CN113673156A - Photo-thermal power generation capacity optimization method suitable for multiple scenes of full renewable energy base - Google Patents
Photo-thermal power generation capacity optimization method suitable for multiple scenes of full renewable energy base Download PDFInfo
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Abstract
The invention discloses a photo-thermal power generation capacity optimization method suitable for multiple scenes of a full renewable energy base in the technical field of power system planning. The method comprises the following steps: reading historical annual wind power, photovoltaic, load and outgoing related information of a full renewable energy base; establishing a typical operation scene of a full renewable energy base; establishing a photo-thermal power station adjusting model; establishing a full renewable energy base photo-thermal power generation capacity optimization model by taking the minimum sum of the system power abandonment and the load loss risk value as a target; a photo-thermal power generation capacity optimization method of a fully renewable energy base is provided. The invention provides a photo-thermal power generation capacity optimization method for a full renewable energy power generation base, which realizes that photo-thermal power generation completely replaces thermal power to participate in power grid regulation by optimal configuration of photo-thermal power generation capacity, promotes the consumption of wind power and photovoltaic power, and provides a theoretical basis for building the full renewable energy base by utilizing photo-thermal power generation.
Description
Technical Field
The invention belongs to the technical field of power system planning, and particularly relates to a photo-thermal power generation capacity optimization method suitable for multiple scenes of a full renewable energy base.
Background
With the continuous and rapid development of original renewable energy sources in China, the scale renewable energy sources can gradually replace conventional power supplies, and the inevitable development trend is achieved, the extraction of 100% renewable energy sources is carried out at once, and the construction of a full renewable energy base becomes an important trend and direction of the future clean low-carbon transformation development of energy sources in China. Renewable energy power generation becomes the first large power supply in Qinghai and Gansu provinces, and part of the areas are built into a full renewable energy power generation base. However, the randomness, intermittence and other characteristics of wind power and photovoltaic enable the operation scene of the power grid to be more complex and changeable, large-scale grid connection of the power grid brings great challenges to operation and scheduling of a power system, and consumption and further development of renewable energy sources are hindered.
Photo-thermal power generation is another important form of solar power generation following photovoltaic power generation. The photo-thermal power generation is a renewable energy power generation form naturally having energy storage, can inhibit the influence of the random fluctuation of solar energy on the power generation power, can realize continuous and stable power generation for 24 hours, is superior to a conventional thermal power generating unit in the aspects of speed and depth adjustment, and is a renewable energy power generation form capable of being scheduled and controlled. The 'Chinese renewable energy development route map' indicates that the photo-thermal installed capacity of China is estimated to reach 500GW in 2050, and photo-thermal power generation becomes an important choice for replacing the traditional thermal power, promoting the consumption of renewable energy and accelerating the construction of a full renewable energy base. Therefore, research on a photo-thermal power generation capacity optimization method suitable for multiple scenes of a fully renewable energy base is urgently needed.
Due to the harsh construction conditions and high initial investment cost of the photo-thermal power generation, the photo-thermal power generation industry in China just starts, the commercial photo-thermal power station still operates with stable self output and maximized self income at present, and the research on the aspect of capacity configuration of the photo-thermal power station flexibly participating in power grid regulation is less. In the literature, the complementary characteristics of wind power and photo-thermal power generation are considered, a strategy for flexibly arranging the operation of a photo-thermal power station is provided, and the uncertainty and the intermittency of wind power output can be reduced. The existing established photothermal power station is used as an auxiliary peak regulation resource of traditional thermal power, and the thermal power and photothermal combined peak regulation is proved to improve the system regulation capacity, so that the renewable energy is promoted to be consumed. The above documents study the regulation characteristics and scheduling method of the photo-thermal power generation, but there is still no study on photo-thermal power generation capacity configuration in a complex operation scene of a fully renewable energy base in which photo-thermal power is completely substituted for thermal power.
In summary, on the basis of the existing research, the invention provides a photo-thermal power generation capacity optimization method suitable for multiple scenes of a fully renewable energy base, and photo-thermal power generation is used for replacing the traditional thermal power to bear base load and participate in system adjustment, so that the wind power and the photovoltaic are promoted to be absorbed, and a theoretical basis is provided for the photo-thermal power station planning and construction of the fully renewable energy base.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a photo-thermal power generation capacity optimization method suitable for multiple scenes of a full renewable energy base, which is used for solving the problem of power generation capacity configuration of the full renewable energy base containing photo-thermal power generation under the condition of meeting the scene of multiple operation of wind, light and electricity and provides a reference for planning and building a photo-thermal power station. The method mainly comprises the following steps:
s1: reading historical annual wind power, photovoltaic, load and outgoing related information of a full renewable energy base;
s2: establishing a typical operation scene of a full renewable energy base;
s3: establishing a photo-thermal power station adjusting model;
s4: establishing a full renewable energy base photo-thermal power generation capacity optimization model by taking the minimum risk value of system power abandonment and load loss as a target;
s5: a photo-thermal power generation capacity optimization method of a fully renewable energy base is provided.
Wherein S1 includes the steps of:
s101: based on typical days of spring, summer, autumn and winter: obtaining a historical wind power active power output curve PW，F(t) obtaining a photovoltaic historical active power output curve PPV，F(t) obtaining a load active power output curve PD(t), obtaining a historical illumination radiation intensity curve R (t). Obtaining total renewable energy base outgoing power limit PL(t)。
S2 includes the steps of:
s201: establishing a typical solar wind-solar power output scene set in each season of spring, summer, autumn and winter;
s202: forming four seasonal typical wind-solar-electric power output scenes based on a K-means + + clustering algorithm, which specifically comprises the following steps: big wind and big light, big wind and small light, small wind and big light, small wind and small light scenes.
S3 includes the steps of:
s301: the method comprises the following steps of establishing equation constraint of energy flow of the photo-thermal power station, specifically comprising: light-heat energy conversion in the light gathering system, heat energy transfer in the heat transfer working medium, heat charge and discharge processes in the heat storage system and energy flow constraint of heat-electric energy conversion in the power generation system;
s302: the method comprises the following steps of establishing inequality constraints of operation of the photo-thermal power station, and specifically comprises the following steps: the upper and lower limits of the output power, the minimum start-stop time, the climbing speed, the heat charging and discharging power of the heat storage system and the operation constraint of the heat storage capacity.
S4 includes the steps of:
s401: calculating a system operation risk measurement index based on a condition risk value theory;
s402: establishing an objective function according to the minimum sum of the system power abandon and the load loss risk value;
s403: and establishing system operation constraints including system power balance constraint, photo-thermal power station operation constraint, wind power output constraint, photovoltaic output constraint and system standby constraint.
S5 includes the steps of:
s501: solving the photo-thermal power generation capacity optimization model of the full renewable energy base by using the improved DESO algorithm to obtain the photo-thermal optimal power generation capacity PCSP，max。
The invention discloses a photo-thermal power generation capacity optimization method suitable for multiple scenes of a full renewable energy base in the technical field of power system planning. The method comprises the following steps: reading historical annual wind power, photovoltaic, load and outgoing related information of a full renewable energy base; establishing a typical operation scene of a full renewable energy base; establishing a photo-thermal power station adjusting model; establishing a full renewable energy base photo-thermal power generation capacity optimization model; a photo-thermal power generation capacity optimization method of a fully renewable energy base is provided. The invention provides a photo-thermal power generation capacity optimization method suitable for multiple scenes of a full renewable energy base, which improves the adjusting capacity of a system for coping with complex operation scenes of the full renewable energy base through the optimal configuration of the photo-thermal power generation capacity and promotes the consumption of wind power and photovoltaic power.
Drawings
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
FIG. 1 is a flow chart of a photo-thermal power generation capacity optimization method for adapting to multiple scenes of a full renewable energy base provided by the invention;
FIG. 2 is a network diagram of an IEEE RTS-24 node test system in example 2 provided by the present invention;
FIG. 3 is typical solar wind power active output information in historical years for example 2 provided by the present invention;
FIG. 4 is typical solar photovoltaic active output information over a historical year in example 2 provided by the present invention;
FIG. 5 is exemplary daily load active power output information for a historical year in example 2 provided by the present invention;
FIG. 6 is typical solar radiation intensity information over historical years for example 2 provided by the present invention;
FIG. 7 is a typical solar wind power output scene in spring of example 2 provided by the present invention;
FIG. 8 is a typical wind-solar power scenario for the summer-season day of example 2 provided by the present invention;
FIG. 9 is a typical solar photovoltaic power generation scenario in autumn of example 2 provided by the present invention;
FIG. 10 is a typical solar photovoltaic output scenario for the winter season of example 2 provided by the present invention;
FIG. 11 is a risk index of system power curtailment at different photothermal power generation capacities in example 2 according to the present invention
Loss of load risk indicator
And a total risk indicator F.
Detailed Description
In order to clearly understand the technical solution of the present invention, a detailed structure thereof will be set forth in the following description. It is apparent that the specific implementation of the embodiments of the present invention is not limited to the specific details familiar to those skilled in the art. Exemplary embodiments of the invention are described in detail below, and other embodiments in addition to those described in detail are possible.
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Example 1
FIG. 1 is a flow chart of a photo-thermal power generation capacity optimization method adapting to multiple scenes of a full renewable energy base. In fig. 1, a flow chart of a photo-thermal power generation capacity optimization method adapted to multiple scenarios of a full renewable energy base provided by the invention includes:
s1: reading the historical wind power, photovoltaic, load and relevant information sent out all year round in the spring, summer, autumn and winter of the whole renewable energy base;
s2: establishing a typical daily operation scene of a full renewable energy base;
s3: establishing a photo-thermal power station adjusting model;
s4: establishing a full renewable energy base photo-thermal power generation capacity optimization model by taking the minimum value risk of system power abandonment and load loss conditions as a target;
s5: a photo-thermal power generation capacity optimization method of a fully renewable energy base is provided.
The S1 includes the steps of:
s101: based on typical days of spring, summer, autumn and winter: obtaining a historical wind power active power output curve PW，F(t) obtaining a photovoltaic historical active power output curve PPV，F(t) obtaining a load active power output curve PD(t), obtaining a historical illumination radiation intensity curve R (t), and obtaining the outward power limit P of the fully renewable energy baseL(t)。
The S2 includes the steps of:
s201: dividing a typical daily wind power and photovoltaic active power output curve into season scene sets according to spring of 3-5 months, summer of 6-8 months, autumn of 9-11 months and winter of 12 and 1-2 months;
s202: establishing typical scene of wind, light and electricity output under four seasons
In order to effectively reduce the number of output scenes of a fully renewable energy base and accurately describe the output characteristics of wind power and photovoltaic power in each season, a clustering algorithm is adopted to generate a typical wind-solar-electricity output scene.
The traditional K-means algorithm extracts an initial center due to uncertainty, so that the difference between a clustering result and the actual data distribution is large. The K-means + + reduction method selected by the method has better fault-tolerant capability and clustering effect by improving the initial seed point selection process. The algorithm is briefly described as follows:
(1) selecting a clustering number of 4, and randomly selecting a number sequence from a wind-solar output sequence in a certain season as a first initial clustering center;
(2) for a given cluster center, calculating the distance D (x) of each data point x in the set to the center point;
(3) calculating the probability of each sample being selected as the next cluster center point
Selecting the next clustering center point by a wheel disc method;
(4) repeating the steps (2) and (3), and so on until the last clustering center is selected;
(5) for each sample X in the datasetiCalculating the distance from the cluster center to each cluster center, and classifying the cluster center to the class corresponding to the cluster center with the minimum distance;
(6) for each class CiRecalculating its clustering center, i.e. the centroid of all samples belonging to the class;
(7) and (5) repeating the step (5) and the step (6) until the position of the cluster center is not changed any more.
(8) Repeating the steps (1) to (7) until generating typical output scenes of all seasons, specifically including 16 typical scenes including strong wind, weak wind and weak wind in all seasons, and calculating the probability of each scene.
The S3 includes the steps of:
s301: equality constraints for building energy flows in photothermal power stations
Firstly, the photo-thermal power station converts light energy reflected by a mirror field into heat energy through a heat collecting device, and calculates the solar thermal power received by a light-gathering heat collecting system as follows:
PS-H(t)＝ηSFSSFR(t) (2)
in the formula, PS-H(t) is the secondary polymerization of heat transfer working mediumHeat power absorbed in the optical heat collection system; etaSFThe light-heat conversion efficiency of the light-gathering and heat-collecting system is obtained; sSFIs the mirror field area.
Regarding the heat transfer working medium as a node, the power balance relation of the obtained system is as follows:
PS-H(t)-PH-P(t)+PT-H(t)-PH-T(t)＝0 (3)
in the formula, PH-P(t) the thermal power delivered to the power generation system by the heat transfer working medium; pT-H(t)、PH-TAnd (t) is the heat charging and discharging power between the heat transfer working medium and the heat storage system.
The heat storage capacity of the heat storage system of the photo-thermal power station can be expressed as follows:
ECSP(t)＝ECSP(t-1)+PH-T(t)Δt-PT-H(t)Δt (4)
in the formula: eCSP(t) is the total energy in the thermal storage system at time t.
Loss caused in the process of providing electric energy for the power generation system by the heat transfer working medium, and the characteristic expressed by the heat-electricity conversion efficiency is as follows:
PCSP(t)＝ηTEPH-P(t) (5)
in the formula, PCSP(t) the output of the photo-thermal unit at the moment t; etaTEThe heat-electricity conversion efficiency from the heat transfer working medium to the power generation system.
S302: establishing inequality constraints for operation of a photothermal power station
(1) The photothermal power station generates electricity through the steam turbine set, and therefore has similar operation constraints as the conventional steam turbine set.
The photothermal minimum output is defined as 20% of rated power, the maximum output is defined as rated power, namely photothermal power generation capacity, and the output limit of the photothermal unit is constrained as follows:
20％PCSP，max≤PCSP(t)≤PCSP，max (6)
in the formula: pCSP，maxThe capacity of photo-thermal power generation.
And the climbing constraint of the photothermal unit is expressed as follows:
in the formula: pCSP，upAnd PCSP，downThe maximum climbing capacity and the maximum climbing capacity of the photo-thermal power generation are respectively.
(2) The photo-thermal power station is provided with a heat storage system, so that the photo-thermal power generator set can keep stable power output and is not influenced by illumination intensity change, and the operation of the photo-thermal power station is mainly limited by capacity constraint.
The maximum capacity of the thermal storage system of a photothermal power plant is usually measured in terms of the "hours at Full Load (FLH)" for the steam turbine set. For example, the heat storage capacity of a typical photothermal power station is 9FLH, which means that the photothermal power station has the capacity of operating the unit at full load for 9h without light. Meanwhile, in order to ensure the safety of the system, for example, to avoid the solidification of molten salt, the heat storage system also has the minimum heat storage limit. The relevant constraints are:
ECSP，min≤ECSP(t)≤ρTESPCSP，max (8)
in the formula: eCSP，minThe minimum heat storage quantity of the heat storage system; rhoTESIs the maximum capacity of the heat storage system described in units of FLH.
The heat charging/discharging power of the heat storage system is continuously adjustable within a limited range, and relevant constraints are as follows:
in the formula: pH-T，maxAnd PT-H，maxThe maximum heat charging power and the maximum heat discharging power are respectively.
The S4 includes the steps of:
s401: the system risk index under different scenes is calculated by using the conditional risk value.
The conditional risk value (CVaR) is used to characterize the expected risk value for loss over VaR at a certain confidence level, and is specifically expressed as follows:
let the probability density function of the random variable xi be p (xi), note the lossThe distribution function of the loss function f (X, ξ) is phi (X, τ) ═ integral-f(X，ξ)≤τp (ξ) d ξ, for a given confidence level β, the VaR for decision X is:
VaRβ＝αβ(X)＝min{τ∈R|φ(X，τ)≥β} (10)
the expression for conditional risk value CVaR is:
wherein: [ f (X, ξ) -VaRβ]+Denotes max { f (X, ξ) -VaRβ，0}。
In general, αβThe analysis table of (X) needs to introduce an auxiliary function Fβ(X, ξ) to calculate the CVaR value:
when the analytic expression of probability distribution of the random variable xi is difficult to solve, a probability scene can be used for converting a stochastic programming problem into a deterministic programming problem, and then the estimated value of CVaR is changed into the following formula:
when the wind power-photovoltaic-photo-thermal combined output is larger than the load requirement, part of wind power or photovoltaic needs to be abandoned; when the wind power-photovoltaic-photothermal combined output is smaller than the load requirement, part of the load needs to be cut off to meet the power balance, and risks are brought to system scheduling operation. The electric quantity abandoned and the load loss are respectively as follows:
wherein:
respectively the electric quantity discarded and the load loss; pW(t) wind power planned output at time t; pPVAnd (t) is the photovoltaic planned output at the moment t.
Electricity abandon punishment cost
To discard electricity
And a power-abandon penalty coefficient CLOECThe product of (a); loss of load
Is the loss of load
And loss of load value CLOLCThe product of (a). Namely, it is
In the formula: cLOEC、CLOLCRespectively is a wind abandoning and load losing punishment coefficient; engineering general term CLOECIs 140 yuan/(MW h), CLOLCIs 700 yuan/(MW & h).
Wind-solar-electricity typical operation scene S ═ { S } considering all-renewable energy basek，k＝1，2，...，NZIn which N iskObtaining the CVaR value of the power abandoning risk and the load loss risk as the total number of the scenes
In the formula: alpha is alpha1The critical value of the risk loss of the electricity abandonment is obtained; alpha is alpha2Is the loss critical value of the load loss risk.
S402: considering the economic investment and the system operation risk of the photo-thermal power station, determining the photo-thermal power generation capacity by taking the minimum sum of the system electricity abandonment and the load loss risk value as a target, wherein the target function is as follows:
in the formula:
the risk value of the power abandonment at the time t;
the value of the load loss risk at the moment t.
S403: establishing system operation constraints including system power balance constraints, photo-thermal power station operation constraints, wind power output constraints, photovoltaic output constraints and system standby constraints, and specifically comprising the following steps:
1) system constraints
Power balance constraint
PPV(t)+PW(t)+PCSP(t)＝PD(t)+PL(t) (21)
② rotate for standby
In the formula: delta PW,up(t)，ΔPW,low(t) respectively keeping positive and negative rotation for standby at the moment t, wherein the positive and negative rotation is required for responding to wind power prediction errors; delta PPV,up(t)，ΔPPV,lowAnd (t) respectively keeping positive and negative rotation standby required for dealing with photovoltaic prediction errors at the moment t.
2) Photo-thermal unit operation constraint condition
Upper and lower limits of output power
20％PCSP，max≤PCSP(t)≤PCSP，max (23)
② restriction of climbing speed
Heat storage capacity constraint of heat storage system
ECSP，min≤ECSP(t)≤ρTESPCSP，max (25)
3) Wind power operation constraint condition
0≤PW(t)≤PW，F(t) (26)
4) Photovoltaic operating constraints
0≤PPV(t)≤PPV，F(t) (27)
The S5 includes the steps of:
s501: and solving a photo-thermal power generation capacity optimization model with the minimum sum of the system power abandonment and load loss risk values as a target by adopting a DESO algorithm. The DESO algorithm is a random parallel direct global search algorithm, has the advantages of simplicity and easiness in use in solving a nonlinear model, can ensure the effectiveness and the calculation efficiency of solution, but cannot ensure that a global optimal solution is accurately and timely found by a standard DESO algorithm when complex problems of high dimension and nonlinearity are processed, and is easy to fall into local optimal. Therefore, the model is solved by adopting a double mutation strategy based on population similarity and an improved DESO algorithm of adaptive cross probability. The double variation strategy ensures the diversity of the population, so that the model is not easy to fall into the local optimal solution; the self-adaptive cross probability can be self-adaptively adjusted according to the individual excellence, so that the population individuals can move to the individuals which are successfully updated, and the convergence of the algorithm is improved.
The method for solving the photo-thermal power generation capacity optimization model of the full renewable energy base by adopting the improved DESO algorithm comprises the following steps.
a. Inputting DESO algorithm parameters and system parameters. The algorithm parameters comprise maximum evolution algebra G and population scale MPIndividual dimension D, scaling factor S and cross probability CR. The system parameters comprise wind, light and electricity prediction data, adjustable capacity data of the photo-thermal unit and the like.
b. And (5) initializing a population. Production initialization population
Each individual in the population represents a set of control variables, including planned photothermal output, planned wind farm output, and planned photovoltaic power plant output.
c. And calculating the fitness. And calculating the fitness of each individual in the population, and selecting the individual with the optimal fitness.
d. The constraints are processed. And when the individual does not meet the model constraint condition, modifying the fitness value of the individual to eliminate the individual.
e. Performing mutation operation by adopting double mutation strategy. And calculating the similarity of the population, and selecting a proper variation strategy according to the similarity of the current population.
f. And (4) crossing and selecting. And performing population crossing, and selecting a new generation of population from the population crossing.
g. And adaptively adjusting the cross probability. And carrying out self-adaptive adjustment on the cross probability according to the individual superiority, and reserving the cross probability of the superior individual to the next generation.
h. Repeating the steps c-g until the maximum evolution algebra is reached, and outputting the optimal photo-thermal power generation capacity PCSP，maxRisk index of system power abandon
Loss of load risk indicator
And a total risk indicator F.
Example 2
Fig. 2 is a modified IEEE RTS-24 node test system, which is used as an example to verify that the photo-thermal power generation capacity optimization method applicable to multiple scenarios of a fully renewable energy base provided by the present invention:
s1: reading related historical information of wind power, photovoltaic, load and delivery of a full renewable energy base;
in a regional power grid, the rated power of a wind power cluster is 1200MW, the rated power of a photovoltaic cluster is 1000MW, and the limit of an outgoing channel is 600 MW. Typical solar wind power active power output information in the historical years is shown in figure 3, photovoltaic active power output information is shown in figure 4, load active power information is shown in figure 5, and solar radiation intensity information is shown in figure 6.
S2: establishing a typical operation scene of a full renewable energy base;
typical solar photovoltaic output scenes of big wind and light, small wind and light, big wind and light and small wind and light in four seasons of spring, summer, autumn and winter are respectively generated based on a K-means + + clustering algorithm. A typical solar wind-solar power output scene in spring is shown in fig. 7, a typical solar wind-solar power output scene in summer is shown in fig. 8, a typical solar wind-solar power output scene in autumn is shown in fig. 9, and a typical solar wind-solar power output scene in winter is shown in fig. 10. Typical daily scene probabilities are shown in the table below.
S3: establishing a photo-thermal power station adjusting model; the photothermal power generation adjustment information is shown in the following table.
| Upper limit of output/MW of photo-thermal power station | PCSP，max |
| Lower limit of output P of photo-thermal power stationCSP，min/MW | 20％PCSP，max |
| Number of hours rho of full-load heat storage operation of photo-thermal power stationTES/ |
10 |
| Ramp rate/MW & min of photo- |
9％PCSP，max |
| Thermoelectric conversion efficiency ηTE/％ | 40 |
| Efficiency of photothermal conversion etaSF/％ | 50 |
S4: establishing a full renewable energy base photo-thermal power generation capacity optimization model by taking the minimum sum of the system power abandonment and the load loss risk value as a target;
s5: and solving the model to obtain the optimal photo-thermal power generation capacity of 1160MW of the full renewable energy base. System electricity abandonment risk index under different photo-thermal power generation capacities
Loss of load risk indicator
And the overall risk indicator F is shown in figure 11. Comparisons of various risk indicators with other photothermal capacity configurations are shown in the table below. As can be seen from the table, when the photothermal power generation capacity is configured to be 1100MW, although the electricity abandonment risk index value is slightly reduced, the load loss risk of 9.799 ten thousand yuan is generated due to the excessively small photothermal power generation capacity and the insufficient system power generation capacity, so that the total operation risk is increased by 9.148 ten thousand yuan; when the photo-thermal power generation capacity is configured to 1200MW, although the load loss risk of 0.085 ten thousand yuan is reduced, the wind and light abandoning condition is intensified, and the light abandoning condition is intensifiedThe electricity risk index is increased by 1.779 ten thousand yuan, so that the total risk index is increased by 1.694 ten thousand yuan, the sum of the system electricity abandonment and load loss risk value under the photo-thermal power generation capacity configuration is minimum, the wind, light and electricity consumption is promoted on the premise of ensuring the safe and economical operation of the system, and the effectiveness of the method is proved.
Finally, it should be noted that: although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can make modifications and equivalents to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is set forth in the claims appended hereto.
Claims (6)
1. A photo-thermal power generation capacity optimization method suitable for multiple scenes of a full renewable energy base comprises the following steps:
s1: reading historical annual wind power, photovoltaic, load and outgoing related information of a full renewable energy base;
s2: establishing a typical operation scene of a full renewable energy base;
s3: establishing a photo-thermal power station adjusting model;
s4: establishing a full renewable energy base photo-thermal power generation capacity optimization model by taking the minimum risk value of system power abandonment and load loss as a target;
s5: and solving the photo-thermal power generation capacity optimization model of the fully renewable energy base to obtain the photo-thermal optimal power generation capacity.
2. The full renewable energy base multi-scenario photo-thermal power generation capacity optimization method of claim 1, wherein the S1 comprises the following steps:
s101: based on typical days of spring, summer, autumn and winter: obtainingHistorical wind power output curve PW，F(t) obtaining a photovoltaic historical active power output curve PPV，F(t) obtaining a load historical active power output curve PD(t) obtaining a historical illumination radiation intensity curve R (t); obtaining total renewable energy base outgoing power limit PL(t)。
3. The full renewable energy base multi-scenario photo-thermal power generation capacity optimization method of claim 1, wherein the S2 comprises the following steps:
s201: establishing an original scene set of typical solar wind-electricity output in spring, summer, autumn and winter;
s202: forming four seasonal typical wind-solar-electric power output scenes based on a K-means + + clustering algorithm, which specifically comprises the following steps: big wind and big light, big wind and small light, small wind and big light, small wind and small light scenes.
4. The full renewable energy base multi-scenario photo-thermal power generation capacity optimization method of claim 1, wherein the S3 comprises the following steps:
s301: the method comprises the following steps of establishing equation constraint of energy flow of the photo-thermal power station, specifically comprising: light-heat energy conversion in the light gathering system, heat energy transfer in the heat transfer working medium, heat charge and discharge processes in the heat storage system and energy flow constraint of heat-electric energy conversion in the power generation system;
s302: the method comprises the following steps of establishing inequality constraints of operation of the photo-thermal power station, and specifically comprises the following steps: the upper and lower limits of the output power, the minimum start-stop time, the climbing speed, the heat charging and discharging power of the heat storage system and the operation constraint of the heat storage capacity.
5. The full renewable energy base multi-scenario photo-thermal power generation capacity optimization method of claim 1, wherein the S4 comprises the following steps:
s401: calculating a system operation probability risk index considering the wind-solar power uncertainty influence based on a condition risk value theory;
s402: and determining the photo-thermal power generation capacity by taking the minimum sum of the system electricity abandonment and the load loss risk value as a target, wherein the target function is as follows:
in the formula:
the risk value of the power abandonment at the time t;
the value of the load loss risk at the moment t.
S403: and establishing system operation constraints including system power balance constraint, photo-thermal power station operation constraint, wind power output constraint, photovoltaic output constraint and system standby constraint.
6. The full renewable energy base multi-scenario photo-thermal power generation capacity optimization method of claim 1, wherein the S5 comprises the following steps:
s501: solving the photo-thermal power generation capacity optimization model of the full renewable energy base by using the improved DESO algorithm to obtain the photo-thermal optimal power generation capacity PCSP，max。
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