CN116488250B - Capacity optimization configuration method for hybrid energy storage system - Google Patents
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Abstract
The invention provides a capacity optimization configuration method of a hybrid energy storage system, which belongs to the technical field of energy storage, and comprises the following steps: establishing an artificial fish swarm according to the capacity range of each unit in the hybrid energy storage system, wherein the food concentration of each fish in the artificial fish swarm is the sum of the minimum consumption resource and the maximum food concentration of a certain unit in the hybrid energy storage system as an objective function, and establishing constraint conditions; optimizing an objective function by utilizing an artificial fish swarm algorithm, and obtaining a plurality of food concentration combinations by simulating predation behaviors of artificial fish; optimizing the obtained food concentration combination by using a firefly algorithm, taking the renewable energy permeability of the hybrid energy storage system as the brightness in the firefly algorithm, and comparing the brightness to obtain the optimal food concentration combination. The invention combines two algorithms to optimize, makes up for the shortages, improves the local searching capability, simultaneously prevents the problem of global optimization, and greatly improves the optimizing capability.
Description
Technical Field
The invention belongs to the technical field of energy storage, and particularly relates to a capacity optimization configuration method of a hybrid energy storage system.
Background
With the increase of the demand of people for electric energy, the development of electric power systems is required to be continuously integrated with new technologies. Distributed energy development is becoming an indispensable link for reducing the pressure of the power grid, and energy storage technology is rapidly becoming a new development target.
At present, a large number of hybrid energy storage systems in China are emerging, and distributed energy systems of wind power generation, photovoltaic power generation, thermal energy storage (CSP) and storage Batteries (BESS) become effective ways for promoting renewable energy development. Most of the prior art considers the advantages of clean accessibility of renewable energy sources such as wind power generation, photovoltaic power generation and the like, and the defects of intermittence and instability of the renewable energy sources, and simultaneously coordinates and optimizes the capacity of a storage battery and the capacity of a thermal energy storage of the system. However, for the energy storage of the traditional storage battery, the prior art does not formulate reasonable charging times, the service life of the storage battery is greatly consumed due to frequent charging and discharging, the objective function only ensures sufficient energy storage, and the problems of accelerated aging and the like caused by the charging times on equipment are not considered, so that the storage battery needs to be frequently configured and replaced, and a large amount of resources are consumed in the aspects of depreciation, maintenance and the like.
Thermal energy storage is typically represented as a dispatchable power plant, with the extra solar energy stored during the day being converted to thermal energy, which is used during the night to generate electricity. Because the investment consumption resource is lower than that of the storage battery, the thermal energy storage system can greatly lighten the load of the power grid, is beneficial to saving the consumption resource, enhances the stability of the system, improves the power generation utilization rate and improves the renewable energy utilization rate. How to optimize the capacity of each unit in a hybrid energy storage system comprising a thermal energy storage system and a storage battery is one of important methods for reducing the consumption of resources in distributed energy production.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a capacity optimization configuration method of a hybrid energy storage system, and aims to solve the problems that the conventional optimization method does not design a thermal energy storage unit and does not consider that the resource consumption is too high due to frequent charging of a storage battery.
In order to achieve the above object, the present invention provides a method for optimally configuring capacity of a hybrid energy storage system, the method comprising the steps of:
s1, establishing an artificial fish swarm according to the capacity range of each unit in the hybrid energy storage system, wherein the food concentration of each fish in the artificial fish swarm is the capacity of a certain unit in the hybrid energy storage system;
s2, aiming at the hybrid energy storage system, taking the sum of the minimum consumed resources and the maximum food concentration as an objective function, and establishing constraint conditions;
s3, optimizing the objective function by using an artificial fish swarm algorithm, and obtaining a plurality of food concentration combinations by simulating predation behaviors of artificial fish;
and S4, optimizing the food concentration combination obtained in the step S3 by utilizing a firefly algorithm, taking the renewable energy permeability of the hybrid energy storage system as brightness in the firefly algorithm, and comparing the brightness to obtain the optimal food concentration combination so as to finish capacity optimization of the hybrid energy storage system.
As a further preferred feature, the hybrid energy storage system comprises a thermal energy storage unit, a battery unit, a wind power generation unit and a photovoltaic power generation unit.
As a further preferred aspect, in step S2, the minimum consumed resource is:
where Cmin is the minimum resource consumption, vbess is the battery cell capacity, t is time,charging power for a battery cell, ">For discharging the battery cell, ">For the maximum capacity of the storage battery unit, pr is the consumed resource of the storage battery unit in one cycle, ccsp is the consumed resource of the thermal energy storage unit, cdep is the depreciated consumed resource, cmt is the maintenance consumed resource, and Coper is the operation consumed resource;
the constraint conditions include:
Puser(t)≤Psolar(t)+Pwind(t)+Ppower(t)+Ploss(t)
SOCmin＜SOCbess，SOCcsp＜SOCmax
wherein, purifier (t) is the total power required by the user, psolar (t) is the total power generated by the heat energy storage unit, pwind (t) is the total power generated by the wind power generation unit, ppower (t) is the total power generated by the storage battery unit, ploss (t) is the consumption of the line network,minimum permissible transmission power for a battery cell, +.>For the maximum allowable transmission power of the battery unit,storing power for a battery cell, < >>For discharging the battery cell, ">Minimum allowable power for thermal energy storage unit, +.>Maximum allowable power for thermal energy storage unit, < >>Storing electric power for the thermal energy storage unit, < >>For the discharge power of the thermal energy storage unit, SOCmin is the lowest electric quantity, SOCmax is the highest electric quantity, SOCbess is the initial electric quantity state of the storage battery unit, SOCcsp is the initial electric quantity state of the thermal energy storage unit, SOCover is the actual residual electric quantity state of renewable energy sources, SOCstart is the initial electric quantity state of renewable energy sources, and SOCobj is the expected residual electric quantity state of renewable energy sources.
As a further preferred option, in step S3, the predation behavior of the simulated artificial fish includes foraging behavior, clustering behavior and rear-end behavior.
As a further preferred aspect, in step S3, the foraging behavior is specifically:
assume that the current state of a certain artificial fish is X i Randomly searching another artificial fish X in the visual field range j ，
X j ＝X i +V×λ(x)
Wherein V is the field of view and λ (x) is a random number between 0 and 1; judgment f (X) i )＞f(X j ) If so, then the value is further changed to X according to the following formula i+1 ，
Wherein S is the step length; if not, then searching another piece again at randomArtificial fish X t And repeating the method to judge whether the advancing condition is met, if the searching end still does not meet the advancing condition in the visual field range, changing the advancing condition into X according to the following random advance i+1 ，
X i+1 ＝X i +[2×rand(1，length(Xi))-1]×V
Where length (Xi) is the state length and V is the field of view.
As a further preferred aspect, in step S3, the clustering behavior is specifically:
a certain current state is X i The number Nn of artificial fish in the field of view is searched for, and the relative crowding factor Xc is calculated using the following formula,
Xc＝Nn/N
wherein N is the total number of artificial fish in the artificial fish swarm; judging whether Xc < delta is established, wherein delta is a crowding factor of the artificial fish school, if yes, the crowding factor is changed into X before going to the center position of the adjacent partner according to the following formula i+1 If not, continuing to find food,
wherein X is j For another artificial fish, S is the step size and λ (x) is a random number between 0 and 1.
As a further preferred aspect, in step S3, the rear-end collision behavior is specifically:
if one or more artificial fish find food, one current state is X i The artificial fish of (2) will advance to become X according to the following formula i+1 ，
Wherein X is max For artificial fish with highest food concentration, S is the step size and λ (x) is a random number between 0 and 1.
As a further preferred, step S4 specifically comprises the following sub-steps:
s41, sequentially arranging the plurality of food concentration combinations obtained in the step S3 to obtain a plurality of firefly positions;
s42, selecting the maximum value of each unit capacity in the hybrid energy storage system, calculating the renewable energy permeability, and taking the renewable energy permeability as an initial optimal value of brightness;
s43, calculating the brightness of the current firefly position, comparing the brightness with the current optimal value, and if the brightness is larger than the current optimal value, replacing the brightness by using the current firefly position; if the current optimal value is less than or equal to the current optimal value, directly entering a step S44, wherein the current optimal value is an initial optimal value in the first calculation;
s44, repeating the step S43 until iteration is completed, and outputting the finally obtained firefly position as an optimal food concentration combination, thereby completing capacity optimization of the hybrid energy storage system.
As a further preferred aspect, in step S42, the renewable energy permeability is:
wherein H is the permeability of renewable energy, wwind (t) is the transmission power of the wind power generation unit, vwind is the capacity of the wind power generation unit, wpv (t) is the transmission power of the photovoltaic power generation unit, and Vpv: and the capacity of the photovoltaic power generation unit, and t is time.
As a further preferred aspect, in step S43, the calculation formula of the brightness is:
wherein L (r) is brightness, L 0 For initial brightness, i.e. initial optimum, η is the fixed light absorption coefficient and r is the distance.
In general, the above technical solutions conceived by the present invention have the following beneficial effects compared with the prior art:
1. according to the invention, the artificial fish swarm algorithm is utilized, when the optimization is solved, the requirements on initial values are not high, the robustness is strong, the parallel processing capability is realized, the global optimization capability is better, the local advantage can be quickly jumped out, the hybrid energy storage system is optimized to obtain a plurality of food concentration combinations, meanwhile, the optimization effect of the artificial fish swarm algorithm is limited by taking the parameters such as step length, visual field and the like into consideration, the combination of the artificial fish swarm algorithm and the firefly algorithm is provided, the advantages of few parameters, simplicity in operation and good stability of the firefly algorithm are utilized, the local optimization is carried out on a plurality of food concentration combinations, the optimal food concentration combinations are obtained, the two algorithms are utilized for optimizing, the long-term complement is realized, the global optimization problem is prevented from occurring while the local searching capability is improved, and the optimization capability is greatly improved;
2. meanwhile, the invention can optimize the hybrid energy storage system comprising the thermal energy storage unit and the storage battery unit, can consider the optimization of the charging times of the storage battery, and saves maintenance and depreciation resources on the basis of ensuring sufficient energy storage;
3. in addition, the method and the device optimize the specific process of the firefly algorithm, and can further improve the searching capability of local optimization, thereby improving the optimizing effect.
Drawings
Fig. 1 is a flowchart of a method for optimizing and configuring a capacity of a hybrid energy storage system according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and 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.
As shown in fig. 1, the invention provides a capacity optimizing configuration method of a hybrid energy storage system, which comprises the following steps:
s1, establishing an artificial fish swarm according to the capacity range of each unit in the hybrid energy storage system, wherein the artificial fish swarm comprises m artificial fish, and the state vector of the artificial fish swarm is represented by X= (X) 1 ，X 2 ，…，X m ) The degree of realization of the objective function Y is expressed as y=f (X) by the density of the artificial fish school hereThe food concentration of each fish is the capacity of a certain unit in the hybrid energy storage system;
s2, aiming at the hybrid energy storage system, taking the sum of the minimum consumption resource and the maximum food concentration as an objective function Y, and establishing constraint conditions, wherein the objective function Y is as follows:
Y＝Cmin+Vmax (1)
where Cmin is the minimum consumed resource, vmax is the maximum food concentration, vbess is the battery cell capacity, t is time,charging power for a battery cell, ">For discharging the battery cell, ">For the maximum capacity of the storage battery unit, pr is the consumed resource of the storage battery unit in one cycle, ccsp is the consumed resource of the thermal energy storage unit, cdep is the depreciated consumed resource, cmt is the maintenance consumed resource, and Coper is the operation consumed resource;
the constraint conditions are as follows:
Puser(t)≤Psolar(t)+Pwind(t)+Ppower(t)+Ploss(t) (3)
SOCmin＜SOCbess，SOCcsp＜SOCmax (5)
wherein purifier (t) is the total power needed for the power needed, psolar (t)For the total power generated by the heat energy storage unit, pwind (t) is the total power generated by the wind power generation unit, ppower (t) is the total power generated by the storage battery unit, ploss (t) is the consumption of a line network,minimum permissible transmission power for a battery cell, +.>For the maximum allowable transmission power of the battery unit,storing power for a battery cell, < >>For discharging the battery cell, ">Minimum allowable power for thermal energy storage unit, +.>Maximum allowable power for thermal energy storage unit, < >>Storing electric power for the thermal energy storage unit, < >>SOCmin is the lowest electric quantity, SOCmax is the highest electric quantity, SOCbess is the initial charge state of the storage battery unit, SOCcsp is the initial charge state of the thermal energy storage unit, SOcover is the actual residual charge state of renewable energy, SOCstart is the initial charge state of renewable energy, and SOCobj is the expected residual charge state of renewable energy;
s3, optimizing an objective function by utilizing an artificial fish swarm algorithm, and obtaining a plurality of food concentration combinations by simulating predation behaviors of artificial fish including foraging behaviors, clustering behaviors and rear-end collision behaviors;
and S4, optimizing the food concentration combination obtained in the step S3 by utilizing a firefly algorithm, taking the renewable energy permeability of the hybrid energy storage system as brightness in the firefly algorithm, and comparing the brightness to obtain the optimal food concentration combination so as to finish capacity optimization of the hybrid energy storage system.
Further, the optimized hybrid energy storage system preferably comprises a thermal energy storage unit, a storage battery unit, a wind power generation unit and a photovoltaic power generation unit, wherein the thermal energy storage unit stores energy generated by solar energy through heat absorption and converts the energy into electric energy at night, and the storage battery unit stores the energy when the thermal energy storage unit is saturated, so that the charge and discharge times of the storage battery unit can be effectively reduced. The invention optimizes the hybrid energy storage system comprising the thermal energy storage unit, fully considers the influence of the charging times of the storage battery unit on the consumed resources, and can save the consumed resources for maintenance and depreciation on the basis of ensuring sufficient energy storage.
Further, in step S3, the foraging behavior is specifically:
fish moves in water to a place with high food concentration, and the current state of a certain artificial fish is assumed to be X i Randomly searching another artificial fish X in the visual field range j ，
X j ＝X i +V×λ(x) (7)
Wherein V is the field of view, lambda (X) is a random number between 0 and 1, and f (X) i )＞f(X j ) If so, then the value is further changed to X according to the formula (8) i+1 ，
In the formula, S is the step length, if not, another state X is randomly searched again t And repeating the above method to determine whether the forward condition is satisfied, if the search end in the visual field range still does not satisfy the forward condition, changing to X further before random according to formula (9) i+1 ，
X i+1 ＝X i +[2×rand(1，length(Xi))-1]×V (9)
Where length (Xi) is the state length and V is the field of view.
The clustering behavior is specifically:
the fish is usually moved towards the adjacent partner, and in order to prevent overcrowding, a certain current state is X i The number Nn of artificial fish in the artificial fish search field of view, and calculate the relative crowding factor Xc using equation (10),
Xc＝Nn/N (10)
wherein N is the total number of artificial fish in the artificial fish swarm, whether Xc < delta is established or not is judged, if delta is the crowding factor of the artificial fish swarm, more food is indicated nearby and less crowding is carried out, if not, foraging is continued according to the formula (8) until the artificial fish is located at the center of the nearby partner.
The rear-end collision behavior is specifically as follows:
when a fish shoal finds food, one or more fish will find food along with the fish finding food, so that one current state is X i The artificial fish of (2) advances according to the formula (11) to become X i+1 ，
Wherein X is max Is artificial fish with highest food concentration.
Further, the step S4 specifically includes the following sub-steps:
s41, sequentially arranging the plurality of food concentration combinations obtained in the step S3 to obtain a plurality of firefly positions;
s42, selecting the maximum value of the capacities of all units in the hybrid energy storage system, calculating the renewable energy permeability, taking the maximum value as the initial optimal value of brightness, and adopting a calculation formula of the renewable energy permeability as follows:
wherein H is the permeability of renewable energy, wwind (t) is the transmission power of the wind power generation unit, vwind is the capacity of the wind power generation unit, and Wpv (t) is the transmission power of the photovoltaic power generation unit; vpv: photovoltaic power generation unit capacity;
s43, calculating the brightness of the current firefly position, wherein the calculation formula of the brightness is as follows:
wherein L (r) is brightness, L 0 For initial brightness, namely an initial optimal value, eta is a fixed light absorption coefficient, r is a distance, comparing the fixed light absorption coefficient with a current optimal value, and if the fixed light absorption coefficient is larger than the current optimal value, replacing by using the current firefly position; if the current optimal value is less than or equal to the current optimal value, directly entering a step S44, wherein the current optimal value is the initial optimal value when calculated for the first time;
s44, repeating the step S43 until iteration is completed, and outputting the finally obtained firefly position as an optimal food concentration combination, thereby completing capacity optimization of the hybrid energy storage system.
It will be readily appreciated by those skilled in the art that the foregoing description is merely a preferred embodiment of the invention and is not intended to limit the invention, but any modifications, equivalents, improvements or alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (7)
1. The capacity optimization configuration method of the hybrid energy storage system is characterized by comprising the following steps of:
s1, establishing an artificial fish swarm according to the capacity range of each unit in the hybrid energy storage system, wherein the food concentration of each fish in the artificial fish swarm is the capacity of a certain unit in the hybrid energy storage system;
s2, aiming at the hybrid energy storage system, taking the sum of the minimum consumed resources and the maximum food concentration as an objective function, and establishing constraint conditions;
s3, optimizing the objective function by using an artificial fish swarm algorithm, and obtaining a plurality of food concentration combinations by simulating predation behaviors of artificial fish;
s4, optimizing the food concentration combination obtained in the step S3 by utilizing a firefly algorithm, taking the renewable energy permeability of the hybrid energy storage system as brightness in the firefly algorithm, and comparing the brightness to obtain the optimal food concentration combination so as to complete capacity optimization of the hybrid energy storage system, wherein the method specifically comprises the following sub-steps:
s41, sequentially arranging the plurality of food concentration combinations obtained in the step S3 to obtain a plurality of firefly positions;
s42, selecting the maximum value of the capacities of all units in the hybrid energy storage system, calculating the renewable energy permeability, taking the maximum value as the initial optimal value of brightness, and adopting a calculation formula of the renewable energy permeability as follows:
wherein H is the permeability of renewable energy, wwind (t) is the transmission power of the wind power generation unit, vwind is the capacity of the wind power generation unit, and Wpv (t) is the transmission power of the photovoltaic power generation unit; vpv: the capacity of the photovoltaic power generation unit, T is time, and T is the upper limit of time;
s43, calculating the brightness of the current firefly position, wherein the calculation formula of the brightness is as follows:
wherein L (r) is brightness, L 0 For initial brightness, namely an initial optimal value, eta is a fixed light absorption coefficient, r is a distance, comparing the fixed light absorption coefficient with a current optimal value, and if the fixed light absorption coefficient is larger than the current optimal value, replacing by using the current firefly position; if the current optimal value is less than or equal to the current optimal value, directly entering a step S44, wherein the current optimal value is the initial optimal value when calculated for the first time;
s44, repeating the step S43 until iteration is completed, and outputting the finally obtained firefly position as an optimal food concentration combination, thereby completing capacity optimization of the hybrid energy storage system.
2. The method of optimizing capacity of a hybrid energy storage system of claim 1, wherein the hybrid energy storage system comprises a thermal energy storage unit, a storage battery unit, a wind power generation unit, and a photovoltaic power generation unit.
3. The method for optimizing capacity of a hybrid energy storage system according to claim 1, wherein in step S2, the minimum consumed resources are:
where Cmin is the minimum resource consumption, vbess is the battery cell capacity, t is time,charging power for a battery cell, ">For discharging the battery cell, ">For the maximum capacity of the storage battery unit, pr is the consumed resource of the storage battery unit in one cycle, ccsp is the consumed resource of the thermal energy storage unit, cdep is the depreciated consumed resource, cmt is the maintenance consumed resource, and Coper is the operation consumed resource;
the constraint conditions include:
Puser(t)≤Psolar(t)+Pwind(t)+Ppower(t)+Ploss(t)
SOCmin<SOCbess，SOCcsp<SOCmax
wherein, purifier (t) is the total power required by the user, psolar (t) is the total power generated by the heat energy storage unit, pwind (t) is the total power generated by the wind power generation unit, ppower (t) is the total power generated by the storage battery unit, ploss (t) is the consumption of the line network,minimum permissible transmission power for a battery cell, +.>Maximum allowable power for battery cell, +.>Storing power for a battery cell, < >>For discharging the battery cell, ">Minimum allowable power for thermal energy storage unit, +.>Maximum allowable power for thermal energy storage unit, < >>Storing electric power for the thermal energy storage unit, < >>For discharging power of the thermal energy storage unit, SOCmin is the lowest electric quantity, SOCmax is the highest electric quantity, and SOCbess is the initial electric quantity of the storage battery unitSOCcsp is the initial state of charge of the thermal energy storage unit, SOcover is the actual state of charge of the renewable energy source, SOCstart is the initial state of charge of the renewable energy source, and SOCobj is the expected state of charge of the renewable energy source.
4. The method for optimizing capacity of a hybrid energy storage system according to claim 1, wherein in step S3, predation behavior of artificial fish is simulated including foraging behavior, clustering behavior and rear-end collision behavior.
5. The method for optimizing capacity of a hybrid energy storage system according to claim 4, wherein in step S3, the foraging behavior is specifically:
assume that the current state of a certain artificial fish is X i Randomly searching another artificial fish X in the visual field range j ，
X j ＝X i +V×λ(x)
Wherein V is the field of view and λ (x) is a random number between 0 and 1; let f (X) be the current food concentration of the artificial fish X, and judge f (X) i )＞f(X j ) If so, then the value is further changed to X according to the following formula i+1 ，
Wherein S is the step length; if not, searching another artificial fish X again at random t And repeating the method to judge whether the advancing condition is met, if the searching end still does not meet the advancing condition in the visual field range, changing the advancing condition into X according to the following random advance i+1 ，
X i+1 ＝X i +[2×rand(1，length(Xi))-1]×V
Where length (Xi) is the state length, V is the field of view, and rand is the random function.
6. The method for optimizing capacity of a hybrid energy storage system according to claim 4, wherein in step S3, the clustering behavior is specifically:
a certain current state is X i The number Nn of artificial fish in the field of view is searched for, and the relative crowding factor Xc is calculated using the following formula,
Xc＝Nn/N
wherein N is the total number of artificial fish in the artificial fish swarm; judging Xc<If delta is true, delta is a crowding factor of the artificial fish school, if so, the factor is changed to X further before going to the center position of the adjacent partner according to the following formula i+1 If not, continuing to find food,
wherein X is j For another artificial fish, S is the step size and λ (x) is a random number between 0 and 1.
7. The method for optimizing the capacity of a hybrid energy storage system according to claim 4, wherein in step S3, the rear-end collision behavior is specifically:
if one or more artificial fish find food, one current state is X i The artificial fish of (2) will advance to become X according to the following formula i+1 ，
Wherein X is max For artificial fish with highest food concentration, S is the step size and λ (x) is a random number between 0 and 1.
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