CN110334878B - Photo-thermal energy storage power station power generation amount optimization method based on typical static model - Google Patents
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Abstract
A photo-thermal power station generating capacity optimization method based on a static model comprises the steps of firstly, analyzing a controllable part which influences the generating capacity of a power station in the static model; secondly, establishing an optimized mathematical model according to the constraint conditions that the maximum generated energy is an objective function and a static model, and providing an optimization method; and finally, introducing a Smith estimation compensation mechanism to reduce the influence of a large hysteresis link appearing in the control system on the stability and accuracy of the system. The optimization method can effectively improve the generating capacity and the operating efficiency of the photo-thermal energy storage power station.
Description
Technical Field
The invention relates to a generating capacity optimization method based on a photo-thermal energy storage power station static model.
Background
In recent years, the photo-thermal storage Solar Power (CSP) system has become an important Power generation system for solving the problems of new energy Power generation and grid fluctuation and randomness by virtue of its advantage of fast output adjustment. The existing ways for improving the generating capacity of the photo-thermal energy storage power station mainly include ways of improving a heat storage medium, optimizing a heat storage way and a control method based on a thermodynamic dynamic model, but the methods are not suitable for optimizing the photo-thermal energy storage power station on a long-time scale. The control method based on the thermodynamic dynamic model has excessive detection quantity, and the working environment of the detection element is severe, so that the control method is not suitable for long-term work.
On the basis of a typical static model of the CSP power station, the influence of a large hysteresis link on a control system is eliminated by optimizing the heat charging and discharging energy flow mode of a heat storage system and combining a Smith pre-estimation compensation mechanism, so that the aims of improving the generating capacity and the operating efficiency of the CSP power station are fulfilled.
Disclosure of Invention
In order to realize the maximum optimized operation of the power generation capacity of the CSP power station, the invention provides a power generation capacity optimization method of the CSP power station based on a typical static model, which mainly comprises the following steps:
step 1: comprehensively analyzing a typical static model and an operation mode of the CSP power station to obtain a characteristic model which can be obtained by optimizing a thermal energy storage subsystemCharging power P of system (Thermal Storage Subsystem, TSS)t S-TWith heat release power Pt T-PThe method achieves the purpose of optimizing the generating capacity of the CSP power station. Introducing a heat charging power coefficient alpha epsilon [0,1 ∈ ]]With heat release power coefficient beta ∈ [0,1 ]]The subsequent charge and discharge energy flow can be expressed as:
in the formula, Pt solarCollecting power of energy for a condenser and heat collection Subsystem (SFS) at time t; delta PfLoss in energy transfer; pt S-PPower to provide energy to a turbine power generation Subsystem (PS) for the SFS;
power to energize the SFS to the TSS, i.e., charging power; 
Power to energize the TSS to the PS, i.e., heat release power; gamma belongs to {0,1} is the state variable of PS work; u. oftStarting the number of the turbo generator units at the time t; pSUThe minimum energy required for starting the turbo-generator set.
Step 2: the maximum generated energy of the CSP power station is taken as an optimized objective function, and the generated energy of the power station is maximized by optimizing a charging and discharging control strategy of the TSS when the energy input to the system is fixed. The corresponding optimization constraint condition is a static energy flow mathematical model of the CSP power station introducing control variables alpha and beta.
And step 3: on the basis of the optimized mathematical model, the main method for optimizing the strategy is as follows: the corresponding specific optimization mode is obtained by detecting the energy flow of each subsystem and the energy flow among the subsystems, so that the generated energy of the CSP power station and the overall operation efficiency are improved.
In order to ensure that the CSP power station can still generate electric energy under the weather condition that the direct illumination radiation intensity (DNI) is continuously low, the optimization strategy is set as follows: in the process of persistence
Under the condition that the TSS is charged with heat to
When the PS is started, the TSS and the SFS jointly supply energy meeting the requirement to the PS. A specific block diagram of the optimization strategy is shown in fig. 1.
And 4, step 4: in the optimization strategy, the core idea of the heating power optimization control strategy is as follows: order to
And as a given value, adjusting the charging power coefficient alpha through a feedback regulator, so that the energy collected by the SFS is completely sent to the TSS for storage, or the energy is completely sent to the TSS for storage after the minimum energy required by the PS is removed, thereby reducing the waste of the energy. Aiming at a large time-lag link generated in the control process, a Smith pre-estimation compensator is introduced to reduce the influence on a control system. A specific block diagram of the charging power control strategy is shown in fig. 2.
And 5: in the optimization strategy, the core idea of the heat release power optimization control strategy is as follows: order to
And as a given value, adjusting the heat release power coefficient beta through a feedback regulator, so that the TSS and the SFS together provide energy meeting the normal working requirement for the PS, thereby ensuring the normal working of the CSP power station. Aiming at a large time-lag link generated in the control process, a Smith pre-estimation compensator is introduced to reduce the influence on a control system. A specific heat release power control strategy diagram is shown in fig. 3.
The invention has the advantages that: a CSP power station generated energy optimization model based on a static model is provided, and the problems that the work environment of a detection element is harsh in a dynamic optimization strategy and the like are solved. Meanwhile, an optimization strategy based on a static model can provide a method for optimal scheduling of the CSP power station after grid connection in future. And secondly, the Smith pre-estimation compensator is adopted, so that the influence of a large time-lag link on a control system is solved.
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FIG. 1 is a block diagram of an optimization strategy flow, FIG. 2 is a block diagram of a heat charge control strategy, and FIG. 3 is a block diagram of a heat discharge control strategy.
Detailed Description
The invention relates to a generating capacity maximum optimization method based on a CSP power station static model. As shown in fig. 1, the charging and discharging control strategy of the TSS in the CSP power station can be optimized to achieve the purpose of increasing the CSP power generation and the overall operation efficiency. Aiming at a large hysteresis link existing in the control process, a Smith pre-estimation compensator is adopted for compensation, and the method specifically comprises the following steps:
step 1: comprehensively analyzing a typical static model and an operation mode of the CSP power station to obtain the charging power P capable of being optimized by a Thermal Storage Subsystem (TSS)t S-TWith heat release power Pt T-PThe method achieves the purpose of optimizing the generating capacity of the CSP power station. Introducing a heat charging power coefficient alpha epsilon [0,1 ∈ ]]With heat release power coefficient beta ∈ [0,1 ]]The subsequent charge and discharge energy flow can be expressed as:
in the formula, Pt solarCollecting power of energy for a condenser and heat collection Subsystem (SFS) at time t; delta PfLoss in energy transfer; pt S-PPower to provide energy to a turbine power generation Subsystem (PS) for the SFS; 
Power to energize the SFS, i.e., charging power, to the TSS;
power to energize the TSS to the PS, i.e., exothermic power; gamma belongs to {0,1} is the state variable of PS work; u. oftStarting the number of the turbo generator units at the time t; pSUThe minimum energy required for starting the turbo-generator set.
Step 2: taking the maximum generated energy of the CSP power station as an optimized objective function, and when the energy input into the system is fixed, optimizing a charging and discharging control strategy of a TSS (transient state service) to enable the generated energy of the power station to reach the maximum, wherein the corresponding objective function is as follows:
max ett (formula five)
In the formula, etThe output electric power of the CSP power station at the moment t; and T is the power generation time of the CSP power station.
The corresponding optimization constraint condition is a static energy flow mathematical model of the CSP power station introducing control variables alpha and beta.
And step 3: on the basis of the optimized mathematical model, the main method for optimizing the strategy is as follows: the corresponding specific optimization mode is obtained by detecting the energy flow of each subsystem and the energy flow among the subsystems, so that the generated energy of the CSP power station and the overall operation efficiency are improved. The concrete expression is as follows: firstly, when sunlight is insufficient and PS stops working, all energy collected by SFS is stored in TSS; when the PS works, the TSS and the SFS jointly provide energy meeting the working requirement for the PS; secondly, when the sunlight is sufficient, the redundant energy which can not be utilized by the PS is stored in the TSS as completely as possible; when there is no sunlight, in order to ensure the normal work of CSP system, TSS releases heat with maximum power, and provides energy meeting the requirement for PS.
In order to ensure that the CSP power station can still generate electric energy under the weather condition that the direct illumination radiation intensity (DNI) is continuously low, the optimization strategy is set as follows: in the process of persistence
Under the condition that the TSS is charged with heat to
When the PS is started, the TSS and the SFS jointly supply energy meeting the requirement to the PS. A specific block diagram of the optimization strategy is shown in fig. 1.
And 4, step 4: in the optimization strategy, the core idea of the heating power optimization control strategy is as follows: order to
And as a given value, adjusting the charging power coefficient alpha through a feedback regulator, so that the energy collected by the SFS is completely sent to the TSS for storage, or the energy is completely sent to the TSS for storage after the minimum energy required by the PS is removed, thereby reducing the waste of the energy. The heat charging energy flow is further analyzed, and the following steps are included:
can be solved to obtain:
transforming the linear function with respect to time t into the frequency domain, there are:
therefore, it can be obtained that: the charging power coefficient α is positively correlated with the main variation (DNI). Aiming at a large time-lag link generated in the control process, a Smith pre-estimation compensator is introduced to reduce the influence on a control system. In addition, a PID regulator is adopted to achieve the control target in consideration of the purposes of overcoming the larger inertia of the system, eliminating the deviation and the like. A specific block diagram of the charging control strategy is shown in fig. 2.
And 5: in the optimization strategy, the core idea of the heat release power optimization control strategy is as follows: order to
And as a given value, adjusting the heat release power coefficient beta through a feedback regulator, so that the TSS and the SFS together provide energy meeting the normal working requirement for the PS, thereby ensuring the normal working of the CSP power station. The exothermic energy flow was further analyzed by:
since the CSP power generation system is in normal operation when the TSS is in a heat release state, Δ P in (equation four)fCan be approximately ignored, and gamma is 1, ut＝0，Pt solar≈Pt S-PAt this time, substituting (equation four) into the above equation can be solved:
the above equation is transformed to the frequency domain in the same way. Comprises the following steps:
it can be derived that: the heat release power coefficient β is inversely related to the main variation (DNI). Similarly, aiming at a large time-lag link generated in the control process, a Smith pre-estimation compensator is introduced to reduce the influence on the control system. In addition, a PID regulator is adopted to achieve the control target in consideration of the purposes of overcoming the larger inertia of the system, eliminating the deviation and the like. A specific heat release control strategy diagram is shown in fig. 3.
The above is one of the implementation methods of the present invention, and it is obvious to a person skilled in the art that various changes can be made to the above embodiments without any creative effort, and the object of the present invention can be achieved. It will be apparent that such variations are intended to be included within the scope of the invention as defined in the claims.
Claims (1)
1. A method for optimizing the generated energy of a photo-thermal energy storage power station based on a typical static model is characterized by comprising the following steps:
step 1: comprehensively analyzing a typical static model and an operation mode of the photo-thermal energy storage power station to obtain the heat charging and discharging power P capable of optimizing the heat energy storage subsystemt S-PWith heat release power Pt T-PThe method optimizes the generating capacity of the photo-thermal energy storage power station; introducing a heat charging power coefficient alpha epsilon [0,1 ∈ ]]With heat release power coefficient beta ∈ [0,1 ]]The subsequent charge and discharge energy flow can be expressed as:
in the formula, Pt solarCollecting the power of the energy for the light-gathering and heat-collecting subsystem at the moment t;
ΔPfloss in energy transfer;
Pt S-Pproviding power of energy for the light-gathering and heat-collecting subsystem to the steam turbine power generation subsystem;
gamma belongs to {0,1} is a working state variable of the steam turbine power generation subsystem;
utstarting the number of the turbo generator units at the time t;
PSUminimum energy required for starting the turbo generator set;
step 2: the maximum generated energy of the photo-thermal energy storage power station is used as an optimized objective function, and when the energy input into the system is constant, the generated energy of the power station is maximized by optimizing a charging and discharging control method of the thermal energy storage subsystem; the corresponding optimization constraint condition is a static energy flow mathematical model of the photo-thermal energy storage power station with control variables alpha and beta introduced;
And step 3: on the basis of the optimization model, the implementation way of the optimization method is as follows: the corresponding specific control mode is obtained by detecting the energy flow of each subsystem and the energy flow among the subsystems, so that the generated energy and the overall operating efficiency of the photo-thermal energy storage power station are improved; in order to ensure that the photo-thermal energy storage power station can still generate electric energy under the weather condition that the direct illumination radiation intensity-DNI (deep ultraviolet) is continuously low, the optimization method is set as follows:
in the process of persistence
Under the condition, if the thermal energy storage subsystem is charged to
When the system is started, the steam turbine power generation subsystem is started, and the heat energy storage subsystem and the light-gathering and heat-collecting subsystem provide energy meeting requirements for the steam turbine power generation subsystem together;
and 4, step 4: in the optimization method, the realization approach of the heat charging power optimization control method is as follows: order to
As a given value, adjusting the heat charging power coefficient alpha through a feedback regulator; when the steam turbine power generation subsystem does not work, all the energy collected by the light and heat collection subsystem is sent to the heat energy storage subsystem for storage; when the steam turbine power generation subsystem works for power generation, the energy collected by the light-gathering and heat-collecting subsystem is completely sent to the heat energy storage subsystem for storage after the minimum energy required by the steam turbine power generation subsystem is removed; the heat charging energy flow is further analyzed, and the following steps are included:
Can be solved as follows:
transforming the linear function with respect to time t into the frequency domain, there are:
therefore, it can be obtained that: the heat charging power coefficient alpha is in positive correlation with the main variable quantity, and a Smith pre-estimation compensator and a PI regulator are introduced to adjust the heat charging power coefficient alpha;
and 5: in the optimization method, the heat release power optimization control method is realized by the following steps: order to
As a given value, adjusting the heat release power coefficient beta through a feedback regulator to enable the heat energy storage subsystem and the light gathering and heat collecting subsystem to provide energy meeting normal working requirements for the steam turbine power generation subsystem together; the exothermic power was further analyzed to be:
when the thermal energy storage subsystem is in a heat release state, the photo-thermal energy storage power generation system is in normal operation, and delta PfCan be ignored, and gamma is 1, ut＝0，Pt solar≈Pt S-PAt this time, the following can be solved:
the same way transforms the above equation to the frequency domain, with:
therefore, it can be obtained that: the heat release power coefficient beta is inversely related to the main variation; and a Smith pre-estimation compensator and a PI regulator are introduced to adjust the Smith pre-estimation compensator and the PI regulator.
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