CN-114977217-B - Configuration method and device of electric-hydrogen hybrid energy storage system

CN114977217BCN 114977217 BCN114977217 BCN 114977217BCN-114977217-B

Abstract

The invention relates to the technical field of hybrid energy storage systems, in particular to a configuration method and device of an electric-hydrogen hybrid energy storage system. The method comprises the steps of constructing a multi-objective optimization model of the electric-hydrogen hybrid energy storage system, iteratively calculating a pareto solution set of the multi-objective optimization model by using the full life cycle loss, the power loss, the load fluctuation and the voltage fluctuation of the electric-hydrogen hybrid energy storage system as objective functions, until iteration termination conditions are met, outputting an optimal pareto solution set, calculating an optimal compromise solution of the optimal pareto solution set by using an ash target decision method of an entropy weight method, and obtaining an optimal configuration scheme of the electric-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 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.

Inventors

Zhuo Yingjun
LU SIYU
ZHOU BAORONG
ZOU JIN
WANG JIAYANG
XIE PINGPING

Assignees

南方电网科学研究院有限责任公司
中国南方电网有限责任公司

Dates

Publication Date: 20260505
Application Date: 20220616

Claims (10)

1. An electro-hydrogen hybrid energy storage system configuration method, comprising: Constructing a multi-objective optimization model of an electric-hydrogen hybrid energy storage system, wherein the multi-objective optimization model takes the minimum total life cycle loss, power loss, load fluctuation and voltage fluctuation of the electric-hydrogen hybrid energy storage system as objective functions; iterative computation is carried out on the pareto solution set of the multi-objective optimization model by adopting a politics optimization algorithm until the iteration termination condition is met, and the optimal pareto solution set is output; And calculating an optimal compromise solution of the optimal pareto solution set by adopting an ash target decision method of an entropy weight method to obtain an optimal configuration scheme of the electric-hydrogen hybrid energy storage system, wherein the optimal configuration scheme comprises an optimal installation node, configuration capacity and configuration power.
2. The method of claim 1, wherein the constructing a multi-objective optimization model of an electro-hydrogen hybrid energy storage system comprises: Acquiring power distribution network parameters, and constructing an objective 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: In the formula, Is an objective function; is a full life cycle loss cost, Is power loss, Is a voltage fluctuation, Is load fluctuation; H (x) is a constraint condition, wherein the constraint condition comprises node power balance constraint, node voltage constraint, grid-connected point power constraint, capacity and power constraint of the electro-hydrogen hybrid energy storage system, battery energy storage system charge and discharge constraint and hydrogen energy storage system charge and discharge constraint; The full lifecycle loss cost includes: Wherein, the For the full life cycle loss cost of a battery energy storage system, The full life cycle loss cost of the hydrogen energy storage system; is the investment cost of the battery energy storage system, Investment cost for the hydrogen energy storage system; For the maintenance cost of the battery energy storage system, Maintenance cost for the hydrogen energy storage system; for the cost of operation of the battery energy storage system, The operation cost of the hydrogen energy storage system; For the replacement cost of the battery energy storage system, The replacement cost of the hydrogen energy storage system; for disposal and recycling costs of the battery energy storage system, The disposal and recovery costs for the hydrogen energy storage system; Represents a capital recovery coefficient; representing the installation quantity of battery energy storage systems in the power distribution network; Cost for a single cell; representing engineering, procurement and construction costs and developer costs of the battery energy storage system; Is a government patch; is the capacity of the i-th battery energy storage system; Representing the annual fixed maintenance cost of a single battery energy storage system; the power of the i-th battery energy storage system is that of the i-th battery energy storage system, wherein T is 24 hours; And The electricity purchase price is respectively; And Charging and discharging power of the ith battery energy storage system respectively; And t is the life and replacement times of the battery respectively, alpha is the annual cost loss rate of the battery, r is the discount rate calculated according to the weighted average fund cost; Is the recovery benefit of the battery energy storage system; And Costs for the fuel cell and the electrolyzer, respectively; And Cost and capacity for the hydrogen storage tank; the power of the ith hydrogen energy storage system; EPC cost representing hydrogen storage system; And Costs for the fuel cell and the electrolyzer, respectively; Representing the annual maintenance costs of the fuel cell; the power of the ith hydrogen energy storage system; And Representing the charge and discharge power of the ith hydrogen energy storage system; Indicating the total hydrogen production in a day of the hydrogen storage system; Hydrogen production for electricity per kilowatt-hour; Mu is the ratio of the hydrogen delivery amount to the generated energy; Number of replacements HESSs; Annual cost loss rate for hydrogen energy storage systems; The recovery benefit of the fuel cell; the power loss includes: L is the total number of interconnecting lines of the electro-hydrogen hybrid system; The resistance on the j-th tie line is indicated, t indicates the time, Is the current on the j-th tie line; the load fluctuation includes: Wherein, the , And Respectively carrying out load, photovoltaic and wind power output on an electric-hydrogen mixed system in a t period; The voltage fluctuation includes: In the formula, The total node number of the system; the voltage of the node j; the average voltage of the j node in the T period is shown; the node power balance constraint is: In the formula, Active power injected for node i at time t; the reactive power injected for the node i at the moment t; The voltage phase angle difference between the nodes i and j at the moment t; And Respectively representing the voltages of a node i and a node j in a t period; And Line conductance and susceptance between nodes i and j, respectively; the node voltage constraint is: In the formula, And Respectively nodes Upper and lower voltage limits of (2); the grid-connected point power constraint is as follows: In the formula, , , And The lower limit and the upper limit of active power and reactive power of the grid-connected point are respectively; The capacity and power constraints of the electro-hydrogen mixing system are as follows: In the formula, And The upper and lower limits of the capacity of the battery energy storage system; And (3) with The upper and lower power limits of the battery energy storage system; And Is the upper and lower limit of the capacity of the hydrogen energy storage system; And (3) with The upper and lower power limits of the hydrogen energy storage system; The charge and discharge constraint of the battery energy storage system is as follows: In the formula, And Charging efficiency and discharging efficiency of the battery energy storage system respectively; The charge and discharge constraint of the hydrogen energy storage system is as follows: In the formula, And And the charging and discharging efficiencies of the hydrogen energy storage system are respectively.
3. The method of claim 2, wherein iteratively computing the pareto solution set of the multi-objective optimization model according to a political optimization algorithm until an iteration termination condition is met, the obtaining an optimal pareto solution set comprising: S1, initializing algorithm parameters according to the acquired power distribution network parameters and the objective function, and storing the algorithm parameters in a storage pool, wherein the algorithm parameters comprise members of a population and fitness functions of the population; s2, sequentially performing operations of competitive activities, inter-political party exchange, election and conference transactions on the population, updating 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 of the storage pool, and replacing dominant solutions in the pareto solution set according to a comparison result; and S4, iterating the steps S2-S3 until the iteration times reach a preset iteration times threshold value, and outputting an optimal pareto solution set.
4. The method of claim 3, wherein the computing the optimal tradeoff of the optimal pareto solution set according to the gray target decision method of the entropy weight method results in an optimal configuration of the electro-hydrogen hybrid energy storage system, comprising: establishing a sample matrix according to the optimal pareto solution set and the objective function; dimensionless treatment is carried out on the sample matrix to obtain an operator; constructing a decision matrix according to the operator and the sample matrix, and determining a bulls-eye of the decision matrix; and calculating a first Euclidean distance between each solution in the decision sample matrix and the bulls-eye, and taking a solution corresponding to the shortest first Euclidean distance as an optimal compromise solution to obtain an optimal configuration scheme of the electro-hydrogen hybrid energy storage system.
5. The method of claim 4, wherein the 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 an objective 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 objective function and the second Euclidean distance.
6. An electro-hydrogen hybrid energy storage system configuration apparatus, the apparatus comprising: the construction module is used for constructing a multi-objective optimization model of the electric-hydrogen hybrid energy storage system, and the multi-objective optimization model takes the minimum full life cycle loss, power loss, load fluctuation and voltage fluctuation of the electric-hydrogen hybrid energy storage system as objective functions; 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 the iteration termination condition is met, and outputting an optimal pareto solution set; The second calculation module is used for calculating the optimal compromise solution of the optimal pareto solution set by adopting an ash target decision method of an entropy weight method to obtain an optimal configuration scheme of the electric-hydrogen hybrid energy storage system, wherein the optimal configuration scheme comprises an optimal installation node, configuration capacity and configuration power.
7. The apparatus of claim 6, wherein the build module comprises: The acquisition unit is used for acquiring the parameters of the power distribution network; The construction unit is used for constructing an objective 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: In the formula, Is an objective function; is a full life cycle loss cost, Is power loss, Is a voltage fluctuation, Is load fluctuation; H (x) is a constraint condition, wherein the constraint condition comprises node power balance constraint, node voltage constraint, grid-connected point power constraint, capacity and power constraint of the electro-hydrogen hybrid energy storage system, battery energy storage system charge and discharge constraint and hydrogen energy storage system charge and discharge constraint; The full lifecycle loss cost includes: Wherein, the For the full life cycle loss cost of a battery energy storage system, The full life cycle loss cost of the hydrogen energy storage system; is the investment cost of the battery energy storage system, Investment cost for the hydrogen energy storage system; For the maintenance cost of the battery energy storage system, Maintenance cost for the hydrogen energy storage system; for the cost of operation of the battery energy storage system, The operation cost of the hydrogen energy storage system; For the replacement cost of the battery energy storage system, The replacement cost of the hydrogen energy storage system; for disposal and recycling costs of the battery energy storage system, The disposal and recovery costs for the hydrogen energy storage system; Represents a capital recovery coefficient; representing the installation quantity of battery energy storage systems in the power distribution network; Cost for a single cell; representing engineering, procurement and construction costs and developer costs of the battery energy storage system; Is a government patch; is the capacity of the i-th battery energy storage system; Representing the annual fixed maintenance cost of a single battery energy storage system; the power of the i-th battery energy storage system is that of the i-th battery energy storage system, wherein T is 24 hours; And The electricity purchase price is respectively; And Charging and discharging power of the ith battery energy storage system respectively; And t is the life and replacement times of the battery respectively, alpha is the annual cost loss rate of the battery, r is the discount rate calculated according to the weighted average fund cost; Is the recovery benefit of the battery energy storage system; And Costs for the fuel cell and the electrolyzer, respectively; And Cost and capacity for the hydrogen storage tank; the power of the ith hydrogen energy storage system; EPC cost representing hydrogen storage system; And Costs for the fuel cell and the electrolyzer, respectively; Representing the annual maintenance costs of the fuel cell; the power of the ith hydrogen energy storage system; And Representing the charge and discharge power of the ith hydrogen energy storage system; Indicating the total hydrogen production in a day of the hydrogen storage system; Hydrogen production for electricity per kilowatt-hour; Mu is the ratio of the hydrogen delivery amount to the generated energy; Number of replacements HESSs; Annual cost loss rate for hydrogen energy storage systems; The recovery benefit of the fuel cell; the power loss includes: L is the total number of interconnecting lines of the electro-hydrogen hybrid system; The resistance on the j-th tie line is indicated, t indicates the time, Is the current on the j-th tie line; the load fluctuation includes: Wherein, the , And Respectively carrying out load, photovoltaic and wind power output on an electric-hydrogen mixed system in a t period; The voltage fluctuation includes: In the formula, The total node number of the system; the voltage of the node j; the average voltage of the j node in the T period is shown; the node power balance constraint is: In the formula, Active power injected for node i at time t; the reactive power injected for the node i at the moment t; The voltage phase angle difference between the nodes i and j at the moment t; And Respectively representing the voltages of a node i and a node j in a t period; And Line conductance and susceptance between nodes i and j, respectively; the node voltage constraint is: In the formula, And Respectively nodes Upper and lower voltage limits of (2); the grid-connected point power constraint is as follows: In the formula, , , And The lower limit and the upper limit of active power and reactive power of the grid-connected point are respectively; The capacity and power constraints of the electro-hydrogen mixing system are as follows: In the formula, And The upper and lower limits of the capacity of the battery energy storage system; And (3) with The upper and lower power limits of the battery energy storage system; And Is the upper and lower limit of the capacity of the hydrogen energy storage system; And (3) with The upper and lower power limits of the hydrogen energy storage system; The charge and discharge constraint of the battery energy storage system is as follows: In the formula, And Charging efficiency and discharging efficiency of the battery energy storage system respectively; The charge and discharge constraint of the hydrogen energy storage system is as follows: In the formula, And And the charging and discharging efficiencies of the hydrogen energy storage system are respectively.
8. The apparatus of claim 6, wherein the first computing module comprises: The system comprises an initialization unit, an algorithm parameter generation unit, a storage pool and a storage unit, wherein the initialization unit is used for initializing the algorithm parameter according to the acquired power distribution network parameter and the objective function, and storing the algorithm parameter in the storage pool, wherein the algorithm parameter comprises members of a population and an adaptability function of the population; The updating unit is used for sequentially carrying out operations of competitive activities, inter-political party exchange, election and bargaining transaction on the population, updating members in the storage pool and the fitness of the members, and selecting the member with the highest fitness as the pareto solution set; The replacing unit is used for comparing the pareto solution set with the pareto solution set of the storage pool and replacing dominant solutions in the pareto solution set according to a comparison result; And the output unit is used for repeatedly triggering the updating unit and the replacement in turn until the 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 set and the objective function; The first calculation subunit is used for carrying out dimensionless treatment 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 a bulls-eye 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 bulls-eye, and obtaining an optimal configuration scheme of the electric-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; a third calculation subunit, configured to calculate a second euclidean distance 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 objective function and the second Euclidean distance.

Description

Configuration method and device of electric-hydrogen hybrid energy storage system Technical Field The invention relates to the technical field of hybrid energy storage systems, in particular to a configuration method and device of an electric-hydrogen hybrid energy storage system. Background With the continued development and improvement of energy storage technology, the combined use of different types of energy storage systems has become a research hotspot for researchers, such as hybrid battery energy storage systems and electro-hydrogen hybrid energy storage systems for hydrogen energy storage systems. An electro-hydrogen hybrid energy storage system stores electrical energy through the use of 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 connect to the grid due to the limitation of the capacity of the battery energy storage system is avoided 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 electro-hydrogen hybrid energy storage system to reduce the cost of the electro-hydrogen hybrid energy storage system and reduce the power fluctuation and the energy loss at the same time is a problem to be solved. Disclosure of Invention The invention provides a configuration method and a 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 electric-hydrogen hybrid energy storage system, which comprises the following steps: Constructing a multi-objective optimization model of an electric-hydrogen hybrid energy storage system, wherein the multi-objective optimization model takes the minimum total life cycle loss, power loss, load fluctuation and voltage fluctuation of the electric-hydrogen hybrid energy storage system as objective functions; iterative computation is carried out on the pareto solution set of the multi-objective optimization model by adopting a politics optimization algorithm until the iteration termination condition is met, and the optimal pareto solution set is output; And calculating an optimal compromise solution of the optimal pareto solution set by adopting an ash target decision method of an entropy weight method to obtain an optimal configuration scheme of the electric-hydrogen hybrid energy storage system, wherein the optimal configuration scheme comprises an optimal installation node, configuration capacity and configuration power. Optionally, the constructing the multi-objective optimization model of the electro-hydrogen hybrid energy storage system includes: Acquiring power distribution network parameters, and constructing an objective 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 1 is full life cycle loss cost, f 2 is power loss, f 3 is voltage fluctuation, f 4 is load fluctuation, x is a decision variable, h (x) is a constraint condition, wherein the decision variable comprises an installation node, configuration capacity and configuration power of the electro-hydrogen hybrid energy storage system, and the constraint condition comprises node power balance constraint, node voltage constraint, grid-connected point power constraint, electro-hydrogen hybrid system capacity and power constraint, battery energy storage system charge and discharge constraint and hydrogen energy storage system charge and discharge constraint; The full lifecycle loss cost includes: Wherein Q BESSs is the full life cycle loss cost of the battery energy storage system, Q HESSs is the full life cycle loss cost of the hydrogen energy storage system, T B is the investment cost of the battery energy storage system, T H is the investment cost of the hydrogen energy storage system, W B is the maintenance cost of the battery energy storage system, W H is the maintenance cost of the hydrogen energy storage system, Y B is the operation cost of the battery energy storage system, Y H is the operation cost of the hydrogen energy storage system, G B is the battery energy storage system, G H is the replacement cost of the hydrogen energy storage system, C B is the disposal and recovery cost of the battery energy storage system, C H is the disposal and recovery cost of the hydrogen energy storage system, mu CRF,B represents the capital recovery coefficient, N BESS represents the installation number