CN105205562A - Operation optimization method of tower-type solar power station receiver - Google Patents

Abstract

The invention discloses an operation optimization method of a tower-type solar power station receiver. The method comprises the following steps that 1, a distributed parameter model of the tower-type solar power station receiver is built, and when the net power generation efficiency of a power station is the highest, numeric values of the outlet temperature of the receiver under different illumination intensities are obtained through simulation; 2, a PID controller is designed, a control variable of the controller serves as the flow rate of a heat-transfer medium at the inlet of the receiver, and a controlled variable serves as the temperature of the heat-transfer medium at the outlet of the receiver; 3, maximum all-day power station net generation is taken as an optimization objective, a dynamic optimization problem is built; 4, the control variable of a continuous NLP optimization problem is discretized through CVP_SS, and solving is conducted by adopting an SQP algorithm. According to the operation optimization method of the tower-type solar power station receiver, on the premise that it is guaranteed that the receiver runs stably, the net generation of the power station is increased simultaneously, and a reference is provided for commercial operation of a tower-type solar power station.

Description

The running optimizatin method of solar power tower receiver

Technical field

The present invention relates to solar energy generation technology field, particularly relate to acceptor device, provide a kind of running optimizatin method for tower type solar receiver.

Background technology

Tower type solar thermo-power station utilizes the heliostat device of multiple independently tracked sun, solar light focusing is fixed on the receiver of reception top of tower to one, the heat transfer medium that heating flows through receiver inside becomes high temperature refrigerant, and the heat energy of recycling high temperature refrigerant drives steam turbine, electrical power generators.It is the one that in all large solar generation technologies, cost is minimum, light and heat collection is most effective, has a wide range of applications.

In tower type solar thermo-power station, the function of receiver absorbs the sun power of heliostat focusing and the heat transfer medium generation high temperature heat for heating its internal flow.The working temperature of heat transfer medium is by the clean generating efficiency in the whole power station of impact, conventionally the working temperature of heat transfer medium is more high better, this is because temperature gets over that the efficiency of high power converting system (being generally Rankine cycle system) is higher and the pump of system consumption can also by less.But, heat-transfer working medium working temperature increase the increase that will inevitably cause system thermal loss.Therefore, in solar power station, heat-transfer working medium working temperature is optimized and has great importance.

The domestic and international research to solar receiver running optimizatin is little at present.In the research of existing receiver running optimizatin, the conversion efficiency of power conversion system is considered as the constant irrelevant with receiver running temperature by some technology, ignores the impact of receiver outlet temperature on power conversion system; The receiver model that some technology adopt in running optimizatin process is lumped parameter model, have ignored the exemplary distribution parameter characteristic of receiver, can not reflect power station actual conditions well.

Summary of the invention

The invention provides a kind of running optimizatin method of tower type solar thermo-power station receiver, under ensureing the prerequisite of receiver even running, improve the net electric generation in power station, for the commercialized running of solar power tower provides reference simultaneously.

The technical solution used in the present invention is as follows:

A kind of step of running optimizatin method of solar power tower receiver is as follows:

1) under the distributed parameter model building solar power tower receiver also emulates and obtains different illumination intensity, when the clean generating efficiency in power station is the highest, the numerical value of receiver outlet temperature;

2) design PID controller, the control variable of its middle controller is receiver porch heat transfer medium flow velocity, and controlled variable is receiver exit heat-transfer medium temperature;

3) optimization aim is to the maximum with whole day power station net electric generation, constitution optimization problem;

4) by the control variable discretize of CVP_SS by N continuous LP optimization problem, and then SQP algorithm is adopted to solve, the change curve of receiver exit heat-transfer medium temperature setting value when obtaining power station whole day Energy Maximization.

Described step 1) be:

1.1 according to energy conservation equation, and the distributed parameter model of receiver is:

${\mathrm{\ρ}}_m{C}_m{V}_m\frac{\∂T_m}{\∂t}={\mathrm{I\η}}_{opt}{A}_{mirror}-{\mathrm{\ϵ\σA}}_o{T}_m^4-h_o{A}_o(T_m-T_a)-h_i{A}_i(T_m-T_f)---\left(1\right)$

${\mathrm{\ρ}}_f{C}_f{V}_f\frac{\∂T_f}{\∂t}+{\mathrm{\ρ}}_f{C}_{f}m\frac{\∂T_f}{\∂x}=h_i{A}_i(T_m-T_f)---\left(2\right)$

Wherein, A _ifor receiver inner surface of tube wall area, A _mirrorfor the heliostat field total area, A _ofor receiver pipeline sensitive surface external area, C _mfor receiver tube wall specific heat, C _ffor heat transfer medium specific heat, h _ifor receiver tube wall and internal heat transfer medium convection transfer rate, h _ofor receiver tube wall and external environment condition convection transfer rate, I is intensity of illumination, and m is heat transfer medium flow velocity, and t is the time, T _afor environment temperature, T _ffor heat-transfer medium temperature, T _mfor receiver pipe surface temperature, V _ffor heat transfer medium volume in receiver pipeline, V _mfor receiver tube wall volume, x is receiver length, ε blackness, η _optfor mirror field overall efficiency, ρ _ffor heat transfer medium density, ρ _mfor receiver tube wall density, σ is blackbody radiation constant;

1.2 choose Intensity of the sunlight I is respectively I ₀, I ₀+ Δ, I ₀+ 2 Δs, I ₀+ 3 Δs, I ₀the representative value of+4 Δs; Wherein I ₀for the minimum intensity of illumination that can power station generate electricity, I ₀+ 4 Δs are maximum intensity of illumination in a day;

The running optimizatin of 1.3 solar power tower receiver parts needs to do following calculating to power conversion system and electric pump:

Power conversion system is equivalent to a Rankine cycle model, its efficiency eta _rankfor:

${\mathrm{\η}}_{rank}=K_1(1-\frac{T_a}{T_{out}})---\left(3\right)$

Wherein, T _outfor receiver outlet temperature,

The power P that electric pump consumes _pumpfor:

$P_{pump}=\frac{K_2{\mathrm{Lm}}^3}{{\mathrm{g\π}}^2{d}^5{\mathrm{\η}}_{pump}}---\left(4\right)$

Wherein, K ₁, K ₂be constant with g, L is the length of pipeline, and d is the diameter of pipeline;

Meanwhile, the power P of the heat transfer medium absorption of flowing in receiver _solarfor:

P _solar＝mC _f(T _out-T _fin)(5)

Therefore, the clean generated output P of power station under certain fixing Intensity of the sunlight _netfor:

$P_{net}=P_{solar}{\mathrm{\η}}_{rank}-P_{pump}={\mathrm{mC}}_f(T_{out}-T_{fin})\×K_1(1-\frac{T_a}{T_{out}})-\frac{K_2{\mathrm{Lm}}^3}{{\mathrm{g\π}}^2{d}^5{\mathrm{\η}}_{pump}}---\left(6\right)$

1.4 emulation to obtain under different illumination intensity I the clean generating efficiency in power station about the curve of receiver exit heat-transfer medium temperature, wherein, and clean generating efficiency η _netcomputing formula be:

${\mathrm{\η}}_{net}=\frac{P_{net}}{{\mathrm{IA}}_{mirror}}=\frac{P_{solar}{\mathrm{\η}}_{rank}-P_{pump}}{{\mathrm{IA}}_{mirror}}---\left(7\right)$

Wherein, I is a certain representative value selected in step 1.2,

1.5 observe the clean generating efficiency in power station about the relation of receiver exit heat-transfer medium temperature by curve, obtain when clean generating efficiency is the highest, the temperature of receiver exit heat transfer medium.

Described step 2) be:

Adopt feedforward to add the multiplex control system of single loop feedback, determine the K of feedforward controller _p1and the K of single loop feedback controller _p2with K _i, make controlled variable receiver exit heat-transfer medium temperature can tracking fixed valure.

Described step 3) be:

When do not consider that electricity price changes affect time, the influence factor of power station income is the running cost of thermal loss in conversion process of energy and operation, the i.e. power station net electric generation that can provide, in power station one day, the gross generation of n hour is the integration of each hour generated energy, consider practical significance, the optimal problem that the setting value being receiver exit heat-transfer medium temperature with decision variable and optimized variable, whole day power station net electric generation are optimization aim to the maximum can be described as:

max $Q={\∫}_{t_1}^{t_n}{P}_{net}\left(t\right)dt$

$s.t.\left\{\begin{array}{c}{P}_r\≤P_{net}\≤P_{max}\\ P_{net}\left(t_i\right)-P_{net}\left(t_{i-1}\right)\≤{\mathrm{\ΔP}}_{net}\\ m_{min}\≤m\≤m_{max}\\ T_{f\min }(x,t_i)\≤T_f(x,t_i)\≤T_{fmax}(x,t_i)\\ T_f(x,t_i)-T_f(x,t_{i-1})\≤{\mathrm{\ΔT}}_{fmax}\end{array}\right.---\left(8\right)$

。

Described step 4) be:

Step 3) in optimal problem be continuous print nonlinear problem, the CVP_SS method based on control vector parametric method is adopted on solving, only discretize control vector and hold mode vector is constant, two_point boundary value problem is converted into initial-value problem solve, specific implementation step is as follows:

4.1 initialization, setting-up time piecewise constant N, is divided into N section by time interval [0, T], and the time span of every section is δ _k, k=1 ..., N, this tittle is not necessarily all equal, but will meet following equation:

$T=\underset{k=1}{\overset{N}{\Σ}}{\mathrm{\δ}}_k---\left(9\right)$

Control variable u (t) the i.e. decision variable of optimization problem is separated into parameter vector ζ according to the segmentation of time, and optimization problems is just converted into nonlinear programming problem;

The initial value of 4.2 setup parameter vectors put j=0, wherein, k represents a kth time slice;

4.3 according to calculate target function value Q _j;

4.4 compute gradient information ▽ Q _j, and obtain following iteration point according to gradient based on SQP algorithm

If 4.5 meet stopping criterion, then algorithm stops; Otherwise, put j=j+1; Repeat step 4.3 and 4.4.

Accompanying drawing explanation

Fig. 1 is tower type solar thermo-power station receiver running optimizatin method flow diagram;

Fig. 2 is heat transfer medium flows schematic diagram in the receiver in example;

Fig. 3 is the receiver control system calcspar in example;

Fig. 4 is the clean generating efficiency in power station temperature variant curve under different illumination intensity in example;

Fig. 5 is Intensity of the sunlight change curve in example;

Fig. 6 is that in example, power station generated energy optimum results and non-optimum results contrast.

Embodiment

As shown in Figure 1, a kind of step of running optimizatin method of solar power tower receiver is as follows:

1) under the distributed parameter model building solar power tower receiver also emulates and obtains different illumination intensity, when the clean generating efficiency in power station is the highest, the numerical value of receiver outlet temperature;

2) design PID controller, the control variable of its middle controller is receiver porch heat transfer medium flow velocity, and controlled variable is receiver exit heat-transfer medium temperature;

3) optimization aim is to the maximum with whole day power station net electric generation, constitution optimization problem;

4) by the control variable discretize of CVP_SS by N continuous LP optimization problem, and then SQP algorithm is adopted to solve, the change curve of receiver exit heat-transfer medium temperature setting value when obtaining power station whole day Energy Maximization.

Described step 1) be:

1.1 according to energy conservation equation, and the distributed parameter model of receiver is:

${\mathrm{\ρ}}_m{C}_m{V}_m\frac{\∂T_m}{\∂t}={\mathrm{I\η}}_{opt}{A}_{mirror}-{\mathrm{\ϵ\σA}}_o{T}_m^4-h_o{A}_o(T_m-T_a)-h_i{A}_i(T_m-T_f)---\left(1\right)$

${\mathrm{\ρ}}_f{C}_f{V}_f\frac{\∂T_f}{\∂t}+{\mathrm{\ρ}}_f{C}_{f}m\frac{\∂T_f}{\∂x}=h_i{A}_i(T_m-T_f)---\left(2\right)$

Wherein, A _ifor receiver inner surface of tube wall area, A _mirrorfor the heliostat field total area, A _ofor receiver pipeline sensitive surface external area, C _mfor receiver tube wall specific heat, C _ffor heat transfer medium specific heat, h _ifor receiver tube wall and internal heat transfer medium convection transfer rate, h _ofor receiver tube wall and external environment condition convection transfer rate, I is intensity of illumination, and m is heat transfer medium flow velocity, and t is the time, T _afor environment temperature, T _ffor heat-transfer medium temperature, T _mfor receiver pipe surface temperature, V _ffor heat transfer medium volume in receiver pipeline, V _mfor receiver tube wall volume, x is receiver length, ε blackness, η _optfor mirror field overall efficiency, ρ _ffor heat transfer medium density, ρ _mfor receiver tube wall density, σ is blackbody radiation constant;

1.2 choose Intensity of the sunlight I is respectively I ₀, I ₀+ Δ, I ₀+ 2 Δs, I ₀+ 3 Δs, I ₀the representative value of+4 Δs; Wherein I ₀for the minimum intensity of illumination that can power station generate electricity, I ₀+ 4 Δs are maximum intensity of illumination in a day;

The running optimizatin of 1.3 solar power tower receiver parts needs to do following calculating to power conversion system and electric pump:

Power conversion system is equivalent to a Rankine cycle model, its efficiency eta _rankfor:

${\mathrm{\η}}_{rank}=K_1(1-\frac{T_a}{T_{out}})---\left(3\right)$

Wherein, T _outfor receiver outlet temperature,

The power P that electric pump consumes _pumpfor:

$P_{pump}=\frac{K_2{\mathrm{Lm}}^3}{{\mathrm{g\π}}^2{d}^5{\mathrm{\η}}_{pump}}---\left(4\right)$

Wherein, K ₁, K ₂be constant with g, L is the length of pipeline, and d is the diameter of pipeline;

Meanwhile, the power P of the heat transfer medium absorption of flowing in receiver _solarfor:

P _solar＝mC _f(T _out-T _fin)(5)

Therefore, the clean generated output P of power station under certain fixing Intensity of the sunlight _netfor:

$P_{net}=P_{solar}{\mathrm{\η}}_{rank}-P_{pump}={\mathrm{mC}}_f(T_{out}-T_{fin})\×K_1(1-\frac{T_a}{T_{out}})-\frac{K_2{\mathrm{Lm}}^3}{{\mathrm{g\π}}^2{d}^5{\mathrm{\η}}_{pump}}---\left(6\right)$

1.4 emulation to obtain under different illumination intensity I the clean generating efficiency in power station about the curve of receiver exit heat-transfer medium temperature, wherein, and clean generating efficiency η _netcomputing formula be:

${\mathrm{\η}}_{net}=\frac{P_{net}}{{\mathrm{IA}}_{mirror}}=\frac{P_{solar}{\mathrm{\η}}_{rank}-P_{pump}}{{\mathrm{IA}}_{mirror}}---\left(7\right)$

Wherein, I is a certain representative value selected in step 1.2,

1.5 observe the clean generating efficiency in power station about the relation of receiver exit heat-transfer medium temperature by curve, obtain when clean generating efficiency is the highest, the temperature of receiver exit heat transfer medium.

Described step 2) be:

Adopt feedforward to add the multiplex control system of single loop feedback, determine the K of feedforward controller _p1and the K of single loop feedback controller _p2with K _i, make controlled variable receiver exit heat-transfer medium temperature can tracking fixed valure.

Described step 3) be:

When do not consider that electricity price changes affect time, the influence factor of power station income is the running cost of thermal loss in conversion process of energy and operation, the i.e. power station net electric generation that can provide, in power station one day, the gross generation of n hour is the integration of each hour generated energy, consider practical significance, the optimal problem that the setting value being receiver exit heat-transfer medium temperature with decision variable and optimized variable, whole day power station net electric generation are optimization aim to the maximum can be described as:

max $Q={\∫}_{t_1}^{t_n}{P}_{net}\left(t\right)dt$

$s.t.\left\{\begin{array}{c}{P}_r\≤P_{net}\≤P_{max}\\ P_{net}\left(t_i\right)-P_{net}\left(t_{i-1}\right)\≤{\mathrm{\ΔP}}_{net}\\ m_{min}\≤m\≤m_{max}\\ T_{f\min }(x,t_i)\≤T_f(x,t_i)\≤T_{fmax}(x,t_i)\\ T_f(x,t_i)-T_f(x,t_{i-1})\≤{\mathrm{\ΔT}}_{fmax}\end{array}\right.---\left(8\right)$

。

Described step 4) be:

Step 3) in optimal problem be continuous print nonlinear problem, the CVP_SS method based on control vector parametric method is adopted on solving, only discretize control vector and hold mode vector is constant, two_point boundary value problem is converted into initial-value problem solve, specific implementation step is as follows:

4.1 initialization, setting-up time piecewise constant N, is divided into N section by time interval [0, T], and the time span of every section is δ _k, k=1 ..., N, this tittle is not necessarily all equal, but will meet following equation:

$T=\underset{k=1}{\overset{N}{\Σ}}{\mathrm{\δ}}_k---\left(9\right)$

Control variable u (t) the i.e. decision variable of optimization problem is separated into parameter vector ζ according to the segmentation of time, and optimization problems is just converted into nonlinear programming problem;

The initial value of 4.2 setup parameter vectors put j=0, wherein, k represents a kth time slice;

4.3 according to calculate target function value Q _j;

4.4 compute gradient information ▽ Q _j, and obtain following iteration point according to gradient based on SQP algorithm

If 4.5 meet stopping criterion, then algorithm stops; Otherwise, put j=j+1; Repeat step 4.3 and 4.4.

Based on shown in Fig. 2 with fused salt be heat transfer medium tower type solar receiver simulation object adopt invention has been optimization.This power station receiver is high 6.2m, and diameter 5.1m's is cylindrical, is made up of 24 pieces of dash receivers, and every block plate has 32 endothermic tubes straight up, endothermic tube pipe diameter 2.1cm, endothermic tube pipe thickness 1.2mm.With this power station for prototype builds distributed parameter model, and design con-trol variable is receiver porch heat transfer medium flow velocity, and controlled variable is the controller of the heat-transfer medium temperature in receiver exit, and its control system calcspar as shown in Figure 3.Under different illumination intensity, (choose typical 400,500,600,700,800) emulate the clean generating efficiency in power station that obtains to the curve of temperature as shown in Figure 4, can find out that the clean generating efficiency in power station constantly increases along with receiver outlet temperature increases, reach maximal value and can reduce along with the increase of temperature on the contrary later.When under employing different illumination intensity, the clean generating efficiency in power station is maximum, receiver outlet temperature is that initial value is optimized the power station that the consecutive variations time is 6:30 to 17:00, and the now change of Intensity of the sunlight as shown in Figure 5.After optimizing, power station net electric generation and non-optimum results contrast as shown in Figure 6, and when intensity of illumination is not strong, before and after optimizing, the increment of power station net electric generation is not very high, and minimum rate of growth occurs between 6:30 to 7:00, is only 1.15%; When the time-division, intensity of illumination was the strongest at noon, the increment of power station net electric generation reaches the highest, is 8.28%; After optimizing in one day, the average growth rate of power station net electric generation is 6.76%.

Claims (5)

1. a running optimizatin method for solar power tower receiver, is characterized in that its step is as follows:

1) under the distributed parameter model building solar power tower receiver also emulates and obtains different illumination intensity, when the clean generating efficiency in power station is the highest, the numerical value of receiver outlet temperature;

2) design PID controller, the control variable of its middle controller is receiver porch heat transfer medium flow velocity, and controlled variable is receiver exit heat-transfer medium temperature;

3) optimization aim is to the maximum with whole day power station net electric generation, constitution optimization problem;

4) by the control variable discretize of CVP_SS by N continuous LP optimization problem, and then SQP algorithm is adopted to solve, the change curve of receiver exit heat-transfer medium temperature setting value when obtaining power station whole day Energy Maximization.

2. the running optimizatin method of a kind of solar power tower receiver as claimed in claim 1, is characterized in that described step 1) be:

1.1 according to energy conservation equation, and the distributed parameter model of receiver is:

${\mathrm{\ρ}}_m{C}_m{V}_m\frac{\∂T_m}{\∂t}={\mathrm{I\η}}_{opt}{A}_{mirror}-{\mathrm{\ϵ\σA}}_o{T}_m^4-h_o{A}_o(T_m-T_a)-h_i{A}_i(T_m-T_f)---\left(1\right)$

${\mathrm{\ρ}}_f{C}_f{V}_f\frac{\∂T_f}{\∂t}+{\mathrm{\ρ}}_f{C}_{f}m\frac{\∂T_f}{\∂x}=h_i{A}_i(T_m-T_f)---\left(2\right)$

Wherein, A _ifor receiver inner surface of tube wall area, A _mirrorfor the heliostat field total area, A _ofor receiver pipeline sensitive surface external area, C _mfor receiver tube wall specific heat, C _ffor heat transfer medium specific heat, h _ifor receiver tube wall and internal heat transfer medium convection transfer rate, h _ofor receiver tube wall and external environment condition convection transfer rate, I is intensity of illumination, and m is heat transfer medium flow velocity, and t is the time, T _afor environment temperature, T _ffor heat-transfer medium temperature, T _mfor receiver pipe surface temperature, V _ffor heat transfer medium volume in receiver pipeline, V _mfor receiver tube wall volume, x is receiver length, ε blackness, η _optfor mirror field overall efficiency, ρ _ffor heat transfer medium density, ρ _mfor receiver tube wall density, σ is blackbody radiation constant;

1.2 choose Intensity of the sunlight I is respectively I ₀, I ₀+ Δ, I ₀+ 2 Δs, I ₀+ 3 Δs, I ₀the representative value of+4 Δs; Wherein I ₀for the minimum intensity of illumination that can power station generate electricity, I ₀+ 4 Δs are maximum intensity of illumination in a day;

The running optimizatin of 1.3 solar power tower receiver parts needs to do following calculating to power conversion system and electric pump:

Power conversion system is equivalent to a Rankine cycle model, its efficiency eta _rankfor:

${\mathrm{\η}}_{rank}=K_1(1-\frac{T_a}{T_{out}})---\left(3\right)$

Wherein, T _outfor receiver outlet temperature,

The power P that electric pump consumes _pumpfor:

$P_{pump}=\frac{K_2{\mathrm{Lm}}^3}{{\mathrm{g\π}}^2{d}^5{\mathrm{\η}}_{pump}}---\left(4\right)$

Wherein, K ₁, K ₂be constant with g, L is the length of pipeline, and d is the diameter of pipeline;

Meanwhile, the power P of the heat transfer medium absorption of flowing in receiver _solarfor:

P _solar＝mC _f(T _out-T _fin)(5)

Therefore, the clean generated output P of power station under certain fixing Intensity of the sunlight _netfor:

$P_{net}=P_{solar}{\mathrm{\η}}_{rank}-P_{pump}={\mathrm{mC}}_f(T_{out}-T_{fin})\×K_1(1-\frac{T_a}{T_{out}})-\frac{K_2{\mathrm{Lm}}^3}{{\mathrm{g\π}}^2{d}^5{\mathrm{\η}}_{pump}}---\left(6\right)$

1.4 emulation to obtain under different illumination intensity I the clean generating efficiency in power station about the curve of receiver exit heat-transfer medium temperature, wherein, and clean generating efficiency η _netcomputing formula be:

${\mathrm{\η}}_{net}=\frac{P_{net}}{{\mathrm{IA}}_{mirror}}=\frac{P_{solar}{\mathrm{\η}}_{rank}-P_{pump}}{{\mathrm{IA}}_{mirror}}---\left(7\right)$

Wherein, I is a certain representative value selected in step 1.2,

1.5 observe the clean generating efficiency in power station about the relation of receiver exit heat-transfer medium temperature by curve, obtain when clean generating efficiency is the highest, the temperature of receiver exit heat transfer medium.

3. the running optimizatin method of a kind of solar power tower receiver as claimed in claim 1, is characterized in that described step 2) be:

Adopt feedforward to add the multiplex control system of single loop feedback, determine the K of feedforward controller _p1and the K of single loop feedback controller _p2with K _i, make controlled variable receiver exit heat-transfer medium temperature can tracking fixed valure.

4. the running optimizatin method of a kind of solar power tower receiver as claimed in claim 1, is characterized in that described step 3) be:

When do not consider that electricity price changes affect time, the influence factor of power station income is the running cost of thermal loss in conversion process of energy and operation, the i.e. power station net electric generation that can provide, in power station one day, the gross generation of n hour is the integration of each hour generated energy, consider practical significance, the optimal problem that the setting value being receiver exit heat-transfer medium temperature with decision variable and optimized variable, whole day power station net electric generation are optimization aim to the maximum can be described as:

$\begin{array}{cc}\max & Q={\∫}_{t_1}^{t_n}{P}_{net}\left(t\right)dt\end{array}$

$\begin{array}{cc}s.t.& \left\{\begin{array}{c}{P}_r\≤P_{net}\≤P_{max}\\ P_{net}\left(t_i\right)-P_{net}\left(t_{i-1}\right)\≤{\mathrm{\ΔP}}_{net}\\ m_{min}\≤m\≤m_{max}\\ T_{f\min }(x,t_i)\≤T_f(x,t_i)\≤T_{fmax}(x,t_i)\\ T_f(x,t_i)-T_f(x,t_{i-1})\≤{\mathrm{\ΔT}}_{fmax}\end{array}\right.\end{array}---\left(8\right).$

5. the running optimizatin method of a kind of solar power tower receiver as claimed in claim 1, is characterized in that described step 4) be:

Step 3) in optimal problem be continuous print nonlinear problem, the CVP_SS method based on control vector parametric method is adopted on solving, only discretize control vector and hold mode vector is constant, two_point boundary value problem is converted into initial-value problem solve, specific implementation step is as follows:

4.1 initialization, setting-up time piecewise constant N, is divided into N section by time interval [0, T], and the time span of every section is δ _k, k=1 ..., N, this tittle is not necessarily all equal, but will meet following equation:

$T=\underset{k=1}{\overset{N}{\Σ}}{\mathrm{\δ}}_k---\left(9\right)$

Control variable u (t) the i.e. decision variable of optimization problem is separated into parameter vector ζ according to the segmentation of time, and optimization problems is just converted into nonlinear programming problem;

The initial value of 4.2 setup parameter vectors put j=0, wherein, k represents a kth time slice;

4.3 according to calculate target function value Q _j;

4.4 compute gradient information ▽ Q _j, and obtain following iteration point according to gradient based on SQP algorithm

If 4.5 meet stopping criterion, then algorithm stops; Otherwise, put j=j+1; Repeat step 4.3 and 4.4.