CN115085227A - Micro-grid source storage capacity configuration method and device - Google Patents
Micro-grid source storage capacity configuration method and device Download PDFInfo
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Abstract
The invention discloses a method and a device for configuring the source storage capacity of a microgrid, wherein an interval uncertainty set model description is respectively constructed to describe the uncertainty of cost parameters by comprehensively considering uncertainty factors such as cost parameters, wind-light output and the like in the planning process of the source storage capacity of the microgrid, a convex hull uncertainty set model is constructed to describe the correlation of the uncertain wind-light output at multiple moments, and a microgrid source storage capacity optimal configuration model considering uncertainty is established by taking the minimum cost as a target; the optimal configuration model of the micro-grid source storage capacity is solved based on the dual robust algorithm, so that the optimal configuration of the micro-grid source storage capacity under the consideration of uncertainty factors such as uncertainty cost parameters, wind and light output and the like is realized, and the economic and stable operation of the micro-grid system is ensured.
Description
Technical Field
The invention relates to the technical field of micro-grids, in particular to a micro-grid source storage capacity configuration method and device.
Background
In recent years, renewable energy sources represented by wind turbines and photovoltaic systems are widely applied to micro-grids, but due to the fact that output power of the renewable energy sources is intermittent, fluctuating and uncertain, research on technicians is difficult. The reasonable configuration of the capacity of power supply and energy storage facilities in the micro-grid is a key step for effectively solving the problems, and has important significance in the aspects of ensuring the economy, stable operation and the like of the system.
The existing micro-grid source storage capacity configuration has the following defects: in the aspect of uncertainty modeling, most documents do not consider the correlation of wind and light output, so that the configuration optimization result can deal with many scenes which cannot occur, and the planning cost is increased. The solution method is divided into an artificial intelligence algorithm and a robust optimization algorithm, the search performance and the convergence of most artificial intelligence algorithms depend on the selection of control parameters, and the global optimal solution is difficult to obtain; the robust optimization algorithm generally adopts an uncertain set to describe the uncertainty of uncertain parameters, however, the robust optimization itself comprises a max-min double-layer optimization model, so that most of solving algorithms are complex in design and the situation of iteration unconvergence is possible to occur. In the aspect of comprehensive measures, although the influence of the fluctuation of wind and light output on the system can be effectively reduced by the energy storage and demand response load, the influence of the uncertainty of the power supply cost parameter on the configuration result is comprehensively considered by few documents on the basis of certain cost parameters. The above measures have great limitations because of the difficulty in model solution caused by improper parameter selection.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method and the device for configuring the storage capacity of the micro-grid source can realize the optimal configuration of the capacity of the power source and the storage facility and improve the configuration economy.
In order to solve the technical problems, the invention adopts the technical scheme that:
a micro-grid source storage capacity configuration method comprises the following steps:
establishing an interval uncertainty set model considering the uncertainty of the cost parameters;
establishing a convex hull uncertainty set model of multi-time uncertain wind-solar output correlation;
constructing a cost minimum objective function based on the interval uncertainty set model, and establishing a microgrid source storage capacity optimization configuration model considering uncertainty by combining the convex hull uncertainty set model;
and solving the microgrid source storage capacity optimization configuration model by using a dual robust algorithm to obtain an optimization configuration result.
In order to solve the technical problem, the invention adopts another technical scheme as follows:
a microgrid source storage capacity configuration apparatus comprising:
the model building module is used for building an interval uncertainty set model considering uncertainty of cost parameters, building a convex hull uncertainty set model of multi-time uncertain wind-solar output correlation, building a cost minimum objective function based on the interval uncertainty set model, and building a micro-grid source storage capacity optimization configuration model considering uncertainty by combining the convex hull uncertainty set model;
and the optimization solving module is used for solving the microgrid source storage capacity optimization configuration model by using a dual robust algorithm to obtain an optimization configuration result.
Further, the model building module comprises:
the data acquisition module is used for acquiring uncertain cost parameters, wherein the uncertain cost parameters comprise energy storage unit capacity replacement cost, fuel price, energy storage unit capacity operation and maintenance cost, generator unit capacity investment cost, generator unit capacity power generation cost, energy storage unit capacity investment cost, fan unit capacity investment cost, photovoltaic unit capacity investment cost, fan unit capacity operation and maintenance cost, generator unit capacity operation and maintenance cost and photovoltaic unit capacity operation and maintenance cost;
and the data interval establishing module is used for establishing an interval set of the uncertain cost parameters.
Further, the model building module further comprises:
the constraint model establishing module is used for establishing a wind and light capacity constraint model at the moment without considering the correlation:
NC Wt ≥P Wt (t)，NC PV ≥P PV (t)；
in the formula, P Wt (t)、P PV (t) represents the historical output of the fan and the photovoltaic respectively, NC Wt 、NC PV Representing the capacity of the fan and the photovoltaic, respectively.
Further, the model building module is configured to form a convex hull uncertainty set model from a plurality of linear inequalities of wind-solar power:
[A Wt (t)A PV (t)]*[P Wt '(t)P PV '(t)] T ≤b(t)；
in the formula, A Wt (t)、A PV (t), b (t) fan output coefficient matrix, photovoltaic output coefficient matrix and constant matrix respectively representing convex hull linear inequality, P Wt '(t)、P PV ' (t) denotes the wind and photovoltaic outputs, respectively, at time t taking into account the wind-solar output dependence.
Further, the model building module comprises:
an objective function establishing module for establishing the investment cost of the power supplyC cap Power supply operation and maintenance cost C OM Energy storage replacement cost C rep，batt Generator fuel cost C fuel And the start-stop cost of the generator C onoff And generator generation cost C e As an optimization objective, an objective function is established:
min(C cap +C OM +C rep，batt +C fuel +C onoff +C e )。
further, the model construction module is used for obtaining a convex hull uncertain set constraint model of historical wind and light output, establishing a system power constraint model, an energy storage charging and discharging state constraint model and a power constraint model, a generator minimum start-stop time constraint model and a generator output model and a microgrid demand response load power constraint model, and obtaining a microgrid source and storage capacity optimal configuration model considering uncertainty by combining the constraint models.
Further, the optimization solution module comprises:
the model conversion module is used for describing the microgrid source storage capacity optimization configuration model as a robust optimization problem based on the interval uncertainty set model and the convex hull uncertainty set model;
and the model solving module is used for converting the robust optimization problem into a deterministic problem by using a dual robust algorithm, and solving the deterministic problem to obtain an energy storage capacity configuration result with optimal cost.
The invention has the beneficial effects that: by comprehensively considering uncertainty factors such as cost parameters, wind-light output and the like in the planning process of the micro-grid source storage capacity, respectively constructing an interval uncertainty set model description to describe the uncertainty of the cost parameters, constructing a convex hull uncertainty set model to describe the correlation of multi-time uncertain wind-light output, and establishing a micro-grid source storage capacity optimization configuration model considering uncertainty by taking the minimum cost as a target; the optimal configuration model of the micro-grid source storage capacity is solved based on the dual robust algorithm, so that the optimal configuration of the micro-grid source storage capacity under the consideration of uncertainty factors such as uncertainty cost parameters, wind and light output and the like is realized, and the economic and stable operation of the micro-grid system is ensured.
Drawings
Fig. 1 is a flowchart of a method for configuring a storage capacity of a microgrid source according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a microgrid source storage capacity configuration device according to an embodiment of the present invention;
FIG. 3 is a flowchart of an embodiment of the invention for solving an optimization model by a dual robust algorithm;
FIG. 4 is a statistical chart of exemplary calendar history data according to a second embodiment of the present invention;
FIG. 5 is a graph of the effect of uncertain cost parameters on total cost for example two of the present invention;
fig. 6 is a statistical graph of annual wind and light output at time t of 7-10 h according to the second embodiment of the invention.
Detailed Description
In order to explain technical contents, achieved objects, and effects of the present invention in detail, the following description is made with reference to the accompanying drawings in combination with the embodiments.
Referring to fig. 1, an embodiment of the present invention provides a method for configuring a source storage capacity of a microgrid, including the steps of:
establishing an interval uncertainty set model considering the uncertainty of the cost parameters;
establishing a convex hull uncertainty set model of multi-time uncertain wind-solar output correlation;
constructing a cost minimum objective function based on the interval uncertainty set model, and establishing a micro-grid source storage capacity optimization configuration model considering uncertainty by combining the convex hull uncertainty set model;
and solving the microgrid source storage capacity optimization configuration model by using a dual robust algorithm to obtain an optimization configuration result.
From the above description, the beneficial effects of the present invention are: by comprehensively considering uncertainty factors such as cost parameters, wind-light output and the like in the planning process of the micro-grid source storage capacity, respectively constructing an interval uncertainty set model description to describe the uncertainty of the cost parameters, constructing a convex hull uncertainty set model to describe the correlation of multi-time uncertain wind-light output, and establishing a micro-grid source storage capacity optimization configuration model considering uncertainty by taking the minimum cost as a target; the optimal configuration model of the micro-grid source storage capacity is solved based on the dual robust algorithm, so that the optimal configuration of the micro-grid source storage capacity under the consideration of uncertainty factors such as uncertainty cost parameters and wind-light output is realized, and the economic and stable operation of the micro-grid system is ensured.
Further, the establishing an interval uncertainty set model considering uncertainty of the cost parameter includes:
obtaining uncertain cost parameters, wherein the uncertain cost parameters comprise energy storage unit capacity replacement cost, fuel price, energy storage unit capacity operation and maintenance cost, generator unit capacity investment cost, generator unit capacity power generation cost, energy storage unit capacity investment cost, fan unit capacity investment cost, photovoltaic unit capacity investment cost, fan unit capacity operation and maintenance cost, generator unit capacity operation and maintenance cost and photovoltaic unit capacity operation and maintenance cost;
and establishing an interval set of the uncertain cost parameters.
According to the description, the operation and maintenance costs and the investment costs of the fan, the photovoltaic, the generator and the energy storage are considered in the uncertain cost parameters, and the replacement cost of the energy storage is used as the uncertain parameters, so that the optimal configuration of the micro-grid source storage capacity under the uncertain factors such as the uncertain cost parameters, the wind-light output and the like is considered, and the economic and stable operation of the micro-grid system is ensured.
Further, the establishing a convex hull uncertainty set model of the multi-time uncertain wind-solar output correlation comprises:
establishing a wind-solar capacity constraint model at a moment without considering the correlation:
NC Wt ≥P Wt (t)，NC PV ≥P PV (t)；
in the formula, P Wt (t)、P PV (t) historical output magnitudes of the fan and the photovoltaic, NC Wt 、NC PV Respectively representing the capacity of the fan and the photovoltaic.
According to the description, the wind-solar capacity constraint model without considering the correlation moment is established, so that the convex hull uncertainty set model of the uncertain wind-solar output correlation at multiple moments can be conveniently established subsequently.
Further, the establishing a convex hull uncertainty set model of the multi-time uncertain wind-solar output correlation includes:
when the correlation is considered, limiting the wind-solar output at each moment in a convex hull uncertainty set constructed by the wind-solar historical output data, wherein the convex hull uncertainty set model consists of a plurality of linear inequalities of the wind-solar output:
[A Wt (t)A PV (t)]*[P Wt '(t)P PV '(t)] T ≤b(t)；
in the formula, A Wt (t)、A PV (t), b (t) fan output coefficient matrix, photovoltaic output coefficient matrix and constant matrix respectively representing convex hull linear inequality, P Wt '(t)、P PV ' (t) denotes the wind and photovoltaic outputs, respectively, at time t taking into account the wind-solar output dependence.
According to the description, strong correlation exists in the output of new energy represented by wind and light in certain time periods, if the correlation of the output of the wind and light is not considered in the considered uncertain set, the result of robust optimization can deal with many scenes which cannot occur, and therefore the correlation performance is considered, so that the optimal configuration of the source storage capacity of the micro-grid can be obtained conveniently in the follow-up process.
Further, constructing a cost-minimum objective function based on the interval uncertainty set model comprises:
investment cost of power supply C cap Power supply operation and maintenance cost C OM Energy storage replacement cost C rep，batt Generator fuel cost C fuel And the start-stop cost of the generator C onoff And generator generation cost C e As an optimization objective, an objective function is established:
min(C cap +C OM +C rep，batt +C fuel +C onoff +C e )。
according to the description, the objective function with the minimum cost is constructed, and the microgrid source storage capacity optimization configuration model can be conveniently established subsequently.
Further, establishing a microgrid source storage capacity optimization configuration model considering uncertainty by combining the convex hull uncertainty set model comprises:
acquiring a convex hull uncertain set constraint model of historical wind and light output, and establishing a system power constraint model, an energy storage charging and discharging state constraint model, a power constraint model, a generator minimum start-stop time constraint model, a generator output model and a microgrid demand response load power constraint model;
and obtaining a micro-grid source storage capacity optimization configuration model considering uncertainty by combining the constraint model.
According to the description, the micro-grid source storage capacity optimization configuration model considering uncertainty is established by integrating the interval uncertainty set model, the convex hull uncertainty set model and the constraint model of the energy storage and power generator, so that the micro-grid source storage capacity optimization configuration considering uncertainty factors such as uncertainty cost parameters, wind and light output and the like is realized.
Further, the using a dual robust algorithm to solve the microgrid source storage capacity optimization configuration model to obtain an optimization configuration result includes:
describing the microgrid source storage capacity optimization configuration model as a robust optimization problem based on the interval uncertainty set model and the convex hull uncertainty set model;
and converting the robust optimization problem into a deterministic problem by using a dual robust algorithm, and solving the deterministic problem to obtain an energy storage capacity configuration result with optimal cost.
According to the description, uncertainty in the robust optimization problem is eliminated through the dual robust algorithm, and the robust optimization problem is converted into a deterministic problem, so that the deterministic problem can be solved conveniently in the subsequent process, and an energy storage capacity configuration result with the optimal cost is obtained.
Referring to fig. 2, another embodiment of the present invention provides a device for configuring a source storage capacity of a micro grid, including:
the model building module is used for building an interval uncertainty set model considering uncertainty of cost parameters, building a convex hull uncertainty set model of multi-time uncertain wind-solar output correlation, building a cost minimum objective function based on the interval uncertainty set model, and building a micro-grid source storage capacity optimization configuration model considering uncertainty by combining the convex hull uncertainty set model;
and the optimization solving module is used for solving the microgrid source storage capacity optimization configuration model by using a dual robust algorithm to obtain an optimization configuration result.
According to the description, by comprehensively considering uncertainty factors such as cost parameters, wind-solar output and the like in the planning process of the micro-grid source storage capacity, an interval uncertainty set model description is respectively constructed to describe the uncertainty of the cost parameters, a convex hull uncertainty set model is constructed to describe the correlation of the multi-time uncertain wind-solar output, and a micro-grid source storage capacity optimization configuration model considering the uncertainty is established by taking the minimum cost as a target; the optimal configuration model of the micro-grid source storage capacity is solved based on the dual robust algorithm, so that the optimal configuration of the micro-grid source storage capacity under the consideration of uncertainty factors such as uncertainty cost parameters, wind and light output and the like is realized, and the economic and stable operation of the micro-grid system is ensured.
Further, the model building module comprises:
the data acquisition module is used for acquiring uncertain cost parameters, wherein the uncertain cost parameters comprise energy storage unit capacity replacement cost, fuel price, energy storage unit capacity operation and maintenance cost, generator unit capacity investment cost, generator unit capacity power generation cost, energy storage unit capacity investment cost, fan unit capacity investment cost, photovoltaic unit capacity investment cost, fan unit capacity operation and maintenance cost, generator unit capacity operation and maintenance cost and photovoltaic unit capacity operation and maintenance cost;
and the data interval establishing module is used for establishing an interval set of the uncertain cost parameters.
According to the description, the operation and maintenance costs and the investment costs of the fan, the photovoltaic, the generator and the energy storage are considered in the uncertain cost parameters, and the replacement cost of the energy storage is used as the uncertain parameters, so that the optimal configuration of the micro-grid source storage capacity under the uncertain factors such as the uncertain cost parameters, the wind-light output and the like is considered, and the economic and stable operation of the micro-grid system is ensured.
Further, the model building module further comprises:
the constraint model establishing module is used for establishing a wind and light capacity constraint model at the moment without considering the correlation:
NC Wt ≥P Wt (t)，NC PV ≥P PV (t)；
in the formula, P Wt (t)、P PV (t) historical output magnitudes of the fan and the photovoltaic, NC Wt 、NC PV Respectively representing the capacity of the fan and the photovoltaic.
According to the description, the wind-solar capacity constraint model without considering the correlation time is established, so that the convex hull uncertainty set model with uncertain wind-solar output correlation at multiple times can be conveniently established subsequently.
Further, the model building module is configured to form a convex hull uncertainty set model from a plurality of linear inequalities of wind-solar power:
[A Wt (t)A PV (t)]*[P Wt '(t)P PV '(t)] T ≤b(t)；
in the formula, A Wt (t)、A PV (t), b (t) fan output coefficient matrix, photovoltaic output coefficient matrix and constant matrix respectively representing convex hull linear inequality, P Wt '(t)、P PV ' (t) denotes the wind and photovoltaic outputs, respectively, at time t taking into account the wind-solar output dependence.
According to the description, strong correlation exists in the output of new energy represented by wind and light in certain time periods, if the correlation of the output of the wind and light is not considered in the considered uncertain set, the result of robust optimization can deal with many scenes which cannot occur, and therefore the correlation performance is considered, so that the optimal configuration of the source storage capacity of the micro-grid can be obtained conveniently in the follow-up process.
Further, the model building module comprises:
an objective function establishing module for establishing an objective function,for reducing the investment cost C of the power supply cap Power supply operation and maintenance cost C OM Energy storage replacement cost C rep，batt Generator fuel cost C fuel And the start-stop cost of the generator C onoff And cost of generator generation C e As an optimization objective, an objective function is established:
min(C cap +C OM +C rep，batt +C fuel +C onoff +C e )。
according to the description, the objective function with the minimum cost is constructed, and the microgrid source storage capacity optimization configuration model can be conveniently established subsequently.
Further, the model construction module is used for obtaining a convex hull uncertain set constraint model of historical wind and light output, establishing a system power constraint model, an energy storage charging and discharging state constraint model and a power constraint model, a generator minimum start-stop time constraint model and a generator output model and a microgrid demand response load power constraint model, and obtaining a microgrid source and storage capacity optimal configuration model considering uncertainty by combining the constraint models.
According to the description, the micro-grid source storage capacity optimization configuration model considering uncertainty is established by integrating the interval uncertainty set model, the convex hull uncertainty set model and the constraint model of the energy storage and power generator, so that the micro-grid source storage capacity optimization configuration considering uncertainty factors such as uncertainty cost parameters, wind and light output and the like is realized.
Further, the optimization solution module comprises:
the model conversion module is used for describing the microgrid source storage capacity optimization configuration model as a robust optimization problem based on the interval uncertainty set model and the convex hull uncertainty set model;
and the model solving module is used for converting the robust optimization problem into a deterministic problem by using a dual robust algorithm, and solving the deterministic problem to obtain an energy storage capacity configuration result with optimal cost.
According to the description, uncertainty in the robust optimization problem is eliminated through the dual robust algorithm, and the robust optimization problem is converted into a deterministic problem, so that the deterministic problem can be solved conveniently in the subsequent process, and an energy storage capacity configuration result with the optimal cost is obtained.
According to the method and the device for configuring the storage capacity of the micro-grid source, on the basis of a micro-grid optimization configuration model, by constructing an interval uncertain set of power source cost parameters, uncertainty of the cost parameters can be considered, the problem that the model cannot be solved due to unreasonable cost parameter setting is avoided, the correlation of multi-time uncertain wind and light output can be considered, and the optimal configuration of the capacities of the power source and the energy storage facility is realized, and the following description is given by a specific implementation mode:
example one
Referring to fig. 1, a method for configuring a source storage capacity of a microgrid includes the steps of:
and S1, establishing an interval uncertainty set model considering the uncertainty of the cost parameters.
S11, obtaining uncertain cost parameters, wherein the uncertain cost parameters comprise energy storage unit capacity replacement cost, fuel price, energy storage unit capacity operation and maintenance cost, generator unit capacity investment cost, generator unit capacity power generation cost, energy storage unit capacity investment cost, fan unit capacity investment cost, photovoltaic unit capacity investment cost, fan unit capacity operation and maintenance cost, generator unit capacity operation and maintenance cost and photovoltaic unit capacity operation and maintenance cost.
And S12, establishing an interval set of the uncertain cost parameters.
In this embodiment, the operation and maintenance cost per unit capacity of the energy storage, the operation and maintenance cost per unit capacity of the fan, the operation and maintenance cost per unit capacity of the generator, and the operation and maintenance cost per unit capacity of the photovoltaic system are regarded as the operation and maintenance cost of the power supply; and the investment cost per unit capacity of the generator, the investment cost per unit capacity of the energy storage, the investment cost per unit capacity of the fan and the investment cost per unit capacity of the photovoltaic system are taken as the investment cost of the power supply.
Specifically, each uncertain cost parameter specifically is:
in the formula, G represents the total number of power supplies in the wind-solar-diesel-storage micro-grid, and i represents the type of the power supplies; c cap Represents the power supply investment cost, C cap，unit (i) NC (i) represents investment cost per unit capacity and total capacity of the power supply, respectively, and
in the formula, C OM Represents the power supply operation and maintenance cost, C OM，unit (i) T represents the unit capacity operation and maintenance cost and the total time of the ith power supply respectively, and
p (i, t) represents the power supply capacity.
In the formula, C rep,batt Representing the cost of replacement of stored energy, repyr, C rep，batt，unit Respectively represent the replacement year and the replacement cost per unit capacity of the energy storage battery, yr represents the year, and
N batt indicating the number of energy storage cells, P rated，batt Indicating the rated capacity, R, of the energy storage battery d Indicating the system discount rate.
In the formula, C fuel Representing fuel cost and YR representing system operating age. C fuel，unit Represents the fuel price per unit volume of the system, is diesel oil, and
V fuel (t)＝aP g，onoff (t)+bP g (t)；
in the formula, V fuel (t) represents the volume of fuel consumed at each moment in time, a, b are coefficients of the fuel curve, P g，onoff (t)、P g And (t) refers to the power of the diesel engine during startup and shutdown and normal operation respectively.
In the formula, C onoff Means the start-stop cost of the diesel generator, C on，unit、 C off，unit Respectively represents the cost of each startup and shutdown of the diesel generator.
In the formula, C e Represents the cost of generator generation, C g，unit Represents the power generation cost per unit power of the diesel generator, and
and S2, establishing a convex hull uncertainty set model of the multi-time uncertain wind-solar output correlation.
S21, establishing a wind-solar capacity constraint model at the moment without considering the correlation:
NC Wt ≥P Wt (t)，NC PV ≥P PV (t)；
in the formula, P Wt (t)、P PV (t) historical output magnitudes of the fan and the photovoltaic, NC Wt 、NC PV Respectively representing the capacity of the fan and the photovoltaic.
And S22, establishing a convex hull uncertainty set model.
Specifically, when the correlation is considered, the wind-solar output at each moment needs to be limited in a convex hull uncertain set constructed by the wind-solar historical output data, and the uncertain set is composed of a plurality of linear inequalities of the wind-solar output, that is:
[A Wt (t)A PV (t)]*[P Wt '(t)P PV '(t)] T ≤b(t)；
in the formula, A Wt (t)、A PV (t), b (t) fan output coefficient matrix, photovoltaic output coefficient matrix and constant matrix respectively representing convex hull linear inequality, P Wt '(t)、P PV ' (t) denotes the wind and photovoltaic outputs, respectively, at time t taking into account the wind-solar output dependence.
S3, constructing a cost minimum objective function based on the interval uncertainty set model, and establishing a micro-grid source storage capacity optimization configuration model considering uncertainty by combining the convex hull uncertainty set model.
S31, investment cost C of power supply cap Power supply operation and maintenance cost C OM Energy storage replacement cost C rep，batt Generator fuel cost C fuel And the start-stop cost of the generator C onoff And generator generation cost C e As an optimization objective, an objective function is established:
min(C cap +C OM +C rep，batt +C fuel +C onoff +C e )。
s32, obtaining a convex hull uncertain set constraint model of historical wind and light output, establishing a system power constraint model, an energy storage charging and discharging state constraint model and a power constraint model, a generator minimum start-stop time constraint model and a generator output model and a microgrid demand response load power constraint model, and combining the constraint models to obtain a microgrid source storage capacity optimal configuration model considering uncertainty.
Specifically, the convex hull uncertain set constraint of the wind-solar historical output is established in step S22.
The model that considers the system power constraint at the moment of correlation is:
in the formula eta inv For photovoltaic inverter efficiency, P dis '(t)、P ch (t) is the stored energy charge and discharge power, P load (t) is the power of the load,
respectively representing the up/down power of the demand responsive load at time t.
The energy storage charging and discharging state constraint model is as follows:
in the formula (I), the compound is shown in the specification,
respectively representing the charging and discharging states of the energy storage battery when the energy storage battery is charged
Is 1, and vice versa
1, belonging to the variable 0-1, T represents the total time,
represents the upper limit value of the number of charge-discharge state transitions.
The energy storage system charge and discharge power constraint model comprises the following steps:
E(t)＝E(t-1)(1-δΔt)+P ch (t)η ch Δt-P dis (t)/η dis Δt；
SOC min N batt P rated，batt ≤E(t)≤SOC max N batt P rated，batt ；
in the formula, E (t), P ch (t)、P dis () And delta t respectively represents the residual electric quantity (kWh), the charging and discharging power (kW) and the time interval (h) of the energy storage battery at the t moment, delta and eta ch 、η dis Respectively representing the hourly self-discharge rate and the charge-discharge efficiency of the energy storage battery, N batt 、P rated，batt、 SOC min、 SOC max Respectively representing the number of the energy storage batteries, the rated capacity of a single energy storage battery and the minimum value and the maximum value of the percentage of the residual electric quantity.
The minimum start-stop time constraint model of the diesel engine is as follows:
in the formula, T min，on 、T min，off Representing the minimum on-time and the minimum off-time of the diesel generator, respectively.
The output constraint model of the diesel generator is as follows:
P g，onoff (t)P g，min ≤P g (t)≤P g，onoff (t)P g，max ；
in the formula, P g，max 、P g，min Respectively representing the upper and lower limits of the generator output, P g，onoff (t) indicates diesel power generation at each timeStarting and stopping state of the machine, starting up is 1, stopping is 0, P g (t) represents the diesel generator output at each moment.
The micro-grid demand response load power constraint model is as follows:
0≤E DR (t)≤w DR max(P load (t))Δt；
in the formula, E DR (t) represents the amount of electricity of the demand response load at time t, w DR A scaling factor representing the total load for the demand responsive load.
And S4, solving the microgrid source storage capacity optimization configuration model by using a dual robust algorithm to obtain an optimization configuration result.
And S41, describing the microgrid source storage capacity optimization configuration model as a robust optimization problem based on the interval uncertainty set model and the convex hull uncertainty set model.
Considering an interval set of uncertain cost parameters and a convex hull uncertain set of wind-light historical output, describing a microgrid source and storage capacity optimization configuration model as a robust optimization problem:
s.t.G X (X)≥0
wherein X represents a set of decision variables, and X ═ X; y ], x, y represent the set of continuous variables and the set of integer variables, respectively:
w n w respectively represent uncertain parameters and uncertain sets, n represents the number of uncertain parameters, and W n ＝[C cap_unit (i)、C OM_unit (i)、C rep_batt_unit 、C fuel_unit 、C g_unit ],i∈G；
F X (X) 、 G X (X) represents an objective function and a constraint condition containing only a continuous variable X and an integer variable y, respectively;
S42, please refer to fig. 3, the dual robust algorithm is used to convert the robust optimization problem into a deterministic problem, and the deterministic problem is solved to obtain an energy storage capacity configuration result with the optimal cost.
S421, adding auxiliary variable function
Then there is
Constrain this to
Together with the elimination of uncertainty.
S422, mixingConstraint decomposition of original problem into containing X and w simultaneously n Is restricted by
Constraint G containing only X X (X) and containing only w n Is restricted by
At this time
I.e. the indeterminate set W.
S423, selecting a dual robust algorithm according to the robust constraint characteristics to eliminate the dual robust algorithm through the following steps
Uncertainty of (2).
S4231, according to the fact whether the expression contains uncertain parameters w or not n Decomposing the robust constraint into (c + A) T X) T w n And (b) T X+d)。
S4232, extracting respectively
The equality constraint and inequality constraint coefficients are transposed into a matrix of
S4233, extracting respectively
The equality constraint and inequality constraint constants are transposed to E eqT ,E ineqT 。
S4234, introducing a constraint variable Z eq 、Z ineq The formula variable contains only deterministic constraints for the decision variable X:
s424, so far, the robust optimization problem is converted into a deterministic problem in the following form:
and S425, aiming at the deterministic problem, a solver can be called to solve the optimization model.
Example two
According to the embodiment of the invention, a method and a device for configuring the storage capacity of a micro-grid source especially considering uncertainty are established, and the method comprises the following steps:
s1, establishing an interval uncertainty set model considering the uncertainty of the cost parameters;
s2, establishing a convex hull uncertainty set model of multi-time uncertain wind-solar output correlation;
s3, constructing a cost minimum objective function based on the interval uncertainty set model, and establishing a micro-grid source storage capacity optimization configuration model considering uncertainty by combining the convex hull uncertainty set model;
and S4, solving the microgrid source and storage capacity optimization configuration model by using a dual robust algorithm to obtain an optimization configuration result.
Description of the basic data:
the annual historical data of the embodiment of the invention are derived from actual engineering data of 32 European countries, and the example data of a typical solar fan, photovoltaic output and load obtained according to the local typical wind speed and illumination intensity is shown in figure 1. The micro-grid system takes 24h as the total planning time, the time interval is 1h, the discount rate is 8% in the embodiment of the invention, and the system operation year is 20 years.
The model parameters comprise parameters of an energy storage model, a diesel generator model, a photovoltaic inverter model and a demand response load model, and values are as follows:
the energy storage adopted by the embodiment of the invention is a lead-acid storage battery, the rated capacity of the lead-acid storage battery is 6kWh, the rated power is 1.2kW, the charging and discharging efficiencies are all 86%, the self-discharging rate of the storage battery is 0.01%, the maximum value of the percentages of the initial electric quantity and the residual electric quantity is 100%, the minimum value is 20%, the upper limit value of the charging and discharging state conversion times is 4 times, and the design life is 10 years.
According to the diesel generator adopted by the embodiment of the invention, the minimum startup and shutdown time is 2 hours, the minimum output and the maximum output are 0kW and 20kW respectively, the startup and shutdown cost is 2 yuan/time and 0.5 yuan/time respectively, and the coefficients a and b of a fuel curve are 0.280L and 0.251L/kW respectively. In addition, the photovoltaic inverter efficiency is 97%, and the proportionality coefficient of the total load for the demand response load is 30%.
Since most of documents do not consider the replacement cost of wind, light and diesel, the embodiment of the invention only considers the replacement cost of energy storage as an uncertain parameter, and converts the units of the energy storage cost parameter, the fuel price and the residual cost into yuan/kW, yuan/L and yuan/kW respectively. In the embodiment of the invention, 11 uncertain cost parameters are considered totally, and the over-high cost parameters are ignored and summarized in table 1. The values of the parameters, when the uncertainty of the parameters is not taken into account, are shown on the right side of table 1.
TABLE 1 summary of uncertain cost parameters
Further, step S1 is specifically:
based on a robust optimization model, capacity optimization configuration is performed on the microgrid system, in order to verify the influence of uncertain cost parameters on the total cost of the system, the embodiment of the invention respectively considers scenes only with the certain cost parameters, 1 uncertain cost parameter and 11 uncertain cost parameters on the basis of the typical daily data of fig. 4, and the size of the total cost under each scene is compared and is shown in fig. 5. Wherein C0 represents the case of only definite cost parameters, and C1 ~ 11 represent the case of considering 11 uncertain cost parameters. As can be seen from fig. 5, the cost parameter is not determined and the number of parameters has a great influence on the total cost. When the cost parameters of the micro-grid power supply are determined, the total cost is 57.25 ten thousand yuan. When the system only has a single uncertain cost parameter, the difference between the first 7 results and the results obtained without considering the uncertain cost parameter is not large, because when the parameters participate in the robust optimization calculation, a proper optimization scene is selected, so that the results are basically consistent with the results obtained without considering the uncertainty of the parameters.
C11 is the operation and maintenance cost of the photovoltaic unit capacity, the total cost is the highest, the interval change range of the parameter is 0.0096-375 yuan/kW, the deterministic parameter is 0.12 yuan/kW, and a worse scene can be easily obtained when the parameter participates in robust optimization calculation, namely, the value of the operation and maintenance cost of the photovoltaic unit capacity is far more than 0.12 yuan/kW. When the upper limit of the parameter is changed to 230 yuan/kW, the total cost at this time can be calculated to be 100.39 ten thousand yuan, which is reduced by 27.2 ten thousand yuan compared with the previous total cost. Besides that, the result of considering 11 uncertain cost parameters is the superposition of the individual scenes, so the total cost is the highest.
In order to study the influence of the interval set on the capacity configuration result, the embodiment of the invention changes the upper limit of the operation and maintenance cost per unit capacity of the generator, namely C10, to be 96 yuan/kW and 1408.1145 yuan/kW respectively, and the planning result is shown in table 2. As can be seen from table 2, when the operation and maintenance cost per unit capacity of the generator is increased, in order to effectively reduce the economy, the diesel generator reduces the output, which results in the capacity reduction of the diesel generator. In order to ensure the normal operation of the system, the energy storage planning capacity is increased, so that the total cost is increased sharply.
TABLE 2C10 Effect of Upper bounds on planning results
| C10 Upper Limit/(yuan/kW) | Energy storage/kWh | Diesel generator/kW | Total cost/ten thousand yuan |
| 96 | 6 | 19.5583 | 91.63 |
| 1408.1145 | 90 | 13.8844 | 533.87 |
Further, step S2 is specifically:
for a certain micro-grid system, the new energy output represented by wind and light has strong correlation in certain time periods. If the uncertain set considered does not consider the correlation of the wind and light output, the result of the robust optimization can deal with many scenes which do not occur, such as the blank area shown in fig. 6. When the uncertain parameters are described by adopting the convex hull uncertain set, namely the parameters are considered to be influenced mutually, the convex hull uncertain set is converted into the linear inequality constraint represented by the red solid line in the figure 6, and the economy of the system is greatly improved because unnecessary scenes do not need to be dealt with.
In order to better study the correlation of the wind-solar output, the absolute value of the correlation coefficient r of the t-3 h-20 h is calculated by neglecting the moment when the photovoltaic output is 0. Generally, the closer to 1 the | r | is, the stronger the correlation between two quantities is, whereas the closer to 0 the | r | is, the weaker the correlation between two quantities is. When t is 7 h-10 h, | r | is in the range of 0.4-0.69, the two are moderately correlated, and the correlation degree is strongest, so the embodiment of the invention only takes t 7 h-10 h as an example to analyze the influence of the correlation between wind and light output on the planning result.
Further, step S3 is specifically:
in order to verify the effectiveness of the solving algorithm in the embodiment of the present invention, an Enumeration Robust peer-to-peer algorithm (ERC), an Maximization Robust peer-to-peer algorithm (EMRC), and a dual Robust peer-to-peer algorithm (DRC) are respectively adopted to calculate. The maximum value and the minimum value of the fan output and the photovoltaic output at the moment are determined by counting the output data of the fan and the photovoltaic at t-7-10 h, the wind and light output are independent, and the correlation of the wind and light output can be considered. The results of the solution are shown in table 3.
TABLE 3 comparison of the impact of wind-solar output correlation on the planning results
As can be seen from table 3, the energy storage capacity does not change, because the energy storage has a good effect in absorbing the new energy output. The total cost is 92.45 ten thousand dollars when considering the correlation, the economy is better because no extra useless scenes need to be dealt with, and the result is mainly realized by reducing the capacity of the generator configuration. When the wind and light output correlation is considered, the worst scenario result of the output of the diesel generator is better at most of the time, so that the configuration capacity can be optimized by considering the wind and light output correlation, and the system economy is improved.
TABLE 4 comparison of results of three robust optimization algorithms
| Algorithm | Energy storage capacity/kWh | Total cost/ten thousand yuan | Solution time/s |
| EMRC | 48 | 93.53 | 11.07 |
| ERC | 120 | 92.45 | 3080.37 |
| DRC | 48 | 92.45 | 22.28 |
As shown in table 4, the solving algorithms are compared with each other according to the three indexes of the energy storage configuration capacity, the total cost and the solving time, and the results are compared. And in the calculation of the MRC algorithm, the model has stronger conservative property and shortest solving time of 10.104250s because the wind-solar output correlation does not need to be considered, and the cost is the largest and the economical efficiency is the worst. The model conservation degree is relatively low and the total cost is relatively small during calculation of the ERC algorithm, but the configuration energy storage capacity is the largest, so that the efficiency of a robust model with relatively complex calculation is slow. The DRC algorithm avoids the defects of the two algorithms in the three aspects of energy storage configuration capacity, total cost and solving time, the calculation result is more reasonable, and the DRC algorithm has reference and reference significance for solving a robust optimization model containing a large number of scenes.
EXAMPLE III
Referring to fig. 2, a microgrid source and storage capacity configuration device includes:
the model building module is used for building an interval uncertainty set model considering uncertainty of cost parameters, building a convex hull uncertainty set model of multi-time uncertain wind-solar output correlation, building a cost minimum objective function based on the interval uncertainty set model, and building a microgrid source storage capacity optimization configuration model considering uncertainty by combining the convex hull uncertainty set model;
and the optimization solving module is used for solving the microgrid source storage capacity optimization configuration model by using a dual robust algorithm to obtain an optimization configuration result.
In some embodiments, the model building module comprises:
the data acquisition module is used for acquiring uncertain cost parameters, wherein the uncertain cost parameters comprise energy storage unit capacity replacement cost, fuel price, energy storage unit capacity operation and maintenance cost, generator unit capacity investment cost, generator unit capacity power generation cost, energy storage unit capacity investment cost, fan unit capacity investment cost, photovoltaic unit capacity investment cost, fan unit capacity operation and maintenance cost, generator unit capacity operation and maintenance cost and photovoltaic unit capacity operation and maintenance cost;
and the data interval establishing module is used for establishing an interval set of the uncertain cost parameters.
In some embodiments, the model building module further comprises:
the constraint model establishing module is used for establishing a wind and light capacity constraint model at the moment without considering the correlation:
NC Wt ≥P Wt (t)，NC PV ≥P PV (t)；
in the formula, P Wt (t)、P PV (t) historical output magnitudes of the fan and the photovoltaic, NC Wt 、NC PV Respectively representing the capacity of the fan and the photovoltaic.
In some embodiments, the model building module is configured to compose a convex hull uncertainty set model from a plurality of linear inequalities of wind-solar power:
[A Wt (t)A PV (t)]*[P Wt '(t)P PV '(t)] T ≤b(t)；
in the formula, A Wt (t)、A PV (t), b (t) fan output coefficient matrix, photovoltaic output coefficient matrix and constant matrix respectively representing convex hull linear inequality, P Wt '(t)、P PV ' (t) denotes the wind and photovoltaic outputs, respectively, at time t taking into account the wind-solar output dependence.
In some embodiments, the model building module comprises:
an objective function establishing module for calculating the power supply investment cost C cap Power supply operation and maintenance cost C OM Energy storage replacement cost C rep，batt Generator fuel cost C fuel And the start-stop cost of the generator C onoff And cost of generator generation C e As an optimization objective, an objective function is established:
min(C cap +C OM +C rep，batt +C fuel +C onoff +C e )。
in some embodiments, the model construction module is configured to obtain a convex hull uncertain set constraint model of historical wind and light output, establish a system power constraint model, an energy storage charging and discharging state constraint model and a power constraint model, a generator minimum start-stop time constraint model and a generator output model, and a microgrid demand response load power constraint model, and obtain a microgrid source storage capacity optimization configuration model considering uncertainty by combining the constraint models.
In some embodiments, the optimization solution module comprises:
the model conversion module is used for describing the microgrid source storage capacity optimization configuration model as a robust optimization problem based on the interval uncertainty set model and the convex hull uncertainty set model;
and the model solving module is used for converting the robust optimization problem into a deterministic problem by using a dual robust algorithm, and solving the deterministic problem to obtain an energy storage capacity configuration result with optimal cost.
In summary, according to the method and the device for configuring the source storage capacity of the microgrid provided by the invention, uncertainty factors such as cost parameters and wind-light output in the planning process of the source storage capacity of the microgrid are comprehensively considered, an interval uncertainty set model is constructed to describe uncertainty of the cost parameters, wherein operation and maintenance costs and investment costs of a fan, a photovoltaic, a generator and energy storage are considered in the uncertainty cost parameters, and replacement cost of the energy storage is taken as the uncertainty parameters, so that optimal configuration of the source storage capacity of the microgrid under the uncertainty factors such as the uncertainty cost parameters and the wind-light output is considered. The method comprises the steps of constructing a convex hull uncertainty set model to describe the correlation of multi-time uncertain wind and light output and aim at minimizing cost, wherein strong correlation exists in the new energy output represented by wind and light in certain time periods, and if the correlation of wind and light output is not considered in the considered uncertainty set, the result of robust optimization can deal with many scenes which cannot occur, so that the correlation performance is considered to be convenient for obtaining the optimal configuration of the micro-grid source storage capacity subsequently. Establishing a target function based on an interval uncertainty set model and establishing a micro-grid source storage capacity optimization configuration model considering uncertainty by combining a convex hull uncertainty set model; solving the optimal configuration model of the micro-grid source storage capacity based on the dual robust algorithm, wherein uncertainty in the robust optimization problem is eliminated through the dual robust algorithm, and the robust optimization problem is converted into a deterministic problem to be solved, so that an optimal energy storage capacity configuration result is obtained, and the economic and stable operation of the micro-grid system is guaranteed.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The scheme in the embodiment of the invention can be realized by adopting various computer languages, such as object-oriented programming language Java and transliterated scripting language JavaScript.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A method for configuring the storage capacity of a micro-grid source is characterized by comprising the following steps:
establishing an interval uncertainty set model considering the uncertainty of the cost parameters;
establishing a convex hull uncertainty set model of multi-time uncertain wind-solar output correlation;
constructing a cost minimum objective function based on the interval uncertainty set model, and establishing a micro-grid source storage capacity optimization configuration model considering uncertainty by combining the convex hull uncertainty set model;
and solving the microgrid source and storage capacity optimization configuration model by using a dual robust algorithm to obtain an optimization configuration result.
2. The method according to claim 1, wherein the establishing an interval uncertainty set model considering uncertainty of cost parameters comprises:
obtaining uncertain cost parameters, wherein the uncertain cost parameters comprise energy storage unit capacity replacement cost, fuel price, energy storage unit capacity operation and maintenance cost, generator unit capacity investment cost, generator unit capacity power generation cost, energy storage unit capacity investment cost, fan unit capacity investment cost, photovoltaic unit capacity investment cost, fan unit capacity operation and maintenance cost, generator unit capacity operation and maintenance cost and photovoltaic unit capacity operation and maintenance cost;
and establishing an interval set of the uncertain cost parameters.
3. The method of claim 1, wherein the establishing a convex hull uncertainty set model of multi-time uncertainty wind-solar-power-output correlation comprises:
establishing a wind-solar capacity constraint model at a moment without considering the correlation:
NC Wt ≥P Wt (t)，NC PV ≥P PV (t)；
in the formula, P Wt (t)、P PV (t) historical output magnitudes of the fan and the photovoltaic, NC Wt 、NC PV Respectively representing the capacity of the fan and the photovoltaic.
4. The method of claim 3, wherein the establishing the convex hull uncertainty set model of the multi-time uncertainty wind-solar-power-output correlation comprises:
a convex hull uncertainty set model is formed by a plurality of linear inequalities of wind-solar output:
[A Wt (t) A PV (t)]*[P Wt '(t) P PV '(t)] T ≤b(t)；
in the formula, A Wt (t)、A PV (t), b (t) fan output coefficient matrix, photovoltaic output coefficient matrix and constant matrix respectively representing convex hull linear inequality, P Wt '(t)、P PV ' (t) denotes the wind and photovoltaic outputs, respectively, at time t taking into account the wind-solar output dependence.
5. The method of claim 1, wherein constructing a cost minimization objective function based on the interval uncertainty set model comprises:
investment cost of power supply C cap Power supply operation and maintenance cost C OM Energy storage replacement cost C rep，batt Generator fuel cost C fuel And the start-stop cost of the generator C onoff And generator generation cost C e As an optimization objective, an objective function is established:
min(C cap +C OM +C rep，batt +C fuel +C onoff +C e )。
6. the method of claim 1, wherein building the uncertainty-considered optimal microgrid source-storage capacity configuration model in combination with the convex hull uncertainty set model comprises:
acquiring a convex hull uncertain set constraint model of historical wind and light output, and establishing a system power constraint model, an energy storage charging and discharging state constraint model, a power constraint model, a generator minimum start-stop time constraint model, a generator output model and a microgrid demand response load power constraint model;
and obtaining a micro-grid source storage capacity optimization configuration model considering uncertainty by combining the constraint model.
7. The method of claim 1, wherein the solving the microgrid source-storage capacity optimization configuration model by using a dual robust algorithm to obtain an optimization configuration result comprises:
describing the microgrid source storage capacity optimization configuration model as a robust optimization problem based on the interval uncertainty set model and the convex hull uncertainty set model;
and converting the robust optimization problem into a deterministic problem by using a dual robust algorithm, and solving the deterministic problem to obtain an energy storage capacity configuration result with optimal cost.
8. A microgrid source storage capacity configuration device, comprising:
the model building module is used for building an interval uncertainty set model considering uncertainty of cost parameters, building a convex hull uncertainty set model of multi-time uncertain wind-solar output correlation, building a cost minimum objective function based on the interval uncertainty set model, and building a micro-grid source storage capacity optimization configuration model considering uncertainty by combining the convex hull uncertainty set model;
and the optimization solving module is used for solving the microgrid source and storage capacity optimization configuration model by using a dual robust algorithm to obtain an optimization configuration result.
9. A microgrid source storage capacity configuration apparatus as claimed in claim 8, the model building module comprising:
an objective function establishing module for calculating the power supply investment cost C cap Power supply operation and maintenance cost C OM Energy storage replacement cost C rep，batt Generator fuel cost C fuel And the starting and stopping cost C of the generator onoff And generator generation cost C e As an optimization objective, an objective function is established:
min(C cap +C OM +C rep，batt +C fuel +C onoff +C e )。
10. the microgrid source storage capacity configuration device according to claim 8, wherein the model building module is used for obtaining a convex hull uncertain set constraint model of historical wind and light output, building a system power constraint model, an energy storage charging and discharging state constraint model and a power constraint model, a generator minimum start-stop time constraint model and a generator output model and a microgrid demand response load power constraint model, and combining the constraint models to obtain a microgrid source storage capacity optimization configuration model considering uncertainty.
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