CN110165707B - Optimal control method of optical storage system based on Kalman filtering and model predictive control - Google Patents
Optimal control method of optical storage system based on Kalman filtering and model predictive control Download PDFInfo
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- H—ELECTRICITY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract
An optimal control method of a light storage system based on Kalman filtering and model prediction control belongs to the technical field of light storage control of a power system and comprises the steps of establishing a structural model of the light storage combined power generation system, establishing a time equation and a state equation of a filter by using an improved Kalman filtering algorithm, establishing a mathematical model of the light storage combined power generation system, performing optimal control on the energy storage system by using a model prediction control method, combining the Kalman filtering algorithm with a model prediction control method to form double-regulation feedback optimal control, and realizing closed-loop control on the light storage combined power generation system through energy storage charge state control parameters. The method disclosed by the invention fully utilizes the prediction characteristics of Kalman filtering and model prediction control, and realizes advanced prediction and timely control on the optical storage combined system, so that the output power of the photovoltaic power generation system is stabilized, and the output of the energy storage system and the charge state of the energy storage system are optimized.
Description
Technical Field
The invention belongs to the technical field of light storage control of a power system, and particularly relates to a control method for light storage combined power generation system by utilizing Kalman filtering and model predictive control combined optimization.
Background
With the development of modern power systems, energy storage technology is gradually introduced into the power systems, and the energy storage systems are widely applied to the fields of smooth wind power generation, photovoltaic power generation, peak clipping and valley filling, frequency modulation and the like at present due to the flexible power throughput characteristics of the energy storage systems. With the increasing of the photovoltaic grid-connected proportion year by year, the fluctuation of the photovoltaic power is not beneficial to the stable operation of a power grid, the energy storage system is introduced into a photovoltaic power station, the fluctuation of the photovoltaic power can be effectively relieved by the energy storage system, and the effect of stabilizing the fluctuation of the photovoltaic power is achieved.
In order to improve the grid-connected capacity of the photovoltaic power station, the photovoltaic power fluctuation of grid connection needs to be stabilized. Because the energy storage system can dynamically absorb or release energy, the energy storage system is used for a photovoltaic power station, the fluctuation of photovoltaic power can be effectively stabilized, and the fluctuation of the photovoltaic power can be effectively stabilized by using an energy storage technology, but the cost of the energy storage system restricts the large-scale application of the energy storage technology. Therefore, there is a need in the art for a new solution to solve this problem.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method fully utilizes the prediction characteristics of Kalman filtering and model prediction control, and realizes advanced prediction and timely control on the optical storage combined system, so that the output power of the photovoltaic power generation system is stabilized, and the output power and the charge state of the energy storage system are optimized.
The optimal control method of the optical storage system based on Kalman filtering and model predictive control is characterized by comprising the following steps: comprises the following steps which are sequentially carried out,
establishing a structural model of a light storage combined power generation system, wherein the structural model comprises a photovoltaic power station, a photovoltaic inverter, an energy storage system, an energy storage converter, a transformer and a microgrid;
establishing a time equation and a state equation of a filter by using an improved Kalman filtering algorithm, and establishing a mathematical model of the optical storage combined power generation system;
thirdly, performing optimization control on the energy storage system in the first step by adopting a model prediction control method;
and step four, combining the Kalman filtering algorithm in the step two with the model prediction control method in the step three to form double-regulation feedback optimization control, and realizing closed-loop control on the light-storage combined power generation system through energy storage charge state control parameters.
An energy storage system controller based on a model predictive controller MPC is arranged in the energy storage system of the first step and is connected with the bus through a DC/AC inverter; and the photovoltaic power station and the energy storage system are connected to a bus and connected to the grid through the energy storage converter.
The method for establishing the filter time equation and the state equation by improving the Kalman filtering algorithm in the second step comprises the following steps,
step one, establishing a time updating equation of a Kalman filter,
wherein (P) v ) k，k+1 For the output power of the photovoltaic power station at the k +1 moment, (P) ess ) k,k The output power of the energy storage system at the moment k, (P) g ) k,k Adding an energy storage system for a photovoltaic power station at k moment and then connecting the grid with a smooth power value P k+1,k Estimating covariance, P, a priori k,k The covariance estimated at time k, Q is the process noise covariance;
calculating an estimated value of a state variable of the photovoltaic power at the current moment, and providing a priori estimated value for the next time state;
step two, establishing a state updating equation of the Kalman filter,
wherein (P) g ) k+1,k+1 Is the power smooth value at the k +1 moment after grid connection, (P) v ) k+1,k Is a prior estimation of the state at the current time K +1 obtained from the current time K of the photovoltaic power station, K k Is the gain value of the Kalman filter, R is the measurement noise covariance, P k+1,k Estimating covariance, P, a priori k+1,k+1 Covariance estimated for time k +1, (P) v ) k+1 Adding photovoltaic output power to the photovoltaic power station before energy storage;
combining the prior estimation and the new measurement variable to provide a new estimation value for the next time state;
introducing an adjusting factor lambda into the Kalman filter gain, and realizing the stabilization of the photovoltaic power fluctuation by dynamically adjusting the filtering gain of the Kalman filter in real time;
step four, introducing a filtering adjustment factor into the Kalman filter to dynamically adjust Kalman filtering gain, wherein the time updating equation is changed into: (P) g ) k+1,k+1 ＝(P v ) k+1,k +K k ·[(P v ) k+1 -(P v ) k+1,k ]+ΔS·P ess The state update equation becomes:
wherein (P) g ) k+1,k+1 Is a power smooth value at the k +1 moment after grid connection, (P) v ) k+1,k Is a prior estimation of the state at the current time K +1 obtained from the current time K of the photovoltaic power station, K k Is the gain value of the Kalman filter, R is the measurement noise covariance, P k+1,k Estimating covariance, P, for a priori ess For the output power of the energy storage system, (P) v ) k+1 Adding photovoltaic output power before energy storage for a photovoltaic power station, wherein delta S is a charge state adjusting variable;
the model predictive control method in the third step comprises the following steps of,
step one, establishing a relation among grid-connected power of a light storage system, power of an energy storage system and actual photovoltaic power in the light storage combined power generation system, wherein the relation is P g (k)＝P v (k)+P ess (k)，
Wherein, P g (k) For grid-connected power of light-storage systems, P v (k) For photovoltaic measured power, P ess (k) The energy storage system power;
step two, optimizing the energy storage system by using a model prediction control methodControlling the time domain to be T and the energy storage capacity to be E b Establishing the charge state expression of the energy storage system:
thirdly, connecting the grid-connected power P of the light storage system g And the state of charge (SOC) of the energy storage system as a state variable, the power P of the energy storage system ess As a control variable, the photovoltaic raw power P is measured v As a disturbance variable, the state space equation of the energy storage system at the time k is as follows:
fourthly, a plurality of energy storage battery monomers are centralized in the energy storage system, the energy storage system in the optical storage combined power generation system is equivalent to an energy storage unit formed by clustering the N independent energy storage battery monomers, and a power prediction model of the energy storage system in the optical storage combined power generation system in a prediction time period is as follows: p is ess,T (k)＝P ess,T0+B (k)-P ess,T0 (k)+ΔP ess (k)
Wherein: p ess,T (k) Equivalent to P ess (k) Synthesizing a T-dimensional vector formed by the output of each energy storage system in the energy storage unit at the moment k; t is a control time domain; b is a prediction time domain; p is ess,T0 (k) Is a certain time (T) before 0 ) Synthesizing B-dimensional vectors formed by output of all energy storage systems in the energy storage units; delta P ess (k) Outputting a power difference value for the energy storage system;
step five, realizing the optimization control of the energy storage system through MPC rolling optimization calculation, and establishing a rolling optimization objective function:
obtaining the power regulating value P of the energy storage system at the next moment according to the formula ess (k + 1) as the actual control quantity, repeating the optimization at the next optimization, solving the control increment at the next moment, and realizing the rolling optimization of the predictive control;
the step four double-regulation feedback optimization control is to introduce a filtering regulation factor into a Kalman filter, and adaptively regulate Kalman filtering gain to enable the energy storage system to coordinate photovoltaic power output and stabilize photovoltaic power fluctuation;
in the case of a model-predictive controller,
according to the power constraint conditions in the operation process of the energy storage system: i P ess (k)|≤P ess,max ，
The state of charge constraint in the operation process of the energy storage system is as the condition: SOC min ≤SOC(k)≤SOC max ，
And grid-connected power fluctuation rate constraint: [ P ] g (k+1)-P g (k)]/P cap And (5) performing model prediction control rolling optimization to obtain the optimal power of the energy storage system and the optimal charge state of the energy storage system at the same time.
Through the design scheme, the invention can bring the following beneficial effects: the method fully utilizes the prediction characteristics of Kalman filtering and model prediction control, and realizes advanced prediction and timely control on the optical storage combined system so as to ensure the output power stability of the photovoltaic power generation system and optimize the output power of the energy storage system and the charge state of the energy storage system.
Drawings
The invention is further described with reference to the following figures and detailed description:
fig. 1 is a schematic structural diagram of the light-storage combined power generation system of the invention.
FIG. 2 is a schematic block diagram of dual-regulation feedback optimization control of the optical storage system based on Kalman filtering and model prediction control.
Fig. 3 is a schematic diagram of an optical storage system optimization control process based on kalman filtering and model predictive control according to the present invention.
Detailed Description
An optical storage system optimization control method based on Kalman filtering and model prediction control is combined with fig. 1, fig. 2 and fig. 3, and the specific implementation manner of the invention is as follows:
establishing a principle diagram of a light storage system according to the working principle of the light storage combined power generation system;
establishing a time updating equation and a state updating equation of the light storage combined power generation system based on Kalman filtering;
in order to improve the photovoltaic power fluctuation stabilizing effect, a filtering adjustment factor lambda is introduced into a Kalman filtering equation, and a time updating equation and a state updating equation are improved; the Kalman filter adjusts the filtering gain according to the estimated state of actual photovoltaic output, and when the SOC of the energy storage system is at a low level and is continuously reduced, in order to avoid over-discharge of the energy storage system, the adjustment factor lambda is increased to charge the energy storage system; similarly, when the SOC of the energy storage system is at a high level and is continuously increased, the adjustment factor lambda is reduced to discharge the energy storage system, and the filtering adjustment factor is dynamically fed back and adjusted in real time to achieve the dynamic adjustment effect;
in the model prediction control loop, the model prediction control rolling optimization is utilized to obtain the optimal energy storage power, and meanwhile, the initial adjustment value of the state of charge (SOC) of the energy storage system is obtained. In order to avoid the severe change of the system SOC of the energy storage system in a short time and meet the requirement of charging or discharging at the next moment, the optimal control of the state of charge adjusting variable Delta S is realized through the regulator 2, the Delta S is optimized by utilizing the system SOC value and the adjustment factor lambda value at the current moment, and the obtained rough SOC value is accurately adjusted through the regulator 1 again and fed back to the Kalman filter to realize rolling optimization so as to enhance the filtering effect.
The prediction characteristics of Kalman filtering and model prediction control are fully utilized, the aim of stabilizing power fluctuation and ensuring the minimum energy storage power in the next period is taken as a target, real-time control is realized by rolling optimization, and the running economy of an energy storage system is improved.
Establishing a predictive control model of the light storage combined power generation system and comprehensively considering all constraint conditions; analyzing from the perspective of the service life of the stored energy, wherein the service life of the stored energy is restricted by factors such as cycle times, discharge depth and the like, comprehensively considering the power constraint, SOC constraint and fluctuation rate constraint of the system, ensuring that the stored energy output of a future control time domain of the power fluctuation stabilizing system is minimum and the state of charge is optimal, and realizing real-time control by using a rolling time domain control strategy in model predictive control; meanwhile, in order to avoid the severe change of the SOC of the energy storage system in a short time and prevent the power distortion of the energy storage system due to out-of-limit, the state of charge of the energy storage system needs to be kept in a reasonable range during use, the requirement of charging or discharging at the next moment is met, and the SOC of the energy storage system is optimized.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (3)
1. The optimal control method of the optical storage system based on Kalman filtering and model predictive control is characterized by comprising the following steps: comprises the following steps which are sequentially carried out,
establishing a structural model of a light storage combined power generation system, wherein the structural model comprises a photovoltaic power station, a photovoltaic inverter, an energy storage system, an energy storage converter, a transformer and a microgrid;
establishing a time equation and a state equation of a filter by using an improved Kalman filtering algorithm, and establishing a mathematical model of the optical storage combined power generation system;
thirdly, performing optimization control on the energy storage system in the first step by adopting a model prediction control method;
step four, combining the Kalman filtering algorithm in the step two with the model prediction control method in the step three to form double-regulation feedback optimization control, and realizing closed-loop control on the light storage combined power generation system through energy storage charge state control parameters;
the method for establishing the filter time equation and the state equation by improving the Kalman filtering algorithm in the second step comprises the following steps,
step one, establishing a time updating equation of a Kalman filter,
wherein (P) v ) k，k+1 For the output power of the photovoltaic power station at the k +1 moment, (P) ess ) k,k For the output power of the energy storage system at the moment k, (P) g ) k,k Adding an energy storage system for a photovoltaic power station k moment and then connecting with a smooth value P of grid power k+1,k Estimating covariance, P, a priori k,k The covariance estimated at time k, Q is the process noise covariance;
calculating an estimated value of a state variable of the photovoltaic power at the current moment, and providing a priori estimated value for the next time state;
step two, establishing a state updating equation of the Kalman filter,
wherein (P) g ) k+1,k+1 Is a power smooth value at the k +1 moment after grid connection, (P) v ) k+1,k Is a priori estimation of the state at the current K +1 moment obtained from the current moment K of the photovoltaic power station, K k Is the gain value of the Kalman filter, R is the measurement noise covariance, P k+1,k Estimating covariance, P, a priori k+1,k+1 Covariance estimated for time k +1, (P) v ) k+1 Adding photovoltaic output power to the photovoltaic power station before energy storage;
combining the prior estimation with a new measurement variable to provide a new estimation value for the next time state;
introducing an adjusting factor lambda into the Kalman filter gain, and realizing the stabilization of the photovoltaic power fluctuation by dynamically adjusting the filtering gain of the Kalman filter in real time;
step four, introducing a filtering adjustment factor into the Kalman filter to dynamically adjust Kalman filtering gain, wherein the time updating equation is changed into: (P) g ) k+1,k+1 ＝(P v ) k+1,k +K k ·[(P v ) k+1 -(P v ) k+1,k ]+ΔS·P ess The state update equation becomes:
wherein (P) g ) k+1,k+1 Is the power smooth value at the k +1 moment after grid connection, (P) v ) k+1,k Is a priori estimation of the state at the current K +1 moment obtained from the current moment K of the photovoltaic power station, K k Is the gain value of the Kalman filter, R is the measurement noise covariance, P k+1,k Estimating covariance, P, for a priori ess For energy storage system output power, (P) v ) k+1 Adding photovoltaic output power before energy storage for a photovoltaic power station, wherein delta S is a charge state regulating variable;
the model predictive control method in the third step comprises the following steps of,
step one, establishing a relation among grid-connected power of a light storage system, power of an energy storage system and actual photovoltaic power in the light storage combined power generation system, wherein the relation is P g (k)＝P v (k)+P ess (k)，
Wherein, P g (k) For grid-connected power of light-storage systems, P v (k) For photovoltaic measured power, P ess (k) The energy storage system power;
and secondly, performing optimal control on the energy storage system by using a model predictive control method, wherein the control time domain is T, and the energy storage capacity is E b And establishing the charge state expression of the energy storage system:
thirdly, connecting the grid-connected power P of the light storage system g And the state of charge (SOC) of the energy storage system is used as a state variable, and the power (P) of the energy storage system ess As a control variable, the photovoltaic raw power P is measured v As a disturbance variable, the state space equation of the energy storage system at the time k is as follows:
step four, a plurality of energy storage battery monomers are centralized in the energy storage system, the energy storage system in the optical storage combined power generation system is equivalent to an energy storage unit after N independent energy storage battery monomer clusters, and a power prediction model of the energy storage system in the optical storage combined power generation system in a prediction time interval is as follows:
wherein: p is ess,T (k) Equivalent to P ess (k) Synthesizing a T-dimensional vector formed by the output of each energy storage system in the energy storage unit at the moment k; t is a control time domain; b is a prediction time domain; p ess,T0 (k) Is a certain time (T) before 0 ) Synthesizing B-dimensional vectors formed by output of all energy storage systems in the energy storage units; delta P ess (k) Outputting a power difference value for the energy storage system;
and fifthly, realizing the optimization control of the energy storage system through MPC rolling optimization calculation, and establishing a rolling optimization objective function:
obtaining the power regulating value P of the energy storage system at the next moment according to the formula ess (k + 1) is used as an actual control quantity, the optimization is repeated at the next optimization, and the control increment at the next moment is solved to realize the rolling optimization of the predictive control.
2. The optimal control method for the light storage system based on the Kalman filtering and the model prediction control as claimed in claim 1, wherein: an energy storage system controller based on a model predictive controller MPC is arranged in the energy storage system of the first step and is connected with the bus through a DC/AC inverter; the photovoltaic power station and the energy storage system are connected to a bus through an energy storage converter and are connected to the grid.
3. The optimal control method for the light storage system based on the Kalman filtering and the model prediction control as claimed in claim 1, wherein: the step four double-regulation feedback optimization control is to introduce a filtering regulation factor into a Kalman filter, and adaptively regulate Kalman filtering gain to enable the energy storage system to coordinate photovoltaic power output and stabilize photovoltaic power fluctuation;
in the case of a model-predictive controller,
through power constraint conditions during operation of the energy storage system: i P ess (k)|≤P ess,max ，
The state of charge constraint in the operation process of the energy storage system is as the condition: SOC min ≤SOC(k)≤SOC max ，
And grid-connected power fluctuation rate constraint: [ P ] g (k+1)-P g (k)]/P cap And (5) performing model prediction control rolling optimization to obtain the optimal power of the energy storage system and the optimal charge state of the energy storage system at the same time.
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