CN110474367B - Micro-grid capacity configuration optimization method considering risk loss - Google Patents
Micro-grid capacity configuration optimization method considering risk loss Download PDFInfo
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Abstract
The invention provides a micro-grid capacity configuration optimization method considering risk loss, which comprises the following steps: obtaining parameters and fitting a probability distribution function of wind power, photovoltaic power and load power; constructing a wind-light-load uncertainty set of a multi-segment interval, and calculating risk power; constructing a two-stage robust optimization model of power supply capacity configuration and decoupling the two-stage robust optimization model into a main problem model and a sub-problem model; and obtaining the optimal configuration of the capacity of the micro-grid by using a column and constraint generation algorithm. The configuration optimization method provided by the invention can solve the capacity configuration scheme of the micro-grid, reduce the risk loss of the capacity configuration scheme of the micro-grid, solve the balance problem of economy and robustness, and the micro-grid configured within the planning period not only has better robustness and economy, but also provides the risk loss range of the scheme, has reference and guidance value for the running mode of the micro-grid in the future, and provides necessary technical support for the micro-grid planning work.
Description
Technical Field
The invention relates to the technical field of micro-grid planning, in particular to a micro-grid capacity configuration optimization method considering risk loss.
Background
Along with the rapid development of economy, the power demand is continuously improved, fossil energy is gradually exhausted, environmental pollution is increasingly serious, and the important development trend of the power industry is to fully utilize renewable clean energy and promote low-carbon power. The micro-grid can be flexibly connected with distributed power sources such as wind driven generators, photovoltaics and the like, so that the micro-grid is widely focused and applied. The power supply capacity optimization configuration is the basis of the micro-grid planning work, and influences the economy and reliability of the micro-grid operation. Because renewable energy sources (wind speed, illumination intensity and the like) and load power have uncertainty and randomness, random fluctuation of source-load power can cause risk losses of different degrees of the micro-grid, such as power supply power surplus caused power abandoning risk loss or load shedding risk loss caused by rapid increase of load demand and the like. Therefore, risk loss caused by uncertainty of renewable energy sources and load power is fully considered in the power capacity configuration work, and a configuration model with strong anti-interference capability (robustness) and good economy is provided.
Random planning and scene analysis methods are generally adopted in the past to solve the risk problem in the optimal configuration of the micro-grid. However, both the scene analysis method and the random programming depend on probability curves, so that probability model fitting may be inaccurate, and actual application scenes cannot be reflected. In engineering practice, it is difficult to obtain a large number of sample points to accurately describe the probability model.
Disclosure of Invention
The invention provides a micro-grid capacity configuration optimization method considering risk loss, which aims to overcome the technical defect that a large number of sample points are difficult to acquire to accurately describe a probability model in the existing micro-grid optimization configuration method.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a micro-grid capacity configuration optimization method considering risk loss comprises the following steps:
s1: obtaining topological structure, basic parameters and power generation data of a micro-grid, and fitting probability distribution functions of wind power, photovoltaic and load power;
s2: constructing a wind-light-load uncertainty set of a multi-segment interval according to probability distribution, and calculating risk power of wind-light-load;
s3: constructing a two-stage robust optimization model of power supply capacity configuration based on risk power;
s4: decoupling a two-stage robust optimization model of power supply capacity configuration into a main problem model and a sub-problem model;
s5: and (3) utilizing a column and constraint generation algorithm to obtain the optimal configuration of the capacity of the micro-grid by using the main problem model and the sub-problem model.
The step S2 specifically includes the following steps:
s21: the wind power uncertainty set is constructed specifically as follows:
setting the boundary value of the wind power uncertainty set as the upper and lower limit values (w) of the maximum wind power accommodation u 、w d ) Dividing the wind power range into M sections, wherein M is E [1, M]；
Let the average value of the wind power in the period t beThe maximum fluctuation amount of the wind power in the t period is +.>The maximum deviation value of the m-th interval of wind power in the t period is Deltaw m,t The uncertainty set W of wind power is expressed as:
wherein,binary variables of the boundary values of the uncertain set are selected, and the values are {0,1}; />Is a 0/1 variable; Γ -shaped structure wt For adjusting parameters, scaling is performed in the interval of the uncertain set, so that flexibility is improved;
s22: constructing a photovoltaic uncertainty set V and a load power uncertainty set L, and obtaining a total uncertainty set expressed as U= { W, V, L };
s23: and calculating the risk power of wind-light-load by adopting a conditional risk value (CVaR) theory.
The step S23 specifically includes the following steps:
s231: the risk power of the wind turbine generator is calculated, and specifically: describing a distribution function of wind power by adopting normal distribution to obtain:
wherein,the upper limit risk power and the lower limit risk power of the wind turbine generator are respectively; w (w) min 、w max Respectively the upper limit value and the lower limit value of wind power; w (t) is a wind power variable; ρ (w (t)) is a probability value of wind power;
s232: calculating risk power of wind turbine generator
S233: calculating risk power of loadAnd->The upper limit risk power of the load is a negative value, and the lower limit risk power is a positive value.
The specific calculation process of the wind power upper limit risk power in step S231 is as follows:
dividing a probability density function of wind power risk power into Q segments, wherein Q is E Q; v q An end point for each piecewise linear function; a, a q 、b q The coefficient of the q-th piecewise function, and thus, when the wind power accommodation limit value is positioned in the q-th piecewise, the wind power upper limit risk power calculation formula is as follows:
wherein the second partial sum is a constant term, so it is combined with b q Is combined into coefficient B q The method comprises the following steps:
in the step S3, the two-stage robust optimization model includes an objective function and a constraint condition, specifically: the objective function is formed by nesting two layers of functions, wherein the inner layer is the running cost of the micro-grid, and the outer layer is the investment cost C of the power supply inv Cost of maintenance C OM Cost of replacement C rep Cost of risk loss E loss Cost of operation C opt The method comprises the steps of carrying out a first treatment on the surface of the Including, but not limited to, power supply installation number constraints, line transmission power constraints, electrical energy self-balancing constraints, system power balancing constraints, energy storage operating constraints, tie line power constraints, and diesel generator operating constraints.
The specific expression of the objective function is as follows:
wherein alpha is a risk preference degree coefficient of the configuration scheme; x is an optimization variable of the outer layer, and comprises the installation number of wind power, photovoltaic, diesel engines and stored energy and an interval boundary value of an uncertain set; u and y are optimization variables of an inner layer, and an uncertainty set variable and unit power; wherein, in units of years, there are:
cost of Power investment C inv Expressed as:
C inv ＝μ CRF (c WT N wt +c PV N pv +c DE N de +c ESS N ess )
in N wt 、N pv 、N de 、N ESS The number of preinstalled generators for wind power, photovoltaic power, diesel generators and energy storage is respectively; c WT 、c PV 、c DE 、c ESS The investment cost of a single unit of wind power, photovoltaic, diesel generators and energy storage is respectively; mu (mu) CRF For the investment amount recovery factor, theThe value is related to the discount rate and the project period;
annual maintenance cost C of unit OM Expressed as:
C OM ＝μ CRF (k WT N wt +k PV N pv +k DE N de +k ESS N ess )
wherein k is WT 、k PV 、k DE 、k ESS Annual maintenance operation cost of a single unit of wind power, photovoltaic, diesel generators and energy storage is respectively realized;
replacement cost C rep Expressed as:
in which L ESS The actual service life of the energy storage can be obtained by using a total throughput method; y is the life cycle of the micro-grid planning project, here set to 20 years;
risk loss cost E loss Expressed as:
wherein:
phi in CVaR_wt,s 、φ CVaR_pv,s 、φ CVaR_L,s The method comprises the steps of respectively obtaining binary quantities of typical solar wind power, photovoltaic power and load power in the s-th season, wherein the binary quantities are corresponding upper limit risk power and lower limit risk power; f (f) C (t) is a risk cost factor; θ dump 、θ cur Penalty coefficients for the electric power discarding and load shedding power of the micro-grid are respectively determined; t (T) s Typical day of the s-th season;
annual operating cost C of micro-grid opt Expressed as:
wherein:
wherein C is grid,s 、C DE,s Representing the total cost of electricity purchase and sale of the micro-grid and the power generation cost of the diesel engine in s seasons respectively; p (P) G2M,s (t)、P G2M,s (t)、P DE,s (t) the micro-grid electricity selling power, electricity purchasing power and diesel engine power generation power in the t-th period of the s season respectively;rated power of the diesel engine; c pur,s (t)、c sel,s (t) are respectively the electricity purchase and selling prices in the t-th period of the s season; c fuel Is the price per unit of fuel; a and b are unit fuel consumption curve coefficients;
so x, u, y are specifically:
wherein, the constraint adjustment is specifically:
the power supply installation number constraint is expressed as:
in the method, in the process of the invention,the maximum installation number of wind power, photovoltaic, diesel generators and energy storage in the micro-grid project is respectively set;
the line transmission power constraint is expressed as:
wherein S is l 、The power of the first branch and the maximum transmission power of the first branch are respectively; n (N) l The total branch number of the system;
the energy self-balancing constraint is expressed as:
wherein E is pur,s 、E Load,s The power purchase amount and the load demand amount of the micro-grid on a day of typical days in s seasons are respectively; beta min Self-powered coefficient representing minimum allowable value of [0,1 ]]；
The system power balance constraint is expressed as:
P grid,s (t)+P ESS,s (t)+P L,s (t)＝P wt,s (t)+P pv,s (t)+P de,s (t)
wherein:
wherein P is grid,s (t)、P ESS,s (t)、P L,s (t)、P wt,s (t)、P pv,s (t)、P de,s (t) is the power of the t period tie line, the energy storage system, the load, wind power, photovoltaic and the diesel generator on the typical day of the s-th season respectively; p (P) ch,s (t)、P dic,s (t) is the t period energy storage system charging and discharging power of the typical day of the s-th season;
the energy storage operation constraint is expressed as:
the energy storage system is composed of a storage battery, and the operation constraint of the storage battery mainly comprises a charge and discharge power constraint and a capacity constraint, and specifically comprises the following steps: the charge and discharge power constraint is as follows:
the relation between the charge state and the charge/discharge power of the storage battery is as follows:
in the method, in the process of the invention,is the maximum charge and discharge power; s is S oc (s, t) is the state of charge of the storage battery at the t period on the s-th typical day; s is S oc_max 、S oc_min The upper limit value and the lower limit value of the charge state are respectively; η (eta) c 、η d The charging and discharging efficiencies of the storage battery are respectively; u (u) ESS (t) is a 0-1 variable to represent the stored energy operating state; t is t 0 、t T Respectively starting and ending time of scheduling;
the tie-line power constraint is expressed as:
in the middle ofLimiting values of the power purchase power and the reverse power of the micro-grid at the t period of the s-th typical day;
the diesel generator operating constraints are expressed as:
u in the formula DE,s And (t) is a 0-1 variable to represent the operating state of the diesel engine.
In the step S4, the two-stage robust optimization model is of a min-max-min structure, and the main problem MP mathematical expression after the decoupling of the two-stage robust optimization model is specifically:
wherein: c T x represents the outer layer optimized objective function C inv +C OM +C rep +α·E loss The method comprises the steps of carrying out a first treatment on the surface of the η is an auxiliary variable to replace the inner layer optimization; constraint d of the first line T y l Objective function C representing inner layer optimization opt The method comprises the steps of carrying out a first treatment on the surface of the The second row constraint represents the installation capacity constraint and the line transmission power constraint above; the third row of constraint represents the constraint of energy storage charge and discharge power, tie line power and diesel generator operation; the fourth row constraint is an electric energy self-balancing constraint; the fifth row constraint is the state of charge constraint of the energy storage system; the sixth row constraint represents the power balance constraint of the system, where u l To determine the value of the variable u after the first iteration,j is the current iteration number; y is l For the solution of the sub-problem after the first iteration, y l ＝[P ESS,s (l,t),P M2G,s (l,t),P G2M,s (l,t),P DE,s (l,t)]；
The sub-problem SP type max-min structure is converted into a single-layer optimization model through a strong dual theory, bilinear terms of the single-layer model are subjected to linearization treatment through a big-M method, and an expression of the sub-problem is obtained, wherein the expression specifically comprises the following steps:
wherein gamma, lambda, v and pi are auxiliary variables introduced after strong dual;deltau is the power vector of wind power, photovoltaic and load, and represents the mean value and the maximum fluctuation of the power uncertainty interval, < ->And +.>ξ + 、ξ - A continuous auxiliary variable introduced for linearization; />A 0-1 binary vector for determining the boundary value of the uncertainty set U; sigma is a relatively large constant; Γ is the robust conservation degree adjustment coefficient of the uncertainty set, +.>
The step S5 specifically includes the following steps:
s51: setting the cost of the optimal configuration scheme upper and lower boundaries respectively is UB = +++ infinity, lb= - ≡, iteration number k=1, worst scene u l (l=1, 2, Λ, k), the gap epsilon for algorithm convergence;
s52: carrying out piecewise linearization on probability density functions of typical solar power, photovoltaic power and load power, and substituting calculation parameters of risk loss cost into a main problem;
s53: solving the optimal solution of the main problemAnd its optimal uncertainty set->To find the optimal uncertainty set boundary per iteration, let worst scenario +.>And sets the lower bound to lb=max { LB, c T x k +η k }；
S54: optimal solution of main problemSubstituting into the sub-problem to obtain the objective functionValue f k Worst scene u corresponding to the same k+1 And regulatory values, juxtaposing an upper bound ub=min { UB, c T x k +f k }；
S55: judging UB-LB is less than or equal to epsilon, if so, stopping iteration and returning to the optimal solution x k Optimal uncertainty setIf not, adding the worst scene u k+1 Is the control variable y of (2) k+1 And conditional constraints, concatenating the iteration number k=k+1, returning to S53;
s56: using the optimal solution x k And an optimal uncertainty setAnd (5) completing the optimal configuration of the capacity of the micro-grid.
Wherein, in the step S55, the condition constraint is specifically expressed as:
compared with the prior art, the technical scheme of the invention has the beneficial effects that:
the invention provides a micro-grid capacity allocation optimization method considering risk loss, which can solve a capacity allocation scheme of a micro-grid, reduce the risk loss of the capacity allocation scheme of the micro-grid, solve the balance problem of economy and robustness of the capacity allocation scheme, wherein the micro-grid comprises investment schemes of wind power units, photovoltaic units, diesel units and energy storage, and the micro-grid allocated within a planning period not only has better robustness and economy, but also provides a risk loss range of the scheme, has reference and guidance values for the running mode of the micro-grid in the future, and provides necessary technical support for the micro-grid planning work.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of a multi-segment interval of wind power output;
FIG. 3 is a normal distribution diagram of wind power output probability distribution functions.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the present patent;
for the purpose of better illustrating the embodiments, certain elements of the drawings may be omitted, enlarged or reduced and do not represent the actual product dimensions;
it will be appreciated by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical scheme of the invention is further described below with reference to the accompanying drawings and examples.
Example 1
As shown in fig. 1, a method for optimizing capacity configuration of a micro-grid in consideration of risk loss includes the following steps:
s1: obtaining topological structure, basic parameters and power generation data of a micro-grid, and fitting probability distribution functions of wind power, photovoltaic and load power;
s2: constructing a wind-light-load uncertainty set of a multi-segment interval according to probability distribution, and calculating risk power of wind-light-load;
s3: constructing a two-stage robust optimization model of power supply capacity configuration based on risk power;
s4: decoupling a two-stage robust optimization model of power supply capacity configuration into a main problem model and a sub-problem model;
s5: and (3) utilizing a column and constraint generation algorithm to obtain the optimal configuration of the capacity of the micro-grid by using the main problem model and the sub-problem model.
In the specific implementation process, in a micro-grid planning area, acquiring data such as a load level, impedance of a line, transmission capacity and the like from a grid energy management system EMS; measuring a local renewable energy power generation resource, acquiring data such as wind speed, illumination intensity, temperature, wind direction and the like, calculating wind power and photovoltaic power generation data according to the data, and screening data of representative typical days (24 hours) all the year round by taking 1 hour as a unit to serve as input of a model.
More specifically, as shown in fig. 2, wind-light power generation and load power data are obtained according to step S1, and a multi-segment interval uncertainty set within 24 periods of 4 typical days is constructed, specifically comprising the following steps:
s21: the wind power uncertainty set is constructed specifically as follows:
setting the boundary value of the wind power uncertainty set as the upper and lower limit values (w) of the maximum wind power accommodation u 、w d ) Dividing the wind power range into M sections, wherein M is E [1, M]；
Let the average value of the wind power in the period t beThe maximum fluctuation amount of the wind power in the t period is +.>The maximum deviation value of the m-th interval of wind power in the t period is Deltaw m,t The uncertainty set W of wind power is expressed as:
wherein,binary variables of the boundary values of the uncertain set are selected, and the values are {0,1}; />Is a 0/1 variable; Γ -shaped structure wt For adjusting parameters, scaling is performed in the interval of the uncertain set, so that flexibility is improved;
s22: constructing a photovoltaic uncertainty set V and a load power uncertainty set L, and obtaining a total uncertainty set expressed as U= { W, V, L };
s23: and calculating the risk power of wind-light-load by adopting a conditional risk value (CVaR) theory.
In practice, renewable energy and load power fluctuations have uncertainty and randomness. When the wind-light-load power is outside the set uncertain collection interval, the risk loss of power discarding and load cutting occurs to the micro-grid operation, and the power outside the interval is called risk power. Calculating the risk power of the micro grid by conditional risk value (CVaR) theory may therefore create risk losses. For power fluctuation probability distribution functions of photovoltaic, wind power and load, normal distribution is generally adopted for description.
More specifically, as shown in fig. 3, the step S23 specifically includes the following steps:
s231: the risk power of the wind turbine generator is calculated, and specifically: describing a distribution function of wind power by adopting normal distribution to obtain:
wherein,the upper limit risk power and the lower limit risk power of the wind turbine generator are respectively; w (w) min 、w max Respectively the upper limit value and the lower limit value of wind power; w (t) is a wind power variable; ρ (w (t)) is a probability value of wind power;
s232: calculating risk power of wind turbine generator
S233: calculating risk power of loadAnd->The upper limit risk power of the load is a negative value, and the lower limit risk power is a positive value.
In a specific implementation process, nonlinear integration exists in the wind-light-load risk power calculation, and the nonlinear integration is difficult to directly solve, but has convexity and monotonicity, so that piecewise linearization is needed for the integration. Therefore, the specific calculation process of the wind power upper limit risk power in step S231 is as follows:
dividing a probability density function of wind power risk power into Q segments, wherein Q is E Q; v q An end point for each piecewise linear function; a, a q 、b q The coefficient of the q-th piecewise function, and thus, when the wind power accommodation limit value is positioned in the q-th piecewise, the wind power upper limit risk power calculation formula is as follows:
wherein the second partial sum is a constant term, so it is combined with b q Is combined into coefficient B q The method comprises the following steps:
more specifically, in the step S3, the two-stage robust optimization model includes an objective function and a constraint condition, specifically: the objective function is formed by nesting two layers of functions, wherein the inner layer is the running cost of the micro-grid, and the outer layer is the investment cost C of the power supply inv Cost of maintenance C OM Cost of replacement C rep Cost of risk loss E loss Cost of operation C opt The method comprises the steps of carrying out a first treatment on the surface of the Including, but not limited to, power supply installation number constraints, line transmission power constraints, electrical energy self-balancing constraints, system power balancing constraints, energy storage operating constraints, tie line power constraints, and diesel generator operating constraints.
The specific expression of the objective function is as follows:
wherein alpha is a risk preference degree coefficient of the configuration scheme; x is an optimization variable of the outer layer, and comprises the installation number of wind power, photovoltaic, diesel engines and stored energy and an interval boundary value of an uncertain set; u and y are optimization variables of an inner layer, and an uncertainty set variable and unit power; wherein, in units of years, there are:
cost of Power investment C inv Expressed as:
C inv ＝μ CRF (c WT N wt +c PV N pv +c DE N de +c ESS N ess )
in N wt 、N pv 、N de 、N ESS The number of preinstalled generators for wind power, photovoltaic power, diesel generators and energy storage is respectively; c WT 、c PV 、c DE 、c ESS The investment cost of a single unit of wind power, photovoltaic, diesel generators and energy storage is respectively; mu (mu) CRF A return coefficient for the investment amount, the value being related to the discount rate and the project period;
annual maintenance cost C of unit OM Expressed as:
C OM ＝μ CRF (k WT N wt +k PV N pv +k DE N de +k ESS N ess )
wherein k is WT 、k PV 、k DE 、k ESS Annual maintenance operation cost of a single unit of wind power, photovoltaic, diesel generators and energy storage is respectively realized;
replacement cost C rep Expressed as:
in which L ESS The actual service life of the energy storage can be obtained by using a total throughput method; y is the life cycle of the micro-grid planning project, here set to 20 years;
risk loss cost E loss Expressed as:
wherein:
phi in CVaR_wt,s 、φ CVaR_pv,s 、φ CVaR_L,s The method comprises the steps of respectively obtaining binary quantities of typical solar wind power, photovoltaic power and load power in the s-th season, wherein the binary quantities are corresponding upper limit risk power and lower limit risk power; f (f) C (t) is a risk cost factor; θ dump 、θ cur Penalty coefficients for the electric power discarding and load shedding power of the micro-grid are respectively determined; t (T) s Typical day of the s-th season;
annual operating cost C of micro-grid opt Expressed as:
wherein:
wherein C is grid,s 、C DE,s Representing the total cost of electricity purchase and sale of the micro-grid and the power generation cost of the diesel engine in s seasons respectively; p (P) G2M,s (t)、P G2M,s (t)、P DE,s (t) the micro-grid electricity selling power, electricity purchasing power and diesel engine power generation power in the t-th period of the s season respectively;rated power of the diesel engine; c pur,s (t)、c sel,s (t) are respectively the electricity purchase and selling prices in the t-th period of the s season; c fuel Is the price per unit of fuel; a and b are unit fuel consumption curve coefficients;
so x, u, y are specifically:
wherein, the constraint adjustment is specifically:
the power supply installation number constraint is expressed as:
in the method, in the process of the invention,the maximum installation number of wind power, photovoltaic, diesel generators and energy storage in the micro-grid project is respectively set;
the line transmission power constraint is expressed as:
wherein S is l 、The power of the first branch and the maximum transmission power of the first branch are respectively; n (N) l The total branch number of the system;
the energy self-balancing constraint is expressed as:
wherein E is pur,s 、E Load,s The power purchase amount and the load demand amount of the micro-grid on a day of typical days in s seasons are respectively; beta min Self-powered coefficient representing minimum allowable value of [0,1 ]]；
The system power balance constraint is expressed as:
P grid,s (t)+P ESS,s (t)+P L,s (t)＝P wt,s (t)+P pv,s (t)+P de,s (t)
wherein:
wherein P is grid,s (t)、P ESS,s (t)、P L,s (t)、P wt,s (t)、P pv,s (t)、P de,s (t) is the power of the t period tie line, the energy storage system, the load, wind power, photovoltaic and the diesel generator on the typical day of the s-th season respectively; p (P) ch,s (t)、P dic,s (t) is the t period energy storage system charging and discharging power of the typical day of the s-th season;
the energy storage operation constraint is expressed as:
the energy storage system is composed of a storage battery, and the operation constraint of the storage battery mainly comprises a charge and discharge power constraint and a capacity constraint, and specifically comprises the following steps: the charge and discharge power constraint is as follows:
the relation between the charge state and the charge/discharge power of the storage battery is as follows:
in the method, in the process of the invention,is the maximum charge and discharge power; s is S oc (s, t) is the state of charge of the storage battery at the t period on the s-th typical day; s is S oc_max 、S oc_min The upper limit value and the lower limit value of the charge state are respectively; η (eta) c 、η d The charging and discharging efficiencies of the storage battery are respectively; u (u) ESS (t) is a 0-1 variable to represent the stored energy operating state; t is t 0 、t T Respectively starting and ending time of scheduling;
the tie-line power constraint is expressed as:
in the middle ofLimiting values of the power purchase power and the reverse power of the micro-grid at the t period of the s-th typical day;
the diesel generator operating constraints are expressed as:
u in the formula DE,s And (t) is a 0-1 variable to represent the operating state of the diesel engine.
More specifically, in the step S4, the two-stage robust optimization model is of a min-max-min structure, and the main problem MP mathematical expression after the decoupling of the two-stage robust optimization model is specifically:
wherein: c T x represents the outer layer optimized objective function C inv +C OM +C rep +α·E loss The method comprises the steps of carrying out a first treatment on the surface of the η is an auxiliary variable to replace the inner layer optimization; constraint d of the first line T y l Objective function C representing inner layer optimization opt The method comprises the steps of carrying out a first treatment on the surface of the The second row constraint represents the installation capacity constraint and the line transmission power constraint above; the third row of constraint represents the constraint of energy storage charge and discharge power, tie line power and diesel generator operation; the fourth row constraint is an electric energy self-balancing constraint; the fifth row constraint is the state of charge constraint of the energy storage system; the sixth row constraint represents the power balance constraint of the system, where u l To determine the value of the variable u after the first iteration,j is the current iteration number; y is l For the solution of the sub-problem after the first iteration, y l ＝[P ESS,s (l,t),P M2G,s (l,t),P G2M,s (l,t),P DE,s (l,t)]；
The sub-problem SP type max-min structure is converted into a single-layer optimization model through a strong dual theory, bilinear terms of the single-layer model are subjected to linearization treatment through a big-M method, and an expression of the sub-problem is obtained, wherein the expression specifically comprises the following steps:
wherein gamma, lambda, v and pi are auxiliary variables introduced after strong dual;deltau is the power vector of wind power, photovoltaic and load, and represents the mean value and the maximum fluctuation of the power uncertainty interval, < ->And +.>ξ + 、ξ - A continuous auxiliary variable introduced for linearization; />A 0-1 binary vector for determining the boundary value of the uncertainty set U; sigma is a relatively large constant; Γ is the robust conservation degree adjustment coefficient of the uncertainty set, +.>
In a specific implementation, a process and method for solving an optimal configuration model using a column and constraint generation algorithm (C & CG). The C & CG algorithm has good effect on solving a two-stage robust model, and the main problem MP is introduced into auxiliary variables to replace an inner-layer objective function by decomposing the model into a main problem and a sub-problem, so that variables and constraints related to the sub-problem are continuously increased; and calculating sub-problem information according to the main problem optimization result and feeding back the sub-problem information to the MP so as to carry out interactive iterative solution. More specifically, as described in step S5, the method specifically includes the following steps:
s51: setting upper and lower bounds for optimizing configuration scheme costRespectively is UB = +++ is a function of, LB = - ≡infinity, the number of iterations k=1, the worst scene u l (l=1, 2, Λ, k), the gap epsilon for algorithm convergence;
s52: carrying out piecewise linearization on probability density functions of typical solar power, photovoltaic power and load power, and substituting calculation parameters of risk loss cost into a main problem;
s53: solving the optimal solution of the main problemAnd its optimal uncertainty set->To find the optimal uncertainty set boundary per iteration, let worst scenario +.>And sets the lower bound to lb=max { LB, c T x k +η k }；
S54: optimal solution of main problemSubstituting into the sub-problem to obtain objective function value f k Worst scene u corresponding to the same k+1 And regulatory values, juxtaposing an upper bound ub=min { UB, c T x k +f k }；
S55: judging UB-LB is less than or equal to epsilon, if so, stopping iteration and returning to the optimal solution x k Optimal uncertainty setIf not, adding the worst scene u k+1 Is the control variable y of (2) k+1 And conditional constraints, concatenating the iteration number k=k+1, returning to S53;
s56: using the optimal solution x k And an optimal uncertainty setAnd (5) completing the optimal configuration of the capacity of the micro-grid.
More specifically, in the step S55, the condition constraint is specifically expressed as:
/>
in a specific implementation process, the method reduces the risk loss of the capacity allocation scheme of the micro-grid and solves the balance problem of economy and robustness. Comprehensively considering the random characteristics of renewable energy sources and load power, and constructing a wind-light-load uncertainty set of a multi-segment interval; the coupling relation between the economical efficiency and the robustness of the configuration scheme is built by quantifying the risk loss of the micro-grid through a conditional risk value theory (CVaR); establishing a two-stage robust optimization model of the capacity configuration of the micro-grid, wherein the outer layer of the model is an optimization configuration layer aiming at the construction cost, the maintenance cost, the replacement cost and the risk loss cost of the micro-grid; the inner layer is an optimized running layer of the micro-grid unit; the mixed integer linear program is decomposed into a main problem and a sub-problem by using a strong dual theory and column and constraint generation algorithm (C & CG), and is subjected to alternate iterative solution. The model not only balances the robustness and the economy of the power supply configuration scheme, but also quantifies the risk loss of the planning scheme, and provides necessary technical support for the micro-grid planning work.
It is to be understood that the above examples of the present invention are provided by way of illustration only and not by way of limitation of the embodiments of the present invention. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the invention are desired to be protected by the following claims.
Claims (4)
1. The utility model provides a micro-grid capacity configuration optimization method considering risk loss, which is characterized by comprising the following steps:
s1: obtaining topological structure, basic parameters and power generation data of a micro-grid, and fitting probability distribution functions of wind power, photovoltaic and load power;
s2: constructing a wind-light-load uncertainty set of a multi-segment interval according to the probability distribution function, and calculating risk power of the wind-light-load;
s3: constructing a two-stage robust optimization model of power supply capacity configuration based on risk power;
the two-stage robust optimization model comprises an objective function and constraint conditions, and specifically comprises the following steps: the objective function is formed by nesting two layers of functions, wherein the inner layer is the running cost of the micro-grid, and the outer layer is the investment cost of a power supply, the maintenance cost, the replacement cost, the risk loss cost and the running cost; the constraint conditions include, but are not limited to, power supply installation number constraint, line transmission power constraint, electric energy self-balancing constraint, system power balance constraint, energy storage operation constraint, tie line power constraint and diesel generator operation constraint;
s4: decoupling a two-stage robust optimization model of power supply capacity configuration into a main problem model and a sub-problem model;
s5: and solving the main problem model and the sub-problem model by using a column constraint generation algorithm to obtain the optimal configuration of the capacity of the micro-grid.
2. The method for optimizing capacity configuration of a micro grid in consideration of risk loss according to claim 1, wherein the step S2 specifically comprises the following steps:
s21: the wind power uncertainty set is constructed specifically as follows:
setting the boundary value of the wind power uncertain set as the upper and lower limit values of the maximum wind power accommodable as w u 、w d Dividing the wind power range into M sections, wherein M is E [1, M]；
Let the average value of the wind power in the period t beThe maximum fluctuation amount of the wind power in the t period is +.>The maximum deviation value of the m-th interval of wind power in the t period is Deltaw m,t The uncertainty set W of wind power is expressed as:
wherein,binary variables of the boundary values of the uncertain set are selected, and the values are {0,1}; />Is a 0/1 variable; Γ -shaped structure wt For adjusting parameters, scaling is performed in the interval of the uncertain set, so that flexibility is improved;
s22: constructing a photovoltaic uncertainty set V and a load power uncertainty set L, and obtaining a total uncertainty set expressed as U= { W, V, L };
s23: and calculating the risk power of wind-light-load by adopting a conditional risk value (CVaR) theory.
3. The method for optimizing capacity configuration of a micro-grid taking into account risk loss according to claim 2, wherein said step S23 specifically comprises the steps of:
s231: calculating upper and lower limit risk power of the wind turbine generator, wherein the upper and lower limit risk power is specifically as follows: and describing a probability distribution function of wind power by adopting normal distribution to obtain:
wherein,the upper limit risk power and the lower limit risk power of the wind turbine generator are respectively; w (w) min 、w max Respectively the upper limit value and the lower limit value of wind power; w (t) is wind powerA variable; ρ (w (t)) is a probability value of wind power;
s232: calculating risk power of wind turbine generatorAnd->
S233: calculating risk power of loadAnd->The upper limit risk power of the load is a negative value, and the lower limit risk power is a positive value.
4. A method for optimizing capacity allocation of a micro-grid in consideration of risk loss according to claim 3, wherein the specific expression of the objective function is:
wherein alpha is a risk preference degree coefficient of the configuration scheme; x is an optimization variable of the outer layer, and comprises the installation number of wind power, photovoltaic, diesel engines and stored energy and an interval boundary value of an uncertain set; u and y are optimization variables of an inner layer, and an uncertainty set variable and unit power; wherein, in units of years, there are:
cost of Power investment C inv Expressed as:
C inv ＝μ CRF (c WT N wt +c PV N pv +c DE N de +c ESS N ess )
in N wt 、N pv 、N de 、N ESS Wind power, photovoltaic, diesel generator and energy storage respectivelyPre-installing the number of the stations; c WT 、c PV 、c DE 、c ESS The investment cost of a single unit of wind power, photovoltaic, diesel generators and energy storage is respectively; mu (mu) CRF A return coefficient for the investment amount, the value being related to the discount rate and the project period;
annual maintenance cost C of unit OM Expressed as:
C OM ＝μ CRF (k WT N wt +k PV N pv +k DE N de +k ESS N ess )
wherein k is WT 、k PV 、k DE 、k ESS Annual maintenance operation cost of a single unit of wind power, photovoltaic, diesel generators and energy storage is respectively realized;
replacement cost C rep Expressed as:
in which L ESS The actual service life of the energy storage can be obtained by using a total throughput method; y is the life cycle of the micro-grid planning project, here set to 20 years;
risk loss cost E loss Expressed as:
wherein:
phi in CVaR_wt,s 、φ CVaR_pv,s 、φ CVaR_L,s The method comprises the steps of respectively obtaining binary quantities of typical solar wind power, photovoltaic power and load power in the s-th season, wherein the binary quantities are corresponding upper limit risk power and lower limit risk power; f (f) C (t) is a risk cost factor; θ dump 、θ cur Penalty coefficients for the electric power discarding and load shedding power of the micro-grid are respectively determined; t (T) s Typical day of the s-th season;
annual operating cost C of micro-grid opt Expressed as:
wherein:
wherein C is grid,s 、C DE,s Representing the total cost of electricity purchase and sale of the micro-grid and the power generation cost of the diesel engine in s seasons respectively; p (P) M2G,s (t)、P G2M,s (t)、P DE,s (t) the micro-grid electricity selling power, electricity purchasing power and diesel engine power generation power in the t-th period of the s season respectively;rated power of the diesel engine; c pur,s (t)、c sel,s (t) are respectively the electricity purchase and selling prices in the t-th period of the s season; c fuel Is the price per unit of fuel; a and b are unit fuel consumption curve coefficients.
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