CN116073445A - Optimal scheduling method and system for wind-light hydrogen storage micro-grid based on semi-physical model - Google Patents
Optimal scheduling method and system for wind-light hydrogen storage micro-grid based on semi-physical model Download PDFInfo
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Abstract
The invention relates to an optimization scheduling method and system based on a semi-physical model wind-light hydrogen storage micro-grid, wherein output power reference functions corresponding to wind-light hydrogen storage modules are set according to the attribute of a wind-light hydrogen storage system, and a day-ahead output function and a load function of each module are obtained from a wind-light hydrogen storage simulation model; determining an operation control strategy of the wind-light hydrogen storage system according to the output power reference function corresponding to each wind-light hydrogen storage module and the day-ahead output function of each module obtained from the wind-light hydrogen storage simulation model; taking the lowest economic consumption of the wind-solar hydrogen storage system as an optimization target, and establishing a multi-target optimization scheduling model; and solving the optimal scheduling model by using an optimal mixed particle swarm algorithm to obtain a day-ahead optimal scheduling scheme of the wind-solar hydrogen storage system, so that the optimal scheduling scheme is obtained. The problem that no scene limitation of a high-power system object is solved by constructing a semi-physical model, and the economy and the safety of the running of the micro-grid system are improved and ensured by optimizing a scheduling method.
Description
Technical Field
The invention belongs to the technical field of micro-grid dispatching, and particularly relates to an optimal dispatching method and system for a wind-solar hydrogen storage micro-grid based on a semi-physical model.
Background
The micro-grid is a micro power supply system composed of a distributed power generation source, a power conversion device, energy storage equipment, a power load, a monitoring system and the like, and can realize self-guarantee safety and control management, so that the micro-grid can be connected into a public grid for grid-connected operation and can also work off the grid. The wind-light hydrogen storage system is a micro-grid system which takes wind power generation and photovoltaic power generation as distributed energy sources and takes hydrogen production, storage battery packs and the like as energy storage units. It uses clean energy sources such as wind energy, light energy, hydrogen energy and the like to generate electricity. The cost of verifying the control strategy by using the actual micro-grid system is high, accidents can be caused by improper control strategy, the safety is low, meanwhile, the micro-grid structure and the operation control thereof are more and more complex, the requirement on real-time performance is higher and higher, and the optimization of energy scheduling of the micro-grid is required.
The PSO algorithm has the advantages of fewer parameters, simple realization, convenient operation and the like; however, the method also has the defects of low optimizing precision, early convergence, early ending of the whole iteration process, sinking into local optimum and the like.
Disclosure of Invention
The invention aims to solve the technical problem of providing an optimal scheduling method and system based on a semi-physical model wind-solar hydrogen storage micro-grid, which solves the problem of no scene limitation of a high-power system object through the construction of the semi-physical model, and improves and ensures the economical efficiency and the safety of the micro-grid system operation through the optimal scheduling method.
The present invention has been achieved in such a way that,
an optimized scheduling method based on a semi-physical model wind-solar hydrogen storage system comprises the following steps:
step 1, setting output power reference functions corresponding to all modules of the wind-light hydrogen storage according to the attribute of a wind-light hydrogen storage system, and acquiring a day-ahead output function and a load function of each module from a wind-light hydrogen storage simulation model;
step 2, determining an operation control strategy of the wind-light hydrogen storage system according to output power reference functions corresponding to all the wind-light hydrogen storage modules and a day-ahead output function of each module obtained from the wind-light hydrogen storage simulation model;
step 3, establishing a multi-objective optimization scheduling model by taking the lowest economic consumption of the wind-solar hydrogen storage system as an optimization objective;
and 4, solving the optimal scheduling model by using an optimal mixed particle swarm algorithm to obtain a day-ahead optimal scheduling scheme of the wind-solar hydrogen storage system, so that the optimal scheduling scheme meets the operation control strategy of the wind-solar hydrogen storage system in the step 2.
Further, a MATLAB/Simulink software platform is used for building a simulation model of the wind-light hydrogen storage system, and each module comprises a wind power generation module, a photovoltaic power generation module, an energy storage module and a hydrogen production module which are all connected with an alternating current bus; the current output function and the load function of the wind-light hydrogen storage system simulation model are obtained through the physical control system, the optimal scheduling scheme is fed back to the wind-light hydrogen storage system simulation model, and the wind-light hydrogen storage system simulation model performs simulation according to the optimal scheduling scheme.
Further, step 2 determines that the operation control strategy of the wind-solar hydrogen storage system is:
the actual power deviation is calculated as follows:
ΔP(t)＝P s (t)-P r (t)
wherein P is s (t)、P r (t) respectively obtaining the current total output power function of each module and the total output power reference power function corresponding to each module of the wind-light hydrogen storage for the wind-light hydrogen storage simulation model;
if delta P (t) is less than or equal to 0, indicating that the actual power is insufficient, and adjusting the output power of the wind-light module or using the energy storage module to compensate the lack of power is needed;
the output power of the wind-light module after adjustment is calculated as follows:
P wind (t)＝α(P r (t)+ΔP(t))
P pv (t)＝(1-α)(P r (t)+ΔP(t))
wherein alpha is the wind power installed capacity ratio in the wind-light module;
if delta P (t) > 0, the actual emitted power exceeds the required value, the higher part is required to absorb the excessive power by utilizing the energy storage module preferentially, if the maximum energy limit of the energy storage module is exceeded, the residual power is distributed to the hydrogen production module, the residual power is used for the hydrogen production of the electrolytic cell, and if the excessive residual power exceeds the maximum working power of the electrolytic cell of the hydrogen production module, the energy generated by the excessive wind and light is abandoned.
Further, the multi-objective optimization scheduling model in step 3 specifically includes:
the objective function is:
wherein, include: wind power generation module adjustment cost CW (t) =a w (αΔP(t)) ２ +b w (αΔP(t))+c w
Wherein a is w ，b w ，c w The regulation coefficient of the wind power generation module;
photovoltaic power generation module adjustment cost CP (t) =a pv ((1-α)ΔP(t)) 2 +b pv ((1-α)ΔP(t))+c pv
Wherein a is pv ，b pv ，c pv The regulation coefficient of the photovoltaic power generation module;
cost of operation of energy storage module CS (t) =a s (ΔP(t)) 2
Wherein a is s The energy storage module running cost coefficient is used;
wind and light cost is abandoned by the system: CR (t) =k r (ΔP(t)-P batmax -P hmax ) 2
Wherein K is r Wind and light cost coefficient, P for system abandon batmax Maximum charge and discharge power of the energy storage module, P hmax The maximum working power of the electrolytic tank of the hydrogen production module is set;
the constraint conditions are as follows:
1) Power balance constraint
P pv (t)+P wind (t)＝Pi oad (t)+P bat (t)+P h (t)
Wherein P is load (t) is the required power of the load in the time period t, P bat (t) is the charge-discharge power of the energy storage module in the time period t, P h (t) is the working power of the electrolytic tank of the hydrogen production module in the time period t;
2) Photovoltaic fan output constraint
Wherein P is pvr ，P windr Rated power of the photovoltaic and wind power modules respectively;
3) Energy storage module restraint
In sigma soct Is the value of the state of charge at time t, sigma socmin Sum sigma socmax Respectively minimum and maximum states of charge, P batmin The minimum charge and discharge power of the energy storage module is obtained;
4) Hydrogen production module restraint
P h (t)≤P hmax 。
Further, the specific step of solving the optimal scheduling model in the step 4 is as follows:
s41: initializing population parameters of particle swarm;
s42: determining a particle constraint condition and correcting a boundary crossing variable;
s43: calculating fitness and finding out a current optimal solution;
s44: dividing individuals of the optimizing population into two parts, and respectively performing PSO optimizing and BA optimizing;
s44: changing the previous optimal solution according to the fitness optimal guiding principle;
s45: adding normal random number disturbance: the normal random number is a random number which obeys normal distribution, and the probability of the expected number appearing closer to the random number is larger, and the probability of the expected number appearing farther from the random number is smaller;
s45: updating the speed and position of the particle according to a speed and position updating formula, the updating formula being as follows:
v(k+1)＝r g {r g v(k)+r g r 1 c 1 [x pi -x(k)+r g ]+r 2 c 2 [x g -x(k)+r g ]}
x(k+1)＝r g [r g x(k)+r g v(k+1)]
wherein r is g R is random number distributed from standard normal 1 、r 2 Is [0,1]Random number on c 1 For individual learning factors, c 2 Is a social learning factor, x pi For the historic optimal position of particle i, x g Is the historical optimal position of the population;
s46: judging whether the maximum population algebra is reached, if so, outputting the position of the optimal particles to obtain an optimal scheduling scheme, if not, continuously updating the population algebra, and returning to the step S44 to continue calculation.
An optimized dispatching system based on a semi-physical model wind-light hydrogen storage system, comprising:
the wind-light hydrogen storage simulation model comprises a wind power generation module, a photovoltaic power generation module, an energy storage module and a hydrogen production module, which are all connected with an alternating current bus and used for simulation;
the physical control system obtains the current output function and the load function of the wind-light hydrogen storage system simulation model, and determines the operation control strategy of the wind-light hydrogen storage system;
the physical control system takes the lowest economic consumption of the wind-solar hydrogen storage system as an optimization target, and establishes a multi-target optimization scheduling model; solving the optimal scheduling model by utilizing an optimal mixed particle swarm algorithm to obtain a current optimal scheduling scheme of the wind-solar hydrogen storage system, enabling the wind-solar hydrogen storage system of the optimal scheduling scheme to run a control strategy, feeding back the optimal scheduling scheme to a wind-solar hydrogen storage system simulation model, and performing simulation on the wind-solar hydrogen storage system simulation model according to the optimal scheduling scheme;
and the physical control system controls the real wind-light hydrogen storage system to execute the optimal scheduling scheme.
Further, the system comprises:
the operation control strategy of the wind-solar hydrogen storage system is as follows:
the actual power deviation is calculated as follows:
ΔP(t)＝P s (t)-P r (t)
wherein P is s (t)、P r (t) respectively obtaining the current total output power function of each module and the total output power reference power function corresponding to each module of the wind-light hydrogen storage for the wind-light hydrogen storage simulation model;
if delta P (t) is less than or equal to 0, indicating that the actual power is insufficient, and adjusting the output power of the wind-light module or using the energy storage module to compensate the lack of power is needed;
the output power of the wind-light module after adjustment is calculated as follows:
P wind (t)＝α(P r (t)+ΔP(t))
P pv (t)＝(1-α)(P r (t)+ΔP(t))
wherein alpha is the wind power installed capacity ratio in the wind-light module;
if delta P (t) > 0, the actual emitted power exceeds the required value, the higher part is required to absorb the excessive power by utilizing the energy storage module preferentially, if the maximum energy limit of the energy storage module is exceeded, the residual power is distributed to the hydrogen production module, the residual power is used for the hydrogen production of the electrolytic cell, and if the excessive residual power exceeds the maximum working power of the electrolytic cell of the hydrogen production module, the energy generated by the excessive wind and light is abandoned.
Further, the multi-objective optimization scheduling model specifically includes:
the objective function is:
wherein, include:wind power generation module adjustment cost CW (t) =a w (αΔP(t)) 2 +b w (αΔP(t))+c w
Wherein a is w ，b w ，c w The regulation coefficient of the wind power generation module;
photovoltaic power generation module adjustment cost CP (t) =a pv ((1-α)ΔP(t)) 2 +b pv ((1-α)ΔP(t))+c pv
Wherein a is pv ，b pv ，c pv The regulation coefficient of the photovoltaic power generation module;
cost of operation of energy storage module CS (t) =a s (ΔP(t)) 2
Wherein a is s The energy storage module running cost coefficient is used;
wind and light cost is abandoned by the system: CR (t) =k r (ΔP(t)-P batmax -P hmax ) 2
Wherein K is r Wind and light cost coefficient, P for system abandon batmax Maximum charge and discharge power of the energy storage module, P hmax The maximum working power of the electrolytic tank of the hydrogen production module is set;
the constraint conditions are as follows:
1) Power balance constraint
P pv (t)+P wind (t)＝P load (t)+P bat (t)+P h (t)
Wherein P is load (t) is the required power of the load in the time period t, P bat (t) is the charge-discharge power of the energy storage module in the time period t, P h (t) is the working power of the electrolytic tank of the hydrogen production module in the time period t;
2) Photovoltaic fan output constraint
Wherein P is pvr ，P windr Rated power of the photovoltaic and wind power modules respectively;
3) Energy storage module restraint
In sigma soct Is the value of the state of charge at time t, sigma socmin Sum sigma socmax Respectively minimum and maximum states of charge, P batmin The minimum charge and discharge power of the energy storage module is obtained;
4) Hydrogen production module restraint
P h (t)≤P hmax 。
Compared with the prior art, the invention has the beneficial effects that:
on one hand, PSO algorithm and BA algorithm are adopted to co-evolve mutually, and the search information between the two corresponding optimizing particles can be shared and used as a reference, so that the optimizing solving accuracy of the optimizing mixed particle swarm algorithm is improved; on the other hand, a normal random number disturbance strategy is adopted, when the algorithm approaches to stagnation or falls into a local optimal solution, a normal random number disturbance term is added, and as the disturbance of the normal random number is concentrated, the speed and position change of particles can be well adapted to further change, the larger normal random number can help the algorithm to jump out of local convergence, the smaller normal random number can effectively help the current local area to carry out deep and fine optimization, and therefore the optimizing capability of the algorithm is improved.
According to the invention, the semi-physical model is adopted to simulate the actual wind-solar hydrogen storage micro-grid, so that the problem that a high-power system is not limited by a real object scene is solved, and the economical efficiency and the safety of optimizing and scheduling strategy verification are improved; the hydrogen production system is led into a micro-grid, and the redundant energy which cannot be stored by the energy storage system in the electricity consumption valley period is converted in the form of water electrolysis hydrogen production, so that the problem of energy waste caused by a large amount of wind and light abandoning in the valley period is avoided.
Drawings
FIG. 1 is a flow chart of an optimized scheduling method of a wind-solar hydrogen storage system based on a semi-physical model.
FIG. 2 is a diagram of a system structure of a wind-solar hydrogen storage semi-physical model.
FIG. 3 is a flowchart of the optimal scheduling control strategy of the wind-solar hydrogen storage system.
Fig. 4 is a flowchart of an improved mixed particle swarm optimization algorithm according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, an optimized scheduling method based on a semi-physical model wind-solar hydrogen storage system comprises the following steps:
step 1, setting output power reference functions corresponding to all modules according to the attribute of a wind-light hydrogen storage system, and acquiring a day-ahead output function and a load function of each module from a wind-light hydrogen storage simulation model;
step 2, determining an operation control strategy of the wind-light hydrogen storage system according to output power reference functions corresponding to all the wind-light hydrogen storage modules and a day-ahead output function of each module obtained from the wind-light hydrogen storage simulation model; each module here includes wind power generation module, photovoltaic power generation module, energy storage module and hydrogen manufacturing module, and based on half physical model scene hydrogen storage system includes: MATLAB/Simulink software platform and physical control system. The MATLAB/Simulink software platform is used for building a simulation model of the wind-light hydrogen storage system; the following finds an optimal scheduling scheme according to the determined operation control strategy of the wind-solar hydrogen storage system, and the method comprises the following steps:
step 3, establishing a multi-objective optimization scheduling model by taking the lowest economic consumption of the wind-solar hydrogen storage system as an optimization objective;
and 4, solving the optimal scheduling model by using an optimal mixed particle swarm algorithm to obtain a day-ahead optimal scheduling scheme of the wind-solar hydrogen storage system.
The physical control system comprises a DSP control module and a man-machine touch control display interface.
Referring to fig. 2, a simulation model is built, each module of the simulation model comprises photovoltaic power generation, wind power generation, an energy storage device and an electrolytic water system, all of which are connected to an alternating current bus, data acquisition is carried out on the simulation model through a physical control system, and scheduling information is transmitted to the simulation model for simulation.
Referring to fig. 3, step 2 determines a control strategy for the operation of the wind-solar hydrogen storage system as follows:
let the actual power deviation be:
ΔP(t)＝P s (t)-P r (t)
wherein P is s (t)、P r (t) respectively obtaining the current total output power function of each module and the total output power reference power function corresponding to each module of the wind-light hydrogen storage for the wind-light hydrogen storage simulation model;
if delta P (t) is less than or equal to 0, indicating that the actual power is insufficient, and adjusting the output power of the wind-light module or using the energy storage module to compensate the lack of power is needed;
the output power of the wind-light module after adjustment is as follows:
P wind (t)＝a(P r (t)+ΔP(t))
P pv (t)＝(1-α)(P r (t)+ΔP(t))
wherein alpha is the wind power installed capacity ratio in the wind-light module;
if delta P (t) > 0, the actual emitted power exceeds the required value, the higher part is required to absorb the excessive power by utilizing the energy storage module preferentially, if the maximum energy limit of the energy storage module is exceeded, the residual power is distributed to the hydrogen production module, the residual power is used for the hydrogen production of the electrolytic cell, and if the excessive residual power exceeds the maximum working power of the electrolytic cell of the hydrogen production module, the energy generated by the excessive wind and light is abandoned.
The multi-objective optimization scheduling model in the step 3 specifically comprises the following steps:
the objective function is:
wind power generation module adjustment cost
CW(t)＝a w (αΔP(t)) 2 +ｂ w (αΔP(t))+c w
Wherein a is w ，b w ，c w The regulation coefficient of the wind power generation module;
photovoltaic power generation module adjustment cost
CP(t)＝a pv ((1-α)ΔP(t)) 2 +b pv ((1-α)ΔP(t))+c pv
Wherein a is pv ，b pv ，c pv The regulation coefficient of the photovoltaic power generation module;
cost of operation of energy storage module
CS(t)＝a s (ΔP(t)) 2
Wherein a is s The energy storage module running cost coefficient is used;
wind and light cost of system abandonment
CR(t)＝K r (ΔP(t)-P batmax -P hmax ) 2
Wherein K is r Wind and light cost coefficient, P for system abandon batmax Maximum charge and discharge power of the energy storage module, P hmax The maximum working power of the electrolytic tank of the hydrogen production module is set;
the constraint conditions are as follows:
1) Power balance constraint
P pv (t)+P wind (t)＝P load (t)+P bat (t)+P h (t)
Wherein P is load (t) is the required power of the load in the time period t, P bat (t) is the charge-discharge power of the energy storage module in the time period t, P h (t) is the working power of the electrolytic tank of the hydrogen production module in the time period t;
2) Photovoltaic fan output constraint
Wherein P is pvr ，P windr Rated power of the photovoltaic and wind power modules respectively;
3) Energy storage module restraint
In sigma SOCt Is the value of the state of charge at time t, sigma SOCmin Sum sigma SOCmax Respectively minimum and maximum states of charge, P batmｉｎ The minimum charge and discharge power of the energy storage module is obtained;
4) Hydrogen production module restraint
P h (t)≤P hmax
Referring to fig. 4, the specific steps of solving the optimized scheduling model in step 4 are as follows:
s41: initializing population parameters of particle swarm;
s42: determining a particle constraint condition and correcting a boundary crossing variable;
s43: calculating fitness and finding out a current optimal solution;
s44: dividing individuals of the optimizing population into two parts, and respectively performing PSO optimizing and BA optimizing;
s44: changing the previous optimal solution according to the fitness optimal guiding principle;
s45: adding normal random number disturbance: the normal random number is a random number which obeys normal distribution, and the probability of the expected number appearing closer to the random number is larger, and the probability of the expected number appearing farther from the random number is smaller;
s45: updating the speed and position of the particle according to a speed and position updating formula, the updating formula being as follows:
v(k+1)＝r g {r g v(k)+r g r 1 c 1 [x pi -x(k)+r g ]+r 2 c 2 [x g -x(k)+r g ]}
x(k+1)＝r g [r g x(k)+r g v(k+1)]
wherein r is g R is random number distributed from standard normal 1 、r 2 Is [0,1]Random number on c 1 For individual learning factors, c 2 Is a social learning factor, x pi For the historic optimal position of particle i, x g Is the history of the populationThe optimal position is beneficial to better adapting to further change of particles because the disturbance of the normal random numbers is concentrated, the larger normal random numbers can help the algorithm to jump out of local convergence, the smaller normal random numbers can effectively help the current local area to carry out intensive optimization, and therefore the optimizing capability of the algorithm is improved;
s46: judging whether the maximum population algebra is reached, if so, outputting the position of the optimal particles to obtain an optimal scheduling scheme, if not, continuously updating the population algebra, and returning to the step S44 to continue calculation.
An optimized dispatching system based on a semi-physical model wind-light hydrogen storage system, comprising:
the wind-light hydrogen storage simulation model comprises a wind power generation module, a photovoltaic power generation module, an energy storage module and a hydrogen production module, which are all connected with an alternating current bus and used for simulation;
the physical control system acquires a day-ahead output function and a load function of a wind-light hydrogen storage system simulation model, and determines an operation control strategy of the wind-light hydrogen storage system;
the physical control system takes the lowest economic consumption of the wind-light hydrogen storage system as an optimization target, and establishes a multi-target optimization scheduling model; solving the optimal scheduling model by utilizing an optimal mixed particle swarm algorithm to obtain a daily optimal scheduling scheme of the wind-solar hydrogen storage system, so that the wind-solar hydrogen storage system of the optimal scheduling scheme operates a control strategy, and feeding back the optimal scheduling scheme to a wind-solar hydrogen storage system simulation model, wherein the wind-solar hydrogen storage system simulation model performs simulation according to the optimal scheduling scheme;
and the physical control system controls the real wind-light hydrogen storage system to execute the optimal scheduling scheme.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (8)
1. An optimized scheduling method based on a semi-physical model wind-solar hydrogen storage system is characterized by comprising the following steps:
step 1, setting output power reference functions corresponding to all modules of the wind-light hydrogen storage according to the attribute of a wind-light hydrogen storage system, and acquiring a day-ahead output function and a load function of each module from a wind-light hydrogen storage simulation model;
step 2, determining an operation control strategy of the wind-light hydrogen storage system according to output power reference functions corresponding to all the wind-light hydrogen storage modules and a day-ahead output function of each module obtained from the wind-light hydrogen storage simulation model;
step 3, establishing a multi-objective optimization scheduling model by taking the lowest economic consumption of the wind-solar hydrogen storage system as an optimization objective;
and 4, solving the optimal scheduling model by using an optimal mixed particle swarm algorithm to obtain a day-ahead optimal scheduling scheme of the wind-solar hydrogen storage system, so that the optimal scheduling scheme meets the operation control strategy of the wind-solar hydrogen storage system in the step 2.
2. The optimal scheduling method based on the semi-physical model wind-light hydrogen storage system according to claim 1 is characterized in that an MATLAB/Simulink software platform is used for building a simulation model of the wind-light hydrogen storage system, and each module comprises a wind power generation module, a photovoltaic power generation module, an energy storage module and a hydrogen production module which are all connected with an alternating current bus; and acquiring a day-ahead output function and a load function of the wind-light hydrogen storage system simulation model through the physical control system, feeding back an optimal scheduling scheme to the wind-light hydrogen storage system simulation model, and performing simulation by the wind-light hydrogen storage system simulation model according to the optimal scheduling scheme.
3. The optimal scheduling method based on the semi-physical model wind-solar hydrogen storage system according to claim 1, wherein step 2 determines that the wind-solar hydrogen storage system operation control strategy is:
the actual power deviation is calculated as follows:
ΔP(t)＝P s (t)-P r (t)
wherein P is s (t)、P r (t) obtaining the total output power function before day and the wind-solar hydrogen storage of each module for the wind-solar hydrogen storage simulation model respectivelyThe total output power reference power function corresponding to each module;
if delta P (t) is less than or equal to 0, indicating that the actual power is insufficient, and adjusting the output power of the wind-light module or using the energy storage module to compensate the lack of power is needed;
the output power of the wind-light module after adjustment is calculated as follows:
P ｗｉnd (t)＝ａ(P r (t)+ΔP(t))
P pv (t)＝(1-α)(P r (t)+ΔP(t))
wherein alpha is the wind power installed capacity ratio in the wind-light module;
if delta P (t) > 0, the actual emitted power exceeds the required value, the higher part is required to absorb the excessive power by utilizing the energy storage module preferentially, if the maximum energy limit of the energy storage module is exceeded, the residual power is distributed to the hydrogen production module, the residual power is used for the hydrogen production of the electrolytic cell, and if the excessive residual power exceeds the maximum working power of the electrolytic cell of the hydrogen production module, the energy generated by the excessive wind and light is abandoned.
4. The optimal scheduling method based on the semi-physical model wind-solar hydrogen storage system according to claim 1, wherein the multi-objective optimal scheduling model in the step 3 specifically comprises:
the objective function is:
wherein, include: wind power generation module adjustment cost CW (t) =a w (αΔP(t)) 2 +b w (aΔP(t))+c w
Wherein a is w ，b w ，c w The regulation coefficient of the wind power generation module;
photovoltaic power generation module adjustment cost CP (t) =a pv ((1-α)ΔP(t)) 2 +b pv ((1-α)ΔP(t))+c pv
Wherein a is pv ，b pv ，c pv For regulating photovoltaic power generation modulesCoefficients;
cost of operation of energy storage module CS (t) =a s (ΔP(t)) 2
Wherein a is s The energy storage module running cost coefficient is used;
wind and light cost is abandoned by the system: CR (t) =k ｒ (ΔP(t)-P batmax -P hmax ) 2
Wherein K is r Wind and light cost coefficient, P for system abandon batmax Is the maximum value of the charge and discharge power of the energy storage module,
P hmax the maximum working power of the electrolytic tank of the hydrogen production module is set;
the constraint conditions are as follows:
1) Power balance constraint
P pv (t)+p wind (t)＝P load (t)+P bat (t)+P h (t)
Wherein P is load (t) is the required power of the load in the time period t, P bat (t) is the charge-discharge power of the energy storage module in the time period t, P h (t) is the working power of the electrolytic tank of the hydrogen production module in the time period t;
2) Photovoltaic fan output constraint
Wherein P is pvr ，P windr Rated power of the photovoltaic and wind power modules respectively;
3) Energy storage module restraint
In sigma SOCt Is the value of the state of charge at time t, sigma SOCmin Sum sigma SOCmax Respectively minimum and maximum states of charge, P batmin The minimum charge and discharge power of the energy storage module is obtained;
4) Hydrogen production module restraint
P h (t)≤P hmax 。
5. The optimal scheduling method based on the semi-physical model wind-solar hydrogen storage system according to claim 1, wherein the specific steps of solving the optimal scheduling model in the step 4 are as follows:
s41: initializing population parameters of particle swarm;
s42: determining a particle constraint condition and correcting a boundary crossing variable;
s43: calculating fitness and finding out a current optimal solution;
s44: dividing individuals of the optimizing population into two parts, and respectively performing PSO optimizing and BA optimizing;
s44: changing the previous optimal solution according to the fitness optimal guiding principle;
s45: adding normal random number disturbance: the normal random number is a random number which obeys normal distribution, and the probability of the expected number appearing closer to the random number is larger, and the probability of the expected number appearing farther from the random number is smaller;
s45: updating the speed and position of the particle according to a speed and position updating formula, the updating formula being as follows:
wherein r is g R is random number distributed from standard normal 1 、r 2 Is [0,1]Random number on c 1 For individual learning factors, c 2 Is a social learning factor, x pi For the historic optimal position of particle i, x g Is the historical optimal position of the population;
s46: judging whether the maximum population algebra is reached, if so, outputting the position of the optimal particles to obtain an optimal scheduling scheme, if not, continuously updating the population algebra, and returning to the step S44 to continue calculation.
6. An optimized dispatching system based on a semi-physical model wind-light hydrogen storage system is characterized in that the system comprises:
the wind-light hydrogen storage simulation model comprises a wind power generation module, a photovoltaic power generation module, an energy storage module and a hydrogen production module, which are all connected with an alternating current bus and used for simulation;
the physical control system acquires a day-ahead output function and a load function of a wind-light hydrogen storage system simulation model, and determines an operation control strategy of the wind-light hydrogen storage system;
the physical control system takes the lowest economic consumption of the wind-solar hydrogen storage system as an optimization target, and establishes a multi-target optimization scheduling model; solving the optimal scheduling model by utilizing an optimal mixed particle swarm algorithm to obtain a daily optimal scheduling scheme of the wind-solar hydrogen storage system, so that the wind-solar hydrogen storage system of the optimal scheduling scheme operates a control strategy, and feeding back the optimal scheduling scheme to a wind-solar hydrogen storage system simulation model, wherein the wind-solar hydrogen storage system simulation model performs simulation according to the optimal scheduling scheme;
and the physical control system controls the real wind-light hydrogen storage system to execute the optimal scheduling scheme.
7. An optimized dispatch system for a semi-physical model based wind and solar hydrogen storage system as claimed in claim 6, wherein the system comprises:
the operation control strategy of the wind-solar hydrogen storage system is as follows:
the actual power deviation is calculated as follows:
ΔP(t)＝P s (t)-P r (t)
wherein P is s (t)、P r (t) acquiring a day-ahead total output power function of each module and a total output power reference power function corresponding to each wind-light hydrogen storage module for the wind-light hydrogen storage simulation model respectively;
if delta P (t) is less than or equal to 0, indicating that the actual power is insufficient, and adjusting the output power of the wind-light module or using the energy storage module to compensate the lack of power is needed;
the output power of the wind-light module after adjustment is calculated as follows:
P wind (t)＝α(P r (t)+AP(t))
P pv (t)＝(1-α)(p r (t)+ΔP(t))
wherein alpha is the wind power installed capacity ratio in the wind-light module;
if delta P (t) > 0, the actual emitted power exceeds the required value, the higher part is required to absorb the excessive power by utilizing the energy storage module preferentially, if the maximum energy limit of the energy storage module is exceeded, the residual power is distributed to the hydrogen production module, the residual power is used for the hydrogen production of the electrolytic cell, and if the excessive residual power exceeds the maximum working power of the electrolytic cell of the hydrogen production module, the energy generated by the excessive wind and light is abandoned.
8. The optimal scheduling system based on the semi-physical model wind-solar hydrogen storage system according to claim 6, wherein the multi-objective optimal scheduling model is specifically:
the objective function is:
wherein, include: wind power generation module adjustment cost CW (t) =a w (αΔP(t)) 2 +b w (αΔP(t))+c w
Wherein a is w ，b w ，c w The regulation coefficient of the wind power generation module;
photovoltaic power generation module adjustment cost CP (t) =a pv ((1-α)ΔP(t)) 2 +b pv ((1-α)ΔP(t))+c pv
Wherein a is pv ，b pv ，c pv The regulation coefficient of the photovoltaic power generation module;
cost of operation of energy storage module CS (t) =a s (ΔP(t)) 2
Wherein a is s The energy storage module running cost coefficient is used;
wind and light cost is abandoned by the system: CR (t) =k r (ΔP(t)-P batax -P hmax ) 2
Wherein K is r Wind and light cost coefficient, P for system abandon batmax Maximum charge and discharge power of the energy storage moduleValue, P hmax The maximum working power of the electrolytic tank of the hydrogen production module is set;
the constraint conditions are as follows:
1) Power balance constraint
P pv (t)+P wind (t)＝P load (t)+Ｐ ｂat (t)+P h (t)
Wherein P is load (t) is the required power of the load in the time period t, P bat (t) is the charge-discharge power of the energy storage module in the time period t, P h (t) is the working power of the electrolytic tank of the hydrogen production module in the time period t;
2) Photovoltaic fan output constraint
Wherein P is pvr ，P windr Rated power of the photovoltaic and wind power modules respectively;
3) Energy storage module restraint
In sigma SOCt Is the value of the state of charge at time t, sigma SOCmin Sum sigma SOCmax Respectively minimum and maximum states of charge, P batmin The minimum charge and discharge power of the energy storage module is obtained;
4) Hydrogen production module restraint
P h (t)≤P hmax 。
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| CN117411087B (en) * | 2023-12-13 | 2024-04-12 | 国网山东省电力公司电力科学研究院 | Collaborative optimization control method and system for wind-solar hydrogen storage combined power generation system |
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