CN116307021B - Multi-target energy management method of new energy hydrogen production system - Google Patents
Multi-target energy management method of new energy hydrogen production system Download PDFInfo
- Publication number
- CN116307021B CN116307021B CN202211221420.3A CN202211221420A CN116307021B CN 116307021 B CN116307021 B CN 116307021B CN 202211221420 A CN202211221420 A CN 202211221420A CN 116307021 B CN116307021 B CN 116307021B
- Authority
- CN
- China
- Prior art keywords
- hydrogen production
- charge
- soc
- storage battery
- energy
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 239000001257 hydrogen Substances 0.000 title claims abstract description 149
- 229910052739 hydrogen Inorganic materials 0.000 title claims abstract description 149
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 title claims abstract description 148
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 99
- 238000007726 management method Methods 0.000 title claims abstract description 44
- 238000004146 energy storage Methods 0.000 claims abstract description 78
- 238000005457 optimization Methods 0.000 claims abstract description 50
- 238000003860 storage Methods 0.000 claims abstract description 38
- 239000002245 particle Substances 0.000 claims abstract description 29
- 238000005265 energy consumption Methods 0.000 claims abstract description 15
- 238000007599 discharging Methods 0.000 claims abstract description 9
- 238000013178 mathematical model Methods 0.000 claims abstract description 5
- 238000000034 method Methods 0.000 claims description 17
- 238000010248 power generation Methods 0.000 claims description 13
- 238000004364 calculation method Methods 0.000 claims description 11
- 108010014173 Factor X Proteins 0.000 claims description 9
- 239000007789 gas Substances 0.000 claims description 7
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 claims description 6
- 238000005868 electrolysis reaction Methods 0.000 claims description 6
- 229910052744 lithium Inorganic materials 0.000 claims description 6
- 238000012163 sequencing technique Methods 0.000 claims description 5
- 238000004458 analytical method Methods 0.000 claims 1
- 238000011161 development Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 5
- 210000004027 cell Anatomy 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000002035 prolonged effect Effects 0.000 description 3
- 210000000352 storage cell Anatomy 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 230000007774 longterm Effects 0.000 description 2
- 230000010355 oscillation Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000002485 combustion reaction Methods 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000012983 electrochemical energy storage Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 150000002431 hydrogen Chemical class 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 238000012882 sequential analysis Methods 0.000 description 1
- 239000011232 storage material Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
Abstract
The invention relates to a multi-target energy management method of a new energy hydrogen production system, and belongs to the technical field of new energy. Introducing an equivalent state of charge SOHC of the hydrogen storage tank; according to the structure of the new energy hydrogen production system, on the premise of meeting the stable and safe operation of the system, determining the energy management flow of the new energy hydrogen production system based on the charge state of the energy storage battery and the equivalent charge state of the hydrogen storage tank; selecting the hydrogen production amount, the new energy consumption rate and the charging and discharging times of the energy storage battery of the new energy hydrogen production system as optimization indexes of the system, and establishing a mathematical model of multi-target energy management of the new energy hydrogen production system under the condition of meeting the operation constraint condition; adding a penalty factor for the state of charge of the energy storage battery, improving the energy management flow, and fully playing the function of an intelligent algorithm; determining the weight of multi-objective optimization based on an improved radar algorithm, and establishing an adaptability function; and adopting an improved particle swarm algorithm to solve the optimal operation scheme of the new energy hydrogen production system.
Description
Technical Field
The invention belongs to the technical field of new energy, relates to the field of energy management of hydrogen production systems, and particularly relates to a multi-target energy management method of a new energy hydrogen production system.
Background
At present, due to the gradual exhaustion of natural resources such as fossil, the world has an increasingly serious environmental problem, the exploration and use of clean energy are being increased in countries around the world, and China is a country supporting the vigorous development of new energy. Wind power generation technology using wind energy and photovoltaic power generation technology using solar energy are main forms of new energy power generation, and in recent years, the wind power generation technology and the photovoltaic power generation technology using solar energy are rapid in development, large in scale and further expand in scale in the future, so that difficulty is brought to the power grid to absorb wind energy and solar energy, and two problems of wind power and photovoltaic power development are needed to be solved (1) due to randomness, intermittence and irregularity of wind and light resources, the power quality of wind and photovoltaic power generation is poor, and great influence is brought to the power quality of the power grid. (2) The traditional electrochemical energy storage, electromagnetic energy storage and physical energy storage cannot meet the energy storage requirements of wind power and photovoltaic power generation in the future due to high operation cost.
The hydrogen energy is a new generation energy source, has high energy density and is convenient to store and transport, only water is generated by combustion, and the hydrogen energy can be a scheme for storing energy under the development of wind power and photovoltaics. At present, a Power to Gas hydrogen production technology which tends to be mature is proposed, a hydrogen storage material is researched, a very promising material is obtained, and better guarantee is provided for hydrogen energy storage. It can be seen that the use of hydrogen energy to store wind power and photovoltaic energy is possible and is full of prospect, and an integrated system with wind, light and hydrogen energy storage as a core is formed in the future, and system energy management is one of important basic works in the system operation process.
With the continuous development of new energy hydrogen production industrial application, further technical requirements are provided for development configuration, operation control and commercial operation of new energy hydrogen production systems based on wind power and photovoltaics. In the aspect of operation control, multi-source heterogeneous characteristics formed by wind power, photovoltaic, hydrogen production and a power grid lead to the need of realizing control strategy research of two levels of an energy management method and a coordination control method respectively for a new energy hydrogen production system, and aiming at the energy management level, how to design a reasonable energy management method, the realization of power balance, electric energy quality optimization and short-term/long-term economic operation optimization of the system operation are one of research hotspots in the field of new energy hydrogen production at present.
According to the invention, the energy management of the new energy hydrogen production system is optimized by taking the influence of the uncertainty problem possibly generated on the running state of the new energy hydrogen production system into consideration in an hour time scale, so that the safety and the economical efficiency are improved. And, unlike the energy management method which only aims at hydrogen production, the invention also aims at new energy consumption and the charge and discharge times of the energy storage battery, thus obtaining a more perfect energy management system.
Disclosure of Invention
The invention mainly solves the energy management problem of the new energy hydrogen production system, and improves the utilization rate and economy of the new energy while ensuring the stability of the new energy hydrogen production system from the aspects of hydrogen production efficiency, new energy consumption rate and service life of the energy storage battery of the system.
The invention provides a multi-target energy management method of a new energy hydrogen production system, which comprises the following steps:
step 1: introducing an equivalent state of charge SOHC of the hydrogen storage tank;
step 2: determining an optimized operation flow of the new energy hydrogen production system based on the state of charge of the energy storage battery and the equivalent state of charge of the hydrogen storage tank;
step 3: selecting the hydrogen production amount, the new energy consumption rate and the charge and discharge times of the energy storage battery of the new energy hydrogen production system as optimization indexes of the system, and establishing a mathematical model of multi-target energy management of the new energy hydrogen production system under the condition of meeting the operation constraint condition;
step 4: adding a penalty factor for the state of charge of the energy storage battery, and improving an energy management flow;
step 5: determining the weight of multi-objective optimization based on an improved radar algorithm, and establishing an adaptability function;
step 6: and adopting an improved particle swarm algorithm to solve the optimal operation scheme of the new energy hydrogen production system.
Further, the equivalent state of charge SOHC of the hydrogen storage tank described in step 1 is introduced in order to reflect the internal conditions of the hydrogen storage tank. Similar to the state of charge SOC of the energy storage battery, the equivalent state of charge of the hydrogen storage tank is:
wherein P is N Is the maximum allowable pressure of the hydrogen storage tank, P sto The expression for the current pressure of the hydrogen storage tank is:
wherein T is sto For hydrogen storage tank temperature, V sto For the volume of the hydrogen storage tank, R is a universal gas constant, generally 8.3145J/(mol.K), n sto Is the hydrogen storage amount of the hydrogen storage tank.
Further, the optimizing operation flow of the new energy hydrogen production system established based on the state of charge of the energy storage battery and the equivalent state of charge of the hydrogen storage tank in the step 2 is as follows: the system is divided into three states of less than 0.2,0.2 and 0.8 and more than 0.8, the running interval of the system is judged according to the SOHC and the SOC state, and then hydrogen production power, lithium battery charging and discharging and wind-solar power output of each state are controlled, wherein the hydrogen production power is used as the main target as large as possible.
Further, the optimizing indexes of the multi-objective energy management of the new energy hydrogen production system in the step 3 are respectively as follows:
(1) Hydrogen production amount
Wherein P is the electrolysis power input into the electrolytic tank, C 0 Is the number of coulombs per unit, V m Is the molar volume of gas in a standard state, V is the voltage of the accessed direct current, and is expressed in volts (V), N A Representing the avogalileo constant.
Because the units, the magnitudes and the like of the optimization indexes are different, in order to ensure the effectiveness of the system optimization function, the system optimization indexes are required to be subjected to unitization.
The unitization treatment of the hydrogen production amount is mainly realized by the hydrogen production power as an intermediate medium, because the hydrogen production power P hm Always at P hmin To P hmmax It can be used to unitize the hydrogen by comparing it with the maximum hydrogen production power:
(2) New energy consumption rate
Wherein P' pv And P' wt Respectively representing the output of an actual wind turbine generator set and a photovoltaic array, P pv And P wt And respectively representing the maximum power generation power of the wind turbine generator and the photovoltaic array under the wind-light condition. If the maximum power limit is exceeded, the maximum power of the unit is taken.
(3) The number of times of charging and discharging the energy storage battery
Since the total charge and discharge of the energy storage battery can be obtained after the whole period is ended, and the optimization is performed in the period, if the total charge and discharge is used as an index, a pre-calculation is required, the running time of the optimization algorithm can be greatly prolonged, and the efficiency is low. Therefore, the invention changes the method into the method, judges whether the charge and discharge state is changed in each optimizing operation, and if not, factors X 3 Setting 1, X if it is changed 3 Set to 0.8.
Further, the constraint conditions of multi-target energy management of the new energy hydrogen production system in step 3 include:
(1) System electric power constraint
P pv (t)+P wt (t)≥P bt (t)+P hm (t)
Wherein P is pv (t) represents the theoretical power of the photovoltaic unit at time t, P wt (t) represents the theoretical power of the wind power unit at time t, P bt (t) represents the charge and discharge power of the energy storage battery at the moment t, the positive charge and the negative discharge, P hm And (t) represents the input power of the hydrogen production unit at the moment t, and the above formula represents that the energy storage charging power and the hydrogen production power of the system are smaller than the maximum wind power and photovoltaic power capable of generating.
(2) Power limiting of wind and photovoltaic power generation units
0≤P pv (t)≤P pvmax
0≤P wt (t)≤P wtmax
Wherein P is pvmax 、P wtmax The maximum power of the photovoltaic array and the maximum power of the wind driven generator are respectively represented.
(3) Capacity limitation of energy storage cell of energy storage battery
SOC min ≤SOC(t)≤SOC max
Wherein SOC is min And SOC (System on chip) max Representing minimum and maximum states of charge, respectively.
(4) Charge and discharge power limitation of energy storage cell of energy storage battery
-P btmax ≤P bt (t)≤P btmax
Wherein P is btmax Representing the maximum charge-discharge power of the energy storage unit, P when the energy storage battery is charged bt (t) >0, P when the energy storage battery is discharged bt (t)≤0。
(5) Power limiting of electrolytic cells
P hmmin ≤P hm (t)≤P hmmax
Wherein P is hm (t) represents the electrolytic power to be input into the electrolytic cell, P hmmin Represents the minimum electrolytic power, P hmmax Indicating the maximum electrolytic power.
Further, in the step 4, the penalty factor for the state of charge of the energy storage battery is to remove the judgment of the SOC interval and manually add the penalty factor to penalty the state not operating in the interval of 0.2-0.8 in order to fully perform the function of the intelligent algorithm, so that what state is the optimal operation is judged according to the value of the fitness function after penalty addition.
When SOC is smaller than SOC min When (1):
X 4 ＝5*(SOC min -SOC)
when SOC > SOC max When (1):
X 4 ＝5*(SOC-SOC max )
where a multiple of 5 is the result after unitization.
Further, the step of determining the multi-objective optimization weight based on the improved radar algorithm in step 5 is as follows: according to G 1 The relative importance of each index is sequenced by the method, the ratio of the importance degree of the optimized index is determined according to the sequencing, and the weight of each optimized index is calculated; making radar image objective function image, solving its area S and perimeter L, calculating targetThe objective function:
F＝S*L
further, in the improved particle swarm optimization described in step 6, a dynamic inertia weight w is added in the speed update process of the basic particle swarm optimization algorithm, the calculation flow is the same as that of the standard particle swarm optimization algorithm, firstly, the particle speed and the particle position are initialized, the particle fitness value is calculated, the individual extremum and the population extremum are found, and then the global optimal solution of the objective function is continuously and iteratively obtained. Wherein, the calculation formula of the dynamic inertia weight w is as follows:
in the formula, K is the current iteration number, and K is the maximum iteration number.
Based on the above energy management method, the invention has the following advantages:
(1) According to the method, a plurality of optimization indexes such as hydrogen production amount, new energy consumption rate and energy storage battery charge and discharge times are selected, and an objective function is established based on an improved radar algorithm, so that three aspects of stability, economy and new energy utilization rate of the new energy hydrogen production system reach an integrated optimal state, and the energy management of the new energy hydrogen production system is safer, more economical and reasonable.
(2) The invention removes the judgment of the SOC interval and adds a penalty factor on the basis of the energy management of the new energy hydrogen production system according to the states of the SOC and the SOHC, and does not work in the SOC min ～SOC max The state of the section is subjected to fine, so that the state is judged to be optimal in operation according to the value of the fitness function added with fine, and the function of the intelligent algorithm is fully played.
(3) According to the invention, an improved particle swarm algorithm is adopted, and particle optimization in a standard particle swarm algorithm is easy to sink into local optimum and cannot achieve a desired effect, so that dynamic inertia weight w is introduced, the convergence speed can be increased in the later stage while population diversity is prevented from being reduced, the phenomenon that convergence cannot be achieved due to overlarge inertia weight is avoided, and the phenomenon that particles sink into local solution due to overlarge inertia weight is avoided. The dynamic inertia weight introduces a cosine function with periodic oscillation, and based on the total iteration times, each particle obtains oscillation during searching, expands the searching space and increases population diversity, thereby being beneficial to the particles to jump out of a local optimal solution.
Drawings
FIG. 1 is a schematic diagram of an energy management flow of a new energy hydrogen production system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an improved multi-objective energy management process shown in an embodiment of the present invention;
FIG. 3 is a block diagram of a new energy hydrogen generation system according to an embodiment of the present invention;
fig. 4 is a radar chart model shown in an embodiment of the present invention.
Detailed Description
The method of the present invention is further described below using the figures and examples, but the invention is not limited in any way, and any changes or substitutions based on the teachings of the invention are within the scope of the invention.
The invention mainly solves the energy management problem of the new energy hydrogen production system, and improves the utilization rate and economy of the new energy while ensuring the stability of the new energy hydrogen production system from the aspects of hydrogen production efficiency, new energy consumption rate and service life of the energy storage battery of the system.
The technical scheme provided by the invention is as follows: a multi-target energy management method of a new energy hydrogen production system is shown in fig. 3, wherein the new energy hydrogen production system comprises a wind turbine generator, an energy storage battery, an electrolytic hydrogen production device, a photovoltaic array and a system optimization operation controller. As shown in fig. 2, the multi-objective energy management method includes the steps of:
step one, introducing the equivalent state of charge SOHC of the hydrogen storage tank
In order to reflect the internal conditions of the hydrogen storage tank, similar to the state of charge SOC of the energy storage battery, an equivalent state of charge SOHC of the hydrogen storage tank is introduced:
wherein P is N P is the maximum allowable pressure of the hydrogen storage tank sto The expression for the current pressure of the hydrogen storage tank is:
wherein T is sto For hydrogen storage tank temperature, V sto For the volume of the hydrogen storage tank, R is a universal gas constant, generally 8.3145J/(mol.K), n sto Is the hydrogen storage amount of the hydrogen storage tank.
Determining an energy management flow of the new energy hydrogen production system, namely an optimized operation flow before improvement, based on the charge state of the energy storage battery and the equivalent charge state of the hydrogen storage tank;
the operation flow of the new energy hydrogen production system is shown in fig. 1, after wind-solar generating capacity is calculated by input data, the equivalent state of charge (hereinafter referred to as SOHC state) of the hydrogen storage tank is collected, the SOHC state is judged, then the state of charge (hereinafter referred to as SOC state) of the energy storage battery is collected, the operation interval of the system is judged, and reasonable control is carried out on each interval.
In the embodiment, when SOHC is less than 0.2 and SOC is less than 0.2, the wind-solar power generation power is used for preferentially generating hydrogen, and the residual electric quantity charges the lithium battery; when SOHC is less than 0.2 and SOC is more than or equal to 0.2 and less than or equal to 0.8, calculating an optimal operation scheme at the moment by adopting an optimization algorithm; when SOHC is less than 0.2 and SOC is more than 0.8, the lithium battery can only discharge, and the redundant wind and light power is abandoned;
when SOHC is more than or equal to 0.2 and less than or equal to 0.8 and SOC is less than or equal to 0.2, the lithium battery can only be charged, and residual wind-solar power is used for hydrogen production; when SOHC is more than or equal to 0.2 and less than or equal to 0.8, SOC is more than or equal to 0.2 and less than or equal to 0.8, an optimal operation scheme is calculated by adopting an optimization algorithm; when SOHC is more than or equal to 0.2 and less than or equal to 0.8 and SOC is more than 0.8, the lithium battery can only discharge, and redundant wind and light power is abandoned;
when SOHC>0.8, because the system takes hydrogen production as the primary target, H is sent out 2 And (3) restoring the hydrogen storage tank to an initial value, and restoring the system to a certain state before the hydrogen storage tank to continuously produce hydrogen.
Thirdly, selecting the hydrogen production amount, the new energy consumption rate and the charge and discharge times of the energy storage battery of the new energy hydrogen production system as optimization indexes of the system, and establishing a mathematical model of multi-target energy management of the new energy hydrogen production system under the condition of meeting the operation constraint condition;
in the step, the optimizing indexes of the multi-target energy management of the new energy hydrogen production system are respectively as follows:
(1) Hydrogen production amount
Wherein P is the electrolysis power input into the electrolytic tank, C 0 Is the number of coulombs per unit, V m Is the molar volume of gas in a standard state, V is the voltage of the accessed direct current, and is expressed in volts (V), N A Representing the Avgalileo constant and Vh is hydrogen production.
Because the units, the magnitudes and the like of the optimization indexes are different, in order to ensure the effectiveness of the system optimization function, the system optimization indexes are required to be subjected to unitization.
The unitization treatment of the hydrogen production amount is mainly realized by the hydrogen production power as an intermediate medium, because the hydrogen production power P hm Always at P hmin To P hmmax It can be used to unitize the hydrogen by comparing it with the maximum hydrogen production power:
wherein X is 1 Represents the hydrogen production amount after unitization, P hm Represents hydrogen production power, P hmmax Representing the maximum hydrogen production power;
(2) New energy consumption rate
Wherein P' pv And P' wt Respectively represent an actual wind turbine generator system and a photovoltaic arrayColumn force, P pv And P wt Respectively representing the maximum power generation of the wind turbine generator and the photovoltaic array under the wind-light condition; if the maximum power limit is exceeded, the maximum power of the unit is taken.
(3) The number of times of charging and discharging the energy storage battery
Since the total charge and discharge of the energy storage battery can be obtained after the whole period is ended, and the optimization is performed in the period, if the total charge and discharge is used as an index, a pre-calculation is required, the running time of the optimization algorithm can be greatly prolonged, and the efficiency is low. Therefore, the invention introduces the charge and discharge factor X of the energy storage battery 3 For expressing the charge and discharge times of the energy storage battery, judging whether the charge and discharge state is changed in each optimization operation, and if not, charging and discharging the energy storage battery by a factor X 3 Is set to 1, if the energy storage battery is changed, the charge and discharge factor X of the energy storage battery is calculated 3 Set to 0.8.
In this embodiment, constraint conditions of multi-target energy management of the new energy hydrogen production system include:
(1) System electric power constraint
P pv (t)+P wt (t)≥P bt (t)+P hm (t)
Wherein P is pv (t) represents the theoretical power which can be generated by the photovoltaic array at the moment t, P wt (t) represents the theoretical power which can be generated by the wind turbine generator at the moment t, P bt (t) represents the charge and discharge power of the energy storage battery at the moment t, the positive charge and the negative discharge, P hm And (t) represents the input power of the hydrogen production unit at the moment t, and the above formula represents that the energy storage charging power and the hydrogen production power of the system are smaller than the maximum wind power and photovoltaic power capable of generating.
(2) Power limitation of wind turbine generator and photovoltaic array
0≤P pv (t)≤P pvmax
0≤P wt (t)≤P wtmax
Wherein P is pvmax 、P wtmax And respectively representing the maximum power of the photovoltaic array and the wind turbine generator.
(3) Capacity limitation of energy storage cell of energy storage battery
SOC min ≤SOC(t)≤SOC max
Wherein SOC is min And SOC (System on chip) max The minimum charge state and the maximum charge state of the energy storage battery are respectively represented, and the SOC (t) represents the charge state of the energy storage battery at the moment t.
(4) Charge and discharge power limitation of energy storage battery
-P btmax ≤P bt (t)≤P btmax
Wherein P is btmax Representing the maximum charge-discharge power of the energy storage unit, P when the energy storage battery is charged bt (t) >0, P when the energy storage battery is discharged bt (t)≤0。
(5) Power limitation of electrolytic cells
P hmmin ≤P hm (t)≤P hmmax
Wherein P is hm (t) represents the electrolysis power input into the electrolyzer at time t, P hmmin Represents the minimum electrolytic power, P hmmax Indicating the maximum electrolytic power.
And fourthly, adding a penalty factor for the charge state of the energy storage battery, and improving the energy management flow.
The penalty factor for the charge state of the energy storage battery is added to fully play the function of an intelligent algorithm, the judgment on the SOC interval is removed, the penalty factor is manually added, and the battery is not operated in the SOC min ～SOC max The particle state of the section is subjected to fine, so that the state is judged to be the optimal operation according to the value of the fitness function added with fine.
The improved energy management flow is shown in fig. 2.
When SOC is smaller than SOC min When (1):
X 4 ＝5*(SOC min -SOC)
when SOC > SOC max When (1):
X 4 ＝5*(SOC-SOC max )
when SOC is min ≤SOC≤SOC max When (1):
X 4 ＝0
wherein the method comprises the steps of，X 4 Represents the penalty factor, the multiple 5 is the result after the unitization process, in this example, SOC min Taking 0.2, SOC max Taking 0.8.
Fig. 2 shows an improved multi-objective energy management flow of the present invention, in which after wind-solar energy generating capacity is calculated by input data, SOHC states are collected first, and operation intervals of the system are judged according to the SOHC states, so that each interval is reasonably controlled.
Specifically, when SOHC is less than 0.2 or S0HC is more than or equal to 0.2 and less than or equal to 0.8, initializing a particle swarm, predicting an SOC state, and calculating a penalty factor X 4 : when SOC is less than 0.2, X 4 ＝5*(SOC min -SOC); when SOC >0.8, X 4 ＝5*(SOC-SOC max ) The method comprises the steps of carrying out a first treatment on the surface of the Combining the unitized hydrogen production amount X 1 New energy consumption rate X 2 Charging and discharging factor X of energy storage battery 3 And penalty factor X 4 Performing fitness calculation and comparison, and performing iterative calculation to obtain an optimal operation scheme;
when SOHC is more than 0.8, H is sent out at the moment because the system takes hydrogen production as the primary target 2 And (3) restoring the hydrogen storage tank to an initial value, and restoring the system to a certain state before the hydrogen storage tank to continuously produce hydrogen.
And fifthly, establishing the weight of multi-objective optimization based on an improved radar algorithm, and establishing an fitness function.
According to the sequential analysis method (G) 1 Method) determines the weight of each optimization index.
Firstly, determining the relative importance of optimization indexes according to optimization requirements and objective economical conditions, sequencing the sizes of the optimization indexes, calculating the specific numerical value of each index after sequencing the importance of each optimization index of a system is determined, and then calculating the specific weight value of the optimization index, wherein the calculation formula is as follows:
wherein omega k I.e. the specific weight of each optimization index, r k Representing xxx.
Table 1 specific weights, relative weights, of optimization metrics
From the above weight changes, the angles of the optimization indexes in the radar chart are obtained, and as shown in fig. 4, θ1 to θ4 each represent: hydrogen production, new energy consumption rate, charge and discharge times and penalty factors.
Table 2 angles corresponding to the optimization indexes
| Index (I) | Angle (rad) |
| θ 1 | 4.49 |
| θ 2 | 0.95 |
| θ 3 | 0.17 |
| θ 4 | 0.67 |
Then dividing the unit circle according to the angles, namely taking the origin as the center of a circle, starting from the OA line segment, and taking the values of theta 1-theta 4 clockwiseTo do X respectively 1 、X 2 、X 3 、X 4 The sectors of the occupied areas are separated by OB, OC and OD line segments, and then angular bisectors of each area are respectively marked as: OX (OX) 1 、OX 2 、OX 3 、OX 4 Namely, the line segment corresponding to the optimization index is obtained, then, according to the value of the specific index, points with corresponding lengths are made on the corresponding line segment, a, b, c, d, abcd are connected to obtain a quadrilateral abcd, and the area S and the perimeter L of the quadrilateral abcd are calculated as follows:
after calculating the area S and the perimeter L, the fitness function F is defined as follows:
F＝S*L
and step six, adopting an improved particle swarm algorithm to solve the optimal operation scheme of the new energy hydrogen production system.
The improved particle swarm algorithm is to add dynamic inertia weight w in the speed updating process of the basic particle swarm algorithm, the calculation flow is the same as that of the standard particle swarm algorithm, firstly, the particle speed and the particle position are initialized, the particle fitness value is calculated, the individual extremum and the group extremum are found, and then the global optimal solution of the objective function is continuously and iteratively obtained. Wherein, the calculation formula of the dynamic inertia weight w is as follows:
in the formula, K is the current iteration number, and K is the maximum iteration number.
In order to illustrate the implementation effect of the invention, the embodiment uses the data of wind speed, temperature and illumination intensity collected from 1990, 4 months, 1 day to 4 months, 30 days and hours in Shanxi Datong district for verification. Rated power of the wind turbine generator is set to be 50kW, rated power of photovoltaic array power generation is 20kW, rated power of the electrolytic hydrogen production device is 24kW, and maximum charge and discharge power of the energy storage battery is 12kW. The multi-objective optimized energy management method of the present invention is compared with the optimized results of an optimization method that only targets hydrogen production.
TABLE 3 Hydrogen production index
| Single target | Multi-target | |
| For 1 day | 0.334 | 0.332 |
| For 3 days | 0.966 | 0.958 |
| For 7 days | 2.109 | 2.117 |
| 14 days | 4.383 | 4.386 |
| For 30 days | 9.299 | 9.331 |
From table 3, it can be derived that the multi-target system cannot achieve higher hydrogen production index in a short period than the optimization system taking hydrogen production as a single target, which also reflects the emphasis of single target optimization, but the multi-target system has higher hydrogen production than the single target optimization system in a long-term perspective.
TABLE 4 New energy consumption Rate index
| Single target | Multi-target | |
| For 1 day | 0.755 | 0.772 |
| For 3 days | 0.627 | 0.628 |
| For 7 days | 0.769 | 0.774 |
| 14 days | 0.686 | 0.692 |
| For 30 days | 0.659 | 0.670 |
As can be seen from the data of the new energy consumption rate in table 4, the multi-target system is higher than the single-target system in the consumption rate, which shows that the economy is better.
TABLE 5 index of charge and discharge times of energy storage battery
From the data of the charge and discharge times of the energy storage battery in table 5, it can be obviously found that the multi-objective system has obvious superiority in the index, and the multi-objective system can reduce the charge and discharge times of the energy storage battery in each time period to about 50% of that of the single-objective system, so that the service life of the energy storage battery can be greatly prolonged.
The foregoing list is only illustrative of specific embodiments of the invention. Obviously, the invention is not limited to the above embodiments, but many variations are possible. All modifications directly derived or suggested to one skilled in the art from the present disclosure should be considered as being within the scope of the present invention.
Claims (1)
1. A multi-target energy management method for a new energy hydrogen production system, comprising the steps of:
step 1: introducing an equivalent state of charge SOHC of the hydrogen storage tank;
the equivalent state of charge SOHC of the hydrogen storage tank described in step 1 is:
wherein P is N P is the maximum allowable pressure of the hydrogen storage tank sto The expression for the current pressure of the hydrogen storage tank is:
wherein T is sto For hydrogen storage tank temperature, V sto For the volume of the hydrogen storage tank, R is a universal gas constant, generally 8.3145J/(mol.K), n sto Hydrogen storage amount as the hydrogen storage tank;
step 2: determining an energy management flow of the new energy hydrogen production system based on the state of charge of the energy storage battery and the equivalent state of charge of the hydrogen storage tank;
the energy management flow of the new energy hydrogen production system is as follows: the system SOC and SOHC are divided into three states of less than 0.2, between 0.2 and 0.8 and more than 0.8, the operation interval of the system is judged according to the SOHC and the SOC state, and then hydrogen production power, lithium battery charge and discharge and wind-solar output under each state are controlled, wherein the hydrogen production power is used as a main target as large as possible;
step 3: selecting the hydrogen production amount, the new energy consumption rate and the charging and discharging times of the energy storage battery of the new energy hydrogen production system as optimization indexes of the system, and establishing a mathematical model of multi-target energy management of the new energy hydrogen production system under the condition of meeting the operation constraint condition;
the optimizing indexes of the multi-target energy management of the new energy hydrogen production system are respectively as follows:
(1) Hydrogen production amount
Wherein P is the electrolysis power input into the electrolytic tank, C 0 Is the number of coulombs per unit, V m Is the molar volume of gas in a standard state, V is the voltage of the accessed direct current, and is in units of volts, N A Representing the Avgalileo constant, and Vh is hydrogen production;
and (3) unitizing the hydrogen production amount:
wherein X is 1 Represents the hydrogen production amount after unitization, P hm Represents hydrogen production power, P hmmax Representing the maximum hydrogen production power;
(2) New energy consumption rate
Wherein P' pv And P' wt Respectively representing the output of an actual wind turbine generator set and a photovoltaic array, P pv And P wt Respectively representing the maximum power generation of the wind turbine generator and the photovoltaic array;
(3) The number of times of charging and discharging the energy storage battery
Introducing charge-discharge factor X of energy storage battery 3 For expressing the charge and discharge times of the energy storage battery, judging whether the charge and discharge state of the energy storage battery is changed in each optimization operation, and if not, charging and discharging the energy storage battery by a factor X 3 Is set to 1, if the energy storage battery is changed, the charge and discharge factor X of the energy storage battery is calculated 3 Setting to 0.8;
constraint conditions of the mathematical model of the multi-target energy management of the new energy hydrogen production system comprise:
(1) System electric power constraint
P pv (t)+P wt (t)≥P bt (t)+P hm (t)
Wherein P is pv (t) represents the theoretical power which can be generated by the photovoltaic array at the moment t, P wt (t) represents the theoretical power which can be generated by the wind turbine generator at the moment t, P bt (t) represents the charge and discharge power of the energy storage battery at the moment t, the positive charge and the negative discharge, P hm (t) represents the electrolysis power input into the electrolyzer at time t;
(2) Power limitation of wind turbine generator and photovoltaic array
0≤P pv (t)≤P pvmax
0≤P wt (t)≤P wtmax
Wherein P is pvmax 、P wtmax Respectively representing the maximum power of the photovoltaic array and the wind turbine generator;
(3) Capacity limitation of energy storage battery
SOC min ≤SOC(t)≤SOC max
Wherein SOC is min And SOC (System on chip) max Respectively representing the minimum charge state and the maximum charge state of the energy storage battery, wherein SOC (t) represents the charge state of the energy storage battery at the moment t;
(4) Charge and discharge power limitation of energy storage battery
-P btmax ≤P bt (t)≤P btmax
Wherein P is btmax Representing the maximum charge-discharge power of the energy storage battery, P when the energy storage battery is charged bt (t) >0, P when the energy storage battery is discharged bt (t)≤0；
(5) Power limitation of electrolytic cells
P hmmin ≤P hm (t)≤P hmmax
Wherein P is hm (t) represents the electrolysis power input into the electrolyzer at time t, P hmmin Represents the minimum electrolytic power, P hmmax Indicating maximum electrolytic power
Step 4: adding a penalty factor to the state of charge of the energy storage battery, and improving the energy management flow;
the penalty factor for the state of charge of the energy storage battery is expressed as:
when SOC is<SOC min When (1):
X 4 ＝5*(SOC min -SOC)
when SOC is>SOC max When (1):
X 4 ＝5*(SOC-SOC max )
when SOC is min ≤SOC≤SOC max When (1):
X 4 ＝0
wherein X is 4 Represents penalty factors for not operating at SOC min ～SOC max Punishment is carried out on the state of the interval, so that the optimal running state is judged according to the value of the fitness function added with the penalty factor
Step 5: determining multi-objective optimization weights based on an improved radar algorithm, and establishing an adaptability function;
the steps for determining the multi-target optimization weight based on the improved radar algorithm are as follows: sequencing the relative importance of the optimization index in the step (3) and the penalty factor in the step (4) according to a sequence relation analysis method, determining the ratio of the importance degree of the optimization index according to the sequencing, and calculating the weight of each optimization index; and (3) making a radar map objective function image, solving the radar map objective function image area S and the perimeter L, and calculating an objective function:
F＝S*L
wherein F represents an objective function
Step 6: solving an optimal operation scheme of the new energy hydrogen production system by adopting an improved particle swarm algorithm;
the improved particle swarm algorithm is characterized in that dynamic inertia weight w is added in the speed updating process of the basic particle swarm algorithm, particle speed and particle position are initialized firstly, particle fitness value calculation is carried out, individual extremum and population extremum are found, and then the global optimal solution of the objective function is obtained continuously and iteratively; wherein, the calculation formula of the dynamic inertia weight w is as follows:
in the formula, K is the current iteration number, and K is the maximum iteration number.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211221420.3A CN116307021B (en) | 2022-10-08 | 2022-10-08 | Multi-target energy management method of new energy hydrogen production system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211221420.3A CN116307021B (en) | 2022-10-08 | 2022-10-08 | Multi-target energy management method of new energy hydrogen production system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116307021A CN116307021A (en) | 2023-06-23 |
| CN116307021B true CN116307021B (en) | 2024-03-22 |
Family
ID=86787515
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211221420.3A Active CN116307021B (en) | 2022-10-08 | 2022-10-08 | Multi-target energy management method of new energy hydrogen production system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116307021B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116697567A (en) * | 2023-07-07 | 2023-09-05 | 广东技术师范大学 | Energy-saving optimal control method and device for central air conditioner water system |
Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103337001A (en) * | 2013-07-18 | 2013-10-02 | 山东大学 | Wind farm energy storage capacity optimization method in consideration of optimal desired output and charge state |
| WO2017161785A1 (en) * | 2016-03-23 | 2017-09-28 | 严利容 | Method for controlling stable photovoltaic power output based on energy storage running state |
| WO2018072351A1 (en) * | 2016-10-20 | 2018-04-26 | 北京工业大学 | Method for optimizing support vector machine on basis of particle swarm optimization algorithm |
| CN110190628A (en) * | 2019-06-12 | 2019-08-30 | 西南交通大学 | A kind of full Parallel AT tractive power supply system of multiple line based on comprehensive energy and method |
| KR102049020B1 (en) * | 2018-06-21 | 2019-11-27 | (주)지필로스 | Power control apparatus using wind power for water electrolysis device and hydrogen generation system based on wind power |
| JP2020198686A (en) * | 2019-05-31 | 2020-12-10 | 東芝エネルギーシステムズ株式会社 | Control apparatus for hydrogen production system and control method for hydrogen production system |
| CN113489004A (en) * | 2021-07-20 | 2021-10-08 | 中国人民解放军陆军勤务学院 | Optimization method for economic operation of multi-energy power supply system |
| AU2021107028A4 (en) * | 2021-03-15 | 2021-12-02 | Hebei University Of Science & Technology | Method and system for optimal energy allocation of a hydrogen production station |
| CN114186467A (en) * | 2021-12-08 | 2022-03-15 | 中国科学院广州能源研究所 | Multi-objective optimization method for offshore wind power hydrogen production and energy storage system |
| CN114243791A (en) * | 2022-01-19 | 2022-03-25 | 中国电力科学研究院有限公司 | Multi-objective optimization configuration method, system and storage medium for wind-solar-hydrogen storage system |
| CN114595619A (en) * | 2021-11-12 | 2022-06-07 | 国网河南省电力公司焦作供电公司 | Comprehensive energy system optimization method based on hydrogen energy storage |
| CN114696362A (en) * | 2022-04-22 | 2022-07-01 | 国网浙江省电力有限公司金华供电公司 | Power distribution network operation optimization method containing hydrogen production-storage-hydrogenation station |
| CN114861546A (en) * | 2022-05-20 | 2022-08-05 | 华北电力大学 | Electric heating hydrogen multi-energy complementary scheduling method and system based on hydrogen-fired gas turbine |
| CN114938012A (en) * | 2022-04-20 | 2022-08-23 | 中国电力科学研究院有限公司 | Hydrogen-electrochemical energy storage system based on power fluctuation and power allocation method |
| CN114977247A (en) * | 2022-05-16 | 2022-08-30 | 燕山大学 | Particle swarm algorithm applied to energy routing management and used for expanding time axis |
| CN114997544A (en) * | 2022-08-04 | 2022-09-02 | 北京理工大学 | Method and system for optimizing and configuring capacity of hydrogen optical storage charging station |
| CN115115130A (en) * | 2022-07-19 | 2022-09-27 | 中国电建集团西北勘测设计研究院有限公司 | Wind-solar energy storage hydrogen production system day-ahead scheduling method based on simulated annealing algorithm |
| CN115130785A (en) * | 2022-08-10 | 2022-09-30 | 南京工程学院 | Capacity optimization configuration method for renewable energy hydrogen production system |
-
2022
- 2022-10-08 CN CN202211221420.3A patent/CN116307021B/en active Active
Patent Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103337001A (en) * | 2013-07-18 | 2013-10-02 | 山东大学 | Wind farm energy storage capacity optimization method in consideration of optimal desired output and charge state |
| WO2017161785A1 (en) * | 2016-03-23 | 2017-09-28 | 严利容 | Method for controlling stable photovoltaic power output based on energy storage running state |
| WO2018072351A1 (en) * | 2016-10-20 | 2018-04-26 | 北京工业大学 | Method for optimizing support vector machine on basis of particle swarm optimization algorithm |
| KR102049020B1 (en) * | 2018-06-21 | 2019-11-27 | (주)지필로스 | Power control apparatus using wind power for water electrolysis device and hydrogen generation system based on wind power |
| JP2020198686A (en) * | 2019-05-31 | 2020-12-10 | 東芝エネルギーシステムズ株式会社 | Control apparatus for hydrogen production system and control method for hydrogen production system |
| CN110190628A (en) * | 2019-06-12 | 2019-08-30 | 西南交通大学 | A kind of full Parallel AT tractive power supply system of multiple line based on comprehensive energy and method |
| AU2021107028A4 (en) * | 2021-03-15 | 2021-12-02 | Hebei University Of Science & Technology | Method and system for optimal energy allocation of a hydrogen production station |
| CN113489004A (en) * | 2021-07-20 | 2021-10-08 | 中国人民解放军陆军勤务学院 | Optimization method for economic operation of multi-energy power supply system |
| CN114595619A (en) * | 2021-11-12 | 2022-06-07 | 国网河南省电力公司焦作供电公司 | Comprehensive energy system optimization method based on hydrogen energy storage |
| CN114186467A (en) * | 2021-12-08 | 2022-03-15 | 中国科学院广州能源研究所 | Multi-objective optimization method for offshore wind power hydrogen production and energy storage system |
| CN114243791A (en) * | 2022-01-19 | 2022-03-25 | 中国电力科学研究院有限公司 | Multi-objective optimization configuration method, system and storage medium for wind-solar-hydrogen storage system |
| CN114938012A (en) * | 2022-04-20 | 2022-08-23 | 中国电力科学研究院有限公司 | Hydrogen-electrochemical energy storage system based on power fluctuation and power allocation method |
| CN114696362A (en) * | 2022-04-22 | 2022-07-01 | 国网浙江省电力有限公司金华供电公司 | Power distribution network operation optimization method containing hydrogen production-storage-hydrogenation station |
| CN114977247A (en) * | 2022-05-16 | 2022-08-30 | 燕山大学 | Particle swarm algorithm applied to energy routing management and used for expanding time axis |
| CN114861546A (en) * | 2022-05-20 | 2022-08-05 | 华北电力大学 | Electric heating hydrogen multi-energy complementary scheduling method and system based on hydrogen-fired gas turbine |
| CN115115130A (en) * | 2022-07-19 | 2022-09-27 | 中国电建集团西北勘测设计研究院有限公司 | Wind-solar energy storage hydrogen production system day-ahead scheduling method based on simulated annealing algorithm |
| CN114997544A (en) * | 2022-08-04 | 2022-09-02 | 北京理工大学 | Method and system for optimizing and configuring capacity of hydrogen optical storage charging station |
| CN115130785A (en) * | 2022-08-10 | 2022-09-30 | 南京工程学院 | Capacity optimization configuration method for renewable energy hydrogen production system |
Non-Patent Citations (4)
| Title |
|---|
| Adaptive real-time optimal energy management strategy based on equivalent factors optimization for hybrid fuel cell system;Huizhong Gao等;INTERNATIONAL JOURNAL OF HYDROGEN ENERGY;第46卷(第5期);4329-4338 * |
| 新能源制氢系统运行控制策略研究;万志;中国优秀硕士学位论文全文数据库 工程科技Ⅰ辑(第01期);B015-67 * |
| 考虑季节特性及节点电压偏移的储能系统优化配置;吴仁光;王云葛;李凯鹏;林明河;彭家从;孙圳;宁轲;;电力科学与工程(第01期);18-24 * |
| 考虑碳排放成本的风光储多能互补系统优化运行研究;朱晔;兰贞波;隗震;刘波;冯万兴;周盛;陈轩恕;韦古强;孙辰军;张崧;;电力系统保护与控制(第10期);127-133 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116307021A (en) | 2023-06-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113394817B (en) | Multi-energy capacity optimal configuration method of wind, light, water and fire storage system | |
| CN103606969B (en) | Containing the island microgrid Optimization Scheduling of new forms of energy and desalinization load | |
| CN110544935A (en) | electric-hydrogen multi-energy complementary direct-current micro-grid coordinated scheduling method | |
| CN107274085B (en) | Optimal management method for energy storage equipment of double-electric ship | |
| CN109510234B (en) | Hybrid energy storage capacity optimal configuration method and device for micro-grid energy storage power station | |
| CN107482660B (en) | Active power distribution network energy storage configuration method based on double energy storage systems | |
| CN116307021B (en) | Multi-target energy management method of new energy hydrogen production system | |
| CN112070628B (en) | Multi-target economic dispatching method for smart power grid considering environmental factors | |
| CN111327053A (en) | Multi-source microgrid capacity optimal configuration method suitable for polar climate | |
| CN113361715B (en) | Genetic algorithm-based daily operation strategy optimization method for wind-storage hybrid power station | |
| CN111628558B (en) | System and method for optimizing energy management and capacity configuration of hybrid energy storage system | |
| CN104616071B (en) | A kind of wind-light storage complementary power generation system Optimal Configuration Method | |
| CN115940292A (en) | Wind-containing power storage system optimal scheduling method and system based on circle search algorithm | |
| CN114583733A (en) | Echelon utilization energy storage system control method, system, equipment and storage medium | |
| CN116667325A (en) | Micro-grid-connected operation optimization scheduling method based on improved cuckoo algorithm | |
| CN115115130A (en) | Wind-solar energy storage hydrogen production system day-ahead scheduling method based on simulated annealing algorithm | |
| CN114722728A (en) | Wind-solar-energy-storage hybrid energy storage capacity optimal configuration method and system | |
| CN112269966B (en) | Communication base station virtual power plant power generation capacity measurement method considering standby demand | |
| CN113937750A (en) | Wind-solar-storage complementary distributed energy power generation system and control method thereof | |
| CN116646956A (en) | Capacity configuration method and system for wind power hydrogen production micro-grid system | |
| CN110620388A (en) | Capacity configuration method for hybrid energy storage system of power distribution network | |
| CN115796533A (en) | Virtual power plant double-layer optimization scheduling method and device considering clean energy consumption | |
| CN113555901A (en) | Hybrid energy storage capacity optimization method based on improved S-shaped function particle swarm optimization algorithm | |
| CN114039384A (en) | Source-storage coordination optimization scheduling method based on new energy consumption | |
| CN113177860A (en) | Improved ant lion algorithm-based micro-grid optimization scheduling method with electric automobile participation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |