CN115860205A - Two-stage distribution robust hydrogen storage equipment optimal configuration method considering cross-season scheduling - Google Patents
Two-stage distribution robust hydrogen storage equipment optimal configuration method considering cross-season scheduling Download PDFInfo
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Abstract
The invention relates to a two-stage distribution robust hydrogen storage equipment optimal configuration method considering cross-season scheduling, which comprises the following steps: establishing an uncertainty model of the source load data of the target year all year round; step 2, obtaining typical day source load data and a corresponding typical day sequence; step 3, establishing a two-stage distribution robust hydrogen storage equipment optimization configuration model considering cross-season regulation capacity; and 4, solving the two-stage distribution robust hydrogen storage equipment optimization configuration model which is established in the step 3 and takes the cross-season regulation capability into consideration, and obtaining the installation capacity and the installation power of the hydrogen storage equipment. The invention can realize the optimal configuration of the capacity and the power of the hydrogen storage equipment in consideration of the cross-season regulation capacity.
Description
Technical Field
The invention belongs to the technical field of capacity configuration optimization of hydrogen storage equipment in a power distribution network containing hydrogen storage equipment, and relates to a two-stage distribution robust hydrogen storage equipment optimization configuration method, in particular to a two-stage distribution robust hydrogen storage equipment optimization configuration method considering cross-season scheduling.
Background
The access of large-scale high-proportion renewable energy power presents a huge challenge to the construction of a novel power system. Firstly, wind power and photovoltaic have intermittent fluctuation of periodic characteristics in a time dimension and intrinsic resource difference in a space dimension, a novel power system bears complex and heavy consumption tasks, and the flexible response capability of the system needs to be improved in different time scales and different energy carrier forms; secondly, the novel energy storage technical form of the current power system in China mainly takes electricity storage, especially electrochemical energy storage, and is difficult to meet the large-scale, long-period and season-crossing power regulation requirements; thirdly, improving the flexible adjustment capability of the coal-electricity unit and reducing the minimum technical output of the coal-electricity unit are also one of the important means for offering the renewable energy power and improving the utilization level of the renewable energy at the present stage. However, with the continuous promotion of carbon neutralization in China, the improvement of the consumption proportion of non-fossil energy is the most direct and effective way for reducing carbon emission. Therefore, the novel power system needs to dig a multi-element stable clean low-carbon energy carrier, and provides strong support and powerful guarantee for constructing a safe and reliable power system operation system.
The hydrogen energy is used as a secondary energy source with wide source, cleanness, flexibility and rich application scene, and relates to application in the fields of chemical industry, traffic, energy and power, buildings and the like. Under the background of constructing a novel power system, hydrogen energy is an important energy conversion carrier of electric energy, an electric hydrogen application system based on various renewable energy hydrogen production technologies can flexibly realize bidirectional interactive conversion of electric energy and hydrogen energy, a multi-energy complementary system meeting diversified energy requirements of electricity, heat and cold is formed according to local conditions, and the electric energy system is powerful supplement of renewable energy power.
In the existing research on capacity planning of hydrogen storage equipment, methods for selecting and processing source load data are roughly divided into two types: one is to select historical or predicted annual source load data to carry out operation planning for 8760 h; one is to cluster historical or predicted annual source load data and plan by using a typical period obtained by clustering. The two processing methods are all based on the same premise: it is believed that historical data will be completely reproducible or that the year-round predictive information is completely accurate. In practice, it is difficult to accurately predict year-round source load information, and it is not practical to assume that the history is completely reproducible, so it is necessary to consider uncertainty of year-round source load information.
In summary, how to describe the uncertainty of the source load information from the perspective of the whole year or years, and further consider the cross-season regulation capability of hydrogen storage energy to optimize the capacity and power configuration of the hydrogen storage equipment needs further intensive research.
Upon search, no prior art documents that are the same or similar to the present invention have been found.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a two-stage distributed robust hydrogen storage equipment optimization configuration method considering cross-season scheduling, and can realize the optimization configuration of the capacity and power of hydrogen storage equipment considering cross-season regulation capacity.
The invention solves the practical problem by adopting the following technical scheme:
a two-stage distribution robust hydrogen storage equipment optimal configuration method considering cross-seasonal scheduling comprises the following steps:
step 1, extracting a typical year, obtaining experience distribution of occurrence probability of the typical year, and further establishing an uncertainty model of annual source load data of a target year;
step 2, simplifying the time sequence information of the typical year source load data obtained in the step 1, and obtaining typical day source load data and a corresponding typical day sequence;
step 3, establishing a two-stage distribution robust hydrogen storage equipment optimization configuration model considering cross-season regulation capacity based on the uncertainty model of the target annual source load data established in the step 1;
and 4, solving the two-stage distribution robust hydrogen storage equipment optimization configuration model which is established in the step 3 and takes the cross-season regulation capability into consideration based on the typical daily source load data and the corresponding typical daily sequence obtained in the step 2, and obtaining the installation capacity and the installation power of the hydrogen storage equipment.
Further, the specific steps of step 1 include:
(1) Collecting years of historical data, and obtaining a plurality of typical years by using a clustering algorithm;
(2) Acquiring experience distribution of typical annual occurrence probability based on clustering results;
(3) And obtaining the experience distribution of the occurrence probability of the typical year according to the clustering result, describing the uncertainty of the source load data of the target year all the year through the uncertainty of the occurrence probability of the typical year, and establishing an uncertainty model of the source load data of the target year all the year.
Moreover, the specific method in the step (1) of the step 1 comprises the following steps:
firstly, acquiring perennial historical data of a planning region, including original source load data of a fan, photovoltaic output data and electric load power data in the region; secondly, clustering the collected historical source load data year by year, aggregating the source load data of a plurality of original years into source load data of a plurality of typical years, and simultaneously obtaining the corresponding relation between the typical years and the original source load data.
Moreover, the specific method in the step 1 and the step (2) is as follows:
based on the clustering result, the experience distribution of the typical annual occurrence probability is obtained, and the specific calculation method is as follows:
in the formula, s represents a typical year; n is a radical of hydrogen s Representing the number of typical years obtained by clustering; m represents the number of original years;
representing the probability of experience with the occurrence of a typical year s; />
Representing typical years s representing the number of original years.
Thus, the experience probability of all typical years is obtained, and the experience probability distribution p of discrete typical years is obtained 0 ；
Moreover, the specific method in the step 1 and the step (3) is as follows:
this range is expressed in the intersection of confidence intervals in the form of 1-norm and ∞ -norm.
In the formula, omega 1 、Ω ∞ Respectively representing confidence intervals corresponding to the 1-norm and the infinity-norm; p represents the probability distribution of the probability of occurrence of a typical year, p s Representing the probability of occurrence of a typical year s; omega 1 、ω ∞ Respectively representing the allowed value of the probability deviation of the confidence interval of 1-norm and infinity norm;
the confidence levels corresponding to the confidence intervals expressed by the formulas (2) and (3) are as follows:
in the formula, pr represents the probability of an event occurring;
if the confidence coefficient values are respectively alpha 1 、α ∞ I.e. the right side of the inequality numbers of the formulae (4) and (5) is respectively equal to alpha 1 、α ∞ Then, there are:
in this way, the corresponding Ω can be obtained from the set confidence by the equations (2), (3), (6) and (7) 1 、Ω ∞ The uncertainty set Ω of the typical annual occurrence probability is the intersection of these two sets:
equation (8) represents a fuzzy set of typical annual occurrence probability uncertainties.
Further, the specific steps of step 2 include:
(1) Clustering the time sequence information of each typical year according to days, aggregating the source load data of the typical year into source load data of a plurality of typical days, and simultaneously obtaining the corresponding relation between the typical days and each day in the typical year;
(2) Representing data of corresponding days in a typical year by data of typical days, then arranging the data in a time sequence, and considering that operating variables of the same typical day are also the same, therefore, the source load data and the operating variables of 8760h in the typical year are simplified into an ordered arrangement I of a plurality of typical days s ：
This sequence consists of 365 elements, corresponding to 365 days of a typical year, with the element subscript d denoting the day number, the element subscript d representing the day number
Is taken on>
Is its corresponding typical day type, l represents a typical day, N l Representing the typical number of days aggregated in a typical year.
Further, the specific steps of step 3 include:
(1) Establishing an equipment model, wherein the equipment model comprises the following steps:
(1) establishing an electrolytic cell and a fuel cell model:
the operating state of the electrolyzer corresponds to the charging state of the hydrogen storage tank, and the operating state of the fuel cell corresponds to the discharging state of the hydrogen storage tank:
in the formula (I), the compound is shown in the specification,
respectively representing the energy charging and discharging power of the hydrogen storage tank in the t period of the day d of the typical year; />
And &>
Respectively representing the input electric power of the electrolytic cell and the output electric power of the fuel cell; eta ED And η FC Respectively representing the hydrogen production efficiency of the electrolytic cell and the electric energy conversion efficiency of the fuel cell;
the input electric power of the electrolytic cell and the input gas power of the fuel cell should not exceed the installation power of the electrolytic cell, and the electrolytic cell and the fuel cell can be opened only one at the same time.
In the formula, P ED,S And P FC,S Respectively showing the installation power of the electrolytic cell and the fuel cell;
and &>
And the charging and discharging states of the hydrogen energy storage device are represented, and 0 or 1 is selected.
(2) Establishing a hydrogen storage tank model:
within the same typical day, the energy relationship between adjacent moments of the hydrogen storage tank is expressed as follows:
in the formula, E d,t Indicating the energy level of the hydrogen energy stored in the hydrogen storage tank at the time t on the day d; eta char And η dis Respectively representing the charging and discharging efficiency of the hydrogen storage equipment; eta loss Representing the self-depletion efficiency of hydrogen energy storage.
In the same typical year, the energy relationship of the hydrogen storage tank between adjacent days is expressed as follows:
E d+1,0 ＝E d,24 (16)
the energy level of the hydrogen storage tank cannot exceed its installation capacity:
0≤E d,t ≤E S (17)
the simulation run period considered is one year, so there are energy storage run cycle conditions:
E 1,0 ＝E 356,24 (18)
(3) building photovoltaic and fan model
The grid-connected power of the photovoltaic and the fan should not exceed the predicted output.
In the formula (I), the compound is shown in the specification,
respectively representing the grid-connected power of the photovoltaic and the fan; />
And respectively representing the predicted output of the photovoltaic and the fan.
(4) Establishing superior power grid model
The electric power purchased and sold to the upper electric network should not exceed the upper limit of the power of the connecting line, and only the electric power can be purchased or sold to the upper electric network at the same time.
In the formula (I), the compound is shown in the specification,
power for buying or selling electricity to the upper-level power grid is represented; />
And &>
And the system indicates that the power is bought or sold to the superior power grid and takes 0 or 1.
(2) The objective function for establishing the two-stage distribution robust hydrogen storage equipment optimization configuration model considering the cross-seasonal adjustment capacity comprises the equipment investment cost C converted to annual cost inv (x) Annual running cost C op (y)：
Consider the objective function divided into two phases, a planning phase and an operating phase. The optimization variables in the planning stage are the installation power of the electrolytic cell, the installation capacity of the hydrogen storage tank and the installation power of the fuel cell, namely x in the formula (24), and the optimization target is that the annual investment cost of the equipment is the lowest; the optimization variable of the operation stage is the operation power of all equipment in the energy system at each period, namely y in the formula (24), and the optimization aim is to perform simulation operation under the known first-stage equipment configuration scheme so as to minimize the operation cost. The max portion of the equation represents the typical annual occurrence probability p in finding the uncertainty set Ω s The worst case is combined, making the objective function F the smallest in this case.
Wherein, the annual equipment investment cost calculation formula is as follows:
in the formula: r is the discount rate; n is the service life of the equipment; c. C ED,S 、c FC,S Investment cost per unit power of the electrolyzer and the fuel cell, respectively, c HS,S The investment cost per unit volume of the hydrogen storage tank.
The operation cost comprises equipment maintenance cost and transaction cost with a superior power grid:
the equipment maintenance cost calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
respectively representing the unit power maintenance costs of the hydrogen storage tank, the photovoltaic, the electrolyzer and the fuel cell.
The cost calculation formula of the transaction with the superior power grid is as follows:
in the formula (I), the compound is shown in the specification,
respectively representing unit electricity prices for buying and selling electric energy to a superior power grid; />
And &>
Representing the electrical power purchased or sold to an upper level grid.
(3) Establishing a constraint condition of a two-stage distribution robust hydrogen storage equipment optimization configuration model considering the cross-season regulation capacity;
(1) planning phase constraints
The optimization variables in the planning phase are the electrolyzer installation power, the hydrogen storage tank installation capacity, and the fuel cell installation power, and the maximum investment amount of the electrolysis cell installation power should not be exceeded.
(2) Operating phase constraints
The system should meet the electric power balance constraint during the operation phase:
in the formula
Representing the electrical load power.
Each plant should also comply with its respective operational constraints, as in equations (10) - (23).
Moreover, the specific method of the step 4 is as follows:
decomposing the original problem into a Main Problem (MP) and a Sub Problem (SP), and repeatedly iterating and solving by adopting a CCG algorithm until the difference between the optimization values of the main problem and the sub problem is smaller than a certain allowable convergence precision epsilon, and stopping iterating.
The main problems are as follows:
the constraints are as follows for equations (28) - (30) and (33):
in the formula, n represents the number of iterations.
Sub-problem given a planning phase variable x * Then, the solution is performed as follows:
the constraints are equations (6) - (8), (10) - (23), (31).
The invention has the advantages and beneficial effects that:
the invention provides a two-stage distribution robust hydrogen storage equipment optimal configuration method considering cross-season scheduling. Firstly, establishing an uncertainty model of the source load data of a target year all year around by using historical source load data and a distribution robust method based on multiple discrete scenes; secondly, simplifying the obtained multiple discrete scenes and laying a foundation for the operation part of a planning model; thirdly, establishing a two-stage distribution robust hydrogen storage equipment optimization configuration model considering the cross-season regulation capacity based on the annual source load data uncertainty model; finally, the planning model is decomposed into a main problem and a sub problem, and Column and Constraint Generation (CCG) algorithm is adopted for solving, so that optimal configuration of the capacity and power of the hydrogen storage equipment considering the cross-season regulation capability is realized.
Drawings
FIG. 1 is a process flow diagram of the present invention.
Detailed Description
The embodiments of the invention will be described in further detail below with reference to the accompanying drawings:
a two-stage distributed robust hydrogen storage facility optimal configuration method considering cross-seasonal scheduling, as shown in fig. 1, comprising the steps of:
step 1, extracting a typical year, obtaining experience distribution of occurrence probability of the typical year, and further establishing an uncertainty model of annual source load data of a target year;
the specific steps of the step 1 comprise:
(1) Collecting years of historical data, and obtaining a plurality of typical years by using a clustering algorithm;
the specific method in the step (1) of the step 1 comprises the following steps:
firstly, acquiring the perennial historical data of a planning region, including the wind turbine, photovoltaic output data and electric load power data in the region, which are collectively referred to as original source load data. Secondly, clustering the collected historical source load data year by year, aggregating the source load data of a plurality of original years into source load data of a plurality of typical years, and obtaining the corresponding relation between the typical years and the original source load data, specifically, which original years each typical year specifically represents (which original years each typical year is aggregated by).
(2) Acquiring experience distribution of typical annual occurrence probability based on clustering results;
the specific method of the step 1 and the step (2) comprises the following steps:
based on the clustering result, the experience distribution of typical annual occurrence probability can be obtained, and the specific calculation method is as follows:
in the formula, s represents a typical year; n is a radical of hydrogen s Representing the number of typical years obtained by clustering; m represents the number of original years;
representing the probability of experience with the occurrence of a typical year s; />
Representing typical years s representing the number of original years.
Thus, the method obtains experience probability of all typical years, and obtains experience probability distribution p of discrete typical years 0 As in table 1.
TABLE 1 empirical probability distribution table for typical year occurrences
(3) Obtaining experience distribution of the occurrence probability of the typical year according to the clustering result, describing the uncertainty of the source load data of the target year all the year through the uncertainty of the occurrence probability of the typical year, and establishing an uncertainty model of the source load data of the target year all the year:
p 0 is an empirical distribution obtained from the clustering result, and is not necessarily a true distribution of the typical annual occurrence probability, but according to the idea of distribution robustness, it can be considered that the true distribution of the typical annual occurrence probability in the probability space may exist in p 0 A certain range of the surroundings.
The specific method of the step 1 and the step (3) comprises the following steps:
this range is expressed in the intersection of confidence intervals in the form of 1-norm and ∞ -norm.
In the formula, omega 1 、Ω ∞ Respectively representing confidence intervals corresponding to 1-norm and infinity-norm; p represents the probability distribution of the probability of occurrence of a typical year, p s Representing the probability of occurrence of a typical year s; omega 1 、ω ∞ Respectively representing the allowed value of the probability deviation of the confidence interval of 1-norm and infinity norm.
The confidence levels corresponding to the confidence intervals expressed by the formulas (2) and (3) are as follows:
in the formula, pr represents the probability of occurrence of an event.
If the confidence coefficient values are respectively alpha 1 、α ∞ I.e. the right side of the inequality numbers of the formulae (4) and (5) is respectively equal to alpha 1 、α ∞ Then, there are:
in this way, the corresponding Ω can be obtained from the set confidence levels by the equations (2), (3), (6) and (7) 1 、Ω ∞ The uncertainty set Ω of the typical annual occurrence probability is the intersection of these two sets:
the formula (8) is a fuzzy set representing the uncertainty of the probability of occurrence in the typical year, then a two-stage distribution robust model can be established in the step 3 according to the fuzzy set established in the step 1, and the establishment of the fuzzy set in the step 1 lays a foundation for the establishment of the two-stage distribution robust model in the step 3.
Step 2, simplifying the time sequence information of the typical year source load data obtained in the step 1, and obtaining typical day source load data and a corresponding typical day sequence;
the specific steps of the step 2 comprise:
(1) Clustering the time sequence information of each typical year according to days, aggregating the source load data of the typical year into source load data of a plurality of typical days, simultaneously obtaining the corresponding relation between the typical days and each day of the typical year,
in the present embodiment, each typical day specifically represents which days of the typical year.
(2) Representing the data of the corresponding days in the typical year by the data of the typical days, then arranging the data in a time sequence, and considering that the operation variables of the same typical day are also the same, therefore, the source load data and the operation variables of the typical year 8760h are simplified into an ordered arrangement I of a plurality of typical days s ：
This sequence consists of 365 elements, corresponding to 365 days of a typical year, with the element subscript d denoting the day number, the element subscript d representing the day number
Is taken on>
Is its corresponding typical day type, l represents a typical day, N l Representing the typical number of days aggregated in a typical year.
Step 3, establishing a two-stage distribution robust hydrogen storage equipment optimization configuration model considering cross-season scheduling based on the uncertainty model of the target annual source load data established in the step 1;
in the present embodiment, the regional energy system considered includes Photovoltaic (PV), fan (WT), electrolyzer (ED), hydrogen storage tank (HS), fuel Cell (FC), where the photovoltaic generates electrical energy, the electrolyzer converts the electrical energy into hydrogen energy, the fuel cell converts the hydrogen energy into electrical energy, and the hydrogen storage tank stores the hydrogen energy; the insufficient or redundant energy of the regional energy system can be solved through the transaction with the superior power grid. The planning objects are the installation power of the electrolytic cell, the installation capacity of the hydrogen storage tank and the installation power of the fuel cell.
The specific steps of the step 3 comprise:
(1) Establishing an equipment model, wherein the equipment model comprises:
(1) establishing an electrolytic cell and a fuel cell model:
the invention considers that hydrogen produced after the electrolytic cell starts to work is directly stored in the hydrogen storage tank, and the fuel cell directly consumes the hydrogen in the hydrogen storage tank after the fuel cell starts to work, so that the working state of the electrolytic cell corresponds to the charging state of the hydrogen storage tank, and the working state of the fuel cell corresponds to the discharging state of the hydrogen storage tank:
in the formula (I), the compound is shown in the specification,
respectively representing the energy charging and discharging power of the hydrogen storage tank in the t period of the day d of the typical year; />
And &>
Respectively representing the input electric power of the electrolytic cell and the output electric power of the fuel cell; eta ED And η FC Respectively representing the hydrogen production efficiency of the electrolytic cell and the electric energy conversion efficiency of the fuel cell.
The input electric power of the electrolyzer and the input gas power of the fuel cell should not exceed the installation power of the electrolyzer, and the electrolyzer and the fuel cell can be opened only one at the same time.
In the formula, P ED,S And P FC,S Respectively showing the installation power of the electrolytic cell and the fuel cell;
and &>
And the charging and discharging states of the hydrogen energy storage equipment are represented, and 0 or 1 is selected.
(2) Establishing a hydrogen storage tank model:
within the same typical day, the energy relationship between adjacent times of the hydrogen storage tank is expressed as follows:
in the formula, E d,t Indicating the energy level of the hydrogen energy stored in the hydrogen storage tank at the time t on the day d; eta char And η dis Respectively representing the charging and discharging efficiency of the hydrogen storage equipment; eta loss Represents hydrogenSelf-depletion efficiency of energy storage.
In the same typical year, the relationship of the energy of the hydrogen storage tank between adjacent days is expressed as follows:
E d+1,0 ＝E d,24 (16)
the energy level of the hydrogen storage tank cannot exceed its installation capacity:
0≤E d,t ≤E S (17)
the simulation run period considered is one year, so there are energy storage run cycle conditions:
E 1,0 ＝E 356,24 (18)
(3) building photovoltaic and fan model
The grid-connected power of the photovoltaic and the fan should not exceed the predicted output.
In the formula (I), the compound is shown in the specification,
respectively representing the grid-connected power of the photovoltaic and the fan; />
And respectively representing the predicted output of the photovoltaic and the fan.
(4) Establishing superior power grid model
The electric power purchased and sold to the upper electric network should not exceed the upper limit of the power of the connecting line, and only the electric power can be purchased or sold to the upper electric network at the same time.
In the formula (I), the compound is shown in the specification,
power for buying or selling electricity to the upper-level power grid is represented; />
And &>
And the system indicates that the power is bought or sold to the superior power grid and takes 0 or 1.
(2) Establishing an objective function of a two-stage distribution robust hydrogen storage equipment optimization configuration model considering cross-seasonal adjustment capacity:
the specific method of the step 3 and the step (2) is as follows:
the planning is targeted to: and considering the uncertainty of the source load power of the planned target year all the year, the annual cost of the system is the lowest under the worst condition.
In particular, the objective function includes a cost of equipment investment C converted to annuity inv (x) Annual running cost C op (y)。
Consider the objective function divided into two phases, a planning phase and an operating phase. The optimization variables in the planning stage are the installation power of the electrolytic cell, the installation capacity of the hydrogen storage tank and the installation power of the fuel cell, namely x in the formula (24), and the optimization target is that the annual investment cost of the equipment is lowest; the optimization variable of the operation stage is the operation power of all equipment in the energy system at each period, namely y in the formula (24), and the optimization aim is to perform simulation operation under the known first-stage equipment configuration scheme so as to minimize the operation cost. M in the formulaThe ax part represents the typical annual occurrence probability p in finding the uncertainty set omega s The worst case is combined, making the objective function F the smallest in this case.
Wherein, the annual equipment investment cost calculation formula is as follows:
in the formula: r is the discount rate; n is the service life of the equipment; c. C ED,S 、c FC,S Investment cost per unit power of the electrolyzer and the fuel cell, respectively, c HS,S The investment cost per unit volume of the hydrogen storage tank.
The operation cost comprises equipment maintenance cost and transaction cost with a superior power grid:
the equipment maintenance cost calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
respectively representing the unit power maintenance cost of the hydrogen storage tank, the photovoltaic, the electrolyzer and the fuel cell.
The cost calculation formula of the transaction with the superior power grid is as follows:
in the formula (I), the compound is shown in the specification,
respectively representing unit electricity prices for buying and selling electric energy to a superior power grid; />
And &>
Representing the electrical power purchased or sold to an upper level grid.
(3) Establishing a constraint condition of a two-stage distribution robust hydrogen storage equipment optimization configuration model considering the cross-season regulation capacity;
the constraint conditions of the step 3 and the step (3) comprise:
(1) planning phase constraints
The optimization variables in the planning phase are the electrolyzer installation power, the hydrogen storage tank installation capacity, and the fuel cell installation power, and the maximum investment amount of the electrolysis cell installation power should not be exceeded.
(2) Operating phase constraints
The system should meet the electric power balance constraint during the operation phase:
in the formula
Representing the electrical load power.
Each plant should also comply with its respective operational constraints, as in equations (10) - (23).
Finally, an objective function formula (24), planning stage constraint conditions (28) - (30), operation stage constraint conditions (31), (10) - (23) and constraint formulas (6) - (8) describing uncertainty of source load power of the target year all year round form a two-stage distribution robust hydrogen storage equipment optimization configuration model;
and 4, solving the two-stage distribution robust hydrogen storage equipment optimization configuration model which is established in the step 3 and takes the cross-season regulation capability into consideration based on the typical daily source load data and the corresponding typical daily sequence obtained in the step 2, and obtaining the installation capacity and the installation power of the hydrogen storage equipment.
The specific method of 4 is as follows:
decomposing the original problem into a Main Problem (MP) and a Sub Problem (SP), and repeatedly iterating and solving by adopting a CCG algorithm until the difference between the optimization values of the main problem and the sub problem is smaller than a certain allowable convergence precision epsilon, and stopping iterating.
The main problems are as follows:
the constraints are as follows for equations (28) - (30) and (33):
in the formula, n represents the number of iterations.
The subproblem variable x given a planning phase * Then, the solution is performed as follows:
the constraints are the expressions (6) - (8), (10) - (23), (31).
In this embodiment, the discrete typical annual probability values and the operation phase variables in the sub-problem are independent from each other, so the sub-problem can be solved in two steps, that is, the problem of the minimum value of the inner layer in the sub-problem is solved first, and then the problem of the outer layer in the sub-problem is solved. Inner min problem of SP at givenPlanning phase variable x * And in typical years, the method is a mixed integer linear programming problem, gurobi can be directly called to solve, and after the solution of the inner-layer min problem is completed, SP becomes p s To optimize the mixed integer linear programming problem for variables, gurobi may be called again to solve.
The specific solving process is as follows:
step 1, setting a lower bound LB as negative infinity, setting an upper bound UB as positive infinity, and setting the iteration number n =1.
Step 2, solving MP to obtain a group of optimal solutions (x) * ，η * ) Update lower bound LB = max { LB, η } * }。
Step 3, fixing the variable x in the planning stage * Solving SP to obtain p under the worst condition s * Function value f of sum SP SP * . Update the upper bound value
Step 4, if the difference between UB and LB is less than the set convergence precision epsilon, stopping iteration and returning to the optimal solution x * (ii) a Otherwise, update the worst probability distribution in the MP
And adds a new variable->
And the constraint associated with the newly added variable in equation (33).
And 5, updating n = n +1, and returning to the step 2.
The invention provides a two-stage distribution robust hydrogen storage equipment optimization configuration method considering cross-season scheduling, which comprises the steps of firstly, acquiring years of historical data, obtaining a plurality of typical years by using a clustering algorithm, obtaining experience distribution of occurrence probability of the typical years, and establishing an uncertainty probability distribution confidence set of the typical years; secondly, respectively applying a clustering algorithm to each typical year to obtain corresponding typical days and typical day arrangement sequences; thirdly, based on the research, a two-stage distribution robust hydrogen storage equipment optimization configuration model considering the cross-season regulation capability is established, finally, a planning model is decomposed into a main problem and a sub-problem, and a Column and Constraint Generation (CCG) algorithm is adopted for solving, so that the hydrogen storage equipment capacity and power optimization configuration considering the cross-season regulation capability is realized.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Claims (8)
1. A two-stage distribution robust hydrogen storage equipment optimal configuration method considering cross-season scheduling is characterized by comprising the following steps: the method comprises the following steps:
step 1, extracting typical years, obtaining experience distribution of occurrence probability of the typical years, and further establishing an uncertainty model of source load data of a target year all year round;
step 2, simplifying the time sequence information of the typical year source load data obtained in the step 1, and obtaining typical day source load data and a corresponding typical day sequence;
step 3, establishing a two-stage distribution robust hydrogen storage equipment optimization configuration model considering cross-season regulation capacity based on the uncertainty model of the target annual source load data established in the step 1;
and 4, solving the two-stage distribution robust hydrogen storage equipment optimization configuration model which is established in the step 3 and takes the cross-season regulation capability into consideration based on the typical daily source load data and the corresponding typical daily sequence obtained in the step 2, and obtaining the installation capacity and the installation power of the hydrogen storage equipment.
2. The two-stage distributed robust hydrogen storage facility optimal configuration method considering cross-seasonal scheduling according to claim 1, characterized in that: the specific steps of the step 1 comprise:
(1) Collecting years of historical data, and obtaining a plurality of typical years by using a clustering algorithm;
(2) Acquiring experience distribution of typical annual occurrence probability based on clustering results;
(3) And obtaining the experience distribution of the occurrence probability of the typical year according to the clustering result, describing the uncertainty of the source load data of the target year all the year through the uncertainty of the occurrence probability of the typical year, and establishing an uncertainty model of the source load data of the target year all the year.
3. The two-stage distributed robust hydrogen storage facility optimal configuration method considering cross-seasonal scheduling according to claim 2, characterized in that: the specific method in the step (1) of the step 1 comprises the following steps:
firstly, acquiring perennial historical data of a planning region, including original source load data of a fan, photovoltaic output data and electric load power data in the region; secondly, clustering the collected historical source load data year by year, aggregating the source load data of a plurality of original years into source load data of a plurality of typical years, and simultaneously obtaining the corresponding relation between the typical years and the original source load data.
4. The two-stage distributed robust hydrogen storage facility optimal configuration method considering cross-seasonal scheduling according to claim 2, characterized in that: the specific method of the step 1 and the step (2) comprises the following steps:
based on the clustering result, the experience distribution of the typical annual occurrence probability is obtained, and the specific calculation method is as follows:
in the formula, s represents a typical year; n is a radical of s Representing the number of typical years obtained by clustering; m represents the number of original years;
representing the probability of experience with the occurrence of a typical year s; />
Representing typical years s representing the number of original years.
Thus, the experience probability of all typical years is obtained, and the experience probability distribution p of discrete typical years is obtained 0 。
5. The two-stage distributed robust hydrogen storage facility optimal configuration method considering cross-seasonal scheduling according to claim 2, characterized in that: the specific method of the step 1 and the step (3) comprises the following steps:
expressing this range using the intersection of confidence intervals in the form of 1-norm and ∞ -norm;
in the formula, omega 1 、Ω ∞ Respectively representing confidence intervals corresponding to 1-norm and infinity-norm; p represents the probability distribution of the probability of occurrence of a typical year, p s Representing the probability of occurrence of a typical year s; omega 1 、ω ∞ Respectively representing the allowed value of the probability deviation of the confidence interval of 1-norm and infinity norm;
the confidence levels corresponding to the confidence intervals expressed by the equations (2) and (3) are as follows:
in the formula, pr represents the probability of an event occurring;
if the confidence coefficient values are respectively alpha 1 、α ∞ I.e. the right side of the inequality numbers of the formulae (4) and (5) is respectively equal to alpha 1 、α ∞ Then, there are:
in this way, the corresponding Ω can be obtained from the set confidence by the equations (2), (3), (6) and (7) 1 、Ω ∞ The uncertainty set Ω of the typical annual occurrence probability is the intersection of these two sets:
equation (8) represents a fuzzy set of typical annual occurrence probability uncertainties.
6. The two-stage distributed robust hydrogen storage facility optimal configuration method considering cross-seasonal scheduling according to claim 1, characterized in that: the specific steps of the step 2 comprise:
(1) Clustering the time sequence information of each typical year according to days, aggregating the source load data of the typical year into source load data of a plurality of typical days, and simultaneously obtaining the corresponding relation between the typical days and each day in the typical year;
(2) Representing data of corresponding days in a typical year by data of typical days, then arranging the data in a time sequence, and considering that operating variables of the same typical day are also the same, therefore, the source load data and the operating variables of 8760h in the typical year are simplified into an ordered arrangement I of a plurality of typical days s ：
This sequence consists of 365 elements, corresponding to 365 days of a typical year, with the element subscript d denoting the day number, the element subscript d representing the day number
Is taken on>
Is its corresponding typical day type, l represents a typical day, N l Representing the typical number of days aggregated in a typical year.
7. The two-stage distributed robust hydrogen storage facility optimal configuration method considering cross-seasonal scheduling according to claim 1, characterized in that: the specific steps of the step 3 comprise:
(1) Establishing an equipment model, wherein the equipment model comprises:
(1) establishing an electrolytic cell and a fuel cell model:
the operating state of the electrolyzer corresponds to the charging state of the hydrogen storage tank, and the operating state of the fuel cell corresponds to the discharging state of the hydrogen storage tank:
in the formula (I), the compound is shown in the specification,
respectively representing the energy charging and discharging power of the hydrogen storage tank in the t period of the day d of the typical year; />
And
respectively representing the input electric power of the electrolytic cell and the output electric power of the fuel cell; eta ED And η FC Respectively representing the hydrogen production efficiency of the electrolytic cell and the electric energy conversion efficiency of the fuel cell;
the input electric power of the electrolytic cell and the input gas power of the fuel cell should not exceed the installation power of the electrolytic cell, and only one of the electrolytic cell and the fuel cell can be opened at the same time;
in the formula, P ED,S And P FC,S Respectively showing the installation power of the electrolytic cell and the fuel cell;
and &>
Representing the charging and discharging states of the hydrogen energy storage equipment, and taking 0 or 1;
(2) establishing a hydrogen storage tank model:
within the same typical day, the energy relationship between adjacent moments of the hydrogen storage tank is expressed as follows:
in the formula, E d,t Represents the energy level of the hydrogen energy stored in the hydrogen storage tank at time t on day d; eta char And η dis Respectively representing the charging and discharging efficiency of the hydrogen storage equipment; eta loss Represents the self-depletion efficiency of hydrogen energy storage;
in the same typical year, the relationship of the energy of the hydrogen storage tank between adjacent days is expressed as follows:
E d+1,0 ＝E d,24 (16)
the energy level of the hydrogen storage tank cannot exceed its installation capacity:
0≤E d,t ≤E S (17) The simulation run period considered is one year, so there are energy storage run cycle conditions:
E 1,0 ＝E 356 ,24(18)
(3) building photovoltaic and fan model
The grid-connected power of the photovoltaic and the fan should not exceed the predicted output;
in the formula (I), the compound is shown in the specification,
respectively representing the grid-connected power of the photovoltaic and the fan; />
Respectively representing the predicted output of the photovoltaic power and the fan;
(4) establishing superior power grid model
The electric power purchase and sale to the superior electric network should not exceed the upper limit of the power of the connection line, and only the electric power purchase or sale to the superior electric network can be carried out at the same time;
in the formula (I), the compound is shown in the specification,
power for buying or selling electricity to the upper-level power grid is represented; />
And &>
The method comprises the following steps of (1) representing whether to buy or sell power to a superior power grid, and taking 0 or 1;
(2) The objective function for establishing the two-stage distribution robust hydrogen storage equipment optimization configuration model considering the cross-seasonal adjustment capacity comprises the equipment investment cost C converted to annual cost inv (x) Annual running cost C op (y)：
The objective function is divided into two stages, namely a planning stage and an operation stage; the optimization variables in the planning stage are the installation power of the electrolytic cell, the installation capacity of the hydrogen storage tank and the installation power of the fuel cell, namely x in the formula (24), and the optimization target is that the annual investment cost of the equipment is the lowest; the optimization variable of the operation stage is the operation power of all equipment in the energy system at each period, namely y in the formula (24), and the optimization target is to perform simulation operation under the known first-stage equipment configuration scheme so as to minimize the operation cost; the max portion of the equation represents the typical annual occurrence probability p in finding the uncertainty set Ω s Combining the worst case, and minimizing the objective function F in the worst case;
wherein, the annual equipment investment cost calculation formula is as follows:
in the formula: r is the discount rate;n is the service life of the equipment; c. C ED,S 、c FC,S Investment cost per unit power of the electrolyzer and the fuel cell, respectively, c HS,S Investment cost per unit volume of the hydrogen storage tank;
the operation cost comprises equipment maintenance cost and transaction cost with a superior power grid:
the equipment maintenance cost calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
respectively representing the unit power maintenance cost of the hydrogen storage tank, the photovoltaic cell, the electrolysis bath and the fuel cell;
the cost calculation formula of the transaction with the superior power grid is as follows:
in the formula (I), the compound is shown in the specification,
respectively representing unit electricity prices for buying and selling electric energy to a superior power grid; />
And &>
Represents electric power purchased or sold to an upper grid;
(3) Establishing a constraint condition of a two-stage distribution robust hydrogen storage equipment optimization configuration model considering the cross-season regulation capacity;
(1) planning phase constraints
The optimization variables in the planning stage are the installation power of the electrolytic cell, the installation capacity of the hydrogen storage tank and the installation power of the fuel cell, and the maximum investment quantity of the hydrogen storage tank should not be exceeded;
(2) operating phase constraints
The system should satisfy the electric power balance constraint in the operation stage:
in the formula
Representing the electrical load power;
each plant should also comply with its respective operational constraints, as in equations (10) - (23).
8. The two-stage distribution robust hydrogen storage equipment optimization configuration method considering cross-seasonal scheduling according to claim 1, characterized in that: the specific method of the step 4 comprises the following steps:
decomposing the original problem into a Main Problem (MP) and a Sub Problem (SP), and repeatedly iterating and solving by adopting a CCG algorithm until the difference between the optimization values of the main problem and the sub problem is smaller than a certain allowable convergence precision epsilon, and stopping iterating;
the main problems are as follows:
MP：
the constraints are as follows for equations (28) - (30) and (33):
in the formula, n represents the number of iterations;
the subproblem variable x given a planning phase * Then, the solution is performed as follows:
SP：
the constraints are the expressions (6) - (8), (10) - (23), (31).
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| CN117578533A (en) * | 2024-01-15 | 2024-02-20 | 华北电力大学 | Electro-hydrogen fusion collaborative optimization configuration method oriented to electro-hydrogen supply capability improvement |
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| CN117458488A (en) * | 2023-12-25 | 2024-01-26 | 河海大学 | Robust optimization scheduling method, device, equipment and medium for gradient water-light complementary distribution |
| CN117458488B (en) * | 2023-12-25 | 2024-03-01 | 河海大学 | Robust optimization scheduling method, device, equipment and medium for gradient water-light complementary distribution |
| CN117578533A (en) * | 2024-01-15 | 2024-02-20 | 华北电力大学 | Electro-hydrogen fusion collaborative optimization configuration method oriented to electro-hydrogen supply capability improvement |
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