CN114825381A - Capacity configuration method for photo-thermal power station of wind-solar new energy base - Google Patents
Capacity configuration method for photo-thermal power station of wind-solar new energy base Download PDFInfo
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Abstract
The invention discloses a capacity configuration method for a photo-thermal power station of a wind-solar new energy base, and belongs to the technical field of power system planning. The method comprises the steps of obtaining historical wind power, photovoltaic output curves and load curves; dividing a typical operation scene of a new energy base sending end system; the estimated adjustment capability at each confidence level; establishing a photo-thermal power station installed capacity optimal configuration model under the condition of direct current delivery and solving by using an NSGA-II (non-subsampled gate array-II) optimization algorithm; checking a wind and light fluctuation index; and determining the capacity of the photo-thermal power station. The capacity of the photo-thermal power station is optimized, the wind and light abandoning rate is reduced, the adjusting requirement of rapid wind and light power fluctuation is met, the absorption rate of new energy is improved, the stability of a power system is improved, the peak regulation requirement of wind power and photovoltaic direct current delivery is met, and a theoretical basis is provided for photo-thermal power station planning construction.
Description
Technical Field
The invention belongs to the technical field of power system planning, and particularly relates to a capacity configuration method for a photo-thermal power station of a wind-solar new energy base.
Background
While the large-scale development of new energy power generation brings huge economic benefits, due to the characteristics of reverse distribution of wind and light resources and a load center in China, the requirement of large-capacity long-distance transmission of electric energy in Gansu areas exists for a long time, and the construction speed of a power transmission line lags behind the construction speed of a power supply; the controllability of the intermittent new energy is insufficient, the characteristics of multi-time-space fluctuation, inverse peak regulation and the like cause that the frequency modulation and peak regulation of a power grid are difficult, the system stability is threatened, the power output is limited, the economy is difficult to ensure, and the grid-connected mode of a large power supply and a weak power grid causes impact on the safe and stable operation of the power grid. In addition, the current wind and light output prediction level has certain limitation, and under the condition that the wind power permeability is increased, the output plan is influenced more and more. With the continuous rise of the permeability of new energy, when the installed capacities of wind power and photovoltaic power in a power system reach a certain scale, the output fluctuation and uncertainty of the power system can generate power fluctuation, even an electric field integrally quits operation due to reasons, dynamic imbalance between the active output and load of the system can be caused, and the operation safety of a power grid is endangered in serious cases.
Gansu has abundant solar energy direct radiation resource, possess solar power station construction operation experience, and the spring area possesses more sandy land, is favorable to constructing large-scale light and heat power station. The first extra-high voltage project +/-800 kV wine lake extra-high voltage for large-scale transportation of new energy in China, and the electricity transmission amount in the design year is 400 hundred million degrees. By the end of 2018, 4 months, the power transmission to Hunan is only 96.2 hundred million degrees in 10 months, which is far away from the design power transmission capacity. The Gansu spring new energy base also faces the problem of system stability. The reason is that the capacity of the photo-thermal power station needs to be reasonably configured so as to take the wind and light absorption, the direct current delivery requirement and the system stability into consideration.
The traditional new energy output has the characteristics of no rotational inertia and poor voltage stability, the photo-thermal power generation has the advantages of rich resources, strong controllability, high photoelectric conversion efficiency and the like, and when no photo-thermal unit exists in the system, grid accidents such as system instability and even disconnection are easily caused if a line fault occurs.
And the photothermal power station adopts the same steam turbine as the conventional thermal power unit, so that the rotational inertia of the power system and the safety and stability of the system can be improved. The photothermal power station has the function of peak load shifting in the day, and can also bear the adjustment requirements of rapid power, voltage and the like caused by real-time fluctuation of photovoltaic power, wind power and the like. At present, researches on rapid adjustment performance evaluation and capacity configuration of the heat storage capacity of the photo-thermal power station generally aim at optimal scheduling of economic targets, the maximum consumption of wind and light is considered, and the voltage supporting capacity of the photo-thermal power station on a power grid at a transmitting end and the direct current output demand are rarely taken into consideration as targets. The capacity model solution of the optical and thermal power station is divided into an optimization method, a statistical analysis method, an intelligent method and the like.
However, the method has the defects of poor generalization capability, high requirement on data scale, low calculation efficiency and the like. An efficient and accurate heat storage rapid regulation capacity configuration method suitable for rapid fluctuation of photovoltaic power and wind power is needed.
In summary, the invention provides a photo-thermal power station capacity configuration method which is applied to a wind and light new energy base and meets the requirements of transient voltage stability and direct current output on the basis of the existing research, reduces the wind abandoning rate and the light abandoning rate by optimally configuring the capacity of the photo-thermal power station, adapts to the adjustment requirement of wind and light rapid power fluctuation, improves the absorption rate of new energy, improves the stability of a power system, solves the peak regulation requirement of wind power and photovoltaic direct current output, and provides a theoretical basis for the planning and construction of the photo-thermal power station.
Disclosure of Invention
The invention aims to provide a capacity configuration method for a photo-thermal power station of a wind and light new energy base, and aims to solve the problems that the capacity of photo-thermal energy configured by the new energy base can meet the voltage stability and the direct current output requirement.
In order to solve the problems, the technical scheme of the invention is as follows:
a capacity configuration method for a photo-thermal power station of a wind-solar new energy base comprises the following steps:
s1, acquiring historical annual wind power, photovoltaic and photo-thermal output curves and load curves;
s2, dividing a typical operation scene;
s3, estimating and adjusting capacity under each confidence coefficient;
s4, establishing a capacity optimization configuration model of the photo-thermal power station;
the model is as follows:
F＝max(μ 1 f 1 (x)+μ 2 f 2 (x))
in the formula:
f, considering a comprehensive optimization target with the maximum direct current output power and voltage stability margin of the new energy base;
f 1 the maximum delivery capacity target meeting the extra-high voltage direct current delivery requirement is met;
f 2 -a voltage stability margin maximum target;
μ 1 、μ 2 -weight of target 1 and target 2;
s4.1, aiming at the maximum direct current outgoing power quantity;
in the formula:
P DC (t) -the dc delivery power at time t;
Δ t — duration of each period;
s4.2, obtaining a maximum voltage stability margin target;
through comparing the voltage variation of each node before and after the photo-thermal power station is incorporated into the power networks, the voltage stability margin is described specifically, and the calculation formula is:
in the formula:
n is the number of nodes contained in the whole system;
ΔU i -the voltage variation, p.u, of the ith node before and after the disturbance;
U i after the photo-thermal power station is connected to the grid, the voltage phase value of the node i is p.u;
when the active power output P of the known node is available i And reactive power Q i While, U i 、
The calculation can be carried out through the load flow calculation of the power system;
s4.3, establishing planning investment constraint and system operation constraint;
the obtained CSP heat storage expected regulation capacity needs to meet various operation constraints of the photo-thermal power station, and the constraints are used for repeatedly modifying an ideal output curve of a wind-solar system so as to obtain the optimal heat storage capacity;
the method comprises the steps of configuring range constraint of wind and light field capacity and investment budget upper limit constraint; direct current output constraint, system power balance constraint, CSP power station power balance constraint, photovoltaic power station operation constraint and wind power plant operation constraint;
s4.3.1, planning investment constraints;
the configuration range of the optical field capacity is restricted:
and secondly, restricting the upper limit of the investment budget: IC (integrated circuit) min ≤IC≤IC max ；
S4.3.2, system operating constraints;
system power balance constraint: p t PV +P t W +P t CSP ≥P t load ；
In the formula: p t PV 、P t W 、P t CSP Respectively photovoltaic, wind power and photo-thermal on-line electric quantity in t time period, P t load Load power at time t;
constraint of power operation interval: p DC,min ≤P t DC ≤P DC,max ；
In the formula: p DC,min And P DC,max Respectively carrying out on-line quota on the transmission power of the direct current connecting line;
thirdly, direct current tie line power transmission constraint:
in the formula: q is the total electric quantity transmitted by the connecting line, and delta t is the time interval duration;
fourthly, constraint of upper and lower limits of CSP output power: 20% P CSP,max ≤P CSP (t)≤P CSP,max ；
Fifth, photovoltaic power station operation constraint:
sixth, wind power plant operation constraint:
s5, solving the capacity optimization configuration model of the optical and thermal power station;
optimizing the installed capacity configuration of the optical thermal power station by adopting an improved NSGA-II algorithm, and finally determining the optimal installed capacity configuration by combining a Pareto solution set generated by the algorithm;
s6, checking the wind and light fluctuation index;
the wind-solar fluctuation index can be calculated by using an output curve corrected by CSP capacity constraint;
if the fluctuation index is within the range specified by the relevant regulations, two capacity parameters of the CSP are obtained: the installed capacity and the heat storage capacity are optimally matched with the specified wind and light fluctuation scale;
if the fluctuation index exceeds a specified range, the corrected curve is used as a wind-light original output curve, and the proposed method is used for optimizing again until the two parameters are optimized;
the total wind-solar energy reduction and total energy shortage of the whole optimization process are calculated by the following formulas:
wherein V total And V total1 Respectively reducing wind energy and light energy and reducing energy shortage in the whole optimization process;
the wind-solar fluctuation index can be calculated by using an output curve modified by CSP power constraint;
if the fluctuation index is in the range specified by relevant regulations, the obtained heat storage capacity is best matched with the specified wind and light fluctuation scale;
if the fluctuation index exceeds the specified range, the corrected curve is used as a wind-light original output curve, and the proposed method is used for optimizing again until the heat storage parameters are optimized;
s7, determining the optimal installed capacity and heat storage capacity of the photo-thermal power station of the wind-solar new energy base;
and comparing the photo-thermal power generation capacity under different typical scenes to determine the optimal capacity configuration of the photo-thermal power station of the wind and light new energy base.
Further, step S1 includes:
s1.1, acquiring a wind power historical output curve P of a whole-year wind and light new energy base W (t) photovoltaic historical output curve P PV (t) near area photothermal historical output curve P CSP (t) reference and load curve P D (t)；
S1.2, setting the limit P of the direct current outgoing power DC (max) and voltage stability margin;
s1.3, calculating the rolling average value of each datum on the actual wind-solar output curve by adopting a rolling average method to obtain a smooth output curve;
the smoothed wind-light output value may be determined as follows:
P rt and P t Respectively, the t-th on the smooth curve and the original curve th A power value at a time; 2M is the number of power points to calculate the rolling average.
Further, step S2 includes:
s2.1, forming a set of 365 scenes by taking day as a unit and hour scale for 8760 hours of data in the whole year;
s2.2, adopting a k-means multi-scene clustering method, selecting a distance cost criterion function Z as a clustering effectiveness function, clustering a scene set, and reducing the scene set to 4 typical scenes to form a new scene set S; respectively as follows: big wind and big light, big wind and small light, small wind and small light, big wind and small light.
Further, step S3 includes:
s3.1, obtaining a fluctuation component by comparing the original power curve with the smooth curve;
the upper fluctuation component represents excess power generated by the wind-solar system, which should be consumed and stored in the thermal storage system, and the lower part represents the energy shortage that the thermal storage system should compensate for;
each fluctuation component is regarded as one data point, and all the points are divided into two parts (upper and lower) and arranged in time series, respectively;
the parameters contained in each data point mainly comprise the duration of fluctuation components, the maximum difference between an original curve and a smooth curve of each fluctuation interval and the area of a fluctuation area;
the maximum difference between the two curves should be the expected heat storage capacity during the period of the fluctuation;
s3.2, in the time scale of 2-60 minutes, the t position scale distribution has the best fitting effect on the probability density of wind and light fluctuation;
the probability density expression for the t-position scale distribution is as follows:
Γ is the gamma function; μ is a location parameter; σ is a scale parameter and v is a shape parameter;
assuming that the upper layer fluctuation component table and the lower layer fluctuation component table are distributed according to the t position scale, the probability distribution functions of the upper layer fluctuation component table and the lower layer fluctuation component table can be respectively fitted through a Maximum Likelihood Estimation (MLE) method;
according to the t distribution parameters obtained by the fitting method, the confidence interval of each confidence interval can be obtained and used as the estimated adjusting capacity of the photothermal power station.
Further, the modified NSGA-II algorithm flow in step S5 is shown in fig. 2:
after the algorithm starts, the population evolution total algebra is gen, and the evolution algebra i is 0; after an initial population is set, calculating objective functions f1 and f2 according to historical wind and light load data; fast non-dominant sorting, calculating the crowdedness; selecting an operator for screening, and reserving excellent individuals; crossover, polynomial mutation using the tDX operator; obtaining a new parent population according to an elite strategy; when i > gen is satisfied, ending; when i > gen is not satisfied, returning to the initial population cycle.
Further, the flow of the optimal heat storage capacity configuration method for inhibiting the rapid fluctuation of the wind and light system in real time in step S6 is shown in fig. 3:
firstly, processing the wind-light original curve by using a rolling average method after starting, and then calculating the predicted heat storage capacity of the CSPs with different confidence degrees by using a statistical analysis method; modifying the wind-solar curve according to the operation condition conversion constraint; seeking the optimal heat storage capacity by utilizing CSP power constraint; checking the wind-solar fluctuation indicator; when the indicator meets the technical regulation, ending the capacity configuration; when the indicator does not meet the technical specification, the modified wind-solar curve is regarded as the original curve again and then returns to the original configuration.
The invention has the following beneficial effects:
(1) the invention provides a method for optimizing and configuring capacity of a photo-thermal power station, which meets the requirement of direct current output, considers transient voltage stability of a transmitting end and inhibits rapid fluctuation of a wind power system and a photovoltaic system in real time. The method comprises the following steps: acquiring historical wind power, photovoltaic output curves and load curves; dividing a typical operation scene of a new energy base sending end system; the estimated adjustment capability at each confidence level; establishing a photo-thermal power station installed capacity optimal configuration model under the condition of direct current delivery and solving by using an NSGA-II (non-subsampled gate array-II) optimization algorithm; checking a wind and light fluctuation index; and determining the capacity of the photo-thermal power station. The capacity of the photo-thermal power station is optimized, the wind and light abandoning rate is reduced, the adjusting requirement of rapid wind and light power fluctuation is met, the absorption rate of new energy is improved, the stability of a power system is improved, the peak regulation requirement of wind power and photovoltaic direct current delivery is met, and a theoretical basis is provided for photo-thermal power station planning construction.
(2) The optimal heat storage capacity configuration method of the photo-thermal power station considers the stability of wind and light output fluctuation and the transient voltage stability of a sending end and inhibits the rapid fluctuation of wind power and photovoltaic systems in real time, and provides reference for the photo-thermal power station planning in a new energy base.
Drawings
FIG. 1 is a flow chart of the steps of the present invention;
FIG. 2 is a flow chart of the improved NSGA-II algorithm process, i.e. the multi-target installed capacity configuration, in step S5 of the present invention;
fig. 3 is a flowchart of the optimal heat storage capacity allocation method in step S6 according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings of the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention.
Example 1
As shown in fig. 1, a capacity configuration method for a photovoltaic new energy base photo-thermal power station is as follows:
s1, acquiring historical annual wind power, photovoltaic and photo-thermal output curves and load curves;
s1.1, acquiring a wind power historical output curve P of a whole-year wind and light new energy base W (t) photovoltaic historical output curve P PV (t) near area photothermal historical output curve P CSP (t) reference and load curve P D (t)。
S1.2, setting the limit P of the direct current outgoing power DC (max) and voltage stability margin.
And S1.3, calculating the rolling average value of each datum on the actual wind-light output curve by adopting a rolling average method to obtain a smooth output curve.
The smoothed wind-light output value may be determined as follows:
P rt and P t Respectively, the t-th on the smooth curve and the original curve th A power value at a time; 2M is the number of power points to calculate the rolling average.
S2, dividing a typical operation scene;
s2.1, forming a set of 365 scenes by the data of 8760 hours in the whole year in an hour scale by taking days as a unit.
S2.2, adopting a k-means multi-scene clustering method, selecting a distance cost criterion function Z as a clustering effectiveness function, clustering a scene set, and reducing the scene set to 4 typical scenes to form a new scene set S; respectively as follows: big wind and big light, big wind and small light, small wind and small light, big wind and small light.
S3, estimating and adjusting capacity under each confidence coefficient;
and S3.1, obtaining the fluctuation component by comparing the original power curve with the smooth curve.
The upper fluctuation component represents excess power generated by the wind-solar system, which should be consumed and stored in the thermal storage system, and the lower part represents the energy shortage that the thermal storage system should compensate for;
each fluctuation component is regarded as one data point, and all the points are divided into two parts (upper and lower) and arranged in time series, respectively.
The parameters contained in each data point mainly comprise the duration of fluctuation components, the maximum difference between the original curve and the smooth curve of each fluctuation interval and the area of a fluctuation area.
The maximum difference between the two curves should be the expected heat storage capacity for that period of fluctuation.
S3.2, in the time scale of 2-60 minutes, the t position scale distribution has the best fitting effect on the probability density of wind and light fluctuation;
the probability density expression for the t-position scale distribution is as follows:
Γ is the gamma function; μ is a location parameter; σ is a scale parameter and v is a shape parameter.
Assuming that the upper and lower fluctuation sub-tables follow the t-position scale distribution, their probability distribution functions can be fitted separately by a Maximum Likelihood Estimation (MLE) method.
According to the t distribution parameters obtained by the fitting method, the confidence interval of each confidence interval can be obtained and used as the estimated adjusting capacity of the photothermal power station.
S4, establishing a capacity optimization configuration model of the photo-thermal power station;
the model is as follows:
F＝max(μ 1 f 1 (x)+μ 2 f 2 (x))
in the formula:
f, considering a comprehensive optimization target with the maximum direct current output power and voltage stability margin of the new energy base;
f 1 the maximum delivery capacity target meeting the extra-high voltage direct current delivery requirement is met;
f 2 -a voltage stability margin maximum target;
μ 1 、μ 2 -the weight of object 1 and object 2.
S4.1, aiming at the maximum direct current outgoing power quantity;
in the formula:
P DC (t) -the dc delivery power at time t;
Δ t — the duration of each period.
S4.2, obtaining a maximum voltage stability margin target;
through comparing the voltage variation of each node before and after the photo-thermal power station is incorporated into the power networks, the voltage stability margin is described specifically, and the calculation formula is:
in the formula:
n is the number of nodes contained in the whole system;
ΔU i -the voltage variation, p.u, of the ith node before and after the disturbance;
U i after the photo-thermal power station is connected to the grid, the voltage phase value of the node i is p.u;
when the active power output P of the known node is available i And reactive power Q i While, U i 、
The solution can be calculated through the power system load flow.
S4.3, establishing planning investment constraint and system operation constraint;
the obtained CSP heat storage projected regulation capability needs to meet various operating constraints of the photo-thermal power station for iteratively modifying the ideal output curve of the wind/light system to obtain the optimum heat storage capacity.
The method comprises the steps of configuration range constraint of wind and light field capacity and investment budget upper limit constraint; direct current output constraint, system power balance constraint, CSP power station power balance constraint, photovoltaic power station operation constraint and wind power plant operation constraint.
S4.3.1, planning investment constraints;
the configuration range of field capacity is restricted:
and secondly, restricting the upper limit of the investment budget: IC (integrated circuit) min ≤IC≤IC max 。
S4.3.2, system operating constraints;
system power balance constraint: p t PV +P t W +P t CSP ≥P t load ；
In the formula: p t PV 、P t W 、P t CSP Photovoltaic, wind power and photo-thermal on-line electric quantity P in t time period t load Load power at time t.
And secondly, constraint of operation interval: p DC,min ≤P t DC ≤P DC,max ；
In the formula: p DC,min And P DC,max And respectively carrying out on-line quota on the transmission power of the direct current connecting line.
Thirdly, current tie line power transmission constraint:
in the formula: q is the total electric quantity transmitted by the connecting line, and delta t is the time interval duration.
Fourthly, constraint of upper and lower limits of CSP output power: 20% P CSP,max ≤P CSP (t)≤P CSP,max 。
V, restraining the operation of the power station:
sixth, wind power plant operation constraint:
s5, solving the capacity optimization configuration model of the optical and thermal power station;
the method comprises the following steps of optimizing the installed capacity configuration of the optical and thermal power station by adopting an improved NSGA-II algorithm, finally determining the optimal installed capacity configuration by combining a Pareto solution set generated by the algorithm, wherein the flow of the improved NSGA-II algorithm is shown in a figure 2:
after the algorithm starts, the population evolution total algebra is gen, and the evolution algebra i is 0;
after an initial population is set, calculating objective functions f1 and f2 according to historical wind and light load data; fast non-dominant sorting, calculating the crowdedness;
selecting an operator for screening, and reserving excellent individuals; crossover, polynomial mutation using the tDX operator;
obtaining a new parent population according to an elite strategy;
when i > gen is satisfied, ending;
when i > gen is not satisfied, returning to the initial population cycle.
S6, checking the wind and light fluctuation index;
the wind-solar fluctuation index may be calculated by using the CSP capacity constraint modified output curve.
If the fluctuation index is within the range specified by the relevant regulations, two capacity parameters of the CSP are obtained: the installed capacity and the heat storage capacity are optimally matched with the specified wind and light fluctuation scale.
If the fluctuation index exceeds the specified range, the corrected curve is used as the wind-light original output curve and optimized again by using the proposed method until the two parameters are optimized.
The total wind-solar energy reduction and total energy shortage of the whole optimization process are calculated by the following formulas:
wherein V total And V total1 Respectively wind-solar energy reduction and energy shortage in the whole optimization process.
The wind-solar fluctuation index can be calculated by using an output curve modified by CSP power constraint;
if the fluctuation index is within the range specified by the relevant regulations, the obtained heat storage capacity is best matched with the specified wind and light fluctuation scale.
If the fluctuation index exceeds the specified range, the corrected curve is used as the wind and light original output curve and optimized again by the proposed method until the heat storage parameters are optimized.
A flow chart of an optimal heat storage capacity configuration method for inhibiting rapid fluctuation of a wind and light system in real time is shown in fig. 3:
firstly, processing the wind-light original curve by using a rolling average method after starting, and then calculating the predicted heat storage capacity of the CSPs with different confidence degrees by using a statistical analysis method;
modifying the wind-solar curve according to the operation condition conversion constraint;
seeking the optimal heat storage capacity by utilizing CSP power constraint;
checking the wind-solar fluctuation indicator;
when the indicator meets the technical regulation, ending the capacity configuration;
when the indicator does not meet the technical specification, the modified wind-solar curve is regarded as the original curve again and then returns to the original configuration.
S7, determining the optimal installed capacity and heat storage capacity of the photo-thermal power station of the wind-solar new energy base;
and comparing the photo-thermal power generation capacity under different typical scenes to determine the optimal capacity configuration of the photo-thermal power station of the wind and light new energy base.
Claims (6)
1. A capacity configuration method for a photo-thermal power station of a wind-solar new energy base is characterized by comprising the following steps: the method comprises the following steps:
s1, acquiring historical annual wind power, photovoltaic and photo-thermal output curves and load curves;
s2, dividing a typical operation scene;
s3, estimating and adjusting capacity under each confidence coefficient;
s4, establishing a capacity optimization configuration model of the photo-thermal power station;
the model is as follows:
F＝max(μ 1 f 1 (x)+μ 2 f 2 (x))
in the formula:
f, considering a comprehensive optimization target with the maximum direct current output power and voltage stability margin of the new energy base;
f 1 the maximum delivery capacity target meeting the extra-high voltage direct current delivery requirement is met;
f 2 -a voltage stability margin maximum target;
μ 1 、μ 2 -weight of target 1 and target 2;
s4.1, aiming at the maximum direct current outgoing power quantity;
in the formula:
P DC (t) -the dc delivery power at time t;
Δ t — duration of each period;
s4.2, obtaining a maximum voltage stability margin target;
through comparing the voltage variation of each node before and after the photo-thermal power station is incorporated into the power networks, the voltage stability margin is described specifically, and the calculation formula is:
in the formula:
n is the number of nodes contained in the whole system;
ΔU i -the voltage variation, p.u, of the ith node before and after the disturbance;
U i after the photo-thermal power station is connected to the grid, the voltage phase value of the node i is p.u;
when the active power of the known node is P i And reactive power Q i While, U i 、
The calculation can be carried out through the load flow calculation of the power system;
s4.3, establishing planning investment constraint and system operation constraint;
the obtained CSP heat storage expected regulation capacity needs to meet various operation constraints of the photo-thermal power station, and the constraints are used for repeatedly modifying an ideal output curve of a wind-solar system so as to obtain the optimal heat storage capacity;
the method comprises the steps of configuring range constraint of wind and light field capacity and investment budget upper limit constraint; direct current output constraint, system power balance constraint, CSP power station power balance constraint, photovoltaic power station operation constraint and wind power plant operation constraint;
s4.3.1, planning investment constraints;
the configuration range of the optical field capacity is restricted:
and secondly, restricting the upper limit of the investment budget: IC (integrated circuit) min ≤IC≤IC max ；
S4.3.2, system operating constraints;
system power balance constraint: p t PV +P t W +P t CSP ≥P t load ；
In the formula: p t PV 、P t W 、P t CSP Respectively photovoltaic, wind power and photo-thermal on-line electric quantity in t time period, P t load Load power at time t;
and secondly, power operation interval constraint: p DC,min ≤P t DC ≤P DC,max ；
In the formula: p DC,min And P DC,max Respectively carrying out on-line quota on the transmission power of the direct current connecting line;
thirdly, direct current tie line power transmission constraint:
in the formula: q is the total electric quantity transmitted by the connecting line, and delta t is the time interval duration;
fourthly, constraint of upper and lower limits of CSP output power: 20% P CSP,max ≤P CSP (t)≤P CSP,max ；
Fifth, photovoltaic power station operation constraint:
sixth, wind power plant operation constraint:
s5, solving the capacity optimization configuration model of the optical and thermal power station;
optimizing the installed capacity configuration of the optical thermal power station by adopting an improved NSGA-II algorithm, and finally determining the optimal installed capacity configuration by combining a Pareto solution set generated by the algorithm;
s6, checking the wind and light fluctuation index;
the wind-solar fluctuation index can be calculated by using an output curve corrected by CSP capacity constraint;
if the fluctuation index is within the range specified by the relevant regulations, two capacity parameters of the CSP are obtained: the installed capacity and the heat storage capacity are optimally matched with the specified wind and light fluctuation scale;
if the fluctuation index exceeds a specified range, the corrected curve is used as a wind-light original output curve, and the proposed method is used for optimizing again until the two parameters are optimized;
the total wind-solar energy reduction and total energy shortage of the whole optimization process are calculated by the following formulas:
wherein V total And V total1 Respectively reducing wind energy and light energy and reducing energy in the whole optimization process;
the wind-solar fluctuation index can be calculated by using an output curve modified by CSP power constraint;
if the fluctuation index is in the range specified by relevant regulations, the obtained heat storage capacity is best matched with the specified wind and light fluctuation scale;
if the fluctuation index exceeds the specified range, the corrected curve is used as a wind-light original output curve, and the proposed method is used for optimizing again until the heat storage parameters are optimized;
s7, determining the optimal installed capacity and heat storage capacity of the photo-thermal power station of the wind-solar new energy base;
and comparing the photo-thermal power generation capacity under different typical scenes to determine the optimal capacity configuration of the photo-thermal power station of the wind and light new energy base.
2. The capacity configuration method for the wind-solar-new-energy-base photo-thermal power station as claimed in claim 1, wherein the capacity configuration method comprises the following steps: step S1 includes:
s1.1, acquiring a wind power historical output curve P of a whole-year wind and light new energy base W (t) photovoltaic historical output curve P PV (t) near area photothermal historical output curve P CSP (t) reference and load curve P D (t)；
S1.2, setting the limit P of the direct current outgoing power DC (max) and voltage stability margin;
s1.3, calculating the rolling average value of each datum on the actual wind-solar output curve by adopting a rolling average method to obtain a smooth output curve;
the smoothed wind-light output value may be determined as follows:
P rt and P t Respectively, the t-th on the smooth curve and the original curve th A power value at a time; 2M is the number of power points to calculate the rolling average.
3. The capacity configuration method for the wind-solar-new-energy-base photo-thermal power station as claimed in claim 1, wherein the capacity configuration method comprises the following steps: step S2 includes:
s2.1, forming a set of 365 scenes by taking day as a unit and hour scale for 8760 hours of data in the whole year;
s2.2, adopting a k-means multi-scene clustering method, selecting a distance cost criterion function Z as a clustering effectiveness function, clustering a scene set, and reducing the scene set to 4 typical scenes to form a new scene set S;
respectively as follows: big wind and big light, big wind and small light, small wind and small light, big wind and small light.
4. The capacity configuration method for the wind-solar-new-energy-base photo-thermal power station as claimed in claim 1, wherein the capacity configuration method comprises the following steps: step S3 includes:
s3.1, obtaining a fluctuation component by comparing the original power curve with the smooth curve;
the upper fluctuation component represents excess power generated by the wind-solar system, which should be consumed and stored in the thermal storage system, and the lower part represents the energy shortage that the thermal storage system should compensate for;
each fluctuation component is regarded as one data point, and all the points are divided into two parts (upper and lower) and arranged in time series, respectively;
the parameters contained in each data point mainly comprise the duration of fluctuation components, the maximum difference between an original curve and a smooth curve of each fluctuation interval and the area of a fluctuation area;
the maximum difference between the two curves should be the expected heat storage capacity during the period of the fluctuation;
s3.2, in the time scale of 2-60 minutes, the t position scale distribution has the best fitting effect on the probability density of wind and light fluctuation;
the probability density expression for the t-position scale distribution is as follows:
Γ is the gamma function; μ is a location parameter; σ is a scale parameter and υ is a shape parameter;
assuming that the upper layer fluctuation component table and the lower layer fluctuation component table are distributed according to the t position scale, the probability distribution functions can be respectively fitted through a Maximum Likelihood Estimation (MLE) method;
according to the t distribution parameters obtained by the fitting method, the confidence interval of each confidence interval can be obtained and used as the estimated adjusting capacity of the photothermal power station.
5. The capacity configuration method for the wind-solar-new-energy-base photo-thermal power station as claimed in claim 1, wherein the capacity configuration method comprises the following steps: the improved NSGA-II algorithm flow in the step S5 is as follows:
after the algorithm starts, the population evolution total algebra is gen, and the evolution algebra i is 0; after an initial population is set, calculating objective functions f1 and f2 according to historical wind and light load data; fast non-dominant sorting, calculating the crowdedness; selecting an operator for screening, and reserving excellent individuals; crossover, polynomial mutation using the tDX operator; obtaining a new parent population according to an elite strategy; when i > gen is satisfied, ending; when i > gen is not satisfied, returning to the initial population cycle.
6. The capacity configuration method for the wind-solar-new-energy-base photo-thermal power station as claimed in claim 1, wherein the capacity configuration method comprises the following steps: the optimal heat storage capacity configuration method for inhibiting the rapid fluctuation of the wind and light system in real time in the step S6 comprises the following steps: firstly, processing the wind-light original curve by using a rolling average method after starting, and then calculating the predicted heat storage capacity of the CSPs with different confidence degrees by using a statistical analysis method; modifying the wind-solar curve according to the operation condition conversion constraint; seeking the optimal heat storage capacity by utilizing CSP power constraint; checking the wind-solar fluctuation indicator; when the indicator meets the technical regulation, ending the capacity configuration; when the indicator does not meet the technical specification, the modified wind-solar curve is regarded as the original curve again and then returns to the original configuration.
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