impact that cloth power supply intermittence is exerted oneself, controls only by the coordination of different time scales, using the optimization of short-term time scale
To stabilize fluctuation；But, with the large-scale photovoltaic of more high permeability, the access of wind-powered electricity generation, its generated output situation is by weather etc.
Factor affects and changes violent, single real-time optimal control from short-term time scale difficulty that its fluctuation is completely eliminated is steady to power grid security
The impact of fixed operation, it is necessary to just consider that in the Optimized Operation of long period yardstick distributed power supply is exerted oneself uncertainty in advance
The risk brought.
The content of the invention
The technical problem to be solved in the present invention is to provide a kind of exerts oneself probabilistic distribution collaboration for distributed power source
Dispatching method, can solve prior art using the real-time optimal control of short-term time scale to stabilize distributed power source access distribution
The fluctuation that guipure comes, the problem for causing to be difficult to the impact to power network safety operation is completely eliminated.
The present invention is achieved through the following technical solutions：
It is a kind of to exert oneself probabilistic distribution coordinated dispatching method for distributed power source, comprise the following steps：
Step 1：By Power Flow Tracing Method, analysis is tracked to the distributed power source trend flow direction in distribution control area,
Interactive connection in structure distribution between distributed power source, distribution network, power load；
Step 2：By building chance constrained programming, exerting oneself for distributed power source for setting up under given level of confidence is uncertain
Property source net lotus collaboration Optimal Operation Model, be optimized by target of economic benefit；
Step 3：Combined with particle swarm intelligence algorithm solving-optimizing model using Monte Carlo simulation approach, complete collaboration optimization.
The further scheme of the present invention is that, in the step 1, Power Flow Tracing Method is opened up with network by Load flow calculation
Analysis is flutterred, distributed power source in distribution control area and the relation of load are analyzed, solved on each branch road of network or load
The power supply that is derived from of trend, and contribution proportion.
The further scheme of the present invention is that Power Flow Tracing Method is to track distributed power source product respectively with following current tracking
The ratio that raw electric power is consumed by load, comes the source of the consumed electric energy of analysis load with adverse current Power Flow Tracing Method.
The further scheme of the present invention is that the chance constrained programming built in the step 2, constraints include tradition about
Beam is limited and is limited with chance constraint.
The further scheme of the present invention is that the target that the traditional constraints are limited is the determination sex object, including controlled distribution
Being exerted oneself for formula power supply limit up and down, energy storage discharge and recharge is limited.
The further scheme of the present invention is that the target that the chance constraint is limited is distribution uncertainty object, including wind
Bear the probability constraintses of confidence level, source-lotus qualified relation constraint and system power balance, trend security restriction in danger.
The further scheme of the present invention is that the solving-optimizing model of the step 3 is comprised the following steps：
S1:Data prepare, including optimization it is interval in the prediction of exerting oneself of distributed power source, load prediction, adjustable controllable electric power with bear
The regulation of lotus limits data, and the parameter required by particle cluster algorithm, including：Population, iterationses, inertia coeffeicent are upper and lower
Limit, Studying factors C1, Studying factors C2；
S2:Population is initialized, and is exerted oneself as decision vector with day part schedulable object, random in feasible zone to generate population
Initial population；
S3:Stochastic variable, foundation predictive value and historical data actual value and predictive value deviation are processed with Monte Carlo Analogue Method, with
Machine sampling n times generate the representative of N group schedulings scheme and truly exert oneself, and calculate corresponding economic benefit of sampling every time respectively to each particle
Value；
S4:N group scheduling schemes are ranked up according to result of calculation, the big elements of α N are taken as target function value, and foundation
Means of Penalty Function Methods processes constraints, and more new particle fitness, wherein α bear confidence level for risk；
S5:According to particle fitness, it is determined that global and individual particles history optimal value, updates particle rapidity, carries out population iteration；
S6:If not up to maximum iteration time, returns S3, otherwise export global optimum's particle and be optimal case.
Present invention advantage compared with prior art is：
First, under the framework of active distribution network " source-net-lotus " information interaction, Yi Yuan, lotus are object of study, with electrical network as connection
Medium, with technological means such as Tracing power flows, analyzes distributed power source electric power flow direction in network, it is established that distributed power source and load
The point-to-point interaction mechanism of terminal provides support for types of applications；
2nd, consider that distributed power source goes out fluctuation and generating, the uncertainty of load prediction error gives distribution optimization operation band
The uncertain factor come, by the chance constrained programming method for introducing uncertain factor, optimization operation, reduce risk.
Description of the drawings
Fig. 1 is the general frame figure of the present invention.
Fig. 2 is the solving-optimizing model flow figure of the present invention.
Specific embodiment
One kind as shown in Figure 1 is exerted oneself probabilistic distribution coordinated dispatching method for distributed power source, including following
Step：
Step 1：By Power Flow Tracing Method, analysis is tracked to the distributed power source trend flow direction in distribution control area,
Interactive connection in structure distribution between distributed power source, distribution network, power load；Power Flow Tracing Method is to first pass through trend meter
Calculate and obtain current network trend section, then network topology structure is converted to the lossless network of Power Flow Tracing Method requirement, then
What kind of which consumed by load with ratio with the electric power that following current tracking tracks distributed power source generation respectively, with adverse current tide
Stream tracking analyzing the consumed electric energy of a certain load from which power supply, to distributed power source in distribution control area with bear
The relation of lotus is analyzed, and solves the power supply that each branch road of network or the trend on load are derived from, and contribution proportion.
Step 2：By building chance constrained programming, exerting oneself not for distributed power source under given level of confidence is set up
Deterministic source net lotus cooperates with Optimal Operation Model, the optimistic value with economic benefit as optimization aim, i.e., under given confidence level
Obtained economic benefit is better than the value, makes distribution operating cost minimum；Constraints in chance constrained programming includes traditional constraints
Limit and limit with chance constraint；The target that the traditional constraints are limited is the determination sex object, including to gas turbine, fuel electricity
Being exerted oneself for the controlled distribution formula power supply such as pond, small power station limit up and down, and it is adjustable to energy storage, electric automobile, flexible load etc. can
The energy storage discharge and recharge of control load is limited；The target that the chance constraint is limited is distribution uncertainty object, including to wind-powered electricity generation, light
Lie prostrate out fluctuation, and the risk of traditional load prediction deviation bear the probability constraintses of confidence level, source-lotus qualified relation constraint with
And the security restriction such as system power balance, trend.
The chance constraint of the economic benefit and power-balance is limited as shown by the following formula, indicates that the probability of α makes fortune
Battalion's cost is less than, have β probability to use the power deviation that fluctuation causes and be less than preset limit.
Step 3：Combined with particle swarm intelligence algorithm solving-optimizing model using Monte Carlo simulation approach, complete collaboration excellent
Change, specifically include following steps, as shown in Figure 2：
S1:Data prepare, including optimization it is interval in the prediction of exerting oneself of distributed power source, load prediction, adjustable controllable electric power with bear
The regulation of lotus limits data, and the parameter required by particle cluster algorithm, including：Population, iterationses, inertia coeffeicent are upper and lower
Limit, Studying factors C1, Studying factors C2；
S2:Population is initialized, and is exerted oneself as decision vector with day part schedulable object, random in feasible zone to generate population
Initial population；
S3:Stochastic variable, foundation predictive value and historical data actual value and predictive value deviation are processed with Monte Carlo Analogue Method, with
Machine sampling n times generate the representative of N group schedulings scheme and truly exert oneself, and calculate corresponding economic benefit of sampling every time respectively to each particle
Value；
S4:N group scheduling schemes are ranked up according to result of calculation, the big elements of α N are taken as target function value, and foundation
Means of Penalty Function Methods processes constraints, and more new particle fitness, wherein α bear confidence level for risk；
S5:According to particle fitness, it is determined that global and individual particles history optimal value, updates particle rapidity, carries out population iteration；
S6:If not up to maximum iteration time, returns S3, otherwise export global optimum's particle and be optimal case.
The present invention considers distributed photovoltaic, blower fan undulatory property is caused actually to exert oneself and exert oneself what a deviation caused with prediction
Uncertain risk constructs Stochastic Optimization Model, and by particle swarm intelligence algorithm and asking that illiteracy off card sieve simulation method combines
Solution method carries out model solution, and to draw optimal scheduling decision-making, reduce risk improves collaboration effect of optimization.
Claims (7)
1. one kind is exerted oneself probabilistic distribution coordinated dispatching method for distributed power source, it is characterised in that including following step
Suddenly：
Step 1：By Power Flow Tracing Method, analysis is tracked to the distributed power source trend flow direction in distribution control area,
Interactive connection in structure distribution between distributed power source, distribution network, power load；
Step 2：By building chance constrained programming, exerting oneself for distributed power source for setting up under given level of confidence is uncertain
Property source net lotus collaboration Optimal Operation Model, be optimized by target of economic benefit；
Step 3：Combined with particle swarm intelligence algorithm solving-optimizing model using Monte Carlo simulation approach, complete collaboration optimization.
2. as claimed in claim 1 a kind of to exert oneself probabilistic distribution coordinated dispatching method for distributed power source, which is special
Levy and be：In the step 1, Power Flow Tracing Method is by Load flow calculation and Network topology, to distribution control area
Interior distributed power source is analyzed with the relation of load, solves the power supply that each branch road of network or the trend on load are derived from, and
Contribution proportion.
3. as claimed in claim 2 a kind of to exert oneself probabilistic distribution coordinated dispatching method for distributed power source, which is special
Levy and be：Power Flow Tracing Method is the ratio that the electric power for tracking distributed power source generation respectively with following current tracking is consumed by load
Example, comes the source of the consumed electric energy of analysis load with adverse current Power Flow Tracing Method.
4. as claimed in claim 1 a kind of to exert oneself probabilistic distribution coordinated dispatching method for distributed power source, which is special
Levy and be：The chance constrained programming built in the step 2, constraints are included that traditional constraints are limited and are limited with chance constraint.
5. as claimed in claim 4 a kind of to exert oneself probabilistic distribution coordinated dispatching method for distributed power source, which is special
Levy and be：The target that the traditional constraints are limited is the determination sex object, including being exerted oneself for controlled distribution formula power supply limit up and down, store up
Can discharge and recharge restriction.
6. as claimed in claim 4 a kind of to exert oneself probabilistic distribution coordinated dispatching method for distributed power source, which is special
Levy and be：The target that the chance constraint is limited is distribution uncertainty object, including risk bear confidence level probability constraintses,
Source-lotus qualified relation constraint and system power balance, trend security restriction.
7. as claimed in claim 1 a kind of to exert oneself probabilistic distribution coordinated dispatching method for distributed power source, which is special
Levy is that the solving-optimizing model of the step 3 is comprised the following steps：
S1:Data prepare, including optimization it is interval in the prediction of exerting oneself of distributed power source, load prediction, adjustable controllable electric power with bear
The regulation of lotus limits data, and the parameter required by particle cluster algorithm, including：Population, iterationses, inertia coeffeicent are upper and lower
Limit, Studying factors C1, Studying factors C2；
S2:Population is initialized, and is exerted oneself as decision vector with day part schedulable object, random in feasible zone to generate population
Initial population；
S3:Stochastic variable, foundation predictive value and historical data actual value and predictive value deviation are processed with Monte Carlo Analogue Method, with
Machine sampling n times generate the representative of N group schedulings scheme and truly exert oneself, and calculate corresponding economic benefit of sampling every time respectively to each particle
Value；
S4:N group scheduling schemes are ranked up according to result of calculation, the big elements of α N are taken as target function value, and foundation
Means of Penalty Function Methods processes constraints, and more new particle fitness, wherein α bear confidence level for risk；
S5:According to particle fitness, it is determined that global and individual particles history optimal value, updates particle rapidity, carries out population iteration；
S6:If not up to maximum iteration time, returns S3, otherwise export global optimum's particle and be optimal case.
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Cited By (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107147152A (en) * | 2017-06-15 | 2017-09-08 | 广东工业大学 | New energy power distribution network polymorphic type active reactive source cooperates with Optimal Configuration Method and system |
| CN107611970A (en) * | 2017-10-17 | 2018-01-19 | 国网江苏省电力公司宜兴市供电公司 | The optimization method of the uncertain distribution of distributed photovoltaic and electric automobile |
| CN108039737A (en) * | 2017-12-29 | 2018-05-15 | 国网能源研究院有限公司 | One introduces a collection net lotus coordinated operation simulation system |
| CN109409609A (en) * | 2018-11-05 | 2019-03-01 | 南方电网科学研究院有限责任公司 | Probability constraint modeling method and device for multi-energy flow supply and demand balance of comprehensive energy system |
| CN109858774A (en) * | 2019-01-09 | 2019-06-07 | 燕山大学 | Improve the source net lotus planing method of security of system and harmony |
| CN109886472A (en) * | 2019-01-23 | 2019-06-14 | 天津大学 | A kind of distributed photovoltaic and electric car access probabilistic power distribution station capacity method |
| CN110097263A (en) * | 2019-04-18 | 2019-08-06 | 新奥数能科技有限公司 | The equipment of integrated energy system regulates and controls method and device |
| CN110808579A (en) * | 2018-08-06 | 2020-02-18 | 南京理工大学 | Active power distribution network source load coordination operation method |
| CN110854928A (en) * | 2019-10-29 | 2020-02-28 | 广东工业大学 | Large-scale power distribution network risk control optimization method facing distributed power supply and electric automobile |
| CN111030091A (en) * | 2019-11-28 | 2020-04-17 | 新奥数能科技有限公司 | Method and system for determining installed electric capacity of distributed renewable energy |
| CN111555370A (en) * | 2020-05-20 | 2020-08-18 | 云南电网有限责任公司电力科学研究院 | Power distribution network layered coordination scheduling method and device based on cloud edge coordination |
| CN112329210A (en) * | 2020-10-15 | 2021-02-05 | 苏州英迈菲智能科技有限公司 | Solving method for quadratic form optimal load tracking model of power price driving of power system |
| CN112821444A (en) * | 2020-12-30 | 2021-05-18 | 国网浙江海盐县供电有限公司 | Source network load coordination analysis control method for distributed photovoltaic power generation |
| CN113078677A (en) * | 2021-04-08 | 2021-07-06 | 浙江电力交易中心有限公司 | Energy consumption risk eliminating method considering uncertainty of renewable energy |
| CN113421004A (en) * | 2021-06-30 | 2021-09-21 | 国网山东省电力公司潍坊供电公司 | Transmission and distribution cooperative active power distribution network distributed robust extension planning system and method |
| CN113783233A (en) * | 2021-07-27 | 2021-12-10 | 国网河北省电力有限公司电力科学研究院 | Active power distribution network partition optimization operation scheduling method and device and terminal equipment |
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Cited By (27)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107147152A (en) * | 2017-06-15 | 2017-09-08 | 广东工业大学 | New energy power distribution network polymorphic type active reactive source cooperates with Optimal Configuration Method and system |
| CN107611970A (en) * | 2017-10-17 | 2018-01-19 | 国网江苏省电力公司宜兴市供电公司 | The optimization method of the uncertain distribution of distributed photovoltaic and electric automobile |
| CN108039737B (en) * | 2017-12-29 | 2020-09-11 | 国网能源研究院有限公司 | Source-grid-load coordinated operation simulation system |
| CN108039737A (en) * | 2017-12-29 | 2018-05-15 | 国网能源研究院有限公司 | One introduces a collection net lotus coordinated operation simulation system |
| CN110808579A (en) * | 2018-08-06 | 2020-02-18 | 南京理工大学 | Active power distribution network source load coordination operation method |
| CN109409609A (en) * | 2018-11-05 | 2019-03-01 | 南方电网科学研究院有限责任公司 | Probability constraint modeling method and device for multi-energy flow supply and demand balance of comprehensive energy system |
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| CN109858774B (en) * | 2019-01-09 | 2020-10-27 | 燕山大学 | Source network load planning method for improving system safety and coordination |
| CN109886472A (en) * | 2019-01-23 | 2019-06-14 | 天津大学 | A kind of distributed photovoltaic and electric car access probabilistic power distribution station capacity method |
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| CN110097263A (en) * | 2019-04-18 | 2019-08-06 | 新奥数能科技有限公司 | The equipment of integrated energy system regulates and controls method and device |
| CN110854928B (en) * | 2019-10-29 | 2023-10-03 | 广东工业大学 | Large-scale power distribution network risk control optimization method for distributed power supply and electric automobile |
| CN110854928A (en) * | 2019-10-29 | 2020-02-28 | 广东工业大学 | Large-scale power distribution network risk control optimization method facing distributed power supply and electric automobile |
| CN111030091A (en) * | 2019-11-28 | 2020-04-17 | 新奥数能科技有限公司 | Method and system for determining installed electric capacity of distributed renewable energy |
| CN111030091B (en) * | 2019-11-28 | 2021-11-30 | 新奥数能科技有限公司 | Method and system for determining installed electric capacity of distributed renewable energy |
| CN111555370A (en) * | 2020-05-20 | 2020-08-18 | 云南电网有限责任公司电力科学研究院 | Power distribution network layered coordination scheduling method and device based on cloud edge coordination |
| CN112329210A (en) * | 2020-10-15 | 2021-02-05 | 苏州英迈菲智能科技有限公司 | Solving method for quadratic form optimal load tracking model of power price driving of power system |
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| CN112821444A (en) * | 2020-12-30 | 2021-05-18 | 国网浙江海盐县供电有限公司 | Source network load coordination analysis control method for distributed photovoltaic power generation |
| CN113078677A (en) * | 2021-04-08 | 2021-07-06 | 浙江电力交易中心有限公司 | Energy consumption risk eliminating method considering uncertainty of renewable energy |
| CN113078677B (en) * | 2021-04-08 | 2022-05-27 | 浙江电力交易中心有限公司 | Energy consumption risk eliminating method considering uncertainty of renewable energy |
| CN113421004A (en) * | 2021-06-30 | 2021-09-21 | 国网山东省电力公司潍坊供电公司 | Transmission and distribution cooperative active power distribution network distributed robust extension planning system and method |
| CN113783233A (en) * | 2021-07-27 | 2021-12-10 | 国网河北省电力有限公司电力科学研究院 | Active power distribution network partition optimization operation scheduling method and device and terminal equipment |
| CN115377990A (en) * | 2022-10-24 | 2022-11-22 | 国网浙江省电力有限公司宁波市北仑区供电公司 | Power distribution network frame optimization method and system, power distribution network, equipment and medium |
| CN116667390A (en) * | 2023-07-27 | 2023-08-29 | 华北电力大学（保定） | Load frequency control method based on dynamic face consistency algorithm |
| CN116667390B (en) * | 2023-07-27 | 2023-09-29 | 华北电力大学（保定） | Load frequency control method based on dynamic face consistency algorithm |
| CN117674276A (en) * | 2023-11-08 | 2024-03-08 | 国网山东省电力公司潍坊供电公司 | New energy power distribution network collaborative optimization method and system based on distributed regulation and control architecture |
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