CN110474367A - A kind of micro-capacitance sensor capacity configuration optimization method considering risk of loss - Google Patents
A kind of micro-capacitance sensor capacity configuration optimization method considering risk of loss Download PDFInfo
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Abstract
A kind of micro-capacitance sensor capacity configuration optimization method considering risk of loss provided by the invention, comprising: get parms and fit the probability-distribution function of wind-powered electricity generation, photovoltaic and load power；Construct the uncertain collection of wind-light-lotus of more piecewise intervals, calculation risk power；The two stages Robust Optimization Model of building power supply capacity configuration is simultaneously decoupled into primal problem model and subproblem model；The allocation optimum for obtaining the capacity of micro-capacitance sensor using arranging and constraining generating algorithm.The present invention provides method for optimizing configuration, the capacity configuration scheme of micro-capacitance sensor can be solved, reduce the risk of loss of micro-capacitance sensor capacity configuration scheme, solve its economical and robustness equilibrium problem, the micro-capacitance sensor configured in the planning time limit not only has preferable robustness and economy, and the risk of loss range of scheme is given, there is reference and guiding value to the method for operation of micro-capacitance sensor in the future, provide the necessary technical support for micro-capacitance sensor planning.
Description
Technical field
The present invention relates to micro-capacitance sensor planning technology fields, more particularly to a kind of micro-capacitance sensor for considering risk of loss holds
Measure method for optimizing configuration.
Background technique
With the rapid development of economy, electricity needs is continuously improved, fossil energy is petered out, and environmental pollution is increasingly tight
Weight, making full use of renewable and clean energy resource, promoting low-carbon electric power is the important development trend of power industry.Micro-capacitance sensor is due to can
Flexible access wind-driven generator, photovoltaic distributed power supply, therefore obtained extensive concern and application.Power supply capacity is distributed rationally
It is the basis of micro-capacitance sensor planning, affects the economy and reliability of micro-capacitance sensor operation.Due to renewable energy (wind speed,
Intensity of illumination etc.) and load power there is uncertain and randomness, the random fluctuation of source-lotus power micro-capacitance sensor will be caused to occur
Different degrees of risk of loss, as power surplus leads to cutting load wind caused by abandoning risk loss or workload demand surge
Danger loss etc..Therefore, it should fully consider that renewable energy and load power uncertainty are brought in power supply capacity configuration work
Risk of loss, propose a kind of allocation models having compared with strong anti-interference ability (robustness) and better economy.
In the past generally use immediately plan and scene analysis method come solve micro-capacitance sensor distribute rationally in risk problem.So
And scene analysis method and stochastic programming all rely on probability curve, therefore it is likely to occur probabilistic model fitting inaccuracy, and can not reflect
Practical application scene.Engineering in practice, it is difficult to obtain a large amount of sample point accurately to describe probabilistic model.
Summary of the invention
The present invention is that existing micro-capacitance sensor Optimal Configuration Method is overcome to exist to be difficult to obtain a large amount of sample point and come accurately
The technological deficiency for describing probabilistic model provides a kind of micro-capacitance sensor capacity configuration optimization method for considering risk of loss.
In order to solve the above technical problems, technical scheme is as follows:
A kind of micro-capacitance sensor capacity configuration optimization method considering risk of loss, comprising the following steps:
S1: the topological structure of micro-capacitance sensor, basic parameter, the data that generate electricity are obtained and fit wind-powered electricity generation, photovoltaic and load power
Probability-distribution function；
S2: the uncertain collection of wind-light-lotus for constructing more piecewise intervals according to probability distribution calculates wind-light-lotus risk function
Rate；
S3: the two stages Robust Optimization Model based on the building power supply capacity configuration of risk power；
S4: the two stages Robust Optimization Model that power supply capacity configures is decoupled into primal problem model and subproblem model；
S5: using arranging and constraining generating algorithm for primal problem model and subproblem model, the capacity of micro-capacitance sensor is obtained most
Excellent configuration.
Wherein, the step S2 specifically includes the following steps:
S21: the uncertain collection of building wind-powered electricity generation, specifically:
If the uncertain boundary value integrated of wind-powered electricity generation is (w as the maximum open ended upper lower limit value of wind poweru、wd), by wind-powered electricity generation function
Rate range is divided into M section, m ∈ [1, M]；
If mean value of the wind power in the t period isMaximum fluctuation amount of the wind power in the t period beWind-powered electricity generation
Maximum deviation value of the section m of power in the t period is Δ wm,t, then the uncertain collection W of wind power is indicated are as follows:
Wherein,It is the binary variable of the uncertain collection boundary value of selection, value is { 0,1 }；For 0/1 variable；
ΓwtFor adjustment parameter, is scaled in the section of uncertain collection, improve flexibility；
S22: the uncertain collection V of the building photovoltaic and uncertain collection L of load power, obtain always not knowing set representations for U=W, V,
L}；
S23: it is theoretical using Conditional Lyapunov ExponentP (CVaR), calculate wind-light-lotus risk power.
Wherein, the step S23 specifically includes the following steps:
S231: calculating the risk power of Wind turbines, specifically: using normal distribution to the distribution function of wind power into
Row description, obtains:
Wherein,The respectively upper and lower limit risk power of Wind turbines；wmin、wmaxRespectively
The upper limit value and lower limit value of wind power；W (t) is wind power variable；ρ (w (t)) is the probability value of wind power；
S232: the risk power of Wind turbines is calculated
S233: the risk power of calculated loadWithWherein, the upper limit risk power of load is negative
Value, lower limit risk power is then positive value.
Wherein, the specific calculating process of the step S231 wind-powered electricity generation upper limit risk power are as follows:
If the probability density function of wind-powered electricity generation risk power is divided into Q segmentation, wherein q ∈ Q；vqFor each piecewise linear function
Several endpoints；aq、bqFor the coefficient of q-th of piecewise function, it is located at q segmentation when wind power accommodates limit value as a result, then wind-powered electricity generation
Upper limit risk power meter formula are as follows:
Wherein, the second part item that adds up is constant term in formula, therefore by itself and bqIt is merged into coefficient Bq, have:
Wherein, in the step S3, the two stages Robust Optimization Model includes objective function and constraint condition, specifically
Are as follows: the objective function is formed by two layer functions nestings, and internal layer is micro-capacitance sensor operating cost, and outer layer is electric generation investment cost Cinv、
Maintenance cost COM, displacement cost Crep, risk of loss cost ElossAnd operating cost Copt；The constraint condition includes but not only
It is limited to power supply installation number constraint, the constraint of line transmission power constraint, electric energy self-balancing, system power Constraints of Equilibrium, storage energy operation
Constraint, dominant eigenvalues constraint and diesel-driven generator operation constraint.
Wherein, the objective function expression are as follows:
Wherein, α is the risk partiality degree coefficient of allocation plan；X is the optimized variable of outer layer, includes wind-powered electricity generation, photovoltaic, bavin
The interval border value of the installation number and uncertain collection of oil machine and energy storage；U, y is the optimized variable of internal layer, is become by uncertain collection
Amount and the power of the assembling unit；Wherein as unit of year, have:
Electric generation investment cost CinvIt indicates are as follows:
Cinv=μCRF(cWTNwt+cPVNpv+cDENde+cESSNess)
N in formulawt、Npv、Nde、NESSIt is wind-powered electricity generation, photovoltaic, diesel-driven generator and the pre-installation of energy storage number of units respectively；cWT、cPV、
cDE、cESSRespectively wind-powered electricity generation, photovoltaic, diesel-driven generator and the single unit of energy storage investment cost；μCRFIt is recycled for investment amount and is
Number, the value are related with discount rate and the project cycle；
Unit year maintenance cost COMIt indicates are as follows:
COM=μCRF(kWTNwt+kPVNpv+kDENde+kESSNess)
In formula, kWT、kPV、kDE、kESSRespectively wind-powered electricity generation, photovoltaic, diesel-driven generator and the single unit of energy storage year maintenance fortune
Row expense；
Replace cost CrepIt indicates are as follows:
L in formulaESSFor energy storage actual life, can be acquired using the total amount method of handling up；Y is the life of micro-capacitance sensor planned project
The period is ordered, is set as herein 20 years；
Risk of loss cost ElossIt indicates are as follows:
Wherein:
φ in formulaCVaR_wt,s、φCVaR_pv,s、φCVaR_L,sRespectively s-th of season typical day wind-powered electricity generation, photovoltaic and load power
Binary amount, value be corresponding upper limit risk power and lower limit risk power；fCIt (t) is risk cost coefficient；θdump、θcurPoint
Not Wei micro-capacitance sensor abandon electrical power and cutting load power penalty coefficient；TsFor s-th of season typical day number of days；
Micro-capacitance sensor annual operating and maintenance cost CoptIt indicates are as follows:
Wherein:
In formula, Cgrid,s、CDE,sRespectively represent the cost of electricity-generating in micro-capacitance sensor purchase the sale of electricity totle drilling cost and diesel engine in s season；
PG2M,s(t)、PG2M,s(t)、PDE,s(t) it is respectively micro-capacitance sensor sale of electricity power, power purchase power and diesel oil in the s t period in season
Machine generated output；For the rated power of diesel engine；cpur,s(t)、csel,s(t) be respectively the s t period in season purchase, sell
Electricity price lattice；cfuelFor unit fuel price；A and b is unit fuel consumption curve coefficient；
Therefore x, u, y specifically:
Wherein, the constraint is adjusted specifically:
Power supply installs number constraint representation are as follows:
In formula,Respectively wind-powered electricity generation, photovoltaic, diesel-driven generator and storage in micro-capacitance sensor project
The maximum installation number of units of energy；
Line transmission power constraint indicates are as follows:
In formula, Sl、The power and its maximum transmission power of respectively the l articles branch；NlFor total circuitry number of system；
Electric energy self-balancing constraint representation are as follows:
In formula, Epur,s、ELoad,sRespectively one day purchase of electricity and workload demand electricity of the micro-capacitance sensor in typical day in s season；
βminIndicate the confession electrostrictive coefficient of minimum allowable, value range is [0,1]；
System power Constraints of Equilibrium indicates are as follows:
Pgrid,s(t)+PESS,s(t)+PL,s(t)=Pwt,s(t)+Ppv,s(t)+Pde,s(t)
Wherein:
In formula, Pgrid,s(t)、PESS,s(t)、PL,s(t)、Pwt,s(t)、Ppv,s(t)、Pde,sIt (t) is respectively s-th of season allusion quotation
The t period interconnection of type day, energy-storage system, load, wind-powered electricity generation, photovoltaic and diesel-driven generator power；Pch,s(t)、Pdic,s(t)
It is the t period energy-storage system charge and discharge power of in s-th of season typical day；
Storage energy operation constraint representation are as follows:
Energy-storage system is made of battery, and battery operation constraint mainly has charge-discharge electric power constraint and capacity-constrained, tool
Body are as follows: charge-discharge electric power constraint are as follows:
The state-of-charge of battery and the relationship of charge-discharge electric power have:
In formula,For maximum charge-discharge electric power；Soc(s, t) is charged shape of s-th of the battery typical day in the t period
State；Soc_max、Soc_minThe respectively upper limit value and lower limit value of state-of-charge；ηc、ηdIt is battery charge and discharge efficiency respectively；uESS(t) it is
0-1 variable is to indicate storage energy operation state；t0、tTThe whole story moment respectively dispatched；
Dominant eigenvalues constraint representation are as follows:
In formulaFor s-th typical day t period micro-capacitance sensor power purchase power and send the limit of power
Value；
Diesel-driven generator runs constraint representation are as follows:
U in formulaDE,sIt (t) is 0-1 variable to indicate the operating status of diesel engine.
Wherein, in the step S4, the two stages Robust Optimization Model is min-max-min structure, passes through introducing
Auxiliary variable, the primal problem MP mathematic(al) representation after the decoupling of two stages Robust Optimization Model specifically:
In formula: cTX indicates the objective function C of outer layer optimizationinv+COM+Crep+α·Eloss；η is auxiliary variable, to replace
Internal layer optimization；The first row constrains dTylRepresent the objective function C of internal layer optimizationopt；Second row constraint representation installed capacity above
Constraint and line transmission power constraint；The third line constraint represents energy storage charge-discharge electric power, dominant eigenvalues and diesel oil hair in text
The constraint of motor operation；Fourth line is constrained to the constraint of electric energy self-balancing；Fifth line is constrained to the constraint of energy-storage system state-of-charge；The
The power-balance constraint of six row constraint representation systems, wherein ulFor the value of uncertain variables u after the l times iteration,J is current iteration number；ylThe solution for being subproblem after the l times iteration, yl=[PESS,s(l,t),
PM2G,s(l,t),PG2M,s(l,t),PDE,s(l,t)]；
Subproblem SP formula max-min structure, is translated into single layer Optimized model by strong dual theory, and pass through big-
The bilinear terms of single-layer model are carried out linearization process by M method, obtain the expression formula of subproblem, specifically:
γ, λ, ν, π are respectively the auxiliary variable introduced after strong dual in formula；Δ u be wind-powered electricity generation, photovoltaic and load power to
Amount indicates the mean value and maximum fluctuation amount of power indeterminacy section,And
ξ+、ξ-The continuous auxiliary variable introduced for linearisation；To determine uncertain collection U
The 0-1 binary vector of boundary value；σ is larger constant；Γ is the conservative degree adjustment factor of robust of uncertain collection,
Wherein, the step S5 specifically includes the following steps:
S51: the bound of setting configuration scheme cost is respectively UB=+ ∞, LB=- ∞, the number of iterations k=1, most
Severe scene is ul(l=1,2, Λ, k), the gap ε of algorithmic statement；
S52: carrying out piece-wise linearization for the probability density function of typical day wind-powered electricity generation, photovoltaic and load power, and by risk
The calculating parameter for losing cost substitutes into primal problem；
S53: primal problem optimal solution is solvedAnd its optimal uncertain collectionIn order to each
Iteration finds optimal uncertain collection boundary, enables most severe sceneAnd it is LB=max { LB, c that lower bound, which is arranged,Txk+ηk}；
S54: by primal problem optimal solutionIt substitutes into subproblem and is solved, obtain subproblem and obtain target function value
fkAnd its corresponding most severe scene uk+1And control value, the juxtaposition upper bound UB=min { UB, cTxk+fk}；
S55: judging UB-LB≤ε, if so, then stop iteration and returns to optimal solution xkAnd optimal uncertain collectionIf
It is invalid, increase most severe scene uk+1Regulation variable yk+1And constraint, juxtaposition number of iterations are k=k+1, return to S53；
S56: optimal solution x is utilizedkWith optimal uncertain collectionComplete the allocation optimum of micro-capacitance sensor capacity.
Wherein, in the step S55, the constraint is embodied as:
Compared with prior art, the beneficial effect of technical solution of the present invention is:
The present invention provides a kind of micro-capacitance sensor capacity configuration optimization methods for considering risk of loss, can solve micro-capacitance sensor
Capacity configuration scheme, reduce micro-capacitance sensor capacity configuration scheme risk of loss, solve its economical balance with robustness and ask
Topic, wherein comprising by Wind turbines, photovoltaic, diesel engine unit and the capital project of energy storage, the micro- electricity configured in the planning time limit
Net not only has preferable robustness and economy, but also gives the risk of loss range of scheme, to the fortune of micro-capacitance sensor in the future
Line mode has reference and guiding value, provides the necessary technical support for micro-capacitance sensor planning.
Detailed description of the invention
Fig. 1 is the method for the present invention flow chart；
Fig. 2 is the more piecewise interval schematic diagrames of wind power output；
Fig. 3 is wind power output probability-distribution function normal distribution.
Specific embodiment
The attached figures are only used for illustrative purposes and cannot be understood as limitating the patent；
In order to better illustrate this embodiment, the certain components of attached drawing have omission, zoom in or out, and do not represent actual product
Size；
To those skilled in the art, it is to be understood that certain known features and its explanation, which may be omitted, in attached drawing
's.
The following further describes the technical solution of the present invention with reference to the accompanying drawings and examples.
Embodiment 1
As shown in Figure 1, a kind of micro-capacitance sensor capacity configuration optimization method for considering risk of loss, comprising the following steps:
S1: the topological structure of micro-capacitance sensor, basic parameter, the data that generate electricity are obtained and fit wind-powered electricity generation, photovoltaic and load power
Probability-distribution function；
S2: the uncertain collection of wind-light-lotus for constructing more piecewise intervals according to probability distribution calculates wind-light-lotus risk function
Rate；
S3: the two stages Robust Optimization Model based on the building power supply capacity configuration of risk power；
S4: the two stages Robust Optimization Model that power supply capacity configures is decoupled into primal problem model and subproblem model；
S5: using arranging and constraining generating algorithm for primal problem model and subproblem model, the capacity of micro-capacitance sensor is obtained most
Excellent configuration.
In the specific implementation process, it in the region of micro-capacitance sensor planning, is obtained from energy management system EMS negative
The data such as lotus level, the impedance of route, transmission capacity；Local renewable energy power generation resource is measured, acquisition wind speed,
The data such as intensity of illumination, temperature, wind direction, and according to the data calculate wind-powered electricity generation, photovoltaic generated output data, above-mentioned data with
1 hour is unit, filters out input of the data of typical day representative in throughout the year (24 hours) as model.
More specifically, it as shown in Fig. 2, obtaining wind-light power generation and load power data according to step S1, constructs in 4 allusion quotations
The uncertain collection of more piecewise intervals in 24 periods of type day, specifically includes the following steps:
S21: the uncertain collection of building wind-powered electricity generation, specifically:
If the uncertain boundary value integrated of wind-powered electricity generation is (w as the maximum open ended upper lower limit value of wind poweru、wd), by wind-powered electricity generation function
Rate range is divided into M section, m ∈ [1, M]；
If mean value of the wind power in the t period isMaximum fluctuation amount of the wind power in the t period beWind-powered electricity generation
Maximum deviation value of the section m of power in the t period is Δ wm,t, then the uncertain collection W of wind power is indicated are as follows:
Wherein,It is the binary variable of the uncertain collection boundary value of selection, value is { 0,1 }；For 0/1 variable；
ΓwtFor adjustment parameter, is scaled in the section of uncertain collection, improve flexibility；
S22: the uncertain collection V of the building photovoltaic and uncertain collection L of load power, obtain always not knowing set representations for U=W, V,
L}；
S23: it is theoretical using Conditional Lyapunov ExponentP (CVaR), calculate wind-light-lotus risk power.
In the specific implementation process, renewable energy and load power fluctuation have uncertain and randomness.When wind-
When light-lotus power is outside the uncertain collection section set, micro-capacitance sensor operation will appear the risk of loss for abandoning electricity, cutting load, and
Power outside the section is known as risk power.Therefore by Conditional Lyapunov ExponentP (CVaR) theoretical calculation micro-capacitance sensor, there may be wind
The risk power nearly lost.For the power swing probability-distribution function of photovoltaic, wind-powered electricity generation and load, generally using normal distribution into
Row description.
More specifically, as shown in figure 3, the step S23 specifically includes the following steps:
S231: calculating the risk power of Wind turbines, specifically: using normal distribution to the distribution function of wind power into
Row description, obtains:
Wherein,The respectively upper and lower limit risk power of Wind turbines；wmin、wmaxRespectively
The upper limit value and lower limit value of wind power；W (t) is wind power variable；ρ (w (t)) is the probability value of wind power；
S232: the risk power of Wind turbines is calculated
S233: the risk power of calculated loadWithWherein, the upper limit risk power of load is negative
Value, lower limit risk power is then positive value.
In the specific implementation process, there are non-linear integrals in above-mentioned wind-light-lotus risk power calculation, it is difficult to directly ask
Solution, but the integration type has convexity and monotonicity, therefore need to it respectively about progress piece-wise linearization.Therefore, the step
The specific calculating process of S231 wind-powered electricity generation upper limit risk power are as follows:
If the probability density function of wind-powered electricity generation risk power is divided into Q segmentation, wherein q ∈ Q；vqFor each piecewise linear function
Several endpoints；aq、bqFor the coefficient of q-th of piecewise function, it is located at q segmentation when wind power accommodates limit value as a result, then wind-powered electricity generation
Upper limit risk power meter formula are as follows:
Wherein, the second part item that adds up is constant term in formula, therefore by itself and bqIt is merged into coefficient Bq, have:
More specifically, in the step S3, the two stages Robust Optimization Model includes objective function and constraint condition,
Specifically: the objective function is formed by two layer functions nestings, and internal layer is micro-capacitance sensor operating cost, and outer layer is electric generation investment cost
Cinv, maintenance cost COM, displacement cost Crep, risk of loss cost ElossAnd operating cost Copt；The constraint condition include but
It is not limited only to power supply installation number constraint, the constraint of line transmission power constraint, electric energy self-balancing, system power Constraints of Equilibrium, energy storage
Operation constraint, dominant eigenvalues constraint and diesel-driven generator operation constraint.
Wherein, the objective function expression are as follows:
Wherein, α is the risk partiality degree coefficient of allocation plan；X is the optimized variable of outer layer, includes wind-powered electricity generation, photovoltaic, bavin
The interval border value of the installation number and uncertain collection of oil machine and energy storage；U, y is the optimized variable of internal layer, is become by uncertain collection
Amount and the power of the assembling unit；Wherein as unit of year, have:
Electric generation investment cost CinvIt indicates are as follows:
Cinv=μCRF(cWTNwt+cPVNpv+cDENde+cESSNess)
N in formulawt、Npv、Nde、NESSIt is wind-powered electricity generation, photovoltaic, diesel-driven generator and the pre-installation of energy storage number of units respectively；cWT、cPV、
cDE、cESSRespectively wind-powered electricity generation, photovoltaic, diesel-driven generator and the single unit of energy storage investment cost；μCRFIt is recycled for investment amount and is
Number, the value are related with discount rate and the project cycle；
Unit year maintenance cost COMIt indicates are as follows:
COM=μCRF(kWTNwt+kPVNpv+kDENde+kESSNess)
In formula, kWT、kPV、kDE、kESSRespectively wind-powered electricity generation, photovoltaic, diesel-driven generator and the single unit of energy storage year maintenance fortune
Row expense；
Replace cost CrepIt indicates are as follows:
L in formulaESSFor energy storage actual life, can be acquired using the total amount method of handling up；Y is the life of micro-capacitance sensor planned project
The period is ordered, is set as herein 20 years；
Risk of loss cost ElossIt indicates are as follows:
Wherein:
φ in formulaCVaR_wt,s、φCVaR_pv,s、φCVaR_L,sRespectively s-th of season typical day wind-powered electricity generation, photovoltaic and load power
Binary amount, value be corresponding upper limit risk power and lower limit risk power；fCIt (t) is risk cost coefficient；θdump、θcurPoint
Not Wei micro-capacitance sensor abandon electrical power and cutting load power penalty coefficient；TsFor s-th of season typical day number of days；
Micro-capacitance sensor annual operating and maintenance cost CoptIt indicates are as follows:
Wherein:
In formula, Cgrid,s、CDE,sRespectively represent the cost of electricity-generating in micro-capacitance sensor purchase the sale of electricity totle drilling cost and diesel engine in s season；
PG2M,s(t)、PG2M,s(t)、PDE,s(t) it is respectively micro-capacitance sensor sale of electricity power, power purchase power and diesel oil in the s t period in season
Machine generated output；For the rated power of diesel engine；cpur,s(t)、csel,s(t) be respectively the s t period in season purchase, sell
Electricity price lattice；cfuelFor unit fuel price；A and b is unit fuel consumption curve coefficient；
Therefore x, u, y specifically:
Wherein, the constraint is adjusted specifically:
Power supply installs number constraint representation are as follows:
In formula,Respectively wind-powered electricity generation, photovoltaic, diesel-driven generator and storage in micro-capacitance sensor project
The maximum installation number of units of energy；
Line transmission power constraint indicates are as follows:
In formula, Sl、The power and its maximum transmission power of respectively the l articles branch；NlFor total circuitry number of system；
Electric energy self-balancing constraint representation are as follows:
In formula, Epur,s、ELoad,sRespectively one day purchase of electricity and workload demand electricity of the micro-capacitance sensor in typical day in s season；
βminIndicate the confession electrostrictive coefficient of minimum allowable, value range is [0,1]；
System power Constraints of Equilibrium indicates are as follows:
Pgrid,s(t)+PESS,s(t)+PL,s(t)=Pwt,s(t)+Ppv,s(t)+Pde,s(t)
Wherein:
In formula, Pgrid,s(t)、PESS,s(t)、PL,s(t)、Pwt,s(t)、Ppv,s(t)、Pde,sIt (t) is respectively s-th of season allusion quotation
The t period interconnection of type day, energy-storage system, load, wind-powered electricity generation, photovoltaic and diesel-driven generator power；Pch,s(t)、Pdic,s(t)
It is the t period energy-storage system charge and discharge power of in s-th of season typical day；
Storage energy operation constraint representation are as follows:
Energy-storage system is made of battery, and battery operation constraint mainly has charge-discharge electric power constraint and capacity-constrained, tool
Body are as follows: charge-discharge electric power constraint are as follows:
The state-of-charge of battery and the relationship of charge-discharge electric power have:
In formula,For maximum charge-discharge electric power；Soc(s, t) is charged shape of s-th of the battery typical day in the t period
State；Soc_max、Soc_minThe respectively upper limit value and lower limit value of state-of-charge；ηc、ηdIt is battery charge and discharge efficiency respectively；uESS(t) it is
0-1 variable is to indicate storage energy operation state；t0、tTThe whole story moment respectively dispatched；
Dominant eigenvalues constraint representation are as follows:
In formulaFor s-th typical day t period micro-capacitance sensor power purchase power and send the limit of power
Value；
Diesel-driven generator runs constraint representation are as follows:
U in formulaDE,sIt (t) is 0-1 variable to indicate the operating status of diesel engine.
More specifically, in the step S4, the two stages Robust Optimization Model is min-max-min structure, is passed through
Introduce auxiliary variable, the primal problem MP mathematic(al) representation after the decoupling of two stages Robust Optimization Model specifically:
In formula: cTX indicates the objective function C of outer layer optimizationinv+COM+Crep+α·Eloss；η is auxiliary variable, to replace
Internal layer optimization；The first row constrains dTylRepresent the objective function C of internal layer optimizationopt；Second row constraint representation installed capacity above
Constraint and line transmission power constraint；The third line constraint represents energy storage charge-discharge electric power, dominant eigenvalues and diesel oil hair in text
The constraint of motor operation；Fourth line is constrained to the constraint of electric energy self-balancing；Fifth line is constrained to the constraint of energy-storage system state-of-charge；The
The power-balance constraint of six row constraint representation systems, wherein ulFor the value of uncertain variables u after the l times iteration,J is current iteration number；ylThe solution for being subproblem after the l times iteration, yl=[PESS,s(l,t),
PM2G,s(l,t),PG2M,s(l,t),PDE,s(l,t)]；
Subproblem SP formula max-min structure, is translated into single layer Optimized model by strong dual theory, and pass through big-
The bilinear terms of single-layer model are carried out linearization process by M method, obtain the expression formula of subproblem, specifically:
γ, λ, ν, π are respectively the auxiliary variable introduced after strong dual in formula；Δ u be wind-powered electricity generation, photovoltaic and load power to
Amount indicates the mean value and maximum fluctuation amount of power indeterminacy section,And
ξ+、ξ-The continuous auxiliary variable introduced for linearisation；To determine uncertain collection U
The 0-1 binary vector of boundary value；σ is larger constant；Γ is the conservative degree adjustment factor of robust of uncertain collection,
In the specific implementation process, the process and side for arranging and constraining generating algorithm (C&CG) solving optimization allocation models are utilized
Method.C&CG algorithm has the effect of to two stages robust Model is solved, by being primal problem and subproblem, master by model decomposition
Problem MP introduces auxiliary variable to replace internal layer objective function, and is continuously increased the relevant variable of subproblem and constraint；According to master
Problem optimum results calculate subproblem information and are fed back to MP, with the solution of this interactive iteration.More specifically, such as step S5
It is described, specifically includes the following steps:
S51: the bound of setting configuration scheme cost is respectively UB=+ ∞, LB=- ∞, the number of iterations k=1, most
Severe scene is ul(l=1,2, Λ, k), the gap ε of algorithmic statement；
S52: carrying out piece-wise linearization for the probability density function of typical day wind-powered electricity generation, photovoltaic and load power, and by risk
The calculating parameter for losing cost substitutes into primal problem；
S53: primal problem optimal solution is solvedAnd its optimal uncertain collectionIn order to each
Iteration finds optimal uncertain collection boundary, enables most severe sceneAnd it is LB=max { LB, c that lower bound, which is arranged,Txk+ηk}；
S54: by primal problem optimal solutionIt substitutes into subproblem and is solved, obtain subproblem and obtain target function value
fkAnd its corresponding most severe scene uk+1And control value, the juxtaposition upper bound UB=min { UB, cTxk+fk}；
S55: judging UB-LB≤ε, if so, then stop iteration and returns to optimal solution xkAnd optimal uncertain collectionIf
It is invalid, increase most severe scene uk+1Regulation variable yk+1And constraint, juxtaposition number of iterations are k=k+1, return to S53；
S56: optimal solution x is utilizedkWith optimal uncertain collectionComplete the allocation optimum of micro-capacitance sensor capacity.
More specifically, in the step S55, the constraint is embodied as:
In the specific implementation process, the present invention reduces the risk of loss of micro-capacitance sensor capacity configuration scheme, solves its economy
With the equilibrium problem of robustness.The stochastic behaviour for comprehensively considering renewable energy and load power constructs the wind-of more piecewise intervals
The uncertain collection of light-lotus；Quantify the risk of loss of micro-capacitance sensor by Conditional Lyapunov ExponentP theoretical (CVaR) to build allocation plan warp
The coupled relation of Ji property and robustness；Establish the two stages Robust Optimization Model of micro-capacitance sensor capacity configuration, the model outer layer be with
Micro-capacitance sensor construction cost, maintenance cost, displacement cost and risk of loss cost distribute layer rationally for target；Internal layer is micro-capacitance sensor
Unit optimizing operation layer；The mixed integer linear programming is decomposed using strong dual theory and column and constraint generating algorithm (C&CG)
For primal problem and subproblem, and carry out alternating iteration solution.Institute's climbing form type not only weighed power configuration scheme robustness and
Economy, and quantify the risk of loss of programme, it provides the necessary technical support for micro-capacitance sensor planning.
Obviously, the above embodiment of the present invention be only to clearly illustrate example of the present invention, and not be pair
The restriction of embodiments of the present invention.For those of ordinary skill in the art, may be used also on the basis of the above description
To make other variations or changes in different ways.There is no necessity and possibility to exhaust all the enbodiments.It is all this
Made any modifications, equivalent replacements, and improvements etc., should be included in the claims in the present invention within the spirit and principle of invention
Protection scope within.
Claims (10)
1. a kind of micro-capacitance sensor capacity configuration optimization method for considering risk of loss, which comprises the following steps:
S1: the topological structure of micro-capacitance sensor, basic parameter, the data that generate electricity are obtained and fit the general of wind-powered electricity generation, photovoltaic and load power
Rate distribution function；
S2: the uncertain collection of wind-light-lotus for constructing more piecewise intervals according to probability distribution calculates wind-light-lotus risk power；
S3: the two stages Robust Optimization Model based on the building power supply capacity configuration of risk power；
S4: the two stages Robust Optimization Model that power supply capacity configures is decoupled into primal problem model and subproblem model；
S5: it using arranging and constraining generating algorithm for primal problem model and subproblem model, obtains the optimal of the capacity of micro-capacitance sensor and matches
It sets.
2. a kind of micro-capacitance sensor capacity configuration optimization method for considering risk of loss according to claim 1, which is characterized in that
The step S2 specifically includes the following steps:
S21: the uncertain collection of building wind-powered electricity generation, specifically:
If the uncertain boundary value integrated of wind-powered electricity generation is (w as the maximum open ended upper lower limit value of wind poweru、wd), by wind power model
It encloses and is divided into M section, m ∈ [1, M]；
If mean value of the wind power in the t period isMaximum fluctuation amount of the wind power in the t period beWind power
The section m the t period maximum deviation value be Δ wm,t, then the uncertain collection W of wind power is indicated are as follows:
Wherein,It is the binary variable of the uncertain collection boundary value of selection, value is { 0,1 }；For 0/1 variable；Γwt
For adjustment parameter, is scaled in the section of uncertain collection, improve flexibility；
S22: the uncertain collection V of the building photovoltaic and uncertain collection L of load power, obtains always not knowing set representations being U={ W, V, L }；
S23: it is theoretical using Conditional Lyapunov ExponentP (CVaR), calculate wind-light-lotus risk power.
3. a kind of micro-capacitance sensor capacity configuration optimization method for considering risk of loss according to claim 2, which is characterized in that
The step S23 specifically includes the following steps:
S231: calculating the risk power of Wind turbines, specifically: it is retouched using distribution function of the normal distribution to wind power
It states, obtains:
Wherein,The respectively upper and lower limit risk power of Wind turbines；wmin、wmaxRespectively wind-powered electricity generation
The upper limit value and lower limit value of power；W (t) is wind power variable；ρ (w (t)) is the probability value of wind power；
S232: the risk power of Wind turbines is calculated
S233: the risk power of calculated loadWithWherein, the upper limit risk power of load is negative value, lower limit
Risk power is then positive value.
4. a kind of micro-capacitance sensor capacity configuration optimization method for considering risk of loss according to claim 3, which is characterized in that
The specific calculating process of the step S231 wind-powered electricity generation upper limit risk power are as follows:
If the probability density function of wind-powered electricity generation risk power is divided into Q segmentation, wherein q ∈ Q；vqFor each piecewise linear function
Endpoint；aq、bqFor the coefficient of q-th of piecewise function, it is located at q segmentation when wind power accommodates limit value as a result, then the wind-powered electricity generation upper limit
Risk power meter formula are as follows:
Wherein, the second part item that adds up is constant term in formula, therefore by itself and bqIt is merged into coefficient Bq, have:
5. a kind of micro-capacitance sensor capacity configuration optimization method for considering risk of loss according to claim 3, which is characterized in that
In the step S3, the two stages Robust Optimization Model includes objective function and constraint condition, specifically: the target letter
Number is formed by two layer functions nestings, and internal layer is micro-capacitance sensor operating cost, and outer layer is electric generation investment cost Cinv, maintenance cost COM, set
Change this C intorep, risk of loss cost ElossAnd operating cost Copt；The constraint condition includes but are not limited to power supply installation
Number constraint, the constraint of line transmission power constraint, electric energy self-balancing, the constraint of system power Constraints of Equilibrium, storage energy operation, interconnection function
Rate constraint and diesel-driven generator operation constraint.
6. a kind of micro-capacitance sensor capacity configuration optimization method for considering risk of loss according to claim 5, which is characterized in that
The objective function expression are as follows:
Wherein, α is the risk partiality degree coefficient of allocation plan；X is the optimized variable of outer layer, includes wind-powered electricity generation, photovoltaic, diesel engine
With the installation number of energy storage and the interval border value of uncertain collection；U, y is the optimized variable of internal layer, by uncertain collection variable and
The power of the assembling unit；Wherein as unit of year, have:
Electric generation investment cost CinvIt indicates are as follows:
Cinv=μCRF(cWTNwt+cPVNpv+cDENde+cESSNess)
N in formulawt、Npv、Nde、NESSIt is wind-powered electricity generation, photovoltaic, diesel-driven generator and the pre-installation of energy storage number of units respectively；cWT、cPV、cDE、
cESSRespectively wind-powered electricity generation, photovoltaic, diesel-driven generator and the single unit of energy storage investment cost；μCRFFor investment amount recovery coefficient,
The value is related with discount rate and the project cycle；
Unit year maintenance cost COMIt indicates are as follows:
COM=μCRF(kWTNwt+kPVNpv+kDENde+kESSNess)
In formula, kWT、kPV、kDE、kESSRespectively wind-powered electricity generation, photovoltaic, diesel-driven generator and the single unit of energy storage year maintenance operation take
With；
Replace cost CrepIt indicates are as follows:
L in formulaESSFor energy storage actual life, can be acquired using the total amount method of handling up；Y is the Life Cycle of micro-capacitance sensor planned project
Phase is set as 20 years herein；
Risk of loss cost ElossIt indicates are as follows:
Wherein:
φ in formulaCVaR_wt,s、φCVaR_pv,s、φCVaR_L,sRespectively the two of s-th of season typical case's day wind-powered electricity generation, photovoltaic and load power
Member amount, value are corresponding upper limit risk power and lower limit risk power；fCIt (t) is risk cost coefficient；θdump、θcurRespectively
The penalty coefficient of micro-capacitance sensor abandoning electrical power and cutting load power；TsFor s-th of season typical day number of days；
Micro-capacitance sensor annual operating and maintenance cost CoptIt indicates are as follows:
Wherein:
In formula, Cgrid,s、CDE,sRespectively represent the cost of electricity-generating in micro-capacitance sensor purchase the sale of electricity totle drilling cost and diesel engine in s season；PG2M,s
(t)、PG2M,s(t)、PDE,s(t) be respectively the s t period in season micro-capacitance sensor sale of electricity power, power purchase power and diesel engine send out
Electrical power；For the rated power of diesel engine；cpur,s(t)、csel,sIt (t) is purchase in the s t period in season, sale of electricity valence respectively
Lattice；cfuelFor unit fuel price；A and b is unit fuel consumption curve coefficient；
Therefore x, u, y specifically:
7. a kind of micro-capacitance sensor capacity configuration optimization method for considering risk of loss according to claim 6, which is characterized in that
The constraint is adjusted specifically:
Power supply installs number constraint representation are as follows:
In formula,Wind-powered electricity generation respectively in micro-capacitance sensor project, photovoltaic, diesel-driven generator and energy storage
Maximum installation number of units；
Line transmission power constraint indicates are as follows:
In formula, Sl、The power and its maximum transmission power of respectively the l articles branch；NlFor total circuitry number of system；
Electric energy self-balancing constraint representation are as follows:
In formula, Epur,s、ELoad,sRespectively one day purchase of electricity and workload demand electricity of the micro-capacitance sensor in typical day in s season；βminTable
Show the confession electrostrictive coefficient of minimum allowable, value range is [0,1]；
System power Constraints of Equilibrium indicates are as follows:
Pgrid,s(t)+PESS,s(t)+PL,s(t)=Pwt,s(t)+Ppv,s(t)+Pde,s(t)
Wherein:
In formula, Pgrid,s(t)、PESS,s(t)、PL,s(t)、Pwt,s(t)、Ppv,s(t)、Pde,sIt (t) is respectively in s-th of season typical day
T period interconnection, energy-storage system, load, wind-powered electricity generation, photovoltaic and diesel-driven generator power；Pch,s(t)、Pdic,sIt (t) is
The t period energy-storage system charge and discharge power of typical day in s season；
Storage energy operation constraint representation are as follows:
Energy-storage system is made of battery, and battery operation constraint mainly has charge-discharge electric power constraint and capacity-constrained, specifically:
Charge-discharge electric power constraint are as follows:
The state-of-charge of battery and the relationship of charge-discharge electric power have:
In formula,For maximum charge-discharge electric power；Soc(s, t) is state-of-charge of s-th of the battery typical day in the t period；
Soc_max、Soc_minThe respectively upper limit value and lower limit value of state-of-charge；ηc、ηdIt is battery charge and discharge efficiency respectively；uESSIt (t) is 0-1
Variable is to indicate storage energy operation state；t0、tTThe whole story moment respectively dispatched；
Dominant eigenvalues constraint representation are as follows:
In formulaFor s-th typical day t period micro-capacitance sensor power purchase power and send the limiting value of power；
Diesel-driven generator runs constraint representation are as follows:
U in formulaDE,sIt (t) is 0-1 variable to indicate the operating status of diesel engine.
8. a kind of micro-capacitance sensor capacity configuration optimization method for considering risk of loss according to claim 7, which is characterized in that
In the step S4, the two stages Robust Optimization Model is min-max-min structure, by introducing auxiliary variable, two ranks
Primal problem MP mathematic(al) representation after section Robust Optimization Model decoupling specifically:
In formula: cTX indicates the objective function C of outer layer optimizationinv+COM+Crep+α·Eloss；η is auxiliary variable, to replace internal layer
Optimization；The first row constrains dTylRepresent the objective function C of internal layer optimizationopt；The installed capacity constraint above of second row constraint representation
With line transmission power constraint；The third line constraint represents energy storage charge-discharge electric power, dominant eigenvalues and diesel-driven generator in text
The constraint of operation；Fourth line is constrained to the constraint of electric energy self-balancing；Fifth line is constrained to the constraint of energy-storage system state-of-charge；6th row
The power-balance constraint of constraint representation system, wherein ulFor the value of uncertain variables u after the l times iteration,J is current iteration number；ylThe solution for being subproblem after the l times iteration, yl=[PESS,s(l,t),
PM2G,s(l,t),PG2M,s(l,t),PDE,s(l,t)]；
Subproblem SP formula max-min structure is translated into single layer Optimized model by strong dual theory, and passes through big-M method
The bilinear terms of single-layer model are subjected to linearization process, obtain the expression formula of subproblem, specifically:
γ, λ, ν, π are respectively the auxiliary variable introduced after strong dual in formula；Δ u is the vector power of wind-powered electricity generation, photovoltaic and load,
Indicate the mean value and maximum fluctuation amount of power indeterminacy section,And
ξ+、ξ-The continuous auxiliary variable introduced for linearisation；To determine uncertain collection U
The 0-1 binary vector of boundary value；σ is larger constant；Γ is the conservative degree adjustment factor of robust of uncertain collection,
9. a kind of micro-capacitance sensor capacity configuration optimization method for considering risk of loss according to claim 8, which is characterized in that
The step S5 specifically includes the following steps:
S51: the bound of setting configuration scheme cost is respectively UB=+ ∞, LB=- ∞, the number of iterations k=1, most badly
Scene is ul(l=1,2, Λ, k), the gap ε of algorithmic statement；
S52: carrying out piece-wise linearization for the probability density function of typical day wind-powered electricity generation, photovoltaic and load power, and by risk of loss
The calculating parameter of cost substitutes into primal problem；
S53: primal problem optimal solution is solvedAnd its optimal uncertain collectionFor each iteration
Optimal uncertain collection boundary is found, most severe scene is enabledAnd it is LB=max { LB, c that lower bound, which is arranged,Txk+ηk}；
S54: by primal problem optimal solutionIt substitutes into subproblem and is solved, obtain subproblem and obtain target function value fkAnd
Its corresponding most severe scene uk+1And control value, the juxtaposition upper bound UB=min { UB, cTxk+fk}；
S55: judging UB-LB≤ε, if so, then stop iteration and returns to optimal solution xkAnd optimal uncertain collectionIf not at
It is vertical then increase most severe scene uk+1Regulation variable yk+1And constraint, juxtaposition number of iterations are k=k+1, return to S53；
S56: optimal solution x is utilizedkWith optimal uncertain collectionComplete the allocation optimum of micro-capacitance sensor capacity.
10. a kind of micro-capacitance sensor capacity configuration optimization method for considering risk of loss according to claim 9, feature exist
In in the step S55, the constraint is embodied as:
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| CN114722591A (en) * | 2022-03-22 | 2022-07-08 | 东南大学 | Planning method for electric heating hydrogen multi-energy flow energy supply equipment of net zero energy consumption building |
| CN115688970A (en) * | 2022-09-21 | 2023-02-03 | 三峡大学 | Micro-grid two-stage adaptive robust optimization scheduling method based on interval probability uncertainty set |
| CN116611192A (en) * | 2023-05-22 | 2023-08-18 | 上海交通大学 | Flexible power distribution network random expansion planning method and system considering operation risk |
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