CN106355344A - Method for robustly and optimally operating micro-grids on basis of orthogonal arrays - Google Patents
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Abstract
The invention discloses a method for robustly and optimally operating micro-grids on the basis of orthogonal arrays. The method includes steps of extracting static data of micro grid architectures, power types, operation costs of the power types, energy storage system unit operation cots, interaction costs, time-of-use electricity prices and the like; building micro-grid uncertainty set models; screening test scenarios; building micro-grid robust and optimal operation models on the basis of the orthogonal arrays and designing solution strategies on the basis of the test scenarios so as to obtain ultimate micro-grid robust and optimal operation schemes. The method has the advantages that the grid-connection micro-grids with consideration of output of renewable energy distributed power sources and load demand uncertainty can be optimally operated by the aid of the method, and dispatching reference can be provided for operation personnel of the micro-grids.
Description
Technical field
The present invention relates to a kind of micro-capacitance sensor robust optimizing operation method based on orthogonal array, belong to electric power system optimization and adjust
Degree and running technology field.
Background technology
Compare with the regulation and control of non-regeneration energy class distributed power source, the forms of electricity generation of regenerative resource class distributed power source is easy
Affected by factors such as its weather, environment, be there is obvious randomness and intermittence, scale access system will necessarily increase joins
The operating uncertainty of electric system, also can make target, about interfascicular restricting relation more complicated in research modeling.How to adapt to
Various regenerative resource class distributed power sources are exerted oneself, workload demand does not know operating mode, realize multivariate resource good interaction and economy
Operation is the Research Challenges in microgrid energy management and optimization.Thus, how effectively to set up multi-period economic model, and merge
The maximum severe scene objects of interaction cost guide the robust operational plan of feasible region unit, become traffic department and are badly in need of solving
Major issue.
Content of the invention
It is an object of the invention to, a kind of micro-capacitance sensor robust optimizing operation method based on orthogonal array is provided it is achieved that
To distributing rationally of the distribution system resource of regenerative resource class distributed power source and negative rules.
To achieve these goals, the invention provides a kind of micro-capacitance sensor robust optimization operation side based on orthogonal array
Method, including step:
(1) micro-capacitance sensor framework, power supply type and its operating cost, energy-storage system unit operating cost, interaction cost, timesharing are extracted
The static datas such as electricity price；
(2) build regenerative resource class distributed power source exert oneself, the uncertain collection of workload demand；
(3) the robust test scene based on orthogonal array for the screening；
(4) build micro-capacitance sensor robust optimal operation model；
(5) the two benches solution strategies based on test scene for the design, obtain final micro-capacitance sensor robust and optimize operating scheme.
2nd, described micro-capacitance sensor framework includes: power supply type, distributed energy storage system, electric load, dispatching control center, on
Level grid interface.
3rd, described distributed electrical Source Type includes: non-regeneration energy class distributed power source and regenerative resource class are distributed
Power supply.
4th, described interactive cost model represents the economic one-tenth interacting power transmission generation between micro-capacitance sensor and higher level's electrical network
This.
5th, described regenerative resource class distributed power source is exerted oneself, the uncertain collection construction step of workload demand is:
(1) determine that regenerative resource class distributed power source uncertain collection of exerting oneself is upper following according to distributed power source historical data of exerting oneself
Boundary's reference quantity:
Constrain, according to actual regenerative resource class distributed power source unit operation, up-and-down boundary value that it is exerted oneself to be modified:
In formula,、It is respectively regenerative resource class distributed power source to exert oneself up-and-down boundary reference value,、It is respectively regenerative resource class distributed power source after revising to exert oneself up-and-down boundary reference value,For renewable energy
Source class distributed power source is exerted oneself point prediction base value,For forecast error probability density function,ForAnti-
Function representation method,、It is respectively regenerative resource class distributed power source to exert oneself maximin,For confidence
Horizontal parameters,、It is respectively the upper and lower dividing value of confidence level parameter；
(2) thus, the uncertain collection of formation distributed power source:
The uncertain collection that workload demand can be obtained in the same manner is:
In formula,For regenerative resource class distributed power source in the periodActual exert oneself,Exist for workload demand
PeriodActual demand value,For workload demand in the periodPoint prediction base value,、It is respectively negative
Lotus demand is in the periodUp-and-down boundary reference value.
6th, the described robust test scene screening step based on orthogonal array is:
(1) dispatching cycle is divided into t period, then distribution type renewable energy exert oneself within dispatching cycle total t from
Dissipate value, be designated as:, workload demand total t centrifugal pump within dispatching cycle, it is designated as:, therefore one dispatching cycle regenerative resource class distributed power source and workload demand defeated
Enter parameter and have 2 × t；
(2) filler test scene: by orthogonal array and corresponding parameter value horizontal assignment rule screening；
So-called orthogonal array (orthogonal array, oa) matrix refers to be made up of c corresponding difference |input paramete
A × b matrix, intensity d(0≤d≤b) orthogonal array, if in arbitrary a × d submatrix of matrix a × b, arbitrary intensity
Arrangement for d exists justOccur in individual row, be designated as:
Wherein, a is the size of matrix array, here in order to refer to the scene number needing to test under current input parameters value level；b
For parameter sum, refer to regenerative resource class distributed power source here and exert oneself and workload demand |input paramete sum；C is one kind
Value level corresponding single group |input paramete；D is the strength factor of oa；
Thus, total test scene number is obtained according to multigroup difference |input paramete value level.
7. the micro-capacitance sensor robust optimal operation model described in is:
(1) micro-capacitance sensor robust optimal operation model object function is:
Wherein,Represent micro-capacitance sensor totle drilling cost；For conventional power generation usage unit unit cost of electricity-generating, have；、For unit cost of electricity-generating coefficient, concrete value and the generator type chosen and its
Relating to parameters,For conventional power generation usage unit in the periodExert oneself；For energy-storage system regulation and control cost, have；For energy storage device unit regulation and control cost；For energy-storage system output；General
Interactive cost mathematic(al) representation be:,,、Correspond to the period respectivelyMicro-capacitance sensor is to major network
Purchase, the 0-1 state variable of power transmission, set and work asWhen,, whenWhen,, with
Shi You:；For the interactive cost under the most severe scene, meet,sRepresent present test field scape；sFor total test scene；By target letter
In numberExpressed with the mode of interaction cost and scene collection, that is, micro-capacitance sensor robust optimizes operational mode and can be deformed into:
Wherein,For scenesUnder Comprehensive economical operation Optimum cost value；
(2) micro-capacitance sensor robust optimal operation model bound for objective function is:
Conventional power generation usage unit different periods exert oneself need to meet power bound constraint be:
Wherein,、It is respectively the bound that conventional power generation usage unit is exerted oneself；
Conventional power unit climbing power constraint is:
Wherein,、For power limit of climbing up and down；
Conventional power generation usage unit when considering multiple stage unit extended model (multiple stage unit undertakes electrical generation burden), wherein theiPlatform unit
Cost of electricity-generating model be:
Wherein,For conventional generator group # collection,For the start-stop state of period the unit,Table
Show the periodThePlatform unit is in open state, corresponding,Represent the periodTheAt platform unit
In stopped status, corresponding；ForPlatform unit start cost；
Normal power supplies species is expanded to many conventional power generation usages unit situation, correlated variabless pattern is modified:
Wherein,For many conventional power generation usages unit unit cost of electricity-generating,For many conventional power generation usages unit quantity,For
TheThe unit cost of electricity-generating of platform conventional power generation usage unit；
Energy-storage system charge-discharge electric power with the relation of energy storage system capacity is:
Wherein,For energy storage device current capacities state；
The constraint of energy-storage system charge-discharge electric power and capacity-constrained are:
Wherein,、It is respectively unit interval energy storage device charge-discharge electric power bound,、It is respectively energy storage dress
Put capacity bound,、For discharge and recharge cutoff rate
The constraints that interaction power need to meet is:
Wherein,、It is respectively period interaction power transmission upper lower limit value；
Non-firm power constrains:
Wherein,For interacting the non-firm power of power,For conventional power generation usage cost non-firm power,For energy storage
The non-firm power of system,The system that represents is in the periodNeed the minimum non-firm power reaching, according to the appearance of micro-capacitance sensor
Measure and to be configured.
8. described in, interaction cost is dissolved and utilization power as the multi-period system renewable energy power generation resource of reflection, its shadow
The factor of sound includes: micro-capacitance sensor interacts power, purchase electricity price, sale of electricity electricity price with higher level's electrical network；Wherein, micro-capacitance sensor is handed over higher level's electrical network
Cross-power influence factor include: conventional power unit cost economic indicator, purchase sale of electricity valency, the interaction mode of micro-capacitance sensor and higher level's electrical network and
Running status.
9th, described based on the two benches solution strategies step of test scene it is:
(1) initialize, forEach of test scene it is considered to various constraints, be optimized and ask
Solution, thus obtains each scenesUnder、, and calculate, wherein:Represent decision variable initial feasible solution；
Represent scenesUnder decision variable optimal solution；Represent scenesUnder the corresponding interactive cost size of optimal solution；
(2) make,, according toUpdate, and its corresponding、, wherein,Table
Show " the most severe " test scene；Represent the interactive cost under optimized operation scheme " the most severe scene "；Represent decision-making to become
Amount last solution；Represent the Optimum Economic operating cost considering " the most severe scene " robust target；
Using the maximum interaction corresponding operating scheme of cost as micro-capacitance sensor robust optimal operating scheme.
The present invention proposes and exerts oneself and load towards the meter of grid type micro-capacitance sensor and renewable energy type distributed power source
Robust optimal operation model under demand uncertainty and its method for solving.By interval prediction method, to regenerative resource class
Distributed power source is exerted oneself and is carried out uncertain interval quantization with workload demand, produces the uncertain collection for Optimized model；Coordinate
" source-storage " scheduling model can improve micro-capacitance sensor operation motility, economy；Produce checkout area using orthogonal array matrix
Scape is a kind of method simply and effectively screening simulating scenes.
Brief description
Fig. 1 is present invention typical case's micro-capacitance sensor configuration diagram；
Each label implication in accompanying drawing and literary composition:For distribution type renewable energy class in the periodOutput,For storage
Energy system is in the periodOutput,For interactive power between higher level's electrical network for the regional power grid,For region system
Normal power supplies unit output in system,For system total capacity requirement.
Specific implementation method
Below in conjunction with the accompanying drawings, the present invention is done based on the micro-capacitance sensor robust optimizing operation method of orthogonal array further detailed
Thin description.
The present invention is to provide a kind of micro-capacitance sensor robust optimizing operation method based on orthogonal array, including step:
(1) micro-capacitance sensor framework, power supply type and its operating cost, energy-storage system unit operating cost, interaction cost, timesharing are extracted
The static datas such as electricity price；
(2) build regenerative resource class distributed power source exert oneself, the uncertain collection of workload demand；
(3) the robust test scene based on orthogonal array for the screening；
(4) build micro-capacitance sensor robust optimal operation model；
(5) the two benches solution strategies based on test scene for the design, obtain final micro-capacitance sensor robust and optimize operating scheme.
Described micro-capacitance sensor framework includes: power supply type, distributed energy storage system, electric load, dispatching control center, higher level
Grid interface.
Described distributed electrical Source Type includes: non-regeneration energy class distributed power source and regenerative resource class distributed electrical
Source.
Described interactive cost model represents the Financial cost interacting power transmission generation between micro-capacitance sensor and higher level's electrical network.
Described regenerative resource class distributed power source is exerted oneself, the uncertain collection construction step of workload demand is:
(1) determine that regenerative resource class distributed power source uncertain collection of exerting oneself is upper following according to distributed power source historical data of exerting oneself
Boundary's reference quantity:
Constrain, according to actual regenerative resource class distributed power source unit operation, up-and-down boundary value that it is exerted oneself to be modified:
In formula,、It is respectively regenerative resource class distributed power source to exert oneself up-and-down boundary reference value,、It is respectively regenerative resource class distributed power source after revising to exert oneself up-and-down boundary reference value,For renewable energy
Source class distributed power source is exerted oneself point prediction base value,For forecast error probability density function,ForAnti-
Function representation method,、It is respectively regenerative resource class distributed power source to exert oneself maximin,For confidence
Horizontal parameters,、It is respectively the upper and lower dividing value of confidence level parameter；
(2) thus, the uncertain collection of formation distributed power source:
The uncertain collection that workload demand can be obtained in the same manner is:
In formula,For regenerative resource class distributed power source in the periodActual exert oneself,Exist for workload demand
PeriodActual demand value,For workload demand in the periodPoint prediction base value,、It is respectively negative
Lotus demand is in the periodUp-and-down boundary reference value.
The described robust test scene screening step based on orthogonal array is:
(1) dispatching cycle is divided into t period, then distribution type renewable energy exert oneself within dispatching cycle total t from
Dissipate value, be designated as:, workload demand total t centrifugal pump within dispatching cycle, it is designated as:, therefore one dispatching cycle regenerative resource class distributed power source and workload demand defeated
Enter parameter and have 2 × t；
(2) filler test scene: by orthogonal array and corresponding parameter value horizontal assignment rule screening；
So-called orthogonal array (orthogonal array, oa) matrix refers to be made up of c corresponding difference |input paramete
A × b matrix, intensity d(0≤d≤b) orthogonal array, if in arbitrary a × d submatrix of matrix a × b, arbitrary intensity
Arrangement for d exists justOccur in individual row, be designated as:
Wherein, a is the size of matrix array, here in order to refer to the scene number needing to test under current input parameters value level；b
For parameter sum, refer to regenerative resource class distributed power source here and exert oneself and workload demand |input paramete sum；C is one kind
Value level corresponding single group |input paramete；D is the strength factor of oa；
Thus, total test scene number is obtained according to multigroup difference |input paramete value level.
Described micro-capacitance sensor robust optimal operation model is:
(1) micro-capacitance sensor robust optimal operation model object function is:
Wherein,Represent micro-capacitance sensor totle drilling cost；For conventional power generation usage unit unit cost of electricity-generating, have；、For unit cost of electricity-generating coefficient, concrete value and the generator type chosen and its
Relating to parameters,For conventional power generation usage unit in the periodExert oneself；For energy-storage system regulation and control cost, have；For energy storage device unit regulation and control cost；For energy-storage system output；General
Interactive cost mathematic(al) representation be:,,、Correspond to the period respectivelyMicro-capacitance sensor is to major network
Purchase, the 0-1 state variable of power transmission, set and work asWhen,, whenWhen,, with
Shi You:；For the interactive cost under the most severe scene, meet,sRepresent present test field scape；sFor total test scene；By target letter
In numberExpressed with the mode of interaction cost and scene collection, that is, micro-capacitance sensor robust optimizes operational mode and can be deformed into:
Wherein,For scenesUnder Comprehensive economical operation Optimum cost value；
(2) micro-capacitance sensor robust optimal operation model bound for objective function is:
Conventional power generation usage unit different periods exert oneself need to meet power bound constraint be:
Wherein,、It is respectively the bound that conventional power generation usage unit is exerted oneself；
Conventional power unit climbing power constraint is:
Wherein,、For power limit of climbing up and down；
Conventional power generation usage unit when considering multiple stage unit extended model (multiple stage unit undertakes electrical generation burden), wherein theiPlatform unit
Cost of electricity-generating model be:
Wherein,For conventional generator group # collection,For the start-stop state of period the unit,Table
Show the periodThePlatform unit is in open state, corresponding,Represent the periodTheAt platform unit
In stopped status, corresponding；ForPlatform unit start cost；
Normal power supplies species is expanded to many conventional power generation usages unit situation, correlated variabless pattern is modified:
Wherein,For many conventional power generation usages unit unit cost of electricity-generating,For many conventional power generation usages unit quantity,For
TheThe unit cost of electricity-generating of platform conventional power generation usage unit；
Energy-storage system charge-discharge electric power with the relation of energy storage system capacity is:
Wherein,For energy storage device current capacities state；
The constraint of energy-storage system charge-discharge electric power and capacity-constrained are:
Wherein,、It is respectively unit interval energy storage device charge-discharge electric power bound,、It is respectively energy storage dress
Put capacity bound,、For discharge and recharge cutoff rate；
The constraints that interaction power need to meet is:
Wherein,、It is respectively period interaction power transmission upper lower limit value；
Non-firm power constrains:
Wherein,For interacting the non-firm power of power,For conventional power generation usage cost non-firm power,For energy storage
The non-firm power of system,The system that represents is in the periodNeed the minimum non-firm power reaching, according to the appearance of micro-capacitance sensor
Measure and to be configured.
Described interactive cost is dissolved and utilization power as the multi-period system renewable energy power generation resource of reflection, its impact
Factor includes: micro-capacitance sensor interacts power, purchase electricity price, sale of electricity electricity price with higher level's electrical network；Wherein, micro-capacitance sensor interacts with higher level's electrical network
Power influence factor include: conventional power unit cost economic indicator, the interaction mode of purchase sale of electricity valency, micro-capacitance sensor and higher level's electrical network and fortune
Row state.
Described based on the two benches solution strategies step of test scene it is:
(1) initialize, forEach of test scene it is considered to various constraints, be optimized and ask
Solution, thus obtains each scenesUnder、, and calculate, wherein:Represent decision variable initial feasible solution；
Represent scenesUnder decision variable optimal solution；Represent scenesUnder the corresponding interactive cost size of optimal solution；
(2) make,, according toUpdate, and its corresponding、, wherein,Table
Show " the most severe " test scene；Represent the interactive cost under optimized operation scheme " the most severe scene "；Represent decision-making to become
Amount last solution；Represent the Optimum Economic operating cost considering " the most severe scene " robust target；
Using the maximum interaction corresponding operating scheme of cost as micro-capacitance sensor robust optimal operating scheme.
Above-described concrete invention implementation, is entered to the purpose of the present invention, technical scheme and beneficial effect
One step describes in detail, be should be understood that the specific implementation method that the foregoing is only the present invention, does not constitute to this
The restriction of bright protection domain.Any modification made within the spirit and principles in the present invention, equivalent and improvement etc., all should
It is included within the claims of the present invention.
Claims (9)
1. a kind of micro-capacitance sensor robust optimizing operation method based on orthogonal array is it is characterised in that include step:
(1) extract micro-capacitance sensor framework, distributed power source unit operating cost, energy-storage system unit operating cost, interaction cost, divide
When the static data such as electricity price；
(2) build regenerative resource class distributed power source exert oneself, the uncertain collection of workload demand；
(3) the robust test scene based on orthogonal array for the screening；
(4) build micro-capacitance sensor robust optimal operation model；
(5) the two benches solution strategies based on test scene for the design, obtain final micro-capacitance sensor robust and optimize operating scheme.
2. a kind of micro-capacitance sensor robust optimizing operation method based on orthogonal array according to claim 1 it is characterised in that
Described micro-capacitance sensor framework includes: distributed power source, distributed energy storage system, electric load, dispatching control center, higher level's electrical network connect
Mouthful.
3. a kind of micro-capacitance sensor robust optimizing operation method based on orthogonal array according to claim 1 it is characterised in that
Described distributed electrical Source Type includes: non-regeneration energy class distributed power source and regenerative resource class distributed power source.
4. a kind of micro-capacitance sensor robust optimizing operation method based on orthogonal array according to claim 1 it is characterised in that
Described interactive cost model represents the Financial cost interacting power transmission generation between micro-capacitance sensor and higher level's electrical network.
5. a kind of micro-capacitance sensor robust optimizing operation method based on orthogonal array according to claim 1 it is characterised in that
Described regenerative resource class distributed power source is exerted oneself, the uncertain collection construction step of workload demand is:
(1) determine that regenerative resource class distributed power source uncertain collection of exerting oneself is upper following according to distributed power source historical data of exerting oneself
Boundary's reference quantity:
Constrain, according to actual regenerative resource class distributed power source unit operation, up-and-down boundary value that it is exerted oneself to be modified:
In formula,、It is respectively regenerative resource class distributed power source to exert oneself up-and-down boundary reference value,、It is respectively regenerative resource class distributed power source after revising to exert oneself up-and-down boundary reference value,For renewable energy
Source class distributed power source is exerted oneself point prediction base value,For forecast error probability density function,ForAnti-
Function representation method,、It is respectively regenerative resource class distributed power source to exert oneself maximin,For confidence
Horizontal parameters,、It is respectively the upper and lower dividing value of confidence level parameter；
(2) thus, the uncertain collection of formation distributed power source:
The uncertain collection that workload demand can be obtained in the same manner is:
In formula,For regenerative resource class distributed power source in the periodActual exert oneself,Exist for workload demand
PeriodActual demand value,For workload demand in the periodPoint prediction base value,、It is respectively negative
Lotus demand is in the periodUp-and-down boundary reference value.
6. a kind of micro-capacitance sensor robust optimizing operation method based on orthogonal array according to claim 1 it is characterised in that
The described robust test scene screening step based on orthogonal array is:
(1) dispatching cycle is divided into t period, then distribution type renewable energy exert oneself within dispatching cycle total t from
Dissipate value, be designated as:, workload demand total t centrifugal pump within dispatching cycle, it is designated as:, therefore one dispatching cycle regenerative resource class distributed power source and workload demand defeated
Enter parameter and have 2 × t；
(2) filler test scene: by orthogonal array and corresponding parameter value horizontal assignment rule screening；
So-called orthogonal array (orthogonal array, oa) matrix refers to a being made up of c corresponding difference |input paramete
× b matrix, intensity d(0≤d≤b) orthogonal array, if in arbitrary a × d submatrix of matrix a × b, arbitrary intensity is d
Arrangement just existOccur in individual row, be designated as:
Wherein, a is the size of matrix array, here in order to refer to the scene number needing to test under current input parameters value level；b
For parameter sum, refer to regenerative resource class distributed power source here and exert oneself and workload demand |input paramete sum；C is one kind
Value level corresponding single group |input paramete；D is the strength factor of oa；
Thus, total test scene number is obtained according to multigroup difference |input paramete value level.
7. a kind of micro-capacitance sensor robust optimizing operation method based on orthogonal array according to claim 1 it is characterised in that
Described micro-capacitance sensor robust optimal operation model is:
(1) micro-capacitance sensor robust optimal operation model object function is:
Wherein,Represent micro-capacitance sensor totle drilling cost；For conventional power generation usage unit unit cost of electricity-generating, have；、For unit cost of electricity-generating coefficient, concrete value and the generator type chosen and its
Relating to parameters,For conventional power generation usage unit in the periodExert oneself；For energy-storage system regulation and control cost, have；For energy storage device unit regulation and control cost；For energy-storage system output；General
Interactive cost mathematic(al) representation be:,,、Correspond to the period respectivelyMicro-capacitance sensor is to major network
Purchase, the 0-1 state variable of power transmission, set and work asWhen,, whenWhen,, with
Shi You:；For the interactive cost under the most severe scene, meet,sRepresent present test field scape；sFor total test scene；By target letter
In numberExpressed with the mode of interaction cost and scene collection, that is, micro-capacitance sensor robust optimizes operational mode and can be deformed into:
Wherein,For scenesUnder Comprehensive economical operation Optimum cost value；
(2) micro-capacitance sensor robust optimal operation model bound for objective function is:
Conventional power generation usage unit different periods exert oneself need to meet power bound constraint be:
Wherein,、It is respectively the bound that conventional power generation usage unit is exerted oneself；
Conventional power unit climbing power constraint is:
Wherein,、For power limit of climbing up and down；
Conventional power generation usage unit when considering multiple stage unit extended model (multiple stage unit undertakes electrical generation burden), wherein theiPlatform unit
Cost of electricity-generating model be:
Wherein,For conventional generator group # collection,For the start-stop state of period the unit,Represent
PeriodThePlatform unit is in open state, corresponding,Represent the periodThePlatform unit is in
Stopped status, corresponding；ForPlatform unit start cost；
Normal power supplies species is expanded to many conventional power generation usages unit situation, correlated variabless pattern is modified:
Wherein,For many conventional power generation usages unit unit cost of electricity-generating,For many conventional power generation usages unit quantity,ForThe unit cost of electricity-generating of platform conventional power generation usage unit；
Energy-storage system charge-discharge electric power with the relation of energy storage system capacity is:
Wherein,For energy storage device current capacities state；
The constraint of energy-storage system charge-discharge electric power and capacity-constrained are:
Wherein,、It is respectively unit interval energy storage device charge-discharge electric power bound,、It is respectively energy storage dress
Put capacity bound,、For discharge and recharge cutoff rate；
The constraints that interaction power need to meet is:
Wherein,、It is respectively period interaction power transmission upper lower limit value；
Non-firm power constrains:
Wherein,For interacting the non-firm power of power,For conventional power generation usage cost non-firm power,For energy storage
The non-firm power of system,The system that represents is in the periodNeed the minimum non-firm power reaching, according to the appearance of micro-capacitance sensor
Measure and to be configured.
8. a kind of micro-capacitance sensor robust optimizing operation method based on orthogonal array according to claim 7 it is characterised in that
Described interactive cost is dissolved and utilization power as the multi-period system renewable energy power generation resource of reflection, and its influence factor wraps
Include: micro-capacitance sensor interacts power, purchase electricity price, sale of electricity electricity price with higher level's electrical network；Wherein, micro-capacitance sensor interacts power shadow with higher level's electrical network
The factor of sound includes: conventional power unit cost economic indicator, the interaction mode of purchase sale of electricity valency, micro-capacitance sensor and higher level's electrical network and operation shape
State.
9. a kind of micro-capacitance sensor robust optimizing operation method based on orthogonal array according to claim 1 it is characterised in that
Described based on the two benches solution strategies step of test scene it is:
(1) initialize, forEach of test scene it is considered to various constraints, be optimized and ask
Solution, thus obtains each scenesUnder、, and calculate, wherein:Represent decision variable initial feasible solution；
Represent scenesUnder decision variable optimal solution；Represent scenesUnder the corresponding interactive cost size of optimal solution；
(2) make,, according toUpdate, and its corresponding、, wherein,Represent
" the most severe " test scene；Represent the interactive cost under optimized operation scheme " the most severe scene "；Represent decision variable
Last solution；Represent the Optimum Economic operating cost considering " the most severe scene " robust target；
Using the maximum interaction corresponding operating scheme of cost as micro-capacitance sensor robust optimal operating scheme.
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