CN113722903A - Photo-thermal power generation capacity configuration method for full-renewable energy source sending-end system - Google Patents
Photo-thermal power generation capacity configuration method for full-renewable energy source sending-end system Download PDFInfo
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Abstract
The invention discloses a photo-thermal power generation capacity configuration method of a full renewable energy source sending end system, which meets the direct current sending requirement. The method comprises the following steps: acquiring historical annual wind power, photovoltaic and day-ahead output prediction curves and load curves of a sending end system; dividing the operation scene of a fully renewable energy source sending end system; establishing a photo-thermal power station adjusting model; establishing a photo-thermal power station power generation capacity optimal configuration model under the condition of direct current delivery and solving; and determining the photo-thermal minimum power generation capacity adapting to multiple scenes. According to the photo-thermal power generation capacity configuration method for the full renewable energy source sending end system meeting the direct current sending requirement, the photo-thermal power generation is completely substituted for thermal power to participate in power grid regulation by configuring the minimum photo-thermal power generation capacity, the peak regulation requirement of wind power and photovoltaic direct current sending is met, and a theoretical basis is provided for solving the peak regulation requirement of the wind power photovoltaic sending end system by utilizing the 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 configuration method of a full renewable energy source sending end system, which meets the direct current sending requirement.
Background
Under the background of the work arrangement of realizing carbon neutralization in 2030 years by carbon peak 2060 in China, the trend that renewable energy in China gradually replaces traditional thermal power to become dominant energy becomes inevitable, and a full renewable energy power generation system is likely to be built in the future. However, the wind power and photovoltaic dominant renewable energy sources have random fluctuation and non-schedulability, so that the consumption of new energy sources is greatly limited, the operation difficulty of a fully renewable energy source sending end system is increased, and meanwhile, due to the addition of extra-high voltage direct current transmission, the fully renewable energy sources need to meet the direct current sending-out requirement, the external transmission quantity is increased as much as possible on the basis of meeting the local load requirement, and the scheduling problem is more prominent. Therefore, it is highly desirable to reasonably allocate a schedulable photo-thermal power generation capacity to satisfy the maximum demand of local wind power, photovoltaic and dc power delivery.
Photo-thermal power generation is another important form of solar power generation following photovoltaic power generation. The photo-thermal power generation is a new energy power generation mode which naturally has 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 new energy power generation mode which can be 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 new energy and the direct current output and accelerating the construction of a fully renewable energy base. However, the key problem of building a full renewable energy source sending end system is that the photo-thermal energy with a certain amount of power generation capacity can meet the direct current sending requirement and the wind power and the photovoltaic can be maximally consumed on site. If the photo-thermal power generation capacity is configured too small, when wind and solar power generation is small, load shedding or direct current output power reduction caused by the fact that load requirements and direct current output requirements cannot be met may occur; if the configuration of the photo-thermal power generation capacity is too large, the minimum technical output is correspondingly increased, and if wind power and photovoltaic power are generated greatly, a large amount of wind and light abandoning phenomena can be caused, so that the direct current transmission requirement is met, but the economy is poor. Due to the adjustment frequency limit of the direct current power and the photo-thermal minimum technology output limit, the photo-thermal capacity configuration has an optimal solution. Therefore, an optimal power generation capacity, namely the minimum power generation capacity, needs to be configured to meet the direct current delivery requirement, and meanwhile, the wind power and photovoltaic consumption is maximum. Therefore, the method for configuring the photo-thermal power generation capacity of the full renewable energy source sending end system meeting the direct current sending requirement has important practical significance.
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 new energy consumption is promoted. The above documents have studied the regulation characteristics and scheduling method of the photovoltaic power generation, but no research has been conducted on the configuration of the photovoltaic power generation capacity of the total renewable energy base, and no consideration has been given to the configuration of the minimum power generation capacity in the case of satisfying the demand for dc output.
In summary, the invention provides a photo-thermal power generation capacity configuration method of a fully renewable energy source sending end system meeting a direct current sending requirement on the basis of the existing research, and the photo-thermal power generation replaces 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, the direct current sending requirement is met, and a theoretical basis is provided for photo-thermal power station planning and construction of the fully renewable energy source sending end system meeting the direct current sending requirement.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a photo-thermal power generation capacity configuration method of a full renewable energy source sending end system, which meets the direct current sending requirement, is used for solving the problems that the photo-thermal capacity configured in a full renewable energy source base can meet the direct current sending requirement and the new energy can be maximally consumed, and provides a reference for the planning and construction of a photo-thermal power station. The method for configuring the photo-thermal power generation capacity of the full renewable energy source sending end system meeting the direct current sending requirement comprises the following steps of:
s1: acquiring historical annual wind power, photovoltaic and day-ahead output prediction curves and load curves of a sending end system;
s2: dividing the operation scene of a fully renewable energy source sending end system;
s3: establishing a photo-thermal power station adjusting model;
s4: establishing a photo-thermal power station power generation capacity configuration optimization model under the condition of direct current delivery and solving;
s5: and determining the photo-thermal minimum power generation capacity adapting to multiple scenes.
Specifically, S1 includes the following steps:
s101: obtaining a prediction curve P of the wind power day-ahead output of a transmitting end of a full-year-round renewable energy baseW，F(t) photovoltaic day-ahead output prediction curve PPV，F(t) and load day-ahead output prediction curve PD(t)；
S102: setting a DC delivery power limit PDC.maxAnd the number of daily adjustments.
The S2 includes:
s201, calculating to obtain daily wind power and photovoltaic power generation capacity Qwind、QpVThe formula used is:
and S202, aggregating 365 groups of wind-light power generation data into 4 types of wind-light combined power generation scene clusters based on a k-means clustering algorithm, wherein the 4 types of wind-light combined power generation scene clusters comprise a wind-light large operation scene, a wind-light small operation scene and a wind-light small operation scene.
The 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, establishing an objective function by using the sending end system to locally consume the wind power, maximize the photovoltaic electric quantity and maximize the direct current output electric quantity as the target:
in the formula: i is the time period number, TiThe total time period number, delta t, the time length of each time period, W, the algebraic sum of the consumption electricity quantity and the direct current delivery electricity quantity of the system on site, PDC(t) is the DC output power at time t, PD(t) predicting power for the load before day, PW(t) planned output of wind power，PPVAnd (t) is the photovoltaic planned output.
S402, establishing system operation constraints including system power balance constraint, photo-thermal power station operation constraint, wind power output constraint, photovoltaic output constraint, direct current tie line operation constraint and system standby constraint,
s403: solving the photo-thermal power generation capacity configuration optimization model under different scenes by adopting an improved DESO algorithm to output photo-thermal power generation capacity PCSPAnd (t) the minimum power generation capacity, the local consumption of wind power, photovoltaic power and direct current outgoing power by the system.
S5 includes the steps of:
s501, comparing photo-thermal power generation capacity P under different scenesCSP(t), determining a minimum power generation capacity configuration.
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 method for configuring the photo-thermal power generation capacity of a full renewable energy source delivery system to meet the DC delivery requirement according to the present invention;
FIG. 2 is a flow chart of clustering operation of the K-means clustering algorithm;
FIG. 3 is a modified IEEE RTS-24 node test system with DC delivery;
FIG. 4 is a typical solar wind power active output prediction curve over a historical year;
FIG. 5 is a typical solar photovoltaic active power output prediction curve over a historical year;
FIG. 6 is a typical daily load curve;
FIG. 7 is a typical day operational scenario with high wind and high light;
FIG. 8 is a typical day operational scenario with wind, light, and light;
FIG. 9 is a typical day operational scenario with small winds and large lights;
FIG. 10 is a typical day operational scenario with little wind and light;
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 method for configuring the photo-thermal power generation capacity of a full renewable energy source sending end system meeting the direct current sending requirement. In fig. 1, a flow chart of a method for configuring photo-thermal power generation capacity of a full renewable energy delivery system that meets a dc output requirement according to the present invention includes:
s1: acquiring historical annual wind power, photovoltaic and day-ahead output prediction curves and load curves of a sending end system; s101: obtaining a prediction curve P of the wind power day-ahead output of a transmitting end of a full-year-round renewable energy baseW，F(t) photovoltaic day-ahead output prediction curve PPV，F(t) and load day-ahead output prediction curve PD(t)；
S102: setting a DC delivery power limit PDC.maxAnd daily adjustment times and other constraints.
S2: dividing a full renewable energy base operation scene; s201, calculating to obtain daily wind power and photovoltaic power generation capacity Qwind、QpVThe formula used is:
in the formula: ppvijThe average wind power active power output, P, in the ith 15 minuteswindijThe photovoltaic average active power output in the jth 15 min on the ith day.
And S202, aggregating 365 groups of wind-light power generation amount data into 4 types of wind-light combined power generation scene clusters based on a k-means clustering algorithm.
The clustering operation flow chart of the K-means clustering algorithm is shown in the attached figure 2. Firstly, randomly selecting K cluster centers, wherein each initial cluster center approximately represents the average Euclidean average distance or the centroid of a cluster, carrying out Euclidean distance operation on the rest data sets to generate a distance matrix, and endowing the distance matrix with the closest cluster in a corresponding matrix. Then, the average value of each cluster is recalculated, and the criterion function is changed in real time in the repeated calculation process until the criterion function converges.
The algorithm is as follows: based on the distance (euclidean distance) of the dataset in the cluster to the cluster center point in the cluster.
Inputting: data set D containing 365 data objects, the number of clusters generated by clustering 4, D1∩D2∩…∩Dk＝D。
And (3) outputting: the operation scenes of the 4 clustering centers, namely the 4 types of all-renewable energy base stations are respectively large wind and light, small wind and light and large wind and light operation scenes.
S3: establishing a photo-thermal power station adjusting model;
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) (4)
in the formula, PS-H(t) the heat power absorbed by the heat transfer working medium from the light-gathering and heat-collecting 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 (5)
in the formula, PH-P(t) conveying heat transfer working medium to power generation systemThe thermal power of (3); 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 (6)
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) (7)
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 (8)
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 (10)
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.
S4: establishing a photo-thermal power station power generation capacity configuration optimization model under the condition of direct current delivery and solving;
s5: and determining the photo-thermal minimum power generation capacity adapting to multiple scenes.
The S4 includes the steps of:
s401, establishing an objective function by using the sending end system to locally consume the wind power, maximize the photovoltaic electric quantity and maximize the direct current output electric quantity as the target:
in the formula: i is the time period number, TiThe total time period number, delta t, the time length of each time period, W, the algebraic sum of the consumption electricity quantity and the direct current delivery electricity quantity of the system on site, PDC(t) is the DC output power at time t, PD(t) is sunward negativePredicted power on load, PW(t) wind power planned output, PPVAnd (t) is the photovoltaic planned output.
S402, establishing system operation constraints including system power balance constraints, photo-thermal power station operation constraints, wind power output constraints, photovoltaic output constraints, direct current tie line operation constraints and system standby constraints, wherein the system operation constraints comprise the following specific steps:
1) system constraints
Power balance constraint
PPV(t)+PW(t)+PCSP(t)＝PD(t)+PL(t) (13)
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 (15)
② restriction of climbing speed
Heat storage capacity constraint of heat storage system
ECSP，min≤ECSP(t)≤ρTESPCSP，max (17)
3) Wind power operation constraint condition
0≤PW(t)≤PW，F(t) (18)
4) Photovoltaic operating constraints
0≤PPV(t)≤PPV，F(t) (19)
5) Direct current tie line operational constraints
Upper and lower limits of output power
PDC.min≤PDC(t)≤PDC.max (20)
In the formula: pDC.minAnd PDC.maxRespectively carry the upper and lower limits of power for the direct current tie line.
② transmission power adjustment rate constraint
In the formula:
and
the downward and upward adjustment states of the direct current tie line transmission power t time interval are respectively, 1 represents that the tie line transmission power is adjusted, otherwise, the transmission power is not adjusted,
and
the rate limit is adjusted up and down for the tie line power transmission.
③ adjacent time interval can not reverse adjust power restraint
Regulating time limit value of junctor
In the formula: x is the number ofmaxThe maximum adjustment times of the direct current connecting line in one scheduling day are obtained.
S403: 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. Therefore, the improved DESO algorithm can be effectively applied to large-scale power system optimization operation calculation.
The method for solving the optimal configuration model of the photo-thermal power generation capacity in different scenes 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, photo-thermal adjustable capacity data and the like.
b. And (5) initializing a population. Production initialization population
Each individual in the population represents a group of control variables, including planned photothermal output, planned wind farm output, planned photovoltaic power plant output, and direct current (dc) 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 constraint condition of the photo-thermal power generation capacity configuration model, 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 reaching the maximum evolution algebra and outputting the photothermal power capacity PCSPAnd (t) the minimum power generation capacity, the local consumption of wind power photovoltaic electric quantity and direct current outgoing electric quantity by the system.
S5 includes the steps of:
s501, comparing photo-thermal power generation capacity P under different scenesCSP(t), determining the configured minimum power generation capacity.
Example 2
Fig. 3 is a modified IEEE RTS-24 node test system with dc output, and by taking this as an example, it is verified that the optimal configuration method for the photo-thermal power generation capacity of the full renewable energy source sending end system provided by the present invention satisfies the dc output requirement:
s1: acquiring historical annual wind power, photovoltaic and day-ahead output prediction curves and load curves of a sending end system;
in a sending end system, the rated power of a wind power cluster is 1200MW, the rated power of a photovoltaic cluster is 1000MW, and the limit of an outward delivery channel is 600 MW. The wind power active output prediction curve in a typical day in a historical year is shown in a graph 4, the photovoltaic active output prediction curve is shown in a figure 5, and the typical daily load curve is shown in a figure 6.
S2: dividing a full renewable energy base operation scene;
and dividing the operation scenes of the full renewable energy base based on a K-means clustering algorithm to respectively obtain the operation scenes of large wind and light, small wind and light and small wind and light. The typical day operation scene with large wind and large light is shown in fig. 7, the typical day operation scene with large wind and small light is shown in fig. 8, the typical day operation scene with small wind and large light is shown in fig. 9, and the typical day operation scene with small wind and small light is shown in fig. 10.
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 photo-thermal power station power generation capacity configuration optimization model with maximum objective of on-site wind power, photovoltaic electric quantity and direct current outgoing electric quantity consumption of a sending end system, and solving the photo-thermal power station power generation capacity configuration optimization model under different scenes
Solving a photo-thermal power generation capacity configuration optimization model under four typical daily operation scenes of large wind and light, small wind and light and small wind and light respectively to obtain wind-light on-site consumption, direct current output electric quantity and sum W of the wind-light on-site consumption and the direct current output electric quantity corresponding to different photo-thermal power generation capacities under each scene, wherein the following table shows that:
it can be seen from the above table that in a typical daily operation scene with high wind and high light, the power generation capacity is 960MW, and at this time, the W between the local consumed wind and light power and the dc output power is the largest, and the dc output requirement is satisfied. When the capacity is increased or decreased, it can be seen that the sum W of the local wind-solar power and the direct current output power is obviously decreased, so that the 960MW power generation capacity is the optimal power generation capacity. Similarly, under the typical daily operation scene of large wind and small light, the optimal power generation capacity is 1160 MW. Under the typical daily operation scene with small wind and large light, the optimal power generation capacity is 1120 MW. Under the wind low light small typical day operation scene, the optimal power generation capacity is 1100 MW. The maximum on-site wind, light and electricity consumption and the maximum direct current delivery capacity of the system are considered under the photo-thermal power generation capacity configuration obtained in the method, and the effectiveness of the method is proved.
S6: and determining the photo-thermal minimum power generation capacity adapting to multiple scenes.
The generating capacity of the photo-thermal unit adapting to multiple operation scenes can be guaranteed to be the minimum generating capacity. Therefore, as can be seen from the data in the above table 4 class operating scenario, the minimum power generation capacity is 1160 MW.
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. The method for configuring the photo-thermal power generation capacity of the full renewable energy source sending end system meeting the direct current sending requirement comprises the following steps:
s1: acquiring historical annual wind power, photovoltaic and day-ahead output prediction curves and load curves of a sending end system;
s2: dividing the operation scene of a fully renewable energy source sending end system;
s3: establishing a photo-thermal power station adjusting model;
s4: establishing a photo-thermal power station power generation capacity optimal configuration model under the condition of direct current delivery and solving;
s5: and determining the photo-thermal minimum power generation capacity adapting to multiple scenes.
2. The method for configuring solar-thermal power generation capacity of a complete renewable energy delivery system satisfying dc delivery requirement according to claim 1, wherein S1 comprises the steps of:
s101: obtaining a prediction curve P of the wind power day-ahead output of a transmitting end of a full-year-round renewable energy baseW，F(t) photovoltaic day-ahead output prediction curve PPV，F(t) and load day-ahead output prediction curve PD(t)；
S102: setting a DC delivery power limit PDC.maxAnd daily adjustment times constraints.
3. The method for configuring solar-thermal power generation capacity of a complete renewable energy delivery system satisfying dc delivery requirement according to claim 1, wherein S2 comprises the steps of:
s201, calculating to obtain daily wind power and photovoltaic power generation capacity Qwind、QpVThe formula used is:
and S202, aggregating 365 groups of wind power and photovoltaic power generation data based on a k-means clustering algorithm to obtain 4 types of wind-light combined power generation scene clusters, including large wind and light, small wind and light and large wind and light operation scenes.
4. The method for configuring solar-thermal power generation capacity of a complete renewable energy delivery system satisfying dc delivery requirement according to claim 1, wherein S3 comprises 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.
5. The method for configuring solar-thermal power generation capacity of a complete renewable energy delivery system satisfying dc delivery requirement according to claim 1, wherein S4 comprises the steps of:
s401, establishing an objective function by taking the maximum local wind power and photovoltaic consumption and direct current delivery capacity of a delivery end system as a target, and equivalently establishing an optimization model by taking the algebraic sum of the local wind power and photovoltaic consumption capacity and the direct current delivery capacity of the system as an equivalent:
in the formula: i is the time period number, TiThe total time period number, delta t, the time length of each time period, W, the algebraic sum of the consumption electricity quantity and the direct current delivery electricity quantity of the system on site, PDC(t) is the DC output power at time t, PD(t) predicting power for the load before day, PW(t) wind power planned output, PPVAnd (t) is the photovoltaic planned output.
S402, establishing system operation constraints including system power balance constraint, photo-thermal power station operation constraint, wind power output constraint, photovoltaic output constraint, direct current tie line operation constraint and system standby constraint.
S403: with improved DESOSolving the photo-thermal power generation capacity optimization configuration model under different scenes by an algorithm to output photo-thermal power generation capacity PCSP(t), minimum power generation capacity, system wind and light abandoning amount, direct current outgoing power and optimization target W.
6. The method for configuring solar-thermal power generation capacity of a complete renewable energy delivery system satisfying dc delivery requirement according to claim 1, wherein S5 comprises the steps of:
s501, comparing photo-thermal power generation capacity P under different scenesCSP(t), determining the photothermal minimum power generation capacity.
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| CN114825381A (en) * | 2022-05-22 | 2022-07-29 | 国网甘肃省电力公司电力科学研究院 | Capacity configuration method for photo-thermal power station of wind-solar new energy base |
| CN116187559A (en) * | 2023-02-21 | 2023-05-30 | 华润电力技术研究院有限公司 | Centralized wind power ultra-short-term power prediction method, system and cloud platform |
| CN116542475A (en) * | 2023-05-09 | 2023-08-04 | 河海大学 | Two-stage optimization scheduling method considering working mode of photo-thermal power station collector |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114825381A (en) * | 2022-05-22 | 2022-07-29 | 国网甘肃省电力公司电力科学研究院 | Capacity configuration method for photo-thermal power station of wind-solar new energy base |
| CN116187559A (en) * | 2023-02-21 | 2023-05-30 | 华润电力技术研究院有限公司 | Centralized wind power ultra-short-term power prediction method, system and cloud platform |
| CN116187559B (en) * | 2023-02-21 | 2024-03-15 | 华润电力技术研究院有限公司 | Centralized wind power ultra-short-term power prediction method, system and cloud platform |
| CN116542475A (en) * | 2023-05-09 | 2023-08-04 | 河海大学 | Two-stage optimization scheduling method considering working mode of photo-thermal power station collector |
| CN116542475B (en) * | 2023-05-09 | 2023-11-07 | 河海大学 | Two-stage optimization scheduling method considering working mode of photo-thermal power station collector |
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