CN114977217A - Configuration method and device of electricity-hydrogen hybrid energy storage system - Google Patents
Configuration method and device of electricity-hydrogen hybrid energy storage system Download PDFInfo
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- H—ELECTRICITY
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- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- H—ELECTRICITY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
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- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
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- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/30—The power source being a fuel cell
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- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
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Abstract
The invention relates to the technical field of hybrid energy storage systems, in particular to a configuration method and a configuration device of an electricity-hydrogen hybrid energy storage system. The method comprises the following steps: constructing a multi-objective optimization model of the electricity-hydrogen hybrid energy storage system, wherein the multi-objective optimization model takes the minimum of the full life cycle loss, the power loss, the load fluctuation and the voltage fluctuation of the electricity-hydrogen hybrid energy storage system as an objective function; iteratively calculating a pareto solution set of the multi-objective optimization model by adopting a political optimization algorithm until an iteration termination condition is met, and outputting an optimal pareto solution set; and calculating the optimal compromise solution of the optimal pareto solution set by adopting a grey target decision method of an entropy weight method to obtain the optimal configuration scheme of the electricity-hydrogen hybrid energy storage system, wherein the optimal configuration scheme comprises optimal installation nodes, configuration capacity and configuration power, and is used for reducing the cost of configuring the electricity-hydrogen hybrid energy storage system in a power distribution network and improving the problems of power loss, load fluctuation and voltage fluctuation in the system.
Description
Technical Field
The invention relates to the technical field of hybrid energy storage systems, in particular to a configuration method and device of an electricity-hydrogen hybrid energy storage system.
Background
With the continuous development and improvement of energy storage technology, the combined use of different types of energy storage systems is becoming a research hotspot of researchers, such as hybrid battery energy storage systems and electricity-hydrogen hybrid energy storage systems of hydrogen energy storage systems.
An electricity-hydrogen hybrid energy storage system stores electrical energy by using a battery energy storage device and a hydrogen energy storage device. When the cost of the hydrogen energy storage system is fixed, the problem that the energy storage system is difficult to flexibly grid due to the capacity limitation of the battery energy storage system is solved by reasonably configuring the capacity of the hydrogen storage device. However, the energy conversion rate of the hydrogen energy storage system is lower than that of the battery energy storage system, and the cost is higher, so how to reasonably configure the electric-hydrogen hybrid energy storage system to reduce the cost of the electric-hydrogen hybrid energy storage system and reduce the power fluctuation and the energy loss becomes an urgent problem to be solved.
Disclosure of Invention
The invention provides a configuration method and a configuration device of an electric-hydrogen hybrid energy storage system, which are used for reducing the cost of configuring the electric-hydrogen hybrid energy storage system in a power distribution network and improving the problems of power loss, load fluctuation and voltage fluctuation in the system.
The invention provides a configuration method of an electricity-hydrogen hybrid energy storage system, which comprises the following steps:
constructing a multi-objective optimization model of the electricity-hydrogen hybrid energy storage system, wherein the multi-objective optimization model takes the minimum whole life cycle loss, power loss, load fluctuation and voltage fluctuation of the electricity-hydrogen hybrid energy storage system as an objective function;
iteratively calculating a pareto solution set of the multi-objective optimization model by adopting a political optimization algorithm until an iteration termination condition is met, and outputting an optimal pareto solution set;
and calculating the optimal compromise solution of the optimal pareto solution set by adopting a grey target decision method of an entropy weight method to obtain the optimal configuration scheme of the electricity-hydrogen hybrid energy storage system, wherein the optimal configuration scheme comprises the optimal installation node, configuration capacity and configuration power.
Optionally, the constructing a multi-objective optimization model of the electricity-hydrogen hybrid energy storage system comprises:
acquiring parameters of a power distribution network, and constructing a target function with minimum full life cycle loss, power loss, load fluctuation and voltage fluctuation according to the acquired parameters of the power distribution network;
the objective function includes:
wherein f (x) is an objective function; f. of 1 Cost, f, for full life cycle 2 Is power loss, f 3 Is a voltage fluctuation, f 4 Is a load fluctuation; x is a decision variable; h (x) is a constraint condition, wherein the decision variables comprise an installation node, a configuration capacity and a configuration power of the electricity-hydrogen hybrid energy storage system; the constraint conditions comprise node power balance constraint, node voltage constraint, grid-connected point power constraint, capacity and power constraint of an electricity-hydrogen hybrid system, charge and discharge constraint of a battery energy storage system and charge and discharge constraint of a hydrogen energy storage system;
the full lifecycle loss cost comprises:
wherein Q is BESSs Cost, Q, for the full life cycle of a battery energy storage system HESSs For hydrogen storageThe total life cycle loss cost of the energy system; t is a unit of B For the investment cost of the battery energy storage system, T H The investment cost of the hydrogen energy storage system; w B For maintenance costs of the battery energy storage system, W H Maintenance costs for the hydrogen energy storage system; y is B For the operating cost of the battery energy storage system, Y H The operating cost of the hydrogen energy storage system; g B For battery energy storage systems, G H The replacement cost of the hydrogen energy storage system; c B For disposal and recovery costs of the battery energy storage system, C H The disposal and recovery costs for the hydrogen energy storage system; mu.s CRF,B Represents a capital recovery factor; n is a radical of BESS The installation number of the battery energy storage systems in the power distribution network is represented; c. C battery The cost of a single battery; c. C EPCD,B Representing engineering, procurement and construction costs and developer costs of the battery energy storage system; I.C. A sub Is a government subsidy; e BESS,i Is the capacity of the ith battery energy storage system; c. C FMC,B Representing the annual fixed maintenance cost of a single battery energy storage system; p BESS,i Is the power of the ith battery energy storage system; t is 24 hours; c. C pu (t) and c sel (t) the purchase and sale prices are respectively; p ch,Bi (t) and P dis,Bi (t) the charging and discharging power of the ith battery energy storage system respectively; n is B And t is the life of the battery and the number of times of replacement, respectively; α is the annual cost rate of the battery; r represents a discount rate calculated as a weighted average capital cost; gamma ray B The recovery benefit of the battery energy storage system; c. C FC And c E The cost of the fuel cell and the electrolyzer, respectively; c. C HT And Q HT,i Cost and capacity of the hydrogen storage tank; p HESS,i The power of the ith hydrogen energy storage system is shown; c EPCD,H Represents the EPC cost of the hydrogen energy storage system; c. C FC And c E The cost of the fuel cell and the electrolyzer, respectively; c. C FMC,H Represents the annual maintenance cost of the fuel cell; p HESS,i The power of the ith hydrogen energy storage system; p is ch,Hi And P dis,Hi Representing the charging and discharging power of the ith hydrogen energy storage system; q H,i Represents the total hydrogen production of the hydrogen energy storage system in one day; q. q.s H Hydrogen production per kilowatt-hour;
represents the profit generated per kg of hydrogen; p is a radical of H The amount of electricity generated per kg of hydrogen; mu and v are the ratio of hydrogen delivery to power generation; n is H The number of replacement times of the HESSs; beta is the annual cost loss rate of the hydrogen energy storage system; gamma ray H The recycling benefit of the fuel cell is achieved;
the power loss includes:
l is the total number of the connecting lines of the electricity-hydrogen mixing system; r j The resistance on the j-th connecting line is shown; t represents the time, I j The current on the j-th connecting line;
the load fluctuations include:
wherein, P load ，P pv (t) and P wind Respectively the load of the electric-hydrogen hybrid system, the photovoltaic power and the wind power output in the t period;
the voltage fluctuations include:
in the formula, N nodes The number of total nodes of the system; v j Is the voltage at node j;
the average voltage of the j node in the T period;
the node power balance constraint is:
in the formula, P i (t) is the active power injected by the node i at the moment t; q i (t) is the reactive power injected at node i at time t; theta ij (t) is the voltage phase angle difference between nodes i and j at time t; v i (t) and V j (t) represents voltages of the node i and the node j during the period t, respectively; g ij And B ij Respectively, the line conductance and susceptance between nodes i and j;
the node voltage constraint is:
in the formula (I), the compound is shown in the specification,
and
the upper and lower voltage limits of the node i are respectively;
the power constraint of the grid-connected point is as follows:
in the formula (I), the compound is shown in the specification,
and
respectively the lower limit and the upper limit of active power and reactive power of a grid-connected point;
the capacity and power constraints of the electric-hydrogen hybrid system are as follows:
in the formula (I), the compound is shown in the specification,
and
the capacity upper and lower limits of the battery energy storage system are set;
and
the power upper and lower limits of the battery energy storage system;
and
the capacity upper and lower limits of the hydrogen energy storage system;
and with
The power upper and lower limits of the hydrogen energy storage system;
the charging and discharging constraints of the battery energy storage system are as follows:
in the formula eta ch_B And η dis_B Respectively the charging efficiency and the discharging efficiency of the battery energy storage system;
the charge and discharge constraints of the hydrogen energy storage system are as follows:
0≤P ch,Hi (t)≤P HESS,i ·η ch_H
-P HESS,i ·η dis_H ≤P dis,Hi (t)≤0
in the formula eta ch_H And η dis_H Respectively the charge and discharge efficiency of the hydrogen energy storage system.
Optionally, the iteratively calculating the pareto solution set of the multi-objective optimization model according to a political optimization algorithm until an iteration termination condition is satisfied, and obtaining an optimal pareto solution set includes:
s1: initializing algorithm parameters according to the acquired power distribution network parameters and the target function, and storing the algorithm parameters in a storage pool; the algorithm parameters comprise members of the population and a fitness function of the population; the members represent installation nodes, configuration capacity and configuration power of a group of electric-hydrogen energy storage systems;
s2: carrying out the operations of election activities, political party exchange, election and conference affairs on the population in sequence, updating the members in the storage pool and the fitness of the members, and selecting the member with the highest fitness as the pareto solution set;
s3: comparing the pareto solution set with the pareto solution set in the storage pool, and replacing a dominant solution in the pareto solution set according to a comparison result;
s4: and (5) iterating S2-S3 until the iteration number reaches a preset iteration number threshold value, and outputting an optimal pareto solution set.
Optionally, the calculating an optimal compromise solution of the optimal pareto solution set according to a gray target decision method of an entropy weight method to obtain an optimal configuration scheme of the electricity-hydrogen hybrid energy storage system includes:
establishing a sample matrix according to the optimal pareto solution set and the target function;
carrying out dimensionless operation on the sample matrix to obtain an operator;
constructing a decision matrix according to the operator and the sample matrix, and determining the target of the decision matrix;
and calculating a first Euclidean distance between each solution in the decision sample matrix and the target, and taking the solution corresponding to the shortest first Euclidean distance as an optimal compromise solution to obtain an optimal configuration scheme of the electricity-hydrogen hybrid energy storage system.
Optionally, the constructing a sample matrix according to the optimal pareto solution and the objective function includes:
acquiring a non-dominant solution of the optimal pareto solution set, and normalizing a target function corresponding to the non-dominant solution;
calculating a second Euclidean distance between each solution in the optimal pareto solution set and an ideal point;
and establishing a sample matrix according to the normalized target function and the second Euclidean distance.
The invention also provides an electricity-hydrogen hybrid energy storage system configuration device, which is characterized by comprising:
the system comprises a construction module, a calculation module and a control module, wherein the construction module is used for constructing a multi-objective optimization model of the electricity-hydrogen hybrid energy storage system, and the multi-objective optimization model takes the minimum of the full life cycle loss, the power loss, the load fluctuation and the voltage fluctuation of the electricity-hydrogen hybrid energy storage system as an objective function;
the first calculation module is used for iteratively calculating the pareto solution set of the multi-objective optimization model by adopting a political optimization algorithm until an iteration termination condition is met and outputting an optimal pareto solution set;
and the second calculation module is used for calculating the optimal compromise solution of the optimal pareto solution set by adopting a grey target decision method of an entropy weight method to obtain the optimal configuration scheme of the electricity-hydrogen hybrid energy storage system, wherein the optimal configuration scheme comprises the optimal installation node, the optimal configuration capacity and the optimal configuration power.
Optionally, the building module comprises:
the acquisition unit is used for acquiring parameters of the power distribution network;
the construction unit is used for constructing a target function with minimum full life cycle loss, power loss, load fluctuation and voltage fluctuation according to the acquired power distribution network parameters;
the objective function includes:
wherein f (x) is an objective function; f. of 1 Cost, f, for full life cycle 2 Is power loss, f 3 As a voltage fluctuation、f 4 Is a load fluctuation; x is a decision variable; h (x) is a constraint condition, wherein the decision variables comprise an installation node, a configuration capacity and a configuration power of the electricity-hydrogen hybrid energy storage system; the constraint conditions comprise node power balance constraint, node voltage constraint, grid-connected point power constraint, capacity and power constraint of an electricity-hydrogen hybrid system, charge and discharge constraint of a battery energy storage system and charge and discharge constraint of a hydrogen energy storage system;
the full lifecycle loss cost comprises:
wherein Q is BESSs Cost, Q, for the full life cycle of a battery energy storage system HESSs Cost is lost for the full life cycle of the hydrogen energy storage system; t is a unit of B For the investment cost of the battery energy storage system, T H The investment cost of the hydrogen energy storage system; w is a group of B For maintenance costs of the battery energy storage system, W H Maintenance costs for the hydrogen energy storage system; y is B For the operating cost of the battery energy storage system, Y H The operating cost of the hydrogen energy storage system; g B For battery energy storage systems, G H The replacement cost of the hydrogen energy storage system; c B For disposal and recovery costs of the battery energy storage system, C H The disposal and recovery costs for the hydrogen energy storage system; mu.s CRF,B Represents a capital recovery factor; n is a radical of BESS The installation number of the battery energy storage systems in the power distribution network is represented; c. C battery The cost of a single battery; c. C EPCD,B Representing engineering, procurement and construction costs and developer costs of the battery energy storage system; i is sub Is a government subsidy; e BESS,i Is the capacity of the ith battery energy storage system; c. C FMC,B Representing the annual fixed maintenance cost of a single battery energy storage system; p BESS,i Is the power of the ith battery energy storage system; t is 24 hours; c. C pu (t) and c sel (t) the purchase and sale prices are respectively; p ch,Bi (t) and P dis,Bi (t) the charging and discharging power of the ith battery energy storage system respectively; n is B And t is the life of the battery and the number of times of replacement, respectively; α is the annual cost loss rate of the battery; r represents a discount rate calculated as a weighted average capital cost; gamma ray B The recovery benefit of the battery energy storage system; c. C FC And c E The cost of the fuel cell and the electrolyzer, respectively; c. C HT And Q HT,i Cost and capacity of the hydrogen storage tank; p HESS,i The power of the ith hydrogen energy storage system is shown; c EPCD,H Represents the EPC cost of the hydrogen energy storage system; c. C FC And c E The cost of the fuel cell and the electrolyzer, respectively; c. C FMC,H Represents the annual maintenance cost of the fuel cell; p HESS,i The power of the ith hydrogen energy storage system; p ch,Hi And P dis,Hi Representing the charging and discharging power of the ith hydrogen energy storage system; q H,i Represents the total hydrogen production of the hydrogen energy storage system in one day; q. q.s H Hydrogen production per kilowatt-hour of electricity; i is H2 Represents the profit generated per kg of hydrogen; p is a radical of formula H The amount of electricity generated per kg of hydrogen gas; mu and v are the ratio of hydrogen delivery to power generation; n is H The number of replacement times of the HESSs; beta is the annual cost loss rate of the hydrogen energy storage system; gamma ray H The recycling benefit of the fuel cell is achieved;
the power loss includes:
l is the total number of the connecting lines of the electricity-hydrogen mixing system; r j Represents the resistance on the j-th tie line; t represents the time, I j The current on the j-th connecting line;
the load fluctuations include:
wherein, P load ，P pv (t) and P wind Respectively the load of the electric-hydrogen hybrid system, the photovoltaic power and the wind power output in the t period;
the voltage fluctuations include:
in the formula, N nodes The number of total nodes of the system; v j Is the voltage at node j;
the average voltage of the j node in the T period;
the node power balance constraint is:
in the formula, P i (t) is the active power injected by the node i at the moment t; q i (t) is the reactive power injected at node i at time t; theta ij (t) is the voltage phase angle difference between nodes i and j at time t; v i (t) and V j (t) represents voltages of the node i and the node j during the period t, respectively; g ij And B ij Respectively, the line conductance and susceptance between nodes i and j;
the node voltage constraint is:
in the formula (I), the compound is shown in the specification,
and
the upper and lower voltage limits of the node i are respectively;
the power constraint of the grid-connected point is as follows:
in the formula (I), the compound is shown in the specification,
and
respectively the lower limit and the upper limit of active power and reactive power of a grid-connected point;
the capacity and power constraints of the electric-hydrogen hybrid system are as follows:
in the formula (I), the compound is shown in the specification,
and
the capacity upper and lower limits of the battery energy storage system are set;
and
the power upper limit and the power lower limit of the battery energy storage system are set;
and
the capacity upper and lower limits of the hydrogen energy storage system;
and
the power upper limit and the power lower limit of the hydrogen energy storage system are set;
the charging and discharging constraints of the battery energy storage system are as follows:
in the formula eta ch_B And η dis_B Respectively the charging efficiency and the discharging efficiency of the battery energy storage system;
the charge and discharge constraints of the hydrogen energy storage system are as follows:
0≤P ch,Hi (t)≤P HESS,i ·η ch_H
-P HESS,i ·η dis_H ≤P dis,Hi (t)≤0
in the formula eta ch_H And η dis_H Respectively the charge and discharge efficiency of the hydrogen energy storage system.
Optionally, the first computing module comprises:
the initialization unit is used for initializing algorithm parameters according to the acquired power distribution network parameters and the target function and storing the algorithm parameters in a storage pool; the algorithm parameters comprise members of the population and a fitness function of the population; the members represent an optimal installation node, configuration capacity and configuration power of a group of electric-hydrogen energy storage systems;
the updating unit is used for sequentially carrying out the operations of election activities, political party exchange, election and conference affairs on the population, updating the members in the storage pool and the fitness of the members, and selecting the member with the highest fitness as the pareto solution set;
a replacing unit, configured to compare the pareto solution set with the pareto solution set in the storage pool, and replace a dominant solution in the pareto solution set according to a comparison result;
and the output unit is used for repeatedly and sequentially triggering the updating unit and the replacement until the stopping triggering condition is met and outputting the optimal pareto solution set.
Optionally, the second computing module comprises:
the establishing unit is used for establishing a sample matrix according to the optimal pareto solution and the target function;
the first calculating subunit is used for carrying out dimensionless operation on the sample matrix to obtain an operator;
the determining unit is used for constructing a decision matrix according to the operator and the sample matrix and determining the target of the decision matrix;
and the second calculating subunit is used for calculating a first Euclidean distance between each solution in the decision sample matrix and the target, and obtaining an optimal configuration scheme of the electricity-hydrogen hybrid energy storage system by taking a solution corresponding to the shortest first Euclidean distance as an optimal compromise solution.
Optionally, the establishing unit includes:
the normalizing subunit is used for acquiring a non-dominant solution of the optimal pareto solution set and normalizing an objective function corresponding to the non-dominant solution;
the third calculation subunit is used for calculating second Euclidean distances between each solution in the optimal pareto solution set and an ideal point;
and the establishing subunit is used for establishing a sample matrix according to the normalized target function and the second Euclidean distance.
According to the technical scheme, the invention has the following advantages:
the invention provides a method for an electricity-hydrogen hybrid energy storage system, which comprises the steps of constructing a multi-objective optimization model of the electricity-hydrogen hybrid energy storage system, wherein the multi-objective optimization model takes the minimum of the full life cycle loss, the power loss, the load fluctuation and the voltage fluctuation of the electricity-hydrogen hybrid energy storage system as an objective function; the pareto solution set of the multi-objective optimization model is iteratively calculated by adopting a political optimization algorithm with strong global search capability and high convergence rate until an iteration termination condition is met, and the optimal pareto solution set is output, so that the pareto solution set which is uniformly distributed and has good convergence performance can be quickly searched, and a good optimization effect is achieved; the optimal compromise solution of the optimal pareto solution set is calculated by adopting a grey target decision method of an entropy weight method, four optimization targets of the loss cost, the power loss, the voltage fluctuation and the load fluctuation of the whole life cycle can be considered fairly, the scheme of optimal installation nodes, configuration capacity and configuration power of the electricity-hydrogen hybrid energy storage system is obtained, the cost of configuring the electricity-hydrogen hybrid energy storage system in a power distribution network is reduced, and the problems of power loss, load fluctuation and voltage fluctuation in the system are solved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.
Fig. 1 is a schematic flow chart of a configuration method of an electricity-hydrogen hybrid energy storage system according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a configuration method of an electricity-hydrogen hybrid energy storage system according to a second embodiment of the present invention;
fig. 3 is a structural diagram of a configuration device of an electric-hydrogen hybrid energy storage system according to a third embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a configuration method and a configuration device of an electric-hydrogen hybrid energy storage system, which are used for reducing the cost of configuring the electric-hydrogen hybrid energy storage system in a power distribution network and improving the problems of power loss, load fluctuation and voltage fluctuation in the system.
In order to make the objects, features and advantages of the present invention more obvious and understandable, 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, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a schematic flow chart illustrating a configuration method of an electro-hydrogen hybrid energy storage system according to an embodiment of the invention.
The configuration method of the electricity-hydrogen hybrid energy storage system provided by the embodiment comprises the following steps:
101. and constructing a multi-objective optimization model of the electricity-hydrogen hybrid energy storage system, wherein the multi-objective optimization model takes the minimum of the full life cycle loss, the power loss, the load fluctuation and the voltage fluctuation of the electricity-hydrogen hybrid energy storage system as an objective function.
In this embodiment, a multi-objective optimization model of the electricity-hydrogen hybrid energy storage system is established according to the acquired power distribution network data, wherein the power distribution network data comprises load data, generator data, branch data, and wind power and photovoltaic data of the battery energy storage system and the hydrogen energy storage system in the whole power distribution network. The multi-objective optimization model takes the minimum of the full life cycle loss, the power loss, the load fluctuation and the voltage fluctuation as an objective function.
Wherein, the influencing factors of the full life cycle loss comprise the full life cycle loss of the battery energy storage system and the full life cycle loss of the hydrogen energy storage system. The full life cycle losses of the battery energy storage system include the total investment cost, operating cost, replacement cost, and disposal and recovery cost of the battery energy storage system. The full life cycle losses of the hydrogen energy storage system include the total investment cost, operating cost, replacement cost, and disposal and recovery costs of the hydrogen energy storage system.
102. And iteratively calculating the pareto solution set of the multi-objective optimization model by adopting a political optimization algorithm until an iteration termination condition is met, and outputting the optimal pareto solution set.
It should be noted that, the political optimization algorithm is an algorithm for carrying out individual optimization by simulating a multi-stage election process such as election leader of a political party, and the algorithm divides a population into the political party and a selection area, and members from the political party and the selection area carry out election to update the leader of the political party and the winner of the selection area, so that the purpose of searching for an optimal solution is achieved. The identities of the winning discount of the leader and the selection area of the political party can be interchanged, the election can be regarded as an evaluation objective function (namely a fitness function), and the number of tickets obtained by the members in the election can be understood as the fitness obtained by the members in the fitness function. The whole algorithm comprises the processes of political party and conference establishment, election activities, political party exchange, election, conference affairs and the like.
In this embodiment, the members represent installation nodes, configuration capacity and configuration power of a group of hybrid energy storage systems, the installation nodes, configuration capacity and configuration power of each hybrid energy storage system in the group are updated in each iteration by adopting a political optimization algorithm, the overall performance of the hybrid energy storage system is evaluated through four fitness functions (full life cycle loss cost, power loss, voltage fluctuation and load fluctuation) until the iteration is terminated, and excellent members with the best fitness functions are selected and an optimal planning scheme is output. The excellent members represent the optimal feasible solutions of the installation nodes, the configuration capacity and the configuration power which meet the fitness function, and the optimal planning scheme is the optimal feasible solution set, namely the optimal pareto solution set.
In the embodiment, the pareto frontier can be stably searched by iteratively calculating the solution set of the multi-objective optimization model by adopting a political optimization algorithm, so that the pareto solution set with uniform distribution and good convergence performance is found, and the optimal pareto solution set can be more accurately and comprehensively obtained. And by adopting a political optimization algorithm and dividing the candidate solution into two parts, a mechanism of mutual interaction of the candidate solutions is increased, so that the algorithm can quickly explore a new candidate solution.
103. And calculating the optimal compromise solution of the optimal pareto solution set by adopting a grey target decision method of an entropy weight method to obtain the optimal configuration scheme of the electricity-hydrogen hybrid energy storage system, wherein the optimal configuration scheme comprises the optimal installation node, configuration capacity and configuration power.
It should be noted that, by using an entropy weight method to obtain an optimal compromise solution from an optimal pareto solution set, four optimization objectives of a full life cycle loss cost, a power loss, a voltage fluctuation and a load fluctuation can be considered fairly, so that an optimal configuration scheme taking into account the minimum full life cycle loss cost, the minimum power loss, the minimum voltage fluctuation and the minimum load fluctuation, namely an optimal installation node, configuration capacity and configuration power of the electricity-hydrogen hybrid energy storage system, is obtained, the electricity-hydrogen hybrid energy storage system is configured according to the optimal configuration scheme, the configuration cost is reduced, and the problems of power loss, voltage fluctuation and load fluctuation existing in the system are solved.
The embodiment provides a configuration method of an electricity-hydrogen hybrid energy storage system, which comprises the steps of constructing a multi-objective optimization model of the electricity-hydrogen hybrid energy storage system, wherein the multi-objective optimization model takes the minimum of the full life cycle loss, the power loss, the load fluctuation and the voltage fluctuation of the electricity-hydrogen hybrid energy storage system as an objective function; the pareto solution set of the multi-objective optimization model is iteratively calculated by adopting a political optimization algorithm with strong global search capability and high convergence rate until an iteration termination condition is met, and the optimal pareto solution set is output, so that the pareto solution set which is uniformly distributed and has good convergence performance can be quickly searched, and a good optimization effect is achieved; the optimal compromise solution of the optimal pareto solution set is calculated by adopting a grey target decision method of an entropy weight method, four optimization targets of the loss cost, the power loss, the voltage fluctuation and the load fluctuation of the whole life cycle can be considered fairly, the scheme of optimal installation nodes, configuration capacity and configuration power of the electricity-hydrogen hybrid energy storage system is obtained, the cost of configuring the electricity-hydrogen hybrid energy storage system in the power distribution network is reduced, the problems of the power loss, the load fluctuation and the voltage fluctuation in the system are solved, and powerful technical support is provided for the stable operation of the power distribution network.
Referring to fig. 2, fig. 2 is a schematic flow chart of a configuration method of an electricity-hydrogen hybrid energy storage system according to a second embodiment of the present invention, where the configuration method specifically includes:
201. and acquiring parameters of the power distribution network, and constructing a target function with minimum full life cycle loss, power loss, load fluctuation and voltage fluctuation according to the acquired parameters of the power distribution network.
It should be noted that the objective function is as follows:
wherein f (x) is an objective function; f. of 1 Cost, f, for full life cycle 2 Is power loss, f 3 Is a fluctuation in load, f 4 Is a voltage fluctuation; x is a decision variable; h (x) is a constraint condition, wherein the decision variables comprise an installation node, a configuration capacity and a configuration power of the electricity-hydrogen hybrid energy storage system; the constraints including node powerBalance constraint, node voltage constraint, grid-connected point power constraint, capacity and power constraint of an electricity-hydrogen hybrid system, charge and discharge constraint of a battery energy storage system and charge and discharge constraint of a hydrogen energy storage system.
The full life cycle loss cost calculation formula is shown in (2) to (12):
wherein Q is BESSs Cost, Q, for the full life cycle of a battery energy storage system HESSs Cost is lost for the full life cycle of the hydrogen energy storage system; t is B For the investment cost of the battery energy storage system, T H The investment cost of the hydrogen energy storage system; w B For maintenance costs of the battery energy storage system, W H Maintenance costs for the hydrogen energy storage system; y is B For the operating cost of the battery energy storage system, Y H The operating cost of the hydrogen energy storage system; g B For battery energy storage systems, G H The replacement cost of the hydrogen energy storage system; c B For disposal and recovery costs of the battery energy storage system, C H The disposal and recovery costs for the hydrogen energy storage system; mu.s CRF,B Represents a capital recovery factor; n is a radical of BESS The installation number of the battery energy storage systems in the power distribution network is represented; c. C battery The cost of a single battery; c. C EPCD,B Representing engineering, procurement and construction costs and developer costs of the battery energy storage system; i is sub Is a government subsidy; e BESS,i Is the capacity of the ith battery energy storage system; c. C FMC,B Representing the annual fixed maintenance cost of a single battery energy storage system; p is BESS,i Is the power of the ith battery energy storage system; t is 24 hours; c. C pu (t) and c sel (t) the purchase and sale prices are respectively; p ch,Bi (t) and P dis,Bi (t) the charging and discharging power of the ith battery energy storage system respectively; n is B And t is the life of the battery and the number of times of replacement, respectively; α is the annual cost loss rate of the battery; r represents a discount rate calculated as a weighted average capital cost; gamma ray B The recovery benefit of the battery energy storage system; c. C FC And c E The cost of the fuel cell and the electrolyzer, respectively; c. C HT And Q HT,i Cost and capacity of the hydrogen storage tank; p HESS,i The power of the ith hydrogen energy storage system is shown; c EPCD,H Represents the EPC cost of the hydrogen energy storage system; c. C FC And c E The cost of the fuel cell and the electrolyzer, respectively; c. C FMC,H Represents the annual maintenance cost of the fuel cell; p HESS,i The power of the ith hydrogen energy storage system; p ch,Hi And P dis,Hi Representing the charging and discharging power of the ith hydrogen energy storage system; q H,i Represents the total hydrogen production of the hydrogen energy storage system in one day; q. q.s H Hydrogen production per kilowatt-hour;
represents the profit generated per kg of hydrogen; p is a radical of formula H The amount of electricity generated per kg of hydrogen; mu and v are the ratio of hydrogen delivery to power generation; n is H The number of replacement times of the HESSs; beta is the annual cost loss rate of the hydrogen energy storage system; gamma ray H The recycling benefit of the fuel cell is achieved;
it should be noted that the power loss of the electric-hydrogen hybrid energy storage system is calculated by the following formula:
l is the total number of the connecting lines of the electricity-hydrogen mixing system; r is j Represents the resistance on the j-th tie line; t represents the time, I j The current on the j-th connecting line;
it should be noted that the electric-hydrogen hybrid energy storage system can stabilize the load fluctuation, and the load fluctuation can be represented by the daily power fluctuation of the grid-connected point, and specifically, the load fluctuation can be calculated by the formula (14):
wherein, P load ，P pv (t) and P wind Respectively the load of the electric-hydrogen hybrid system, the photovoltaic power and the wind power output in the t period;
the voltage fluctuation can be calculated by equation (15):
in the formula, N nodes The number of total nodes of the system; v j Is the voltage at node j;
the average voltage of the j node in the T period;
it should be noted that the constraint conditions include a point power balance constraint, a node voltage constraint, a grid-connected point power constraint, an electric-hydrogen hybrid system capacity and power constraint, a battery energy storage system charge-discharge constraint, and a hydrogen energy storage system charge-discharge constraint.
1) The node power balance constraint is:
in the formula, P i (t) is the active power injected by the node i at the moment t; q i (t) is the reactive power injected at node i at time t; theta ij (t) is the voltage phase angle difference between nodes i and j at time t; v i (t) and V j (t) represents voltages of the node i and the node j for a period t, respectively; g ij And B ij Respectively, the line conductance and susceptance between nodes i and j;
2) the node voltage constraint is:
in the formula (I), the compound is shown in the specification,
and
respectively the upper and lower voltage limits of the node i;
3) the power constraint of the grid-connected point is:
in the formula (I), the compound is shown in the specification,
and
respectively the lower limit and the upper limit of active power and reactive power of a grid-connected point;
4) the capacity and power constraints of the electro-hydrogen hybrid system are:
in the formula (I), the compound is shown in the specification,
and
the capacity upper and lower limits of the battery energy storage system are set;
and
the power upper and lower limits of the battery energy storage system;
and
the capacity upper and lower limits of the hydrogen energy storage system;
and
the power upper and lower limits of the hydrogen energy storage system;
5) the charging and discharging constraints of the battery energy storage system are as follows:
in the formula eta ch_B And η dis_B Respectively the charging efficiency and the discharging efficiency of the battery energy storage system;
6) the charge and discharge constraints of the hydrogen energy storage system are as follows:
in the formula eta ch_H And η dis_H Respectively the charging and discharging efficiency of the hydrogen energy storage system.
202. And iteratively calculating the pareto solution set of the multi-objective optimization model by adopting a political optimization algorithm until an iteration termination condition is met, and outputting the optimal pareto solution set.
It should be noted that step 202 includes the following sub-steps:
s1: initializing algorithm parameters according to the acquired power distribution network parameters and the target function, and storing the algorithm parameters in a storage pool; the algorithm parameters comprise members of the population and a fitness function of the population; the members represent a set of installation nodes, configuration capacity, and configuration power of the electro-hydrogen energy storage system.
It should be noted that the algorithm population is stored in the storage pool, and the purpose of step S1 is to initialize the algorithm population, i.e., to perform political party and conference organization. In the building of the political parties and the agenda, the population is divided into n political parties and n selection areas, the members in the population are divided into n political parties, each political party Zi consists of n members, and the jth member of each political party
As a candidate solution, each candidate solution
Is a d-dimensional vector, which represents the installation node, the configuration capacity and the configuration power of a group of the electric-hydrogen hybrid energy storage system, and in the embodiment, d is 3. As follows:
Z＝{Z 1 ,Z 2 ,Z 3 ,…,Z n } (22)
in the formula, Z is a political party set, Z i Is the ith political party. When setting population parameters, the number of political parties, the number of selected areas and the number of members in each political party are all set to be the same, and the jth member in each political party
Participate in jth selection X j Reference may be made to the following equations (25) and (26):
X＝{x 1 ,x 2 ,x 3 ,…,x n } (25)
x represents the selection area, wherein the X has n selection areas, each selection area has n members for election, and the member with the highest election ticket number is the winner of the selection area.
In a political party, the most suitable member will be elected as the leader of the political party as follows:
where f () is an objective function containing full life cycle loss cost, power loss, voltage ripple and load ripple,
representing the political party leader of the ith political party.
After election is over, all political party leaders
Form a set, as shown in the following formula:
winners in all selection areas
The component agent, as shown in the following formula:
in the present embodiment, the building of the political party and the conference of the population is completed according to formulas (22) to (29). In this embodiment, algorithm parameters of a political optimization algorithm are initialized through power distribution network parameters and an objective function, the objective function of a multi-objective optimization model is used as a fitness function of a population, members are used as installation nodes, configuration capacities and configuration powers in a group of power-hydrogen hybrid energy storage systems, the fitness of the members is a target function value corresponding to the installation nodes, the configuration capacities and the configuration powers in the group of power-hydrogen hybrid energy storage systems, and political party leaders and selection area winners are excellent members with the best fitness in the population. The initialized population will be stored in the storage pool.
S2: and carrying out the operations of election activities, political party exchange, election and conference affairs on the population in sequence, updating the members in the storage pool and the fitness of the members, and selecting the member with the highest fitness as a pareto solution set.
In this embodiment, the election campaign is to update the member's location through formula (30) and formula (31), and update the member's current fitness by comparing the member's current fitness with the fitness at the past time, and selecting the corresponding formula according to the comparison result, if the member's current fitness is greater than the member's fitness
Fitness than before
If high, the position is updated using equation (30), and otherwise, the position is updated using equation (31).
It should be noted that both the formula (30) and the formula (31) are suitable for updating the positions of the political party leader and the winner of the selection area, and when the position of the political party leader is updated, m is updated * For political party leaders
At the position of the k-dimension, m is updated when the position of the winner of the selection is updated * Is the winner of the selected area
Position in the k-dimension:
in the formula, t represents the number of iterations, and r is a random number in the interval [0,1 ].
In this embodiment, an exchange between political parties refers to each member
Randomly selecting a political party Z according to the probability lambda i And the individuals with the worst fitness among the political parties
The positions are exchanged. Specifically, the fitness worst individual may be determined according to equation (32).
Where λ is an adaptive parameter, from λ max Initially, the linearity is reduced to 0 during the iteration.
In this embodiment, the election activity is performed according to equation (33).
In this embodiment, the bargaining transaction refers to each of the winners in the election area
Randomly selected winner in another selection area
And updates the winner through the formula (34)
After updating
In the position of
The formula (34) is as follows
In this embodiment, the member positions are updated by sequentially performing operations of election activities, political party exchanges, elections and conference transactions on the population, and the member with the highest fitness is selected according to the updated member positions to serve as a pareto solution set of the multi-objective optimization model.
S3: comparing the pareto solution set with the pareto solution set in the storage pool, and replacing a dominant solution in the pareto solution set according to a comparison result;
in the iterative process, the pareto solution sets obtained in step S2 are all stored in the storage pool. To obtain the optimal pareto solution set, the solution sets stored in the pareto solution set storage pool obtained each time in S2 are compared, and if the solution sets stored in the storage pool have solutions in pareto solutions that are better than the solutions in the pareto solution set obtained in S2, the solutions in the pareto solution set obtained in S2 are replaced with the solutions in the storage pool. Specifically, the substitution may be made according to the formula (35).
In the formula (f) k () Is the kth objective function, i.e. one of full life cycle loss cost, power loss, voltage ripple and load ripple; x is the number of i The ith electricity-hydrogen hybrid energy storage system in the population; PT k A pareto frontier distance threshold for the kth objective function value;
and
respectively the maximum value and the minimum value of the kth objective function; n is a radical of r An upper limit for the number of pareto optimal solutions in the storage pool.
It will be appreciated that, when the first comparison and replacement is performed, the solutions in the storage pool are calculated from the algorithm initialization parameters, and in this case, the pareto solution set obtained for the first time in S2 may not be replaced.
S4: and (5) iterating S2-S3 until the iteration number reaches a preset iteration number threshold value, and outputting an optimal pareto solution set.
It should be noted that the threshold value of the number of iterations may be determined according to actual situations.
203. And calculating the optimal compromise solution of the optimal pareto solution set by adopting a grey target decision method of an entropy weight method to obtain the optimal configuration scheme of the electricity-hydrogen hybrid energy storage system, wherein the optimal configuration scheme comprises the optimal installation node, configuration capacity and configuration power.
It should be noted that step 203 further includes the following sub-steps:
c1, establishing a sample matrix according to the optimal pareto solution set and the target function;
it should be noted that, in step C1, an objective function corresponding to the non-dominant solution is normalized by obtaining the non-dominant solution of the optimal pareto solution set, then a second euclidean distance between each solution in the optimal pareto solution set and the ideal point is calculated, and then a sample matrix is established according to the normalized objective function and the second euclidean distance. See, in particular, equations (36) - (38).
In the formula, delta is a sample matrix;
the element of the ith row and the jth column in the sample matrix; n is the number of objective functions; m is the dimension of the objective function;
is the ith dimension of the jth objective function; ED (electronic device) i Euclidean distance of an ith dimension of the target function; o is j For the jth objective functionAnd counting the normalized target ideal points.
In this embodiment, the objective function f is determined by establishing a sample matrix 1 ,f 2 ,f 3 ,f 4 Normalization is performed and the euclidean distance between each solution in the optimal pareto solution set and the ideal point is taken into account in order to determine the optimal compromise solution.
And C2, carrying out dimensionless operation on the sample matrix to obtain an operator.
Note that the operator q can be calculated by equation (39) j
C3, constructing a decision matrix according to the operator and the sample matrix, and determining the target of the decision matrix;
it should be noted that the decision matrix V can be constructed according to the formula (40).
The target of the decision matrix is then:
and C4, calculating a first Euclidean distance between each solution in the decision sample matrix and the target, and taking the solution corresponding to the shortest first Euclidean distance as an optimal compromise solution to obtain an optimal configuration scheme of the electricity-hydrogen hybrid energy storage system.
It should be noted that, in this embodiment, based on the first euclidean distance between each non-dominated solution and the target in the decision sample matrix, each non-dominated solution is ranked, a solution closest to the target is selected as an optimal compromise solution, the optimal compromise solution is the optimal installation node, configuration capacity and configuration power of the electro-hydrogen hybrid energy storage system, and the configuration scheme can be obtained by configuring the installation node, the capacity and the power of the electro-hydrogen hybrid energy storage system according to the optimal compromise solution.
The embodiment provides a configuration method of an electricity-hydrogen hybrid energy storage system, which comprises the steps of obtaining parameters of a power distribution network, and constructing a target function with minimum full life cycle loss, power loss, load fluctuation and voltage fluctuation according to the obtained parameters of the power distribution network; the pareto solution set of the objective function is iteratively calculated by adopting a political optimization algorithm with strong global search capability and high convergence rate until an iteration termination condition is met, and an optimal pareto solution set is output, so that the pareto solution set with uniform distribution and good convergence performance can be quickly searched, and a good optimization effect is achieved; the optimal compromise solution of the optimal pareto solution set is calculated by adopting a grey target decision method of an entropy weight method, four optimization targets of full life cycle loss cost, power loss, voltage fluctuation and load fluctuation can be considered fairly, a scheme of optimal installation nodes, configuration capacity and configuration power of the electricity-hydrogen hybrid energy storage system meeting the objective function is obtained, the cost of configuring the electricity-hydrogen hybrid energy storage system in a power distribution network is reduced, and the problems of power loss, load fluctuation and voltage fluctuation in the system are solved.
Referring to fig. 3, fig. 3 is a structural diagram of an electrical-hydrogen hybrid energy storage system configuration device according to a third embodiment of the present invention. Wherein, the device includes:
the building module 301 is used for building a multi-objective optimization model of the electricity-hydrogen hybrid energy storage system, wherein the multi-objective optimization model takes the minimum whole life cycle loss, power loss, load fluctuation and voltage fluctuation of the electricity-hydrogen hybrid energy storage system as an objective function;
the first calculation module 302 is used for iteratively calculating a pareto solution set of the multi-objective optimization model by adopting a political optimization algorithm until an iteration termination condition is met, and outputting an optimal pareto solution set;
the second calculating module 303 is configured to calculate an optimal compromise solution of the optimal pareto solution set by using a grey target decision method of an entropy weight method, so as to obtain an optimal configuration scheme of the electricity-hydrogen hybrid energy storage system, where the optimal configuration scheme includes an optimal installation node, configuration capacity, and configuration power.
Further, the building block 301 includes:
the acquisition unit is used for acquiring parameters of the power distribution network;
the construction unit is used for constructing a target function with minimum full life cycle loss, power loss, load fluctuation and voltage fluctuation according to the acquired power distribution network parameters;
it should be noted that, the calculation formulas of the objective function, the full life cycle loss, the power loss, the load fluctuation, and the voltage fluctuation may refer to the second embodiment of the present invention, and are not described herein again.
Further, the first calculation module 302 includes:
the initialization unit is used for initializing algorithm parameters according to the acquired power distribution network parameters and the target function and storing the algorithm parameters in a storage pool; the algorithm parameters comprise members of the population and a fitness function of the population; members represent optimal installation nodes, configuration capacity and configuration power of a group of electric-hydrogen energy storage systems;
the updating unit is used for sequentially carrying out the operations of election activities, intercourse exchange, election and conference affairs on the population, updating the members in the storage pool and the fitness of the members, and selecting the member with the highest fitness as a pareto solution set;
a replacement unit, configured to compare the pareto solution set with the pareto solution set in the storage pool, and replace a dominant solution in the pareto solution set according to a comparison result;
and the output unit is used for repeatedly and sequentially triggering the updating unit and the replacing unit until the stopping triggering condition is met, and outputting the optimal pareto solution set.
Further, the second calculation module 303 includes:
the establishing unit is used for establishing a sample matrix according to the optimal pareto solution set and the target function;
the first calculation subunit is used for carrying out dimensionless operation on the sample matrix to obtain an operator;
the determining unit is used for constructing a decision matrix according to the operator and the sample matrix and determining the target of the decision matrix;
and the second calculating subunit is used for calculating the first Euclidean distance between each solution in the decision sample matrix and the target, and obtaining the optimal configuration scheme of the electricity-hydrogen hybrid energy storage system by taking the solution corresponding to the shortest first Euclidean distance as the optimal compromise solution.
Further, the establishing unit includes:
the normalizing subunit is used for acquiring a non-dominant solution of the optimal pareto solution set and normalizing a target function corresponding to the non-dominant solution;
the third calculation subunit is used for calculating second Euclidean distances between each solution in the optimal pareto solution set and the ideal point;
and the establishing subunit is used for establishing a sample matrix according to the normalized target function and the second Euclidean distance.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention, which is substantially or partly contributed by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk, and various media capable of storing program codes.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. An electricity-hydrogen hybrid energy storage system configuration method, comprising:
constructing a multi-objective optimization model of the electricity-hydrogen hybrid energy storage system, wherein the multi-objective optimization model takes the minimum whole life cycle loss, power loss, load fluctuation and voltage fluctuation of the electricity-hydrogen hybrid energy storage system as an objective function;
iteratively calculating a pareto solution set of the multi-objective optimization model by adopting a political optimization algorithm until an iteration termination condition is met, and outputting an optimal pareto solution set;
and calculating the optimal compromise solution of the optimal pareto solution set by adopting a grey target decision method of an entropy weight method to obtain the optimal configuration scheme of the electricity-hydrogen hybrid energy storage system, wherein the optimal configuration scheme comprises the optimal installation node, configuration capacity and configuration power.
2. The method of claim 1, wherein constructing the multi-objective optimization model of the electricity-hydrogen hybrid energy storage system comprises:
acquiring parameters of a power distribution network, and constructing a target function with minimum full life cycle loss, power loss, load fluctuation and voltage fluctuation according to the acquired parameters of the power distribution network;
the objective function includes:
wherein f (x) is an objective function; f. of 1 Cost, f, for full life cycle 2 Is power loss, f 3 Is a voltage fluctuation, f 4 Is a load fluctuation; x is a decision variable; h (x) is a constraint condition, wherein the decision variables comprise an installation node, a configuration capacity and a configuration power of the electricity-hydrogen hybrid energy storage system; the constraint conditions comprise node power balance constraint, node voltage constraint, grid-connected point power constraint, capacity and power constraint of an electricity-hydrogen hybrid system, charge and discharge constraint of a battery energy storage system and charge and discharge constraint of a hydrogen energy storage system;
the full lifecycle loss cost comprises:
wherein Q is BESSs Energy storage system for batteryFull life cycle loss cost, Q HESSs Cost is lost for the full life cycle of the hydrogen energy storage system; t is B For the investment cost of the battery energy storage system, T H The investment cost of the hydrogen energy storage system; w B For maintenance costs of the battery energy storage system, W H Maintenance costs for the hydrogen energy storage system; y is B For the operating cost of the battery energy storage system, Y H The operating cost of the hydrogen energy storage system; g B For battery energy storage systems, G H The replacement cost of the hydrogen energy storage system; c B For disposal and recovery costs of the battery energy storage system, C H Disposal and recovery costs for the hydrogen energy storage system; mu.s CRF,B Represents a capital recovery factor; n is a radical of hydrogen BESS The installation number of the battery energy storage systems in the power distribution network is represented; c. C battery The cost of a single battery; c. C EPCD,B Representing engineering, procurement and construction costs and developer costs of the battery energy storage system; i is sub Is a government subsidy; e BESS,i Is the capacity of the ith battery energy storage system; c. C FMC,B Representing the annual fixed maintenance cost of a single battery energy storage system; p BESS,i Is the power of the ith battery energy storage system; t is 24 hours; c. C pu (t) and c sel (t) the purchase and sale prices are respectively; p ch,Bi (t) and P dis,Bi (t) the charging and discharging power of the ith battery energy storage system respectively; n is B And t is the life of the battery and the number of times of replacement, respectively; α is the annual cost rate of the battery; r represents a discount rate calculated as a weighted average capital cost; gamma ray B The recovery benefit of the battery energy storage system; c. C FC And c E The cost of the fuel cell and the electrolyzer, respectively; c. C HT And Q HT,i Cost and capacity of the hydrogen storage tank; p HESS,i The power of the ith hydrogen energy storage system is shown; c EPCD,H Represents the EPC cost of the hydrogen energy storage system; c. C FC And c E The cost of the fuel cell and the electrolyzer, respectively; c. C FMC,H Represents the annual maintenance cost of the fuel cell; p HESS,i The power of the ith hydrogen energy storage system; p ch,Hi And P dis,Hi Representing the charging and discharging power of the ith hydrogen energy storage system; q H,i To representTotal hydrogen production by the hydrogen energy storage system within one day; q. q of H Hydrogen production per kilowatt-hour;
represents the profit generated per kg of hydrogen; p is a radical of H The amount of electricity generated per kg of hydrogen; mu and v are the ratio of hydrogen delivery to power generation; n is H The number of replacement times of the HESSs; beta is the annual cost loss rate of the hydrogen energy storage system; gamma ray H The recycling benefit of the fuel cell is achieved;
the power loss includes:
l is the total number of the connecting lines of the electricity-hydrogen mixing system; r j The resistance on the j-th connecting line is shown; t represents the time, I j The current on the j-th connecting line;
the load fluctuations include:
wherein, P load ，P pv (t) and P wind Respectively the load of the electric-hydrogen hybrid system, the photovoltaic power and the wind power output in the t period;
the voltage fluctuations include:
in the formula, N nodes The number of total nodes of the system; v j Is the voltage at node j;
the average voltage of the j node in the T period;
the node power balance constraint is:
in the formula, P i (t) is the active power injected by the node i at the moment t; q i (t) is the reactive power injected at node i at time t; theta ij (t) is the voltage phase angle difference between nodes i and j at time t; v i (t) and V j (t) represents voltages of the node i and the node j during the period t, respectively; g ij And B ij Respectively the line conductance and susceptance between nodes i and j;
the node voltage constraint is:
V i min ＜V i ＜V i max
in the formula, V i min And V i max Respectively the upper and lower voltage limits of the node i;
the power constraint of the grid-connected point is as follows:
in the formula (I), the compound is shown in the specification,
and
respectively the lower limit and the upper limit of active power and reactive power of a grid-connected point;
the capacity and power constraints of the electric-hydrogen hybrid system are as follows:
in the formula (I), the compound is shown in the specification,
and
the capacity upper and lower limits of the battery energy storage system are set;
and
the power upper and lower limits of the battery energy storage system;
and
the capacity upper and lower limits of the hydrogen energy storage system;
and
the power upper limit and the power lower limit of the hydrogen energy storage system are set;
the charge and discharge constraint of the battery energy storage system is as follows:
in the formula eta ch_B And η dis_B Respectively the charging efficiency and the discharging efficiency of the battery energy storage system;
the charge and discharge constraints of the hydrogen energy storage system are as follows:
0≤P ch,Hi (t)≤P HESS,i ·η ch_H -P HESS,i ·η dis_H ≤P dis,Hi (t)≤0
in the formula eta ch_H And η dis_H Respectively the charge and discharge efficiency of the hydrogen energy storage system.
3. The method of claim 2, wherein iteratively calculating the pareto solution set of the multi-objective optimization model according to a political optimization algorithm until an iteration termination condition is satisfied, resulting in an optimal pareto solution set comprises:
s1: initializing algorithm parameters according to the acquired power distribution network parameters and the target function, and storing the algorithm parameters in a storage pool; the algorithm parameters comprise members of the population and a fitness function of the population; the members represent installation nodes, configuration capacity and configuration power of a group of electric-hydrogen energy storage systems;
s2: carrying out the operations of competitive election activities, political party exchange, election and conference affairs on the population in sequence, updating the members in the storage pool and the fitness of the members, and selecting the member with the highest fitness as the pareto solution set;
s3: comparing the pareto solution set with the pareto solution set in the storage pool, and replacing a dominant solution in the pareto solution set according to a comparison result;
s4: and (5) iterating S2-S3 until the iteration number reaches a preset iteration number threshold value, and outputting an optimal pareto solution set.
4. The method of claim 3, wherein the calculating the optimal compromise solution of the optimal pareto solution set according to the entropy-weighted grey target decision method to obtain the optimal configuration scheme of the electricity-hydrogen hybrid energy storage system comprises:
establishing a sample matrix according to the optimal pareto solution set and the target function;
carrying out dimensionless operation on the sample matrix to obtain an operator;
constructing a decision matrix according to the operator and the sample matrix, and determining the target of the decision matrix;
and calculating a first Euclidean distance between each solution in the decision sample matrix and the target, and taking the solution corresponding to the shortest first Euclidean distance as an optimal compromise solution to obtain an optimal configuration scheme of the electricity-hydrogen hybrid energy storage system.
5. The method of claim 4, wherein constructing a sample matrix from the optimal pareto solution set and the objective function comprises:
acquiring a non-dominant solution of the optimal pareto solution set, and normalizing a target function corresponding to the non-dominant solution;
calculating a second Euclidean distance between each solution in the optimal pareto solution set and an ideal point;
and establishing a sample matrix according to the normalized target function and the second Euclidean distance.
6. An electricity-hydrogen hybrid energy storage system configuration device, characterized in that the device comprises:
the system comprises a construction module, a calculation module and a control module, wherein the construction module is used for constructing a multi-objective optimization model of the electricity-hydrogen hybrid energy storage system, and the multi-objective optimization model takes the minimum of the full life cycle loss, the power loss, the load fluctuation and the voltage fluctuation of the electricity-hydrogen hybrid energy storage system as an objective function;
the first calculation module is used for iteratively calculating a pareto solution set of the multi-objective optimization model by adopting a political optimization algorithm until an iteration termination condition is met and outputting an optimal pareto solution set;
and the second calculation module is used for calculating the optimal compromise solution of the optimal pareto solution set by adopting a grey target decision method of an entropy weight method to obtain the optimal configuration scheme of the electricity-hydrogen hybrid energy storage system, wherein the optimal configuration scheme comprises the optimal installation node, the optimal configuration capacity and the optimal configuration power.
7. The apparatus of claim 6, wherein the building module comprises:
the acquisition unit is used for acquiring parameters of the power distribution network;
the construction unit is used for constructing a target function with minimum full life cycle loss, power loss, load fluctuation and voltage fluctuation according to the acquired power distribution network parameters;
the objective function includes:
wherein f (x) is an objective function; f. of 1 Cost, f, for full life cycle 2 Is power loss, f 3 Is a voltage fluctuation, f 4 Is a load fluctuation; x is a decision variable; h (x) is a constraint condition, wherein the decision variables comprise an installation node, a configuration capacity and a configuration power of the electricity-hydrogen hybrid energy storage system; the constraint conditions comprise node power balance constraint, node voltage constraint, grid-connected point power constraint, capacity and power constraint of an electricity-hydrogen hybrid system, charge and discharge constraint of a battery energy storage system and charge and discharge constraint of a hydrogen energy storage system;
the full lifecycle loss cost comprises:
wherein Q is BESSs Cost, Q, for the full life cycle of a battery energy storage system HESSs Cost is lost for the full life cycle of the hydrogen energy storage system; t is B For the investment cost of the battery energy storage system, T H The investment cost of the hydrogen energy storage system; w B For maintenance costs of the battery energy storage system, W H Maintenance costs for the hydrogen energy storage system; y is B For the operating cost of the battery energy storage system, Y H The operating cost of the hydrogen energy storage system; g B For battery energy storage systems, G H The replacement cost of the hydrogen energy storage system; c B For disposal and recovery costs of the battery energy storage system, C H Disposal and recovery costs for the hydrogen energy storage system; mu.s CRF,B Represents a capital recovery factor; n is a radical of hydrogen BESS The installation number of the battery energy storage systems in the power distribution network is represented; c. C battery The cost of a single battery; c. C EPCD,B Representing engineering, procurement and construction costs and developers of battery energy storage systemsCost; i is sub Is a government subsidy; e BESS,i Is the capacity of the ith battery energy storage system; c. C FMC,B Representing the annual fixed maintenance cost of a single battery energy storage system; p BESS,i Is the power of the ith battery energy storage system; t is 24 hours; c. C pu (t) and c sel (t) the purchase and sale prices are respectively; p ch,Bi (t) and P dis,Bi (t) the charging and discharging power of the ith battery energy storage system respectively; n is B And t is the life of the battery and the number of times of replacement, respectively; α is the annual cost loss rate of the battery; r represents a discount rate calculated as a weighted average capital cost; gamma ray B The recovery benefit of the battery energy storage system; c. C FC And c E The cost of the fuel cell and the electrolyzer, respectively; c. C HT And Q HT,i Cost and capacity of the hydrogen storage tank; p HESS,i The power of the ith hydrogen energy storage system is shown; c EPCD,H Represents the EPC cost of the hydrogen energy storage system; c. C FC And c E The cost of the fuel cell and the electrolyzer, respectively; c. C FMC,H Represents the annual maintenance cost of the fuel cell; p HESS,i The power of the ith hydrogen energy storage system; p is ch,Hi And P dis,Hi Representing the charging and discharging power of the ith hydrogen energy storage system; q H,i Represents the total hydrogen production of the hydrogen energy storage system in one day; q. q.s H Hydrogen production per kilowatt-hour;
represents the profit generated per kg of hydrogen; p is a radical of H The amount of electricity generated per kg of hydrogen; mu and v are the ratio of hydrogen delivery to power generation; n is H The number of replacement times of the HESSs; beta is the annual cost loss rate of the hydrogen energy storage system; gamma ray H The recovery benefit of the fuel cell;
the power loss includes:
l is the total number of the connecting lines of the electricity-hydrogen mixing system; r j Denotes the jth barA resistor on the tie line; t represents the time, I j The current on the j-th connecting line;
the load fluctuations include:
wherein, P load ，P pv (t) and P wind Respectively the load of the electric-hydrogen hybrid system, the photovoltaic power and the wind power output in the t period;
the voltage fluctuations include:
in the formula, N nodes The number of total nodes of the system; v j Is the voltage at node j;
the average voltage of the j node in the T period;
the node power balance constraint is:
in the formula, P i (t) is the active power injected by the node i at the moment t; q i (t) is the reactive power injected at node i at time t; theta ij (t) is the voltage phase angle difference between nodes i and j at time t; v i (t) and V j (t) represents voltages of the node i and the node j during the period t, respectively; g ij And B ij Respectively, the line conductance and susceptance between nodes i and j;
the node voltage constraint is:
V i min ＜V i ＜V i max
in the formula, V i min And V i max Are respectively provided withThe upper and lower voltage limits of the node i;
the power constraint of the grid-connected point is as follows:
in the formula (I), the compound is shown in the specification,
and
respectively the lower limit and the upper limit of active power and reactive power of a grid-connected point;
the capacity and power constraints of the electric-hydrogen hybrid system are as follows:
in the formula (I), the compound is shown in the specification,
and
the capacity upper and lower limits of the battery energy storage system are set;
and
the power upper and lower limits of the battery energy storage system;
and
the capacity upper and lower limits of the hydrogen energy storage system;
and
the power upper and lower limits of the hydrogen energy storage system;
the charging and discharging constraints of the battery energy storage system are as follows:
in the formula eta ch_B And η dis_B Respectively the charging efficiency and the discharging efficiency of the battery energy storage system;
the charge and discharge constraints of the hydrogen energy storage system are as follows:
0≤P ch,Hi (t)≤P HESS,i ·η ch_H -P HESS,i ·η dis_H ≤P dis,Hi (t)≤0
in the formula eta ch_H And η dis_H Respectively the charge and discharge efficiency of the hydrogen energy storage system.
8. The apparatus of claim 6, wherein the first computing module comprises:
the initialization unit is used for initializing algorithm parameters according to the acquired power distribution network parameters and the target function and storing the algorithm parameters in a storage pool; the algorithm parameters comprise members of the population and a fitness function of the population; the members represent an optimal installation node, configuration capacity and configuration power of a group of electric-hydrogen energy storage systems;
the updating unit is used for sequentially carrying out the operations of election activities, political party exchange, election and conference affairs on the population, updating the members in the storage pool and the fitness of the members, and selecting the member with the highest fitness as the pareto solution set;
a replacing unit, configured to compare the pareto solution set with the pareto solution set in the storage pool, and replace a dominant solution in the pareto solution set according to a comparison result;
and the output unit is used for repeatedly and sequentially triggering the updating unit and the replacement until the stopping triggering condition is met and outputting the optimal pareto solution set.
9. The apparatus of claim 6, wherein the second computing module comprises:
the establishing unit is used for establishing a sample matrix according to the optimal pareto solution and the target function;
the first calculating subunit is used for carrying out dimensionless operation on the sample matrix to obtain an operator;
the determining unit is used for constructing a decision matrix according to the operator and the sample matrix and determining the target of the decision matrix;
and the second calculating subunit is used for calculating a first Euclidean distance between each solution in the decision sample matrix and the target, and obtaining an optimal configuration scheme of the electricity-hydrogen hybrid energy storage system by taking a solution corresponding to the shortest first Euclidean distance as an optimal compromise solution.
10. The apparatus of claim 9, wherein the establishing unit comprises:
a normalization subunit, configured to obtain a non-dominant solution of the optimal pareto solution set, and normalize an objective function corresponding to the non-dominant solution;
the third calculation subunit is used for calculating second Euclidean distances between each solution in the optimal pareto solution set and an ideal point;
and the establishing subunit is used for establishing a sample matrix according to the normalized target function and the second Euclidean distance.
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| CN117134468A (en) * | 2023-10-25 | 2023-11-28 | 深圳联芯微电子科技有限公司 | Stable PD quick charging system and method for dynamically adjusting power |
| CN117477615A (en) * | 2023-12-28 | 2024-01-30 | 国网浙江省电力有限公司电力科学研究院 | Optimal configuration method and equipment for electric-hydrogen composite energy storage system |
| CN117729233A (en) * | 2023-12-14 | 2024-03-19 | 中国人寿保险股份有限公司山东省分公司 | Device resource intensive management system and method based on key multidimensional |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117134468A (en) * | 2023-10-25 | 2023-11-28 | 深圳联芯微电子科技有限公司 | Stable PD quick charging system and method for dynamically adjusting power |
| CN117134468B (en) * | 2023-10-25 | 2024-02-13 | 深圳联芯微电子科技有限公司 | Stable PD quick charging system and method for dynamically adjusting power |
| CN117729233A (en) * | 2023-12-14 | 2024-03-19 | 中国人寿保险股份有限公司山东省分公司 | Device resource intensive management system and method based on key multidimensional |
| CN117477615A (en) * | 2023-12-28 | 2024-01-30 | 国网浙江省电力有限公司电力科学研究院 | Optimal configuration method and equipment for electric-hydrogen composite energy storage system |
| CN117477615B (en) * | 2023-12-28 | 2024-03-26 | 国网浙江省电力有限公司电力科学研究院 | Optimal configuration method and equipment for electric-hydrogen composite energy storage system |
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