CN115237198B - Quick global peak tracking method of photovoltaic system under time-varying local shadow condition - Google Patents
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Abstract
The invention provides a rapid global peak tracking method of a photovoltaic system under a time-varying local shadow condition, which comprises the following steps: inputting a P-V characteristic curve; quantum modeling; green's function monte carlo weighted random walker design; green's function Monte Carlo simulation; the global peak tracking result is output, and the problem that the energy loss is caused by the fact that the local peak can be trapped under partial shadow conditions because the local peak cannot be distinguished from the global peak in the traditional global peak tracking method in the prior art is solved.
Description
Technical Field
The invention relates to the technical field of photovoltaic power generation, in particular to a rapid global peak tracking method of a photovoltaic system under a time-varying local shadow condition.
Background
To address global warming challenges, renewable resources such as wind, solar, water, biomass, and geothermal have been considered as alternatives to their precursors, coal, crude oil, natural gas, and nuclear; solar energy is considered the most common, readily masterable renewable energy source compared to other energy sources.
Photovoltaic systems have been widely used throughout the world, particularly in developing countries, for converting solar energy into electrical energy. However, due to the variability of weather and environmental factors, the output power of a photovoltaic system can be significantly affected by irradiance changes caused by neighboring buildings, clouds or trees, resulting in a large difference in the output power of the photovoltaic system from ideal sunlight.
Under partial shadow conditions, the output level of some photovoltaic cells due to shadows decreases, resulting in a mismatch of the entire photovoltaic system. Furthermore, when the photovoltaic system is in a rapidly changing local shadow condition, multiple peaks of photovoltaic performance can occur. Thus, one of the significant challenges faced in effectively utilizing photovoltaic systems is tracking global peaks from the P-V characteristics to obtain global maximum output power.
Due to the time-varying nature of local shadow conditions, it becomes a challenging problem how to quickly and accurately find a global peak from a plurality of peaks, including local peaks. The existing traditional global peak tracking method cannot distinguish local peaks from global peaks, so that the local peaks can be trapped under partial shadow conditions, and energy loss is caused.
Disclosure of Invention
(One) solving the technical problems
Aiming at the defects of the prior art, the invention provides a rapid global peak tracking method of a photovoltaic system under a time-varying local shadow condition, which solves the problem that the energy loss is caused by the fact that the existing traditional global peak tracking method can not distinguish local peaks from global peaks due to the time-varying characteristic of the local shadow condition and can possibly sink into the local peaks under partial shadow condition.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme: a rapid global peak tracking method of a photovoltaic system under a time-varying local shadow condition comprises the following steps:
Inputting a P-V characteristic curve;
Quantum modeling;
Green's function monte carlo weighted random walker design;
green's function Monte Carlo simulation;
and outputting a global peak tracking result.
Preferably, the quantum modeling includes:
Mapping the global peak tracking problem to a multidimensional random isooctyl model;
Finding the ground state of the lowest Hamiltonian energy state in the multidimensional random isooctyl model;
In quantum annealing, the annealing process changes the lateral field term from a larger value to 0;
in quantum annealing, the total hamiltonian of the global peak tracking problem in the photovoltaic system under local shadow conditions is time dependent and can be written as:
HGPT＝HMRIM+HTF(t)
Wherein H MRIM is the potential energy of the multidimensional random i Xin Moxing, and H TF (t) is the imaginary kinetic energy introduced by the time-dependent transverse field Γ (t);
The multidimensional random isooctyl model consists of a series of spins, each spin can only be set to one of two basic states, and two binary variables 0 and 1 can be respectively represented by two spin variables + -1, so that each possible configuration can be represented by a corresponding state, which is a linear combination of all constant-amplitude states in z representation, namely the lowest eigenstate of hamiltonian:
wherein, Representing the Brix matrix, i.e. the spin/>, at lattice site iThe components of the operator, J ij, are the random nearest neighbors of i Xin Ouge between lattice sites i and J, all peaks match the relevant quantum states of the K spins in the multidimensional random isooct model:
as the time-dependent transverse field Γ (t) decreases, the state vector |ψ (t) > transitions from the expected starting state of the highly constrained multidimensional random key Xin Moxing to the non-trivial ground state.
Preferably, the green function monte carlo weighted random walker design comprises:
Each state of the multidimensional stochastic isooctyl model is represented by a state vector evolving over time as follows:
Where T is the time ordering operator, |ψ 0 > is the initial state;
In the Green's function In (2), the matrix element is written as:
G(y,x；t)＝<y|1-Δt[H(t)-ET]|x>
Wherein x, y represents a ground state, E T is a reference energy;
thus, the state vector may be represented as:
|ψ(t)>＝limG0(tn-1)G0(tn-2)···G0(t1)G0(t0)|ψ0>
Wherein G 0 (t) =1- Δt·h (t) and t n =nΔt, Δt=t/n, in the recursive form:
the following wave function is obtained:
wherein, For normalized probability, w (x; t) is the weight.
Preferably, the green function monte carlo simulation includes:
Randomly preparing an initial wave function psi 0(x0);
generates a random walker S w with probability Moving from a location x 0 to a new location x 1, updating the weight of the random walk S w according to the set location;
Repeating the process with random variables until t=t n-1;
The random walk determines the ground state wave function output.
The invention also provides a rapid global peak tracking system of a photovoltaic system under a time-varying local shadow condition, which comprises:
An input module: for inputting a P-V characteristic;
modeling module: for quantum modeling;
the wander design module: the method is used for the design of a green function Monte Carlo weighted random walk;
and (3) an analog module: the method is used for the green function Monte Carlo simulation;
And a result output module: and the method is used for outputting global peak tracking results.
The invention also provides a rapid global peak tracking terminal of the photovoltaic system under the time-varying local shadow condition, which comprises: input device, output device, memory, processor; the input device, the output device, the memory and the processor are interconnected, wherein the memory is configured to store a computer program comprising program instructions, the processor being configured to invoke the program instructions to perform the method of fast global peak tracking of a photovoltaic system under time-varying local shadow conditions as set forth in any of the preceding claims.
The invention also provides a computer readable storage medium storing a computer program comprising program instructions which, when executed by a processor, perform a method of fast global peak tracking of a photovoltaic system under time-varying local shadow conditions as defined in any one of the preceding.
(III) beneficial effects
The invention provides a rapid global peak tracking method of a photovoltaic system under a time-varying local shadow condition. The beneficial effects are as follows:
According to the global peak tracking method based on quantum annealing of the rapid global peak tracking method of the photovoltaic system under the time-varying local shadow condition, which is provided by the invention, the global peak tracking problem under the partial shadow condition is mapped to the multidimensional random Xin Moxing, and the random conversion between two adjacent solutions in the traditional global peak tracking is replaced by quantum fluctuation and quantum tunneling, so that the global maximum power point tracking efficiency of the photovoltaic system under the partial shadow condition is improved.
Drawings
FIG. 1 is a flow chart of a method for tracking a fast global peak of a photovoltaic system under a time-varying local shadow condition;
FIG. 2 is a block diagram of a fast global peak tracking system for a photovoltaic system under time-varying local shadow conditions provided by the present invention;
FIG. 3 is a diagram of a fast global peak tracking terminal of a photovoltaic system under time-varying local shadow conditions provided by the invention;
FIG. 4 is a graph showing the P-V characteristic of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a method for fast global peak tracking of a photovoltaic system under a time-varying local shadow condition, including:
inputting a P-V characteristic curve; firstly, inputting a P-V characteristic curve shown in FIG. 4;
Quantum modeling;
Green's function monte carlo weighted random walker design;
green's function Monte Carlo simulation;
And outputting a global peak tracking result, and tracking the photovoltaic system as a global maximum power point according to the global peak tracking result.
Preferably, the quantum modeling includes:
Mapping the global peak tracking problem to a multidimensional random isooctyl model;
the Ising model Xin Moxing (Ising model) is a model of a random process (stochastic process) describing the phase change of a substance. The material undergoes phase transition to give new structure and physical properties. Systems in which phase changes occur are generally systems in which there is a strong interaction between molecules, also known as cooperative systems.
The system studied by the isooctyl model consists of a multi-dimensional periodic lattice, the geometric structure of which can be cubic or hexagonal, and each lattice is endowed with a value representing the spin-variable, namely the spin-up or the spin-down. The i Xin Moxing assumes that only the nearest neighbor spins have interactions with each other and the lattice configuration is determined using a set of spin variables. A common two-dimensional map Xin Moxing uses the arrow direction to indicate the spin direction.
Finding the ground state of the lowest Hamiltonian energy state in the multidimensional random isooctyl model;
In quantum annealing, the annealing process changes the lateral field term from a larger value to 0;
in quantum annealing, the total hamiltonian of the global peak tracking problem in the photovoltaic system under local shadow conditions is time dependent and can be written as:
HGPT＝HMRIM+HTF(t)
Wherein H MRIM is the potential energy of the multidimensional random i Xin Moxing, and H TF (t) is the imaginary kinetic energy introduced by the time-dependent transverse field Γ (t);
The multidimensional random isooctyl model consists of a series of spins, each spin can only be set to one of two basic states, and two binary variables 0 and 1 can be respectively represented by two spin variables + -1, so that each possible configuration can be represented by a corresponding state, which is a linear combination of all constant-amplitude states in z representation, namely the lowest eigenstate of hamiltonian:
wherein, Representing the Brix matrix, i.e. the spin/>, at lattice site iThe components of the operator, J ij, are the random nearest neighbors of i Xin Ouge between lattice sites i and J, all peaks match the relevant quantum states of the K spins in the multidimensional random isooct model:
as the time-dependent transverse field Γ (t) decreases, the state vector |ψ (t) > transitions from the expected starting state of the highly constrained multidimensional random key Xin Moxing to the non-trivial ground state.
According to the technical scheme, the global peak tracking method based on quantum annealing maps the global peak tracking problem under the partial shadow condition to the multidimensional random Xin Moxing, uses quantum fluctuation and quantum tunneling to replace random conversion between two adjacent solutions in the traditional global peak tracking, so as to improve the global maximum power point tracking efficiency of the photovoltaic system under the partial shadow condition.
Preferably, the green function monte carlo weighted random walker design comprises:
Each state of the multidimensional stochastic isooctyl model is represented by a state vector evolving over time as follows:
Where T is the time ordering operator, |ψ 0 > is the initial state;
In the Green's function In (2), the matrix element is written as:
G(y,x；t)＝<y|1-Δt[H(t)-ET]|x>
Wherein x, y represents a ground state, E T is a reference energy;
thus, the state vector may be represented as:
|ψ(t)>＝limG0(tn-1)G0(tn-2)···G0(t1)G0(t0)|ψ0>
Wherein G 0 (t) =1- Δt·h (t) and t n =nΔt, Δt=t/n, in the recursive form:
the following wave function is obtained:
wherein, For normalized probability, w (x; t) is the weight.
Preferably, the green function monte carlo simulation includes:
Randomly preparing an initial wave function psi 0(x0);
generates a random walker S w with probability Moving from a location x 0 to a new location x 1, updating the weight of the random walk S w according to the set location;
Repeating the process with random variables until t=t n-1;
The random walk determines the ground state wave function output.
As shown in fig. 2, the embodiment of the present invention further provides a fast global peak tracking system of a photovoltaic system under a time-varying local shadow condition, where the system includes:
An input module: for inputting a P-V characteristic;
modeling module: for quantum modeling;
the wander design module: the method is used for the design of a green function Monte Carlo weighted random walk;
and (3) an analog module: the method is used for the green function Monte Carlo simulation;
And a result output module: and the method is used for outputting global peak tracking results.
As shown in fig. 3, an embodiment of the present invention further provides a fast global peak tracking terminal of a photovoltaic system under a time-varying local shadow condition, including: input device, output device, memory, processor; the input device, the output device, the memory and the processor are interconnected, wherein the memory is configured to store a computer program comprising program instructions, the processor being configured to invoke the program instructions to perform the method of fast global peak tracking of a photovoltaic system under time-varying local shadow conditions as set forth in any of the preceding claims.
Embodiments of the present invention also provide a computer readable storage medium storing a computer program comprising program instructions which, when executed by a processor, perform a method of fast global peak tracking of a photovoltaic system under time-varying local shadow conditions as described in any of the preceding.
The embodiment of the invention discloses a rapid global peak tracking method of a photovoltaic system under a time-varying local shadow condition, which maps a global peak tracking problem under the local shadow condition to a multidimensional random Xin Moxing and approximately solves a negative sign problem in a Monte Carlo simulation process based on a fixed node. Finally, based on quantum classical mapping and projection entanglement pair state, actual operation of quantum annealing is realized by utilizing a green function Monte Carlo simulation technology. The method is particularly suitable for tracking the global maximum power point of the photovoltaic system under the time-varying local shadow condition.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (4)
1. A method for fast global peak tracking of a photovoltaic system under time-varying local shadow conditions, comprising:
Inputting a P-V characteristic curve;
Quantum modeling; the quantum modeling includes:
Mapping the global peak tracking problem to a multidimensional random isooctyl model;
Finding the ground state of the lowest Hamiltonian energy state in the multidimensional random isooctyl model;
In quantum annealing, the annealing process changes the lateral field term from a larger value to 0;
In quantum annealing, the total hamiltonian of the global peak tracking problem in the photovoltaic system under local shadow conditions is time dependent, written as:
HGPT＝HMRIM+HTF(t)
Wherein H MRIM is the potential energy of the multidimensional random i Xin Moxing, and H TF (t) is the imaginary kinetic energy introduced by the time-dependent transverse field Γ (t);
The multidimensional stochastic isooctane model consists of a series of spins, each spin can only be set to one of two basic states, two binary variables 0 and 1 can be respectively represented by two spin variables + -1, each possible configuration is represented by a corresponding state, which is a linear combination of all constant-amplitude states in z representation, namely the lowest eigenstate of hamiltonian:
wherein, Representing the Brix matrix, i.e. the spin/>, at lattice site iThe components of the operator, J ij, are the random nearest neighbors of i Xin Ouge between lattice sites i and J, all peaks match the relevant quantum states of the K spins in the multidimensional random isooct model:
As the time-dependent transverse field Γ (t) decreases, the state vector |ψ (t) > transitions from the expected starting state of the highly constrained multi-dimensional random Xin Moxing to the non-trivial ground state;
green's function monte carlo weighted random walker design; the green function monte carlo weighted random walker design includes:
Each state of the multidimensional stochastic isooctyl model is represented by a state vector evolving over time as follows:
Where T is the time ordering operator, |ψ 0 > is the initial state;
In the Green's function In (2), the matrix element is written as:
G(y,x；t)＝<y|1-Δt[H(t)-ET]|x>
Wherein x, y represents a ground state, E T is a reference energy;
thus, the state vector may be represented as:
|ψ(t)>＝limG0(tn-1)G0(tn-2)···G0(t1)G0(t0)|ψ0>
Wherein G 0 (t) =1- Δt·h (t) and t n =nΔt, Δt=t/n, in the recursive form:
the following wave function is obtained:
wherein, For normalized probability, w (x; t) is weight;
green's function Monte Carlo simulation; the green function monte carlo simulation includes:
Randomly preparing an initial wave function psi 0(x0);
generates a random walker S w with probability Moving from a location x 0 to a new location x 1, updating the weight of the random walk S w according to the set location;
Repeating the process with random variables until t=t n-1;
The random walk determines the ground state wave function output;
and outputting a global peak tracking result.
2. A fast global peak tracking system for a photovoltaic system under time-varying local shadow conditions, the system comprising:
An input module: for inputting a P-V characteristic;
modeling module: for quantum modeling; the quantum modeling includes:
Mapping the global peak tracking problem to a multidimensional random isooctyl model;
Finding the ground state of the lowest Hamiltonian energy state in the multidimensional random isooctyl model;
In quantum annealing, the annealing process changes the lateral field term from a larger value to 0;
In quantum annealing, the total hamiltonian of the global peak tracking problem in the photovoltaic system under local shadow conditions is time dependent, written as:
HGPT＝HMRIM+HTF(t)
Wherein H MRIM is the potential energy of the multidimensional random i Xin Moxing, and H TF (t) is the imaginary kinetic energy introduced by the time-dependent transverse field Γ (t);
The multidimensional stochastic isooctane model consists of a series of spins, each spin can only be set to one of two basic states, two binary variables 0 and 1 can be respectively represented by two spin variables + -1, each possible configuration is represented by a corresponding state, which is a linear combination of all constant-amplitude states in z representation, namely the lowest eigenstate of hamiltonian:
wherein, Representing the Brix matrix, i.e. the spin/>, at lattice site iThe components of the operator, J ij, are the random nearest neighbors of i Xin Ouge between lattice sites i and J, all peaks match the relevant quantum states of the K spins in the multidimensional random isooct model:
As the time-dependent transverse field Γ (t) decreases, the state vector |ψ (t) > transitions from the expected starting state of the highly constrained multi-dimensional random Xin Moxing to the non-trivial ground state;
the wander design module: the method is used for the design of a green function Monte Carlo weighted random walk; the green function monte carlo weighted random walker design includes:
Each state of the multidimensional stochastic isooctyl model is represented by a state vector evolving over time as follows:
Where T is the time ordering operator, |ψ 0 > is the initial state;
In the Green's function In (2), the matrix element is written as:
G(y,x；t)＝<y|1-Δt[H(t)-ET]|x>
Wherein x, y represents a ground state, E T is a reference energy;
thus, the state vector may be represented as:
|ψ(t)>＝limG0(tn-1)G0(tn-2)···G0(t1)G0(t0)|ψ0>
Wherein G 0 (t) =1- Δt·h (t) and t n =nΔt, Δt=t/n, in the recursive form:
the following wave function is obtained:
wherein, For normalized probability, w (x; t) is weight;
And (3) an analog module: the method is used for the green function Monte Carlo simulation; the green function monte carlo simulation includes:
Randomly preparing an initial wave function psi 0(x0);
generates a random walker S w with probability Moving from a location x 0 to a new location x 1, updating the weight of the random walk S w according to the set location;
Repeating the process with random variables until t=t n-1;
The random walk determines the ground state wave function output;
And a result output module: and the method is used for outputting global peak tracking results.
3. A fast global peak tracking terminal for a photovoltaic system under time-varying local shadow conditions, comprising: input device, output device, memory, processor; the input device, the output device, the memory and the processor are interconnected, wherein the memory is for storing a computer program comprising program instructions, the processor being configured to invoke the program instructions to perform the method of fast global peak tracking of a photovoltaic system under time-varying local shadow conditions according to any of claims 1-2.
4. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program comprising program instructions which, when executed by a processor, perform the method of fast global peak tracking of a photovoltaic system under time-varying local shadow conditions according to any of claims 1-2.
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| A global optimization method for multiple peak photovoltaic MPPT;Yuling Li等;《IECON》;全文 * |
| Novel Maximum Power Point Tracking Strategy for Photovoltaic System Under Partially Shaded Condition Based Monte Carlo Algorithm;Lei Tang等;《IEEE International Conference》;全文 * |
| 考虑风电和光伏发电影响的电力系统最优旋转备用容量确定;殷桂梁等;《电网技术》;全文 * |
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