CN107994571A - A kind of Regional Energy net Multiple Time Scales management method of the parameter containing Full Fuzzy - Google Patents
A kind of Regional Energy net Multiple Time Scales management method of the parameter containing Full Fuzzy Download PDFInfo
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- 238000007726 management method Methods 0.000 title claims abstract description 29
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- 238000013439 planning Methods 0.000 claims description 16
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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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- 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
- H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
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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
- H02J3/381—Dispersed generators
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- H02J3/382—
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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
-
- 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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Abstract
The invention discloses a kind of Regional Energy net Multiple Time Scales management method of parameter containing Full Fuzzy, it is related to energy field.The present invention includes the optimum management of Regional Energy being divided into 24h plans a few days ago, in a few days 2h plans, real-time 15min plan three-level Optimized Operation structures according to time scale, realizes the optimal management to Regional Energy；Under different time scales, the operating scheme of reasonable arrangement difference speed of response controllable resources and alternative plan, realize the progressive coordination of power deviation amount, mitigate the scheduling burden of Regional Energy net control centre；By the way that the constraint of traditional deterministic system is converted into Fuzzy Chance Constraint, the confidence level of credible chance constraint is adjacent to be incremented by, and regenerative resource and system loading precision of prediction gradually step up.
Description
Technical field
The present invention relates to the Regional Energy net Multiple Time Scales management of energy field, more particularly to a kind of parameter containing Full Fuzzy
Method.
Background technology
With the rise accessed extensively of distributed energy, Regional Energy net by numerous distributed generation resources, load, energy storage and
Flexible controllable device aggregates into an autonomous area, interactive by the source of the diversification energy, lotus in Local Area Network, promote source and
The reasonable utilization of lotus end resource, realizes and stablizing for external electrical network is accessed, be considered as the important shape of future source of energy internet development
One of state.
However, due to regenerative resource in Regional Energy net and load can uncertainty, in the management of Regional Energy net
The heart faces certain risk when formulating scheduling scheme.In terms of the fluctuation characteristic of uncertain factor is embodied in randomness and ambiguity, lead to
Chang Congsan angle is tackled：When the simulation of uncertain factor, including interval method, fuzz method, randomized, robust method；Two
It is to carry out Multiple Time Scales optimization using the predicted value constantly updated；Third, reasonable standby resources coping with uncertainty factor is set
Influence.Closed in view of uncertain factor with the time and predict further accurate characteristic, Multiple Time Scales Frame Design is in electricity
Quite a few basis has been accumulated in Force system scheduling.But it is pointed out that existing Regional Energy net management strategy is usual
The problem of ignoring spare setting, or certain spare capacity is reserved according only to proportionality coefficient, for example, peak load 10% or most
Large-sized unit capacity etc..In fact, economy and risk balance side of the reasonable disposition of standby resources in Multiple Time Scales management
Face is most important：When standby configuration is insufficient, system will appear from part and lose load risk, and during standby configuration excess, easily cause
Part of generating units is left unused.And mainly for last stage day in the spare setting of strategy at present, and day inside points then consider not enough to fill
Point, and special spare Optimized model is not established in a few days scheduling portion.
Therefore, those skilled in the art is directed to developing a kind of Full Fuzzy ginseng for considering different time scales standby resources
The Multiple Time Scales optimum management strategy of several Regional Energy nets.
The content of the invention
In view of the drawbacks described above of the prior art, the technical problems to be solved by the invention are contained under structure Multiple Time Scales
The local energy network energy management optimization scheme of Full Fuzzy parameter.
To achieve the above object.A kind of Regional Energy net more times of the parameter containing Full Fuzzy are provided the present invention provides a kind of
Scale management method, its feature under different time scales, the operating scheme of reasonable arrangement difference speed of response controllable resources and
Alternative plan, and the requirement under the change embodiment different time scales for passing through credible chance constraint to scheme reliability.Including
The optimum management of Regional Energy is divided into 24h plans a few days ago, in a few days 2h plans, real-time 15min plans according to time scale
Three-level Optimized Operation structure.
In the better embodiment of the present invention, carried out for the uncertain parameter in Regional Energy net using fuzzy variable
Description, while be 24h plans a few days ago, in a few days 2h plans, real-time 15min plans three by the optimum management partition of the scale of Regional Energy
A time scale；
In another better embodiment of the present invention, corresponding Generation Side standby resources are according to startup-shutdown responding ability
It is spare that difference is divided into slow machine unit reserve, quick startup unit reserve and AGC；
In another better embodiment of the present invention, corresponding user side demand response is spare to be divided into IDR, in a few days a few days ago
2 it is small when IDR, in a few days 15minIDR；
In another better embodiment of the present invention, 24h plans a few days ago determine that Unit Commitment arrangement and unit go out substantially
Power operating point；Unit power generating value is corrected in a few days 2h rolling plannings, and 24h plans a few days ago determine Unit Combination state described in fine setting；
Real-time 15min plans further optimization unit output distribution；
In another better embodiment of the present invention, the confidence level in credible chance Reserve Constraint is with time ruler
Degree closes on continuous improvement, to reflect the different time characteristic requirement to operation plan；
In another better embodiment of the present invention, last stage day, according to regenerative resource a few days ago and predicted load,
Gone out clearly by Unit Combination and economic load dispatching model, determine that a few days ago slow machine unit startup-shutdown plan of next day 24h, scheduling are contributed and count
Draw, the spinning reserve that slow machine unit provides and the purchase volume of IDR spare capacities a few days ago；
In another better embodiment of the present invention, in a few days the 2h stages, then the predicted value that the 2h stages provide in advance is combined,
Set state is adjusted on the basis of combine a few days ago, while determines quick start that unit provides spare and in a few days
2hIDR spare capacity purchase volumes；
It is 15min sections real-time in another better embodiment of the present invention, then according to newest regenerative resource and load
Prediction result, determines that AGC is spare and the purchase of 15minIDR spare capacities.
Technique effect
It is described further below with reference to the technique effect of design of the attached drawing to the present invention, concrete structure and generation, with
It is fully understood from the purpose of the present invention, feature and effect.
The present invention is divided into 24h plans a few days ago, in a few days 2h plans, real-time 15min plans three according to according to time scale
Level Optimized Operation structure, and further consider the response of different adjustment rate resource under different time scales, refine model
Fineness and sophistication.And Fuzzy Chance Constraint relaxed system Reserve Constraint is introduced, and fuzzy believable degree is arranged to be incremented by
Parameter, as the time approaches, the uncertain precision of prediction of regenerative resource and load greatly improve.This management strategy can be with
So that Regional Energy net is optimal state in each period energy management, efficiency of energy utilization is improved.
Brief description of the drawings
Fig. 1 is the Regional Energy Multiple Time Scales Optimization Framework schematic diagram of the preferred embodiment of the present invention；
Fig. 2 is the in a few days 2h rolling planning sequence diagrams of the preferred embodiment of the present invention.
Embodiment
Multiple preferred embodiments of the present invention are introduced below with reference to Figure of description, make its technology contents more clear and just
In understanding.The present invention can be emerged from by many various forms of embodiments, and protection scope of the present invention not only limits
The embodiment that Yu Wenzhong is mentioned.
In the accompanying drawings, the identical component of structure is represented with same numbers label, everywhere the similar component of structure or function with
Like numeral label represents.The size and thickness of each component shown in the drawings arbitrarily show that the present invention does not limit
The size and thickness of each component.In order to make diagram apparent, the appropriate thickness for exaggerating component in some places in attached drawing.
As shown in Figure 1, the Multiple Time Scales optimum management strategy of the Regional Energy net of fuzzy parameter of the present invention is included a few days ago
24 plan when small, in a few days 2h rolling plannings, three aspects such as real-time 15min plans.A few days ago plan determine Unit Commitment arrangement and
Unit is contributed operating point substantially；Unit power generating value is corrected in a few days rolling planning, and is finely tuned and planned to determine Unit Combination state a few days ago；
The further optimization unit output distribution of plan in real time.The particular content of each aspect is as follows：
24h plans before 1.1 days
With the regulation goal a few days ago of the minimum energy LAN of system total operating cost, consider that heat, electric equilibrium constrain, heat,
The spare chance constraint of electricity, equipment output bound and the constraint such as Climing constant, unit minimum startup-shutdown time, its scheduling interval are set
It is set to 1h.
24h operational plans a few days ago, with the minimum target of day operation cost, draw whole day unit group using hour as time scale
Close optimal case.
Object function
The target that local energy net is dispatched a few days ago is this network minimal of system synthesis, including is exchanged with the electric energy of external electrical network
The fuel cost of cost, generator and boiler, and the stand-by cost that Demand-side resource is provided.It is worth noting that, this plan
In slightly designing as Demand-side resource is regarded to independent main body, therefore energy LAN is needed in optimizing scheduling to Demand-side resource
Suitable Economic Stimulus is given in the service provided.
Section 1 is Regional Energy net from major network purchases strategies on the right side of formula (1)；Section 2 be Regional Energy net in combustion gas into
This；The operating cost of CHP units and gas fired-boiler in Section 3 and Section 4 corresponding region energy net net；Section 5 corresponds to a few days ago
Unit generation cost；Section 6 corresponds to the stand-by cost of slow machine unit a few days ago；Section 7 represents the spare of Demand-side resource a few days ago
Cost；Section 8 corresponds to and abandons wind punishment cost.Wherein：ρgridRepresent major network electricity price, ρgasRepresent gas price；Pgrid、PgasRespectively
Represent that local energy net buys the performance number of the energy from major network and natural gas companies； CHP units are represented respectively
With the unit operating cost of gas fired-boiler；For the capacity spare with Demand-side a few days ago of controllable unit reserve a few days ago into
This.
Constraints
The constraints of this model is similar with the constraints in conventional power unit built-up pattern, such as unit output bound about
Beam, Climing constant, startup-shutdown constrain, abandon wind capacity-constrained etc., the also electro thermal coupling conversion constraint including CHP units, and combustion gas
Calorific value conversion of boiler etc., therefore details are not described herein again.It is worth noting that, in this paper models, power-balance and spare capacity are set
Put and then had differences with conventional model, embodied as follows.
1) active power balance constraint
In formula：To predict that regenerative resource is contributed and workload demand amount obscures ginseng in Regional Energy net a few days ago
Number；α1For the confidence level planned a few days ago.In view of the prediction error of Uncertainty, constrained using confidence level and ensure energy balance
Condition meets certain probability level.
2) spare capacity constrains
In formula, controllable unit reserve capacity a few days agoDemand-side resource spare capacity a few days agoTo be to be determined
Amount, can be provided spare capacity maximum capacity min { P by unit respectivelyi max-Pi(t),60URiAnd user side provide spare appearance
Measure maximum capacityConstraint；URiFor the minute level creep speed of controllable unit；β1For the confidence water of the constraint of spare capacity a few days ago
It is flat,To provide Demand-side a few days ago spare user agent's set.It is worth noting that, CHP units are used with heat in this programme
Determine power mode, correspondingly CHP units and be not involved in the offer of spare capacity.
2h rolling plannings in 1.2 days
In a few days rolling planning is based on to plan a few days ago, as shown in Fig. 2, for current time, according to regenerative resource and
Load rolls the newest future 2h ultra-short term predicted values reported per 15min, adjusts [t+1, t+9] period optimal scheduling plan.Together
When, to avoid the adjusting repeatedly of in a few days rolling planning, on-line amending control only is carried out to [t+1, t+2] period power generating value, with this
Analogize rolling optimization backward, gradually reduce the output deviation planned a few days ago, the reference of unit output value is provided for future time scale.
1.2.1 object function
The spare and slow machine unit reserve of Demand-side a few days ago stage when in a few days 2 is small is added in the target of in a few days rolling planning
The energy cost of calling.
Section 1 is Regional Energy net from major network purchases strategies in formula；Section 2 is combustion gas cost in Regional Energy net；The
The operating cost of CHP units and gas fired-boiler in three and Section 4 corresponding region energy net net；Section 5 corresponds in a few days unit
Generator operation cost；Section 6 corresponds to the stand-by cost of slow machine unit a few days ago；Section 7 represents the spare of Demand-side resource a few days ago
Cost；Section 8 corresponds to and abandons wind punishment cost；Section 9 is the energy cost that slow machine unit reserve is called when in a few days 2 is small；
Section 10 is the spare energy cost being called when in a few days 2 is small of Demand-side a few days ago.It is worth noting that, under time of day scale
Regional Energy net no longer changes with the power that interacts of extraneous major network.
1.2.2 constraints
1) power-balance constraint
In formula：Predict that regenerative resource is contributed and workload demand amount obscures ginseng when in a few days 2 is small for Regional Energy net
Number；α2Plan the confidence level of active balance constraint in a few days 2h.
2) spare capacity constrains
In formula, the in a few days controllable unit reserve capacity of 2hIn a few days 2h Demand-sides resource spare capacityTo treat really
Fixed amount, by controllable unit reserve capacity to greatest extentThe spare capacity maximum capacity provided with user sideConstraint；β2
The confidence level constrained in a few days 2h spare capacities,To provide in a few days 2h Demand-sides spare user agent's set.
1.3 real-time 15min plans
Real-time 15min plans lay particular emphasis on the on-line amending of the unit output value of next scheduling instance, according to newest
15min ultra-short terms are predicted, correct the deviation that in a few days rolling planning is left.
1.3.1 object function
Spare Demand-side a few days ago, slow machine unit reserve, day are added in a few days 15min Optimized models, in object function
Interior Demand-side is spare and fast machine unit reserve is in the energy cost that in a few days the 15min stages are called.
Section 1 is Regional Energy net from major network purchases strategies in formula；Section 2 is combustion gas cost in Regional Energy net；The
The operating cost of CHP units and gas fired-boiler in three and Section 4 corresponding region energy net net；Section 5 corresponding A GC units
Spare capacity cost；Section 6 represents the stand-by cost of in a few days 15min Demand-sides resource；Section 7 corresponds to and abandons wind punishment cost；
Section 8 and Section 9 are the called energy costs of slow machine unit reserve and fast machine unit reserve；Section 10 and Section 11 are
In Demand-side spare day a few days ago 2 it is small when the spare called energy cost of Demand-side.It is worth noting that, real time execution is region
Energy net no longer changes with the power that interacts of extraneous major network, and Regional Energy net side overweights the internal unit output of adjustment to ensure
The stability interacted with the external world.
1.3.2 constraints
1) power-balance constraint
In formula,Contribute for Regional Energy net regenerative resource and the 15min ultra-short terms of workload demand amount are predicted
Fuzzy parameter；α3For plan confidence level in real time.
2) spare capacity constrains
In formula, controllable unit reserve capacity under real-time 15min plansWith real-time requirement side resource spare capacityFor amount to be determined, by controllable unit reserve capacity to greatest extentThe spare capacity maximum energy provided with user side
PowerConstraint；β3Plan the confidence level of lower spare capacity constraint for real-time 15min,To provide real-time requirement spare use
Family agent list.
Preferred embodiment of the invention described in detail above.It should be appreciated that the ordinary skill of this area is without wound
The property made work can conceive according to the present invention makes many modifications and variations.Therefore, all technician in the art
Pass through the available technology of logical analysis, reasoning, or a limited experiment on the basis of existing technology under this invention's idea
Scheme, all should be in the protection domain being defined in the patent claims.
Claims (10)
1. the Regional Energy net Multiple Time Scales management method of a kind of parameter containing Full Fuzzy, it is characterised in that in different time ruler
Under degree, operating scheme and the alternative plan of different speed of response controllable resources are arranged, and passes through the change of credible chance constraint
Embody the requirement to scheme reliability under different time scales；The method further include by the optimum management of Regional Energy according to when
Between it is sized be divided into a few days ago 24h plan, in a few days 2h plan, real-time 15min plan three-level Optimized Operation structure；It is described a few days ago
24h plans determine that Unit Commitment arrangement and unit are contributed operating point substantially；Unit power generating value is corrected in the in a few days 2h rolling plannings,
And 24h plans a few days ago determine Unit Combination state described in finely tuning；Further optimization unit output divides for the plans of 15min in real time
Match somebody with somebody.
2. the Regional Energy net Multiple Time Scales management method of the parameter containing Full Fuzzy as claimed in claim 1, it is characterised in that
The operational plans of 24h a few days ago are considered with the regulation goal a few days ago of the minimum energy LAN of system total operating cost
Heat, electric equilibrium constraint, heat, the spare chance constraint of electricity, equipment output bound and Climing constant, the unit minimum startup-shutdown time is about
Beam, its scheduling interval are arranged to 1h, and the operational plans of 24h a few days ago are minimum with day operation cost using hour as time scale
Target, draws whole day Unit Combination optimal case.
3. the Regional Energy net Multiple Time Scales management method of the parameter containing Full Fuzzy as claimed in claim 2, it is characterised in that
The constraints of the operational plans of 24h a few days ago is similar with the constraints in conventional power unit built-up pattern, including unit goes out
The constraint of power bound, Climing constant, startup-shutdown constrain, abandon wind capacity-constrained, further include the electro thermal coupling conversion of CHP units about
Beam, and the calorific value conversion of gas fired-boiler, but the constraints power-balance and spare capacity are set with conventional model in the presence of poor
It is different, embody as follows：
Active power balance constraint：
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In formula：To predict that regenerative resource is contributed and workload demand amount fuzzy parameter in the Regional Energy net a few days ago；
α1For the confidence level of the 24h a few days ago plans；In view of the prediction error of Uncertainty, constrained using confidence level and ensure energy
Equilibrium condition meets certain probability level；
Spare capacity constrains：
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<mn>1</mn>
<mi>S</mi>
</msubsup>
</munderover>
<msubsup>
<mi>P</mi>
<mi>i</mi>
<mrow>
<mi>D</mi>
<mi>A</mi>
<mo>_</mo>
<mi>S</mi>
</mrow>
</msubsup>
<mo>+</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>g</mi>
<mi>r</mi>
<mi>i</mi>
<mi>d</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msubsup>
<mover>
<mi>P</mi>
<mo>~</mo>
</mover>
<mi>w</mi>
<mrow>
<mi>D</mi>
<mi>A</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<msubsup>
<mi>P</mi>
<mrow>
<mi>c</mi>
<mi>u</mi>
<mi>r</mi>
<mi>t</mi>
</mrow>
<mrow>
<mi>D</mi>
<mi>A</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msubsup>
<mi>N</mi>
<mn>1</mn>
<mi>D</mi>
</msubsup>
</munderover>
<msubsup>
<mi>P</mi>
<mi>i</mi>
<mrow>
<mi>D</mi>
<mi>A</mi>
<mo>_</mo>
<mi>D</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>&GreaterEqual;</mo>
<msubsup>
<mover>
<mi>P</mi>
<mo>~</mo>
</mover>
<mi>d</mi>
<mrow>
<mi>D</mi>
<mi>A</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>}</mo>
<mo>&GreaterEqual;</mo>
<msub>
<mi>&beta;</mi>
<mn>1</mn>
</msub>
</mrow>
<mrow>
<mn>0</mn>
<mo>&le;</mo>
<msubsup>
<mi>R</mi>
<mi>i</mi>
<mrow>
<mi>D</mi>
<mi>A</mi>
<mo>_</mo>
<mi>S</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>&le;</mo>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
<mo>{</mo>
<msubsup>
<mi>P</mi>
<mi>i</mi>
<mi>max</mi>
</msubsup>
<mo>-</mo>
<msub>
<mi>P</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mn>60</mn>
<msub>
<mi>UR</mi>
<mi>i</mi>
</msub>
<mo>}</mo>
</mrow>
<mrow>
<mn>0</mn>
<mo>&le;</mo>
<msubsup>
<mi>R</mi>
<mi>i</mi>
<mrow>
<mi>D</mi>
<mi>A</mi>
<mo>_</mo>
<mi>D</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>&le;</mo>
<msubsup>
<mi>R</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>max</mi>
</mrow>
<mrow>
<mi>D</mi>
<mi>A</mi>
<mo>_</mo>
<mi>D</mi>
</mrow>
</msubsup>
</mrow>
In formula, controllable unit reserve capacity a few days agoDemand-side resource spare capacity a few days agoFor amount to be determined,
Respectively spare capacity maximum capacity can be provided by unitThe spare capacity maximum energy provided with user side
PowerConstraint；URiFor the minute level creep speed of controllable unit；β1The confidence level constrained for spare capacity a few days ago,For
There is provided Demand-side a few days ago spare user agent's set；CHP units use electricity determining by heat mould in the operational plans of 24h a few days ago
Formula, correspondingly CHP units and is not involved in the offer of spare capacity.
4. the Regional Energy net Multiple Time Scales management method of the parameter containing Full Fuzzy as claimed in claim 1, it is characterised in that
The in a few days 2h rolling-operations plan, for current time, according to regenerative resource and is born based on the 24h a few days ago plans
Lotus rolls the newest future 2h ultra-short term predicted values reported per 15min, adjusts [t+1, t+9] period optimal scheduling plan；Meanwhile
To avoid the adjusting repeatedly of the in a few days 2h rolling plannings, on-line amending control only is carried out to [t+1, t+2] period power generating value, with
It is such to push rear rolling optimization to, the output deviation of the plans of 24h a few days ago is gradually reduced, unit is provided for future time scale and goes out
Force value refers to.
5. the Regional Energy net Multiple Time Scales management method of the parameter containing Full Fuzzy as claimed in claim 4, it is characterised in that
The constraints of the in a few days 2h rolling plannings is：
Power-balance constraint：
<mrow>
<mi>C</mi>
<mi>r</mi>
<mo>{</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>N</mi>
<mrow>
<mi>C</mi>
<mi>H</mi>
<mi>P</mi>
</mrow>
</msub>
</munderover>
<msubsup>
<mi>P</mi>
<mrow>
<mi>e</mi>
<mo>,</mo>
<mi>i</mi>
</mrow>
<mrow>
<mi>C</mi>
<mi>H</mi>
<mi>P</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msubsup>
<mi>N</mi>
<mn>1</mn>
<mrow>
<mi>I</mi>
<mi>D</mi>
</mrow>
</msubsup>
</munderover>
<msubsup>
<mi>P</mi>
<mi>i</mi>
<mrow>
<mi>I</mi>
<mi>D</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>g</mi>
<mi>r</mi>
<mi>i</mi>
<mi>d</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msubsup>
<mover>
<mi>P</mi>
<mo>~</mo>
</mover>
<mi>w</mi>
<mrow>
<mi>I</mi>
<mi>D</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<msubsup>
<mi>P</mi>
<mrow>
<mi>c</mi>
<mi>u</mi>
<mi>r</mi>
<mi>t</mi>
</mrow>
<mrow>
<mi>I</mi>
<mi>D</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msubsup>
<mi>N</mi>
<mn>1</mn>
<mi>S</mi>
</msubsup>
</munderover>
<msubsup>
<mi>S</mi>
<mi>i</mi>
<mrow>
<mi>D</mi>
<mo>_</mo>
<mi>A</mi>
<mo>_</mo>
<mi>S</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msubsup>
<mi>N</mi>
<mn>1</mn>
<mi>D</mi>
</msubsup>
</munderover>
<msubsup>
<mi>S</mi>
<mi>i</mi>
<mrow>
<mi>D</mi>
<mo>_</mo>
<mi>A</mi>
<mo>_</mo>
<mi>D</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msubsup>
<mover>
<mi>P</mi>
<mo>~</mo>
</mover>
<mi>d</mi>
<mrow>
<mi>I</mi>
<mi>D</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>}</mo>
<mo>&GreaterEqual;</mo>
<msub>
<mi>&alpha;</mi>
<mn>2</mn>
</msub>
</mrow>
In formula：Predict that regenerative resource is contributed and workload demand amount obscures ginseng when in a few days 2 is small for the Regional Energy net
Number；α2For the confidence level of in a few days 2h rolling plannings active balance constraint；
Spare capacity constrains：
<mrow>
<mi>C</mi>
<mi>r</mi>
<mfenced open = "{" close = "}">
<mtable>
<mtr>
<mtd>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>N</mi>
<mrow>
<mi>C</mi>
<mi>H</mi>
<mi>P</mi>
</mrow>
</msub>
</munderover>
<msubsup>
<mi>P</mi>
<mrow>
<mi>e</mi>
<mo>,</mo>
<mi>i</mi>
</mrow>
<mrow>
<mi>C</mi>
<mi>H</mi>
<mi>P</mi>
</mrow>
</msubsup>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
<mo>+</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msubsup>
<mi>N</mi>
<mn>1</mn>
<mrow>
<mi>I</mi>
<mi>D</mi>
</mrow>
</msubsup>
</munderover>
<msubsup>
<mi>P</mi>
<mi>i</mi>
<mrow>
<mi>I</mi>
<mi>D</mi>
</mrow>
</msubsup>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
<mo>+</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msubsup>
<mi>N</mi>
<mn>1</mn>
<mi>S</mi>
</msubsup>
</munderover>
<msubsup>
<mi>S</mi>
<mi>i</mi>
<mrow>
<mi>D</mi>
<mo>_</mo>
<mi>A</mi>
<mo>_</mo>
<mi>S</mi>
</mrow>
</msubsup>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
<mo>+</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msubsup>
<mi>N</mi>
<mn>1</mn>
<mi>D</mi>
</msubsup>
</munderover>
<msubsup>
<mi>S</mi>
<mi>i</mi>
<mrow>
<mi>D</mi>
<mo>_</mo>
<mi>A</mi>
<mo>_</mo>
<mi>D</mi>
</mrow>
</msubsup>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<mi>+</mi>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msubsup>
<mi>N</mi>
<mi>2</mi>
<mi>S</mi>
</msubsup>
</munderover>
<msubsup>
<mi>R</mi>
<mi>i</mi>
<mrow>
<mi>I</mi>
<mi>D</mi>
<mo>_</mo>
<mi>S</mi>
</mrow>
</msubsup>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
<mo>+</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msubsup>
<mi>N</mi>
<mi>2</mi>
<mi>D</mi>
</msubsup>
</munderover>
<msubsup>
<mi>R</mi>
<mi>i</mi>
<mrow>
<mi>I</mi>
<mi>D</mi>
<mo>_</mo>
<mi>D</mi>
</mrow>
</msubsup>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
<mo>+</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>g</mi>
<mi>r</mi>
<mi>i</mi>
<mi>d</mi>
</mrow>
</msub>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
<mo>+</mo>
<msubsup>
<mover>
<mi>P</mi>
<mo>~</mo>
</mover>
<mi>w</mi>
<mrow>
<mi>I</mi>
<mi>D</mi>
</mrow>
</msubsup>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
<mo>-</mo>
<msubsup>
<mi>P</mi>
<mrow>
<mi>c</mi>
<mi>u</mi>
<mi>r</mi>
<mi>t</mi>
</mrow>
<mrow>
<mi>I</mi>
<mi>D</mi>
</mrow>
</msubsup>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
<mo>&GreaterEqual;</mo>
<msubsup>
<mover>
<mi>P</mi>
<mo>~</mo>
</mover>
<mi>d</mi>
<mrow>
<mi>I</mi>
<mi>D</mi>
</mrow>
</msubsup>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>&GreaterEqual;</mo>
<msub>
<mi>&beta;</mi>
<mn>2</mn>
</msub>
</mrow>
<mrow>
<mn>0</mn>
<mo>&le;</mo>
<msubsup>
<mi>R</mi>
<mi>i</mi>
<mrow>
<mi>I</mi>
<mi>D</mi>
<mo>_</mo>
<mi>S</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>&le;</mo>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
<mo>{</mo>
<msubsup>
<mi>P</mi>
<mi>i</mi>
<mi>max</mi>
</msubsup>
<mo>-</mo>
<msub>
<mi>P</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mn>15</mn>
<msub>
<mi>UR</mi>
<mi>i</mi>
</msub>
<mo>}</mo>
</mrow>
<mrow>
<mn>0</mn>
<mo>&le;</mo>
<msubsup>
<mi>R</mi>
<mi>i</mi>
<mrow>
<mi>I</mi>
<mi>D</mi>
<mo>_</mo>
<mi>D</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>&le;</mo>
<msubsup>
<mi>R</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>max</mi>
</mrow>
<mrow>
<mi>I</mi>
<mi>D</mi>
<mo>_</mo>
<mi>D</mi>
</mrow>
</msubsup>
</mrow>
In formula, the controllable unit reserve capacity of in a few days 2h rolling planningsWith the in a few days 2h rolling plannings Demand-side resource
Spare capacityFor amount to be determined, by controllable unit reserve capacity to greatest extentThe spare appearance provided with user side
Measure maximum capacityConstraint；β2The confidence level constrained for the in a few days 2h spare capacities,For in a few days 2h is needed described in offer
Seek user agent's set that side is spare.
6. the Regional Energy net Multiple Time Scales management method of the parameter containing Full Fuzzy as claimed in claim 1, it is characterised in that
The plans of 15min in real time lay particular emphasis on the on-line amending of the unit output value of next scheduling instance, surpass according to newest 15min
Short-term forecast, the deviation that in a few days 2h rolling plannings described in amendment are left.
7. the Regional Energy net Multiple Time Scales management method of the parameter containing Full Fuzzy as claimed in claim 6, it is characterised in that
It is described in real time 15min plan constraints be：
Power-balance constraint：
<mrow>
<mi>C</mi>
<mi>r</mi>
<mfenced open = "{" close = "}">
<mtable>
<mtr>
<mtd>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msub>
<mi>N</mi>
<mrow>
<mi>C</mi>
<mi>H</mi>
<mi>P</mi>
</mrow>
</msub>
</munderover>
<msubsup>
<mi>P</mi>
<mrow>
<mi>e</mi>
<mo>,</mo>
<mi>i</mi>
</mrow>
<mrow>
<mi>C</mi>
<mi>H</mi>
<mi>P</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msubsup>
<mi>N</mi>
<mn>1</mn>
<mrow>
<mi>R</mi>
<mi>T</mi>
</mrow>
</msubsup>
</munderover>
<msubsup>
<mi>P</mi>
<mi>i</mi>
<mrow>
<mi>R</mi>
<mi>T</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>g</mi>
<mi>r</mi>
<mi>i</mi>
<mi>d</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msubsup>
<mover>
<mi>P</mi>
<mo>~</mo>
</mover>
<mi>w</mi>
<mrow>
<mi>R</mi>
<mi>T</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<msubsup>
<mi>P</mi>
<mrow>
<mi>c</mi>
<mi>u</mi>
<mi>r</mi>
<mi>t</mi>
</mrow>
<mrow>
<mi>R</mi>
<mi>T</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msubsup>
<mi>N</mi>
<mn>1</mn>
<mi>S</mi>
</msubsup>
</munderover>
<msubsup>
<mi>S</mi>
<mi>i</mi>
<mrow>
<mi>D</mi>
<mo>_</mo>
<mi>A</mi>
<mo>_</mo>
<mi>S</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mo>+</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msubsup>
<mi>N</mi>
<mn>1</mn>
<mi>D</mi>
</msubsup>
</munderover>
<msubsup>
<mi>S</mi>
<mi>i</mi>
<mrow>
<mi>D</mi>
<mo>_</mo>
<mi>A</mi>
<mo>_</mo>
<mi>D</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msubsup>
<mi>N</mi>
<mn>2</mn>
<mi>S</mi>
</msubsup>
</munderover>
<msubsup>
<mi>S</mi>
<mi>i</mi>
<mrow>
<mi>D</mi>
<mo>_</mo>
<mi>B</mi>
<mo>_</mo>
<mi>S</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<munderover>
<mo>&Sigma;</mo>
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In formula：Contribute for the Regional Energy net regenerative resource and the 15min ultra-short terms of workload demand amount predict mould
Paste parameter；α3For plan confidence level in real time；
Spare capacity constrains：
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In formula, controllable unit reserve capacity under the plans of 15min in real timeWith real-time requirement side resource spare capacity
For amount to be determined, by controllable unit reserve capacity to greatest extentThe spare capacity maximum capacity provided with user side
Constraint；β3The confidence level constrained for the lower spare capacity of the plans of 15min in real time,To provide real-time requirement spare user
Agent list.
8. the Regional Energy net Multiple Time Scales management method of the parameter containing Full Fuzzy as claimed in claim 1, it is characterised in that
Obscurity model building is carried out to the regenerative resource in the Regional Energy net and predicted load, the constraint of traditional deterministic system is turned
Turn to Fuzzy Chance Constraint.
9. the Regional Energy net Multiple Time Scales management method of the parameter containing Full Fuzzy as claimed in claim 1, it is characterised in that
Confidence level in the credibility chance Reserve Constraint closes on continuous improvement with time scale, meets the different time
Constraint requirements of the scale operational plan to the stand-by requirement.
10. the Regional Energy net Multiple Time Scales management method of the parameter containing Full Fuzzy, its feature exist as claimed in claim 1
According to the difference of the regulations speed of the different resource, the multiple dimensioned operational management plan carries out the different resource
Interest frequency Coordination Treatment.
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| CN111277005A (en) * | 2020-02-19 | 2020-06-12 | 东北电力大学 | Multi-source power system multi-time scale scheduling method considering source-load coordination optimization |
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| CN112927095A (en) * | 2021-01-11 | 2021-06-08 | 东北电力大学 | Multi-time scale coordinated scheduling method for electric heating combined system |
| CN113034205A (en) * | 2021-04-20 | 2021-06-25 | 上海交通大学 | Energy storage station and transformer substation combined planning method considering capacity-to-load ratio dynamic adjustment |
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| CN110009152A (en) * | 2019-04-03 | 2019-07-12 | 东南大学 | A kind of consideration electricity turns gas and probabilistic regional complex energy system operation robust Optimal methods |
| CN111277005A (en) * | 2020-02-19 | 2020-06-12 | 东北电力大学 | Multi-source power system multi-time scale scheduling method considering source-load coordination optimization |
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| CN112927095A (en) * | 2021-01-11 | 2021-06-08 | 东北电力大学 | Multi-time scale coordinated scheduling method for electric heating combined system |
| CN113034205A (en) * | 2021-04-20 | 2021-06-25 | 上海交通大学 | Energy storage station and transformer substation combined planning method considering capacity-to-load ratio dynamic adjustment |
| CN113034205B (en) * | 2021-04-20 | 2022-03-11 | 上海交通大学 | Energy storage station and transformer substation combined planning method considering capacity-to-load ratio dynamic adjustment |
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| CN114123326B (en) * | 2021-11-19 | 2024-05-10 | 许继集团有限公司 | Layered autonomous cooperative source network load storage optimization operation system and control method |
| CN115081707A (en) * | 2022-06-17 | 2022-09-20 | 国网福建省电力有限公司 | Micro-grid multi-time scale optimization scheduling method based on source and load flexibility |
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