CN111325395A - Multi-time scale source optimization scheduling method for photo-thermal power station to participate in adjustment - Google Patents

Multi-time scale source optimization scheduling method for photo-thermal power station to participate in adjustment Download PDF

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CN111325395A
CN111325395A CN202010099865.3A CN202010099865A CN111325395A CN 111325395 A CN111325395 A CN 111325395A CN 202010099865 A CN202010099865 A CN 202010099865A CN 111325395 A CN111325395 A CN 111325395A
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杨美颖
刘文颖
汪宁渤
张尧翔
周强
王方雨
陈钊
夏鹏
赵亮
张雨薇
王定美
胡阳
朱丽萍
李潇
陈鑫鑫
郇悦
张雯程
刘紫东
曾贇
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State Grid Corp of China SGCC
North China Electric Power University
State Grid Gansu Electric Power Co Ltd
Electric Power Research Institute of State Grid Gansu Electric Power Co Ltd
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State Grid Corp of China SGCC
North China Electric Power University
State Grid Gansu Electric Power Co Ltd
Electric Power Research Institute of State Grid Gansu Electric Power Co Ltd
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Abstract

The invention discloses a multi-time scale source optimization scheduling method for a photo-thermal power station to participate in regulation. The method comprises the following steps: reading the relevant information of various power supplies and loads in time scales before and during the day; establishing a photo-thermal power station adjusting model; establishing a day-ahead wind power-photovoltaic-photo-thermal power combined power generation system optimization scheduling model on a day-ahead time scale with the maximum wind power and photovoltaic power generation amount as a target; on the time scale of a day, new energy prediction errors are considered, the maximum active output of the wind-solar power plant is a target, and an optimization scheduling model of the wind power-photovoltaic-photothermal-thermal power combined generation system in the day is established; and finally, providing a wind power-photovoltaic-photo-thermal power combined generation optimization scheduling method of time scales in the day and the day. The invention provides a multi-time scale source optimization scheduling method for a photothermal power station to participate in regulation, which utilizes the excellent regulation performance of the photothermal power station and improves the wind power and photovoltaic absorption capacity of a power grid through wind power-photovoltaic-photothermal-thermal power coordinated scheduling.

Description

Multi-time scale source optimization scheduling method for photo-thermal power station to participate in adjustment
Technical Field
The invention belongs to the technical field of operation and control of power systems, and particularly relates to a multi-time scale source optimization scheduling method for a photo-thermal power station to participate in adjustment.
Background
With the increasing prominence of energy and environmental issues, it has become common knowledge to gradually change the energy structure and develop renewable energy. However, renewable energy sources such as solar energy and wind energy have the characteristics of randomness, intermittency and the like, and after the renewable energy sources are connected to a power grid in a large scale, great challenges are brought to the operation and scheduling of a power system. The novel solar-thermal power generation form has a large-capacity heat storage system, can inhibit the influence of solar random fluctuation on output, has good scheduling characteristics and peak regulation capacity, has the regulation speed and depth superior to those of a conventional thermal power generating unit, and is a new energy power generation form capable of being scheduled and controlled. Therefore, the research on the wind power-photovoltaic-photo-thermal power combined power generation optimization scheduling method has important significance for stabilizing the power fluctuation of wind power and photovoltaic power generation and promoting the wind power and photovoltaic consumption.
At present, photo-thermal power generation is rapidly developed in China. By the end of 2018, the solar-thermal power generation device is built in China with 21.5 ten thousand kilowatts, and the solar-thermal power generation device still under construction has about 40 ten thousand kilowatts. By the end of 2020, 500 ten thousand kilowatt photo-thermal power generation is built in China. The excellent regulation capacity of a future photo-thermal power station under large-scale construction is considered, the coordination optimization scheduling operation with mature new energy power generation forms such as wind power generation and photovoltaic power generation is researched, the reduction of the power abandonment rate of new energy is facilitated, the safe and economic operation and consumption of various types of new energy power generation are promoted, and the method has an important meaning for constructing a sending-end comprehensive energy power system taking renewable energy as a main energy source.
The photo-thermal power station is started later due to higher initial investment cost, the photo-thermal power stations built at home and abroad are mostly in a test operation stage at present, and partial photo-thermal power station control strategies and operation modes for commercial operation are also based on self optimized operation without considering the dispatching requirement of a power grid. In the aspect of academic research, a photothermal-thermal power combined scheduling model in a time scale of the day before is established in literature, peak regulation pressure of a thermal power unit is relieved by using scheduling flexibility of a photothermal power station, frequent regulation of the thermal power unit is avoided, and economical efficiency of system operation is improved. According to the risk-based day-long unit combination model, complementarity of wind power and photo-thermal power generation is considered, and the photo-thermal power station is proved to have the advantages of stabilizing power fluctuation and promoting wind power consumption under the condition of supporting large-scale wind power grid connection. The active complementary optimization scheduling method of photothermal power, conventional thermal power and wind power is researched by utilizing complementary characteristics of photothermal power generation and other power generation forms in the day ahead and in the day, but the optimization scheduling of a multi-time scale wind power-photovoltaic-thermal power-photothermal multi-source complementary combined power generation system is not involved.
In summary, the invention provides a multi-time scale source optimization scheduling method for a photothermal power station to participate in regulation based on the existing research, and the method improves the regulation capability of the system and promotes the consumption of wind power and photovoltaic power by coordinating and scheduling the photothermal power station.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a multi-time scale source optimization scheduling method for a photo-thermal power station to participate in regulation, which is used for solving the problems of untimely regulation response and large plan tracking error when a high-proportion new energy is accessed into a power grid unit and providing reference for power grid operation.
A multi-time scale source optimization scheduling method for photo-thermal power stations to participate in adjustment comprises the following steps:
s1: reading the relevant information of various power supplies and loads in time scales before and during the day;
s2: establishing an adjusting model of the photo-thermal power station;
s3: establishing an optimization scheduling model of a day-ahead wind power-photovoltaic-photo-thermal power combined power generation system;
s4: establishing an optimized dispatching model of the wind power-photovoltaic-photo-thermal power combined power generation system in a day;
s5: the method for optimizing and scheduling the wind power-photovoltaic-photo-thermal power combined generation in time scales before and during the day is provided.
The S1 includes the steps of:
s101: obtaining prediction information P of day-ahead active power output of wind powerW，PF(t) obtaining photovoltaic day-ahead active power output prediction information PPV，PF(t) obtaining the day-ahead active power output prediction information P of the photothermal power stationjCSP，PF(t) obtaining the active power output prediction information P before the load dayL，PF(t) obtaining day-ahead heat storage plan information E of the photothermal power stationjCSP，PF(t)；
S102: obtaining super-short term prediction information P of active power output in wind power dayW，IF(t) obtaining the ultra-short term prediction information P of the photovoltaic active power output in the dayPV，IF(t) acquiring ultra-short term prediction information P of daily active power output of the photo-thermal power stationjCSP，IF(t) obtaining daily heat storage capacity planning information E of the photothermal power stationjCSP，IF(t)；
S103: and acquiring adjustment information of the thermal power generating unit and the photo-thermal unit, wherein the adjustment information comprises a unit climbing rate, upper and lower unit output limits and upper and lower thermal power station heat storage limits.
The S2 includes the steps of:
s201: 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 output power, the minimum start-stop time, the climbing speed and the operation constraint of heat storage capacity.
The S3 includes the steps of:
s301: in a day-ahead wind power-photovoltaic-photo-thermal power combined power generation system optimization scheduling model, adjusting capabilities of thermal power and a photo-thermal power station are fully utilized, and a target function is established by taking the maximum wind power and photovoltaic power generation amount as a target;
s302: and establishing system operation constraints including day-ahead system power balance constraint, thermal power unit output constraint, photo-thermal power station output constraint, wind power output constraint, photovoltaic output constraint and system standby constraint.
The S4 includes the steps of:
s401: proposing a model prediction control-based intra-day scheduling mode;
s402: in an optimized dispatching model of the wind power-photovoltaic-photothermal-thermal power combined power generation system in the day, the regulating capacity of a thermal power station is fully utilized, and a target function is established by taking the maximum active output of a wind-solar power plant as a target;
s403: and establishing system operation constraints including intraday system power balance constraint, thermal power unit output constraint, photo-thermal power station output constraint, wind power output constraint, photovoltaic output constraint and system standby constraint.
The S5 includes the steps of:
s501: an NSGA-II algorithm containing an elite retention strategy is utilized to solve a day-ahead wind power-photovoltaic-photothermal-thermal power combined power generation system optimization scheduling model to obtain a thermal power generation plan P under a day-ahead scaleiTH，P(t) photovoltaic-thermal power plant Power Generation plan PjCSP，P(t) wind-power generation plan PW，P(t) and photovoltaic Power Generation plan PPV，P(t)；
S502: an optimized scheduling model of the intra-day wind power-photovoltaic-photo-thermal power combined generation system is solved by using an NSGA-II algorithm containing an elite reservation strategy to obtain a thermal power generation plan P under the intra-day scaleiTH，I(t) solar-thermal Power station Power Generation plan PjCSP，I(t) wind-power generation plan PW，I(t) and photovoltaic Power Generation plan PPV，I(t)。
The invention discloses a multi-time scale source optimization scheduling method for a photo-thermal power station to participate in regulation, and belongs to the technical field of operation and control of power systems. The method comprises the following steps: reading the relevant information of various power supplies and loads in time scales before and during the day; establishing an adjusting model of the photo-thermal power station; establishing an optimization scheduling model of a day-ahead wind power-photovoltaic-photo-thermal power combined power generation system; establishing an optimized dispatching model of the wind power-photovoltaic-photo-thermal power combined power generation system in a day; the method for optimizing and scheduling the wind power-photovoltaic-photothermal-thermal power combined power generation in time scales before and in days is provided. The invention provides a multi-time scale source optimization scheduling method for a photo-thermal power station to participate in regulation, which improves the regulation capability of a system and promotes the consumption of wind power and photovoltaic power by coordinating and scheduling the photo-thermal power station.
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 multi-time scale source optimization scheduling method for regulation of a photothermal power plant provided by the invention;
FIG. 2 is a flow chart of a non-dominated sorting genetic algorithm with elite strategy;
FIG. 3 is a network diagram of an IEEE RTS-24 node test system in example 2 provided by the present invention;
FIG. 4 is the wind-power day-ahead active power output prediction information provided by the present invention in example 2;
FIG. 5 is a graph of photovoltaic day-ahead active power output prediction information in example 2 provided by the present invention;
FIG. 6 is the active power output prediction information of the optical thermal power station in the past day of example 2 provided by the present invention;
FIG. 7 is the load day ahead active power output prediction information provided by the present invention in example 2;
FIG. 8 is a chart of heat storage capacity before the day of the optical thermal power plant in example 2 provided by the present invention;
FIG. 9 is the super short term prediction information of the wind power output in the day of the example 2 provided by the present invention;
FIG. 10 is the ultra-short term prediction information of photovoltaic output within a day for example 2 provided by the present invention;
FIG. 11 is the information of ultra-short term prediction of the effective output within the day of the optical-thermal power station in example 2 provided by the present invention;
FIG. 12 is a chart of heat storage capacity within a day of the optical thermal power plant according to example 2 provided by the present invention;
FIG. 13 is a plan for power generation on a live day ahead in example 2 provided by the present invention;
FIG. 14 is a plan for day-ahead power generation of a photothermal power plant in example 2 provided by the present invention;
FIG. 15 is a wind-powered day-ahead power generation schedule of example 2 provided by the present invention;
FIG. 16 is a photovoltaic day-ahead power generation schedule in example 2 provided by the present invention;
FIG. 17 is a plan for power generation within a fire-electricity day in example 2 provided by the present invention;
FIG. 18 is a diurnal power generation plan for a photothermal power plant of example 2 provided by the present invention;
FIG. 19 is a wind-powered intraday power generation plan of example 2 provided by the present invention;
FIG. 20 is a photovoltaic in-day power generation schedule in example 2 provided by the present invention.
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 are possible in addition to those described in detail.
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 multi-time scale source optimization scheduling method for regulation of a photothermal power station. In fig. 1, a flow chart of a multi-time scale source optimization scheduling method for a photothermal power station to participate in regulation provided by the invention includes:
s1: reading the relevant information of various power supplies and loads in time scales before and during the day;
s2: establishing an adjusting model of the photo-thermal power station;
s3: establishing an optimization scheduling model of a day-ahead wind power-photovoltaic-photo-thermal power combined power generation system;
s4: establishing an optimized dispatching model of the wind power-photovoltaic-photo-thermal power combined power generation system in a day;
s5: the method for optimizing and scheduling the wind power-photovoltaic-photo-thermal power combined generation in time scales before and during the day is provided.
The S1 includes the steps of:
s101: obtaining prediction information P of day-ahead active power output of wind powerW，PE(t) obtaining photovoltaic day-ahead active power output prediction information PPV，PE(t) obtaining the day-ahead active power output prediction information P of the photothermal power stationjCSP，PF(t) obtaining the active power output prediction information P before the load dayL，PF(t) obtaining day-ahead heat storage plan information E of the photothermal power stationjCSP，PF(t)；
S102: obtaining super-short term prediction information P of active power output in wind power dayW，IF(t) obtaining the ultra-short term prediction information P of the photovoltaic active power output in the dayPV，IF(t) acquiring ultra-short term prediction information P of daily active power output of the photo-thermal power stationjCSP，IF(t) obtaining daily heat storage capacity planning information E of the photothermal power stationjCSP，IF(t)；
S103: and acquiring adjustment information of the thermal power generating unit and the photo-thermal unit, wherein the adjustment information comprises a unit climbing rate, upper and lower unit output limits and upper and lower thermal power station heat storage limits.
The S2 includes the steps of:
s201: 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.
①, the minimum output of the photo-thermal unit is defined as 20% of rated power, the maximum output is defined as rated power, and the output limit of the photo-thermal unit is constrained as follows:
20％PCSP，N≤PCSP(t)≤PCSP，N(3)
in the formula: pCSP，NThe rated output of the photo-thermal unit.
② the on/off time constraint of the photothermal unit is expressed as:
Figure BDA0002386515170000071
in the formula of UCSP(t) is the operating state of the unit at time t, UCSP(t) ═ 1 shows that the photothermal unit is in operation, UCSP(t) ═ 0 shows that the photothermal unit is in a shutdown state; t isCSP，off(t)、TCSP，on(t) respectively representing the shutdown time and the running time of the photothermal unit at the moment t; t isCSP，min off、TCSP，min onThe shortest downtime and the shortest running time of the unit are respectively.
The climbing constraint of the ③ photothermal unit is expressed as:
Figure BDA0002386515170000072
in the formula: pCSP，upAnd PCSP，downThe maximum up-and-down climbing capacity of the photo-thermal unit is 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.
① the maximum capacity of a thermal storage system of a photovoltaic plant is usually measured in terms of the "Full Load Hours (FLH)" for a steam turbine plant, for example, the thermal storage capacity of a typical thermal plant is 9FLH, which means the ability of a thermal plant to operate at full load for 9h without light.
ECSP，min≤ECSP(t)≤ρTESPCSP，N(6)
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 S3 includes the steps of:
s301: in a day-ahead wind power-photovoltaic-photothermal-thermal power combined power generation system optimization scheduling model, the adjusting capability of thermal power and a thermal power station is fully utilized, and a target function is established by taking the maximum wind power and photovoltaic power generation amount as a target as follows:
Figure BDA0002386515170000081
in the formula: pW，P(t) planned output before the day at the moment t of wind power generation; pPV，P(t) the planned force before the day of photovoltaic time t; t is a scheduling period, and the whole day is divided into 96 time periods.
S302: establishing system operation constraints including day-ahead system power balance constraint, thermal power unit output constraint, photo-thermal power station output constraint, wind power output constraint, photovoltaic output constraint and system standby constraint, and specifically comprising the following steps:
1) system constraints
① power balance constraints
Figure BDA0002386515170000082
In the formula: n is a radical ofTHThe number of thermal power generating units; n is a radical ofCSPThe number of photo-thermal units; t isiTH，P(t) is planned output of thermal power generating unit i at moment t day ahead, PjCSP，P(t) the planned output of the photothermal unit j at the moment t; pL，PF(t) is the predicted output at time t of the load.
② rotating for standby
Figure BDA0002386515170000091
In the formula: u shapeiTH(t) is the starting and stopping state of the thermal power generating unit i at the moment t, UiTHWhen t is 1, U indicates that the thermal power generating unit is in an operating stateiTH(t) 0 indicates that the thermal power generating unit is in a shutdown state; piTH，max，PiTH，minRespectively the maximum and minimum active output of the thermal power generating unit; delta PiW，up(t)，ΔPiW，low(t) respectively keeping positive and negative rotation required for dealing with wind power prediction errors at the moment t for standby; delta PiPV，up(t)，ΔPiP V，lowAnd (t) respectively keeping positive and negative rotation standby required for dealing with photovoltaic prediction errors at the moment t.
2) Thermal power generating unit operation constraint condition
① upper and lower limit constraints of output power
PiTH，min≤PiTH，P(t)≤PiTH，max(10)
② minimum on-off time constraint
Figure BDA0002386515170000092
In the formula: t isiTH，off(t)、TiTH，on(t) respectively representing the shutdown time and the running time of the thermal power generating unit i at the moment t; t isiTH，min off、TiTH，min onThe shortest downtime and the shortest running time of the unit i are respectively.
③ ramp rate constraints
Figure BDA0002386515170000093
In the formula: piTH, up andPiTH，downthe maximum up-down climbing capacity of the thermal power generating unit is respectively.
3) Photo-thermal unit operation constraint condition
① upper and lower limit constraints of output power
20％PjCSP，N≤PjCSP，P(t)≤PjCSP，N(13)
PjCSP，P(t)≤PjCSP，PF(t) (14)
② minimum on-off time constraint
Figure BDA0002386515170000101
③ ramp rate constraints
Figure BDA0002386515170000102
④ Heat storage System Heat storage Capacity constraints
Figure BDA0002386515170000103
4) Wind power operation constraint condition
0≤PW，P(t)≤PW，PF(t) (18)
5) Photovoltaic operating constraints
0≤PPV，P(t)≤PPV，PF(t) (19)
The S4 includes the steps of:
s401: and (3) proposing an intra-day scheduling mode based on model predictive control: starting the wind power and photovoltaic super-short-term predicted values once every 15min by taking the super-short-term predicted values of wind power and photovoltaic as references, obtaining active power output increment of 1h in the future in a rolling mode, only executing a control instruction in the 1 st time period each time, and correcting active power output of a thermal power and a photo-thermal power station in time;
① the rolling prediction model of wind power, photovoltaic and load obtains the prediction value of the future optimization time domain as the input variable, and the actual active power output of each unit in the actual system is used as the initial value P0(k)；
Figure BDA0002386515170000111
In the formula: piTH，0(t)＝[P1TH，0(t)，P2TH，0(t)，...，PnTH，0(t)]TActual output of the thermal power generating unit at the moment t; pjCSP，0(t)＝[P1CSP，0(t)，P2CSP，0(t)，...，PnCSP，0(t)]TActual output of the photothermal unit at time t, PW，0(t)、PPV，0And (t) the actual active power output of the wind power and the photovoltaic at the moment t respectively.
②, establishing an active power output prediction model of each unit, and taking an active power output increment as a control variable;
Figure BDA0002386515170000112
in the formula: p (t + i | t) is the active power output of each unit at the future t + i moment predicted at the t moment; and the delta P (t + k | t) is used for predicting the active increment of each unit in the future [ t + k-1, t + k ] time period at the time t.
③, establishing an objective function with the maximum active power output of the wind-solar power plant station as a target, and optimally solving control variable sequences [ delta P (t +1| t), delta P (t +2| t) ], delta P (t + N | t) in N time periods in the future]T；
④, only issuing a first control variable sequence to obtain the active power output of each unit at the moment of t + 1;
P(t+1|t)＝P0(t)+ΔP(t+1|t) (22)
⑤ takes the actual measurement value at time t +1 as the initial value at time t +1, i.e. P0(t+1)＝PrealAnd (t +1) repeatedly executing ② - ④ until the power generation plan at all times of the day is updated.
S402: in the day-to-day optimization scheduling model, the regulation capacity of the thermal power station is fully utilized, the day-to-day planned output is determined by taking the maximum active output of the wind-solar power station as a target, and the objective function is as follows:
Figure BDA0002386515170000113
in the formula: pW，I(t+i|t)、PPV，I(t + i | t) predicting the planned daily output of wind power and photovoltaic at the future t + i moment for the t moment respectively; pW，0(t)、PPV，0(t) actual active power output of wind power and photovoltaic at the moment t respectively; delta PW，I(t+k|t)、ΔPPV，IAnd (t + k | t) predicting the active increment of wind power and photovoltaic at the future t + k moment respectively at the t moment.
S403: establishing system operation constraints including intraday system power balance constraint, thermal power unit output constraint, photo-thermal power station output constraint, wind power output constraint, photovoltaic output constraint and system standby constraint, and specifically comprising the following steps:
1) system constraints
① power balance constraints
Figure BDA0002386515170000121
② rotating for standby
Figure BDA0002386515170000122
2) Thermal power generating unit operation constraint condition
① upper and lower limit constraints of output power
PiTH，min≤PiTH，I(t)≤PiTH，max(26)
② minimum on-off time constraint
Figure BDA0002386515170000123
③ ramp rate constraints
Figure BDA0002386515170000131
3) Photo-thermal unit operation constraint condition
① upper and lower limit constraints of output power
20％PjCSP，N≤PjCSP，I(t)≤PjCSP，N(29)
PjCSP，I(t)≤PjCSP，IF(t) (30)
② minimum on-off time constraint
Figure BDA0002386515170000132
③ ramp rate constraints
Figure BDA0002386515170000133
④ Heat storage System Heat storage Capacity constraints
Figure BDA0002386515170000134
4) Wind power operation constraint condition
0≤PW，I(t)≤PW，IF(t) (34)
5) Photovoltaic operating constraints
0≤PPV，I(t)≤PPV，IF(t) (35)
The S5 includes the steps of:
s501: the genetic algorithm has the characteristics of simplicity, high adaptability and strong robustness, and has strong adaptability to the economic dispatching problem of the power system with more processing variables and complex constraints. An elite retention strategy is added on the basis of the traditional genetic algorithm, so that the optimal individuals in the parent population can be directly stored in the offspring population, the defects of local optimization, low convergence speed and the like of the traditional algorithm are overcome, and the credibility of the result is effectively improved. Therefore, the standard genetic algorithm containing the elite reservation strategy can be used for solving the day-ahead and day-inside scheduling problems containing multivariable and complex constraints. The algorithm flow is shown in fig. 2.
① calculating a fast dominance sort operator
⑴ for each individual P in the population P, the number n of individuals P dominates the population P is calculatedpAnd storing the individuals governed by p in S;
⑵ let Flayer be 1;
⑶ find all npIndividuals of 0, stored in the array FlayerPerforming the following steps;
⑷ for FlayerOf each individual pp: traverse SpFor each individual l, perform nl＝nl-1；
⑸ layer +1, repeat ⑶.
② calculate crowd distance operator
⑴ congestion distance d for each individual pp＝0，p＝1,2，…,N；
⑵ sorting each individual according to the value of the objective function f;
⑶ for each individual p, d is calculatedp＝dp+(fm(p+1)-fm(p-1)), congestion is preferentially selectedDistance dpA large individual.
The initial population size selected in the genetic algorithm is 300, and the maximum iteration number is 300. The ratio of the number of chromosomes participating in the crossover operation to the total number of chromosomes was 0.9, and the ratio of the number of mutated gene sites to the total number of gene sites of the total number of chromosomes was 0.25.
An NSGA-II algorithm containing an elite retention strategy is utilized to solve a day-ahead wind power-photovoltaic-photothermal-thermal power combined power generation system optimization scheduling model to obtain a thermal power generation plan P under a day-ahead scaleiTH，P(t) photovoltaic plant Power Generation plan PjCSP，P(t) wind-power generation plan PW，P(t) and photovoltaic Power Generation plan PPV，P(t)；
S502: an optimized scheduling model of the intra-day wind power-photovoltaic-photo-thermal power combined generation system is solved by using an NSGA-II algorithm containing an elite reservation strategy to obtain a thermal power generation plan P under the intra-day scaleiTH，I(t) solar-thermal Power station Power Generation plan PjCSP，I(t) wind-power generation plan PW，I(t) and photovoltaic Power Generation plan PPV，I(t)。
Example 2
Fig. 3 is a modified IEEE RTS-24 node test system, and taking this as an example, the invention provides a multi-time scale source optimization scheduling method for regulation of a photothermal power station:
s1: reading the relevant information of various power supplies and loads;
(1) in a regional power grid, the rated power of a wind power cluster is 450MW, the rated power of a photovoltaic cluster is 200MW, the rated power of a thermal power unit is 800MW, the rated power of a photothermal power unit is 100MW, the prediction information of the wind power day-ahead active output is shown in figure 4, the prediction information of the photovoltaic day-ahead active output is shown in figure 5, the prediction information of the photothermal power station day-ahead active output is shown in figure 6, the prediction information of the load day-ahead active output is shown in figure 7, the prediction information of the photothermal power station day-ahead heat storage plan is shown in figure 8, the prediction information of the wind power day-in active output ultra-short term is shown in figure 9, the prediction information of the photovoltaic day-in active output ultra-short term is shown in figure 10, the prediction information of the photothermal power station day-in active output ultra-.
(2) Regulatory information for conventional thermal power generating units
Figure BDA0002386515170000151
Figure BDA0002386515170000161
(3) Photothermal power station information
Bus numbering 10
Upper limit of output P of photothermal power stationCSP，max/MW 100
Lower limit of output P of photo-thermal power stationCSP，min/MW 10
Number of hours rho of full-load heat storage operation of photo-thermal power stationTES/FLH 10
Climbing speed/MW & min of photo-thermal power station unit -1 9
S2: establishing an adjusting model of the photo-thermal power station;
s3: establishing a day-ahead wind power-photovoltaic-photo-thermal power combined power generation system optimization scheduling model by taking the maximum wind power and photovoltaic power generation amount as a target;
s4: considering a new energy prediction error, establishing an optimized dispatching model of the wind power-photovoltaic-photothermal-thermal power combined power generation system in the day by taking the maximum active output of the wind-solar power plant as a target;
s5: calculating the active power output plans of various power supplies in the day and the day;
and solving the models (8) to (19) to obtain a thermal power day-ahead power generation plan as shown in the figure 13, a photo-thermal power station day-ahead power generation plan as shown in the figure 14, a wind power day-ahead power generation plan as shown in the figure 15 and a photovoltaic day-ahead power generation plan as shown in the figure 16.
Solving the models (23) - (35) to obtain a thermal power day-internal power generation plan as shown in the figure 17, a photo-thermal power station day-internal power generation plan as shown in the figure 18, a wind power day-internal power generation plan as shown in the figure 19 and a photovoltaic day-internal power generation plan as shown in the figure 20.
The increased wind power consumption and the increased photovoltaic consumption are compared as shown in the following table, 4.8% of wind abandoning and photoelectric abandoning amount is reduced in a day-ahead plan, and 4.9% of wind abandoning and photoelectric abandoning amount is reduced in a day-in plan, so that the regulation capability of the system can be improved and the wind power and photovoltaic consumption can be promoted through the coordinated dispatching of the photo-thermal power station.
Figure BDA0002386515170000171
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 embodiments of the present invention without departing from the spirit and scope of the present invention, which is set forth in the claims appended hereto.

Claims (6)

1. A multi-time scale source optimization scheduling method for photo-thermal power stations to participate in adjustment comprises the following steps:
s1: reading the relevant information of various power supplies and loads in time scales before and during the day;
s2: establishing an adjusting model of the photo-thermal power station;
s3: establishing an optimization scheduling model of a day-ahead wind power-photovoltaic-photo-thermal power combined power generation system;
s4: establishing an optimized dispatching model of the wind power-photovoltaic-photo-thermal power combined power generation system in a day;
s5: the method for optimizing and scheduling the wind power-photovoltaic-photo-thermal power combined generation in time scales before and during the day is provided.
2. The method for optimizing and scheduling multi-time scale source for regulating participation of photo-thermal power station as claimed in claim 1, wherein said S1 comprises the following steps:
s101: obtaining prediction information P of day-ahead active power output of wind powerW，PF(t) obtaining photovoltaic day-ahead active power output prediction information PPV，PF(t) obtaining the day-ahead active power output prediction information P of the photothermal power stationjCSP，PF(t) obtaining the active power output prediction information P before the load dayL，PF(t) obtaining day-ahead heat storage plan information E of the photothermal power stationjCSP，PF(t)；
S102: obtaining super-short term prediction information P of active power output in wind power dayW，IF(t) obtaining the ultra-short term prediction information P of the photovoltaic active power output in the dayPV，IF(t) acquiring ultra-short term prediction information P of daily active power output of the photo-thermal power stationjCSP，IF(t) obtaining daily heat storage capacity planning information E of the photothermal power stationjCSP，IF(t)；
S103: and acquiring adjustment information of the thermal power generating unit and the photo-thermal unit, wherein the adjustment information comprises a unit climbing rate, upper and lower unit output limits and upper and lower thermal power station heat storage limits.
3. The method for optimizing and scheduling multi-time scale source for regulating participation of photo-thermal power station as claimed in claim 1, wherein said S2 comprises the following steps:
s201: 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 output power, the minimum start-stop time, the climbing speed and the operation constraint of heat storage capacity.
4. The method for optimizing and scheduling multi-time scale source for regulating participation of photo-thermal power station as claimed in claim 1, wherein said S3 comprises the following steps:
s301: in a day-ahead wind power-photovoltaic-photothermal-thermal power combined power generation system optimization scheduling model, the adjusting capability of thermal power and a thermal power station is fully utilized, and a target function is established by taking the maximum wind power and photovoltaic power generation amount as a target as follows:
Figure FDA0002386515160000021
in the formula: pW，P(t) planned output before the day at the moment t of wind power generation; pPV，P(t) planned output before the day of photovoltaic time t; t is a scheduling period, and the whole day is divided into 96 time periods.
S302: and establishing system operation constraints including day-ahead system power balance constraint, thermal power unit output constraint, photo-thermal power station output constraint, wind power output constraint, photovoltaic output constraint and system standby constraint.
5. The method for optimizing and scheduling multi-time scale source for regulating participation of photo-thermal power station as claimed in claim 1, wherein said S4 comprises the following steps:
s401: and (3) proposing an intra-day scheduling mode based on model predictive control: starting the wind power and photovoltaic super-short-term predicted values once every 15min by taking the super-short-term predicted values of wind power and photovoltaic as references, obtaining active power output increment of 1h in the future in a rolling mode, only executing a control instruction in the 1 st time period each time, and correcting active power output of a thermal power and a photo-thermal power station in time;
s402: in an optimized dispatching model of the wind power-photovoltaic-photothermal-thermal power combined power generation system in the day, the regulating capacity of a thermal power station is fully utilized, the maximum active output of a wind-solar power plant is used as a target to determine planned output in the day, and the objective function is as follows:
Figure FDA0002386515160000022
in the formula: pW，I(t+i|t)、PPV，I(t + i | t) predicting the planned daily output of wind power and photovoltaic at the future t + i moment for the t moment respectively; pW，0(t)、PPV，0(t) actual active power output of wind power and photovoltaic at the moment t respectively; delta PW，I(t+k|t)、ΔPPV，IAnd (t + k | t) predicting the active increment of wind power and photovoltaic at the future t + k moment respectively at the t moment.
S403: and establishing system operation constraints including intraday system power balance constraint, thermal power unit output constraint, photo-thermal power station output constraint, wind power output constraint, photovoltaic output constraint and system standby constraint.
6. The method for optimizing and scheduling multi-time scale source for regulating participation of photo-thermal power station as claimed in claim 1, wherein said S5 comprises the following steps:
s501: an NSGA-II algorithm containing an elite retention strategy is utilized to solve a day-ahead wind power-photovoltaic-photothermal-thermal power combined power generation system optimization scheduling model to obtain a thermal power generation plan P under a day-ahead scaleiTH，P(t) solar-thermal Power station Power Generation plan PjCSP，P(t) wind-power generation plan PW，P(t) and photovoltaic Power Generation plan PPV，P(t)；
S502: an optimized scheduling model of the intra-day wind power-photovoltaic-photo-thermal power combined generation system is solved by using an NSGA-II algorithm containing an elite reservation strategy to obtain a thermal power generation plan P under the intra-day scaleiTH，I(t) solar-thermal Power station Power Generation plan PjCSP，I(t) wind-power generation plan PW，I(t) and photovoltaic Power Generation plan PPV，I(t)。
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