CN114914943A - Hydrogen energy storage optimization configuration method for green port shore power system - Google Patents

Hydrogen energy storage optimization configuration method for green port shore power system Download PDF

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CN114914943A
CN114914943A CN202210589697.5A CN202210589697A CN114914943A CN 114914943 A CN114914943 A CN 114914943A CN 202210589697 A CN202210589697 A CN 202210589697A CN 114914943 A CN114914943 A CN 114914943A
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power
energy storage
energy
hydrogen
cost
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文书礼
董晊兴
朱淼
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Shanghai Jiaotong University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • C—CHEMISTRY; METALLURGY
    • C25—ELECTROLYTIC OR ELECTROPHORETIC PROCESSES; APPARATUS THEREFOR
    • C25B—ELECTROLYTIC OR ELECTROPHORETIC PROCESSES FOR THE PRODUCTION OF COMPOUNDS OR NON-METALS; APPARATUS THEREFOR
    • C25B1/00—Electrolytic production of inorganic compounds or non-metals
    • C25B1/01—Products
    • C25B1/02—Hydrogen or oxygen
    • C25B1/04—Hydrogen or oxygen by electrolysis of water
    • C—CHEMISTRY; METALLURGY
    • C25—ELECTROLYTIC OR ELECTROPHORETIC PROCESSES; APPARATUS THEREFOR
    • C25B—ELECTROLYTIC OR ELECTROPHORETIC PROCESSES FOR THE PRODUCTION OF COMPOUNDS OR NON-METALS; APPARATUS THEREFOR
    • C25B9/00—Cells or assemblies of cells; Constructional parts of cells; Assemblies of constructional parts, e.g. electrode-diaphragm assemblies; Process-related cell features
    • C25B9/60—Constructional parts of cells
    • C25B9/65—Means for supplying current; Electrode connections; Electric inter-cell connections
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin

Abstract

The invention provides a hydrogen energy storage optimal configuration method and a hydrogen energy storage optimal configuration system for a green port shore power system, which comprise the following steps: step S1: hydrogen energy is configured in the port comprehensive energy system to form a multi-energy hybrid energy storage system; step S2: establishing an optimized configuration model for a green port multi-energy system; step S3: performing optimization solution on the optimization configuration model by using a particle swarm optimization algorithm to obtain an optimal configuration scheme; the optimal configuration model is based on the hydrogen-containing energy storage multi-energy system of the green port, and the optimal configuration capacity is found for the hydrogen-containing energy storage multi-energy system of the green port according to a preset objective function and a preset constraint condition.

Description

Hydrogen energy storage optimization configuration method for green port shore power system
Technical Field
The invention relates to the field of optimal configuration of comprehensive energy systems, in particular to a hydrogen energy storage optimal configuration method for a green port shore power system.
Background
The port is used as a connection point for sea and land transportation and bears huge energy consumption and pollution emission. Among them, hydrogen energy is a promising secondary energy in this century, has good flexible regulation characteristics, can realize large-scale seasonal storage, and is a clean energy with high comprehensive performance. Therefore, the port comprehensive energy system based on hydrogen energy storage is an ideal means for low-carbon transformation, and is beneficial to improving the economic benefit and the environmental benefit of the port.
However, the conventional port energy system is relatively single in configured energy type, and the overall level of energy scheduling and interconnection is low, so that the huge daily power consumption, high electricity cost and large amount of pollution emission of the port become main factors restricting the comprehensive development of the port. With the popularization of new energy technology, particularly the application of hydrogen energy in energy systems, the port energy supply side structure can be optimized by means of the characteristics of the energy, and the economic cost and the pollution emission are reduced.
According to the invention, hydrogen energy is applied to the port comprehensive energy system, a novel green port comprehensive energy system model with various energy storage forms is established, and a hydrogen energy storage-based multi-energy system optimization configuration scheme is formed based on an intelligent optimization algorithm, so that the wind and light absorption capacity is improved, the economic cost is reduced, and the port comprehensive benefit is improved.
The Energy conversion model is established based on the Energy conversion mode, and a Day-Ahead Economic dispatch model is established to increase the absorption effect of a harbor area on renewable Energy sources, reduce the dependence of onshore equipment and an onshore fuel oil discharge system on fuel oil, and reduce the carbon emission of the onshore fuel oil system. However, the document focuses on scheduling the established energy supply structure, and a configuration scheme is not provided for the energy supply system related to the energy supply structure.
The literature researches the Optimal configuration problem of short-term seasonal power-on island-hydrogen hybrid Energy storage System, and establishes an Optimal configuration model of short-term and seasonal storage of the island type hydrogen System by taking economy as a target. However, the research object of the document is an island, and environmental indexes such as carbon emission and the like are not considered, in contrast, the invention considers the carbon emission cost in the port multi-energy system optimization configuration model, and effectively promotes the green development of the port in a form of combining new energy and an energy storage system.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a hydrogen energy storage optimal configuration method and system for a green port shore power system.
The invention provides a hydrogen energy storage optimal configuration method for a green port shore power system, which comprises the following steps:
step S1: hydrogen energy is configured in the port comprehensive energy system to form a multi-energy hybrid energy storage system;
step S2: establishing an optimized configuration model for a green port multi-energy system;
step S3: performing optimization solution on the optimization configuration model by using a particle swarm optimization algorithm to obtain an optimal configuration scheme;
the optimal configuration model is based on the hydrogen-containing energy storage multi-energy system of the green port, and the optimal configuration capacity is found for the hydrogen-containing energy storage multi-energy system of the green port according to a preset objective function and a preset constraint condition.
Preferably, the multi-energy hybrid energy storage system includes: the system comprises a wind power generation system, a photovoltaic power generation system, an electricity storage device and a hydrogen storage device;
the wind power generation system adopts:
Figure BDA0003664606140000021
wherein, W PT,t The wind power generation power is the time t; ρ is the air density; c p The wind energy conversion coefficient; v. of t The wind speed at the moment t; s is the cross section of the wind sweeping area of the wind power generation equipment;
the photovoltaic power generation system adopts:
Figure BDA0003664606140000031
wherein, P PV Representing the real-time power output by the photovoltaic cell panel; v N And I PV Rated voltage and rated current of the photovoltaic cell panel respectively; p VN Rated power for the photovoltaic panel, K T Is a temperature coefficient, T n Is a standard temperature, G n Is the standard irradiance, T e 、G e The working environment temperature and the radiation illumination of the photovoltaic cell panel at the moment are obtained;
the electricity storage device adopts:
Figure BDA0003664606140000032
wherein: delta is the self-leakage rate of the power storage system; e ehc (t)、E ehc (t-1) the residual electric quantity of the power storage system at the time t and the time t-1 respectively; eta char 、η dis The charging and discharging efficiencies of the battery, respectively; p char (t)、P dis (t) total power of charging and discharging of the power storage system in the tth time period respectively; Δ t is the metering period;
the hydrogen storage device adopts:
firstly, the water electrolysis hydrogen production device converts electric energy into hydrogen energy to be stored in a hydrogen storage tank, and the hydrogen energy is expressed as follows:
Figure BDA0003664606140000033
wherein, P t H And
Figure BDA0003664606140000034
output power and input power, eta, of the water electrolysis hydrogen production device at the moment t H Conversion efficiency for hydrogen production by water electrolysis;
the hydrogen storage level of the hydrogen storage tank is
Figure BDA0003664606140000035
The calculation formula is as follows:
Figure BDA0003664606140000036
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003664606140000037
is t 0 The storage capacity of hydrogen at any moment;
Figure BDA0003664606140000038
the amount of hydrogen produced at time t;
Figure BDA0003664606140000039
the consumption of hydrogen at time t.
Preferably, the objective function in the optimized configuration model adopts:
Figure BDA0003664606140000041
wherein T is the total scheduling time of the scheme; cost min Cost to minimum, Cost grid The electricity purchasing cost for the power grid; p is t GRID Trading the electric quantity for the power grid at the moment t; price t The corresponding electricity purchase price at the time t; cost e Cost for new energy equipment maintenance;
Figure BDA0003664606140000042
Figure BDA0003664606140000043
respectively output at t moment of the electrolytic hydrogen production device, the wind power generation device and the photovoltaic power generation device, and gamma is H 、γ WT 、γ PV The cost coefficients of the operation and maintenance costs of the electrolytic hydrogen production device, the wind power generation device and the photovoltaic power generation device are respectively; cost cut The cost of abandoning light for abandoning wind; p WT,f,t 、P WT,r,t Respectively predicting wind power output and actual output, lambda, at t moment of the fan WT Cost coefficient for waste wind; p is PV,f,t 、P PV,r,t Predicted output and actual output lambda of photovoltaic power at t moment PV Cost coefficient of waste light; cost ehc For the cost of the energy storage device, r and n are depreciation rate and depreciation age limit;
Figure BDA0003664606140000044
charging and discharging power and loss electric quantity for the energy storage device at the moment t; alpha is alpha ehc Is a cost per capacity coefficient; mu.s ehc A cost factor for operation and maintenance; theta ehc Is a loss energy cost factor; cost C C is the carbon emission cost processing coefficient per unit of electricity;
preferably, the constraint conditions in the optimized configuration model adopt:
Figure BDA0003664606140000045
wherein, P L,t Is the electrical load at time t;
Figure BDA0003664606140000046
the maximum output value of the power grid is obtained; p t H Represents the output power, eta, of the water electrolysis hydrogen production device at the time t H Represents the conversion efficiency, P, of hydrogen production by electrolysis of water H,max The maximum working power of the hydrogen storage tank;
Figure BDA0003664606140000051
the maximum value of the charging and discharging power of the energy storage device is obtained; s. the min 、S max Respectively planning the upper limit boundary value and the lower limit boundary value of the capacity for energy storage; s is the rated capacity of energy storage;
Figure BDA0003664606140000052
the energy storage capacity is respectively the minimum value and the maximum value of the energy storage capacity which are met during the energy storage charging and discharging power under the rated energy storage capacity;
Figure BDA0003664606140000053
respectively representing the energy storage capacity at the moment t-1 and the moment t-24;
Figure BDA0003664606140000054
the energy storage system is complementary constraint of energy storage, the unification of energy storage states is limited, and the energy storage system can only be in a charging or discharging state at the same time;
Figure BDA0003664606140000055
representing the discharge power of the energy storage system at time t,
Figure BDA0003664606140000056
represents the charging power of the energy storage system,
Figure BDA0003664606140000057
indicating that the energy storage capacity is restored to the initial value after the energy storage operation is carried out for 24 hours.
Preferably, the step S3 adopts:
step S3.1: initializing particle swarm optimization variables, including time-sharing variables such as power grid output power, energy storage charge-discharge power, new energy output power, shore power exchange power and the like, and determining the capacity of a power storage device and a hydrogen storage device, the upper and lower boundary values of charge-discharge power, the upper and lower boundary values of shore power system power exchange and the maximum value of power grid output;
step S3.2: randomly giving the position and the speed of each particle, and setting a target function;
step S3.3: obtaining an individual optimal finger based on an objective function, and obtaining a global optimal value from all the individual optimal values;
step S3.4: and judging whether the objective function value reaches the optimum, if not, repeatedly triggering the step S3.2 to the step S3.3 until the objective function value reaches the optimum, outputting the optimum objective function, and determining the optimum configuration capacity of the multi-energy system.
The invention provides a hydrogen energy storage optimal configuration system for a green port shore power system, which comprises:
module M1: hydrogen energy is configured in the port comprehensive energy system to form a multi-energy hybrid energy storage system;
module M2: establishing an optimized configuration model for a green port multi-energy system;
module M3: performing optimization solution on the optimization configuration model by using a particle swarm optimization algorithm to obtain an optimal configuration scheme;
the optimal configuration model is based on the hydrogen-containing energy storage multi-energy system of the green port, and the optimal configuration capacity is found for the hydrogen-containing energy storage multi-energy system of the green port according to a preset objective function and a preset constraint condition.
Preferably, the multi-energy hybrid energy storage system includes: the system comprises a wind power generation system, a photovoltaic power generation system, an electricity storage device and a hydrogen storage device;
the wind power generation system adopts:
Figure BDA0003664606140000058
wherein, W PT,t The wind power generation power is the time t; ρ is the air density; c p For rotating wind energyChanging the coefficient; v. of t The wind speed at the moment t; s is the cross section of the wind sweeping area of the wind power generation equipment;
the photovoltaic power generation system adopts:
Figure BDA0003664606140000061
wherein, P PV Representing the real-time power output by the photovoltaic cell panel; v N And I PV Rated voltage and rated current of the photovoltaic cell panel respectively; p VN Rated power, K, of the photovoltaic cell panel T Is a temperature coefficient, T n Is a standard temperature, G n As standard irradiance, T e 、G e The working environment temperature and the radiation illumination of the photovoltaic cell panel at the moment are obtained;
the electricity storage device adopts:
Figure BDA0003664606140000062
wherein: delta is the self-leakage rate of the power storage system; e ehc (t)、E ehc (t-1) the residual electric quantity of the power storage system at the time t and the time t-1 respectively; eta char 、η dis The charging and discharging efficiencies of the battery, respectively; p is char (t)、P dis (t) total power of charging and discharging of the power storage system in the tth time period respectively; Δ t is the metering period;
the hydrogen storage device adopts:
firstly, the water electrolysis hydrogen production device converts electric energy into hydrogen energy to be stored in a hydrogen storage tank, and the hydrogen energy is expressed as follows:
Figure BDA0003664606140000063
wherein, P t H And
Figure BDA0003664606140000064
the output power of the water electrolysis hydrogen production device at the time t is respectivelyInput power, η H Conversion efficiency for hydrogen production by water electrolysis;
the hydrogen storage level of the hydrogen storage tank is
Figure BDA0003664606140000065
The calculation formula is as follows:
Figure BDA0003664606140000066
wherein the content of the first and second substances,
Figure BDA0003664606140000067
is t 0 The storage capacity of hydrogen at any moment;
Figure BDA0003664606140000068
the amount of hydrogen produced at time t;
Figure BDA0003664606140000069
the consumption of hydrogen at time t.
Preferably, the objective function in the optimized configuration model adopts:
Figure BDA0003664606140000071
wherein T is the total scheduling time of the scheme; cost min Cost to a minimum grid The electricity purchasing cost for the power grid; p t GRID Trading the electric quantity for the power grid at the moment t; price t The corresponding electricity purchase price at the time t; cost e Cost for new energy equipment maintenance;
Figure BDA0003664606140000072
Figure BDA0003664606140000073
respectively output at t moment of the electrolytic hydrogen production device, the wind power generation device and the photovoltaic power generation device, and gamma is H 、γ WT 、γ PV Respectively an electrolytic hydrogen production device and windThe cost coefficient of the operation and maintenance costs of the power generation device and the photovoltaic power generation device; cost cut The cost of abandoning light for abandoning wind; p WT,f,t 、P WT,r,t Respectively predicting wind power output and actual output, lambda, at t moment of the fan WT The cost coefficient of the waste wind is obtained; p PV,f,t 、P PV,r,t Predicted output and actual output lambda of photovoltaic power at t moment PV Cost coefficient of waste light; cost ehc For the cost of the energy storage device, r and n are depreciation rate and depreciation age limit;
Figure BDA0003664606140000074
charging and discharging power and loss electric quantity for the energy storage device at the moment t; alpha is alpha ehc Is a cost per capacity coefficient; mu.s ehc A cost factor for operation and maintenance; theta ehc Is a loss energy cost factor; cost C C is the carbon emission cost processing coefficient per unit of electricity;
preferably, the constraint conditions in the optimized configuration model adopt:
Figure BDA0003664606140000075
wherein, P L,t Is the electrical load at time t;
Figure BDA0003664606140000076
the maximum output value of the power grid is obtained; p t H Represents the output power, eta, of the water electrolysis hydrogen production device at the time t H Represents the conversion efficiency, P, of hydrogen production by electrolysis of water H,max The maximum working power of the hydrogen storage tank;
Figure BDA0003664606140000081
the maximum value of the charging and discharging power of the energy storage device is obtained; s min 、S max Respectively planning the upper limit boundary value and the lower limit boundary value of the capacity for energy storage; s is the rated capacity of energy storage;
Figure BDA0003664606140000082
respectively meets the requirements of energy storage charging and discharging power under rated energy storage capacityMinimum and maximum energy storage capacity of (a);
Figure BDA0003664606140000083
respectively representing the energy storage capacity at the time t-1 and the time t-24;
Figure BDA0003664606140000084
the energy storage system is complementary constraint of energy storage, the unification of energy storage states is limited, and the energy storage system can only be in a charging or discharging state at the same time;
Figure BDA0003664606140000085
representing the discharge power of the energy storage system at time t,
Figure BDA0003664606140000086
represents the charging power of the energy storage system,
Figure BDA0003664606140000087
indicating that the energy storage capacity is restored to the initial value after the energy storage operation is carried out for 24 hours.
Preferably, the module M3 employs:
module M3.1: initializing particle swarm optimization variables, including time-sharing variables such as power grid output power, energy storage charge-discharge power, new energy output power, shore power exchange power and the like, and determining the capacity of a power storage device and a hydrogen storage device, the upper and lower boundary values of charge-discharge power, the upper and lower boundary values of shore power system power exchange and the maximum value of power grid output;
module M3.2: randomly giving the position and the speed of each particle, and setting a target function;
module M3.3: obtaining an individual optimal finger based on an objective function, and obtaining a global optimal value from all the individual optimal values;
module M3.4: and judging whether the target function value reaches the optimum, if not, repeatedly triggering the module M3.2 to the module M3.3 until the target function value reaches the optimum, outputting the optimum target function, and determining the optimum configuration capacity of the multi-energy system.
Compared with the prior art, the invention has the following beneficial effects:
1. by means of combining an energy storage technology, wind and light output fluctuation is effectively inhibited, the wind and light utilization rate is improved, and the technical effects of wind and light abandoning cost is reduced;
2. by adopting the optimal configuration scheme of electricity-hydrogen hybrid energy storage, the energy utilization rate of the system is effectively improved, and the technical effects of enhancing the diversity of an energy supply structure and enhancing the overall economy of a port energy system are achieved;
3. by means of a time-of-use electricity price adjusting mechanism and considering energy supply and demand balance, the technical effects of peak clipping and valley filling of the system and reducing electricity purchasing cost are achieved;
4. the technical effects of reducing carbon emission and promoting the benign development of green ports are realized through the technical characteristics of combining the new energy technology and the hydrogen energy storage system technology.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
fig. 1 is a structure diagram of a green harbor multi-energy system.
FIG. 2 is a schematic diagram of particle swarm optimization.
FIG. 3 shows the output power generated by the new energy.
Fig. 4 shows the power output of the power grid and shore power.
Fig. 5 shows energy storage charge and discharge power.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
Example 1
The invention provides a hydrogen energy storage optimal configuration method for a green port shore power system, which comprises the following steps:
step S1: hydrogen energy is configured in the port comprehensive energy system to form a multi-energy hybrid energy storage system;
step S2: an optimized configuration model is established for the green port multi-energy system, and the influence of factors such as port energy supply, demand characteristics and time-of-use electricity price is comprehensively considered, so that the effective utilization of hydrogen energy storage in the port energy system is realized;
step S3: performing optimization solution on the optimization configuration model by using a particle swarm optimization algorithm to obtain an optimal configuration scheme;
the optimization configuration model is established on the basis of the green port hydrogen-containing energy storage multi-energy system and comprises an economic cost objective function and various constraint conditions, wherein the economic cost objective function comprises power grid electricity purchasing cost, new energy equipment maintenance cost, wind and light abandoning cost, energy storage cost and carbon emission cost, and the constraint conditions comprise power balance constraint, energy storage constraint, public power grid constraint and new energy system constraint. The method has the function of finding the optimal configuration capacity for the hydrogen-containing energy storage green port multi-energy system. And finally, the economic cost of each part of the model can be effectively evaluated by using economic indexes.
Compared with single hydrogen energy storage or electricity energy storage, the electric-hydrogen hybrid energy storage provided by the invention saves energy storage capacity, has better economy, enriches the types and structures of energy supply, and combines a hybrid energy storage system with a new energy technology and a time-of-use electricity price mechanism, thereby improving economic benefits and environmental benefits. Therefore, the beneficial effects of the invention on systematicness and integrity are obviously improved.
Specifically, the multi-energy hybrid energy storage system comprises: the system comprises a wind power generation system, a photovoltaic power generation system, an electricity storage device and a hydrogen storage device;
the wind power generation system adopts: the wind power generation power specifically refers to wind energy flowing through the cross section of the swept wind in the vertical direction in unit time. The wind power generation power is related to relevant parameters such as air density, wind energy utilization degree, the cross section area of power generation equipment and the like, and is in a nonlinear relation with the ambient wind speed;
Figure BDA0003664606140000101
wherein, W PT,t The wind power generation power is the time t; ρ is the air density; c p The wind energy conversion coefficient; v. of t The wind speed at the moment t; s is the cross section of the wind sweeping area of the wind power generation equipment;
the photovoltaic power generation system adopts:
Figure BDA0003664606140000102
wherein, P PV Representing the real-time power output by the photovoltaic cell panel; v N And I PV Rated voltage and rated current of the photovoltaic cell panel respectively; p VN Rated power for the photovoltaic panel, K T Is a temperature coefficient, T n Is a standard temperature, G n Is the standard irradiance, T e 、G e The working environment temperature and the radiation illuminance of the photovoltaic cell panel at the moment are obtained;
the electricity storage device adopts:
Figure BDA0003664606140000103
wherein: delta is the self-leakage rate of the power storage system; e ehc (t)、E ehc (t-1) the residual electric quantity of the power storage system at the time t and the time t-1 respectively; eta char 、η dis The charging and discharging efficiencies of the battery, respectively; p char (t)、P dis (t) total power of charging and discharging of the power storage system in the tth time period respectively; Δ t is the metering period;
the hydrogen storage device adopts:
firstly, the water electrolysis hydrogen production device converts electric energy into hydrogen energy to be stored in a hydrogen storage tank, and the hydrogen energy is expressed as follows:
Figure BDA0003664606140000104
wherein, P t H And
Figure BDA0003664606140000105
output power and input power, eta, of the water electrolysis hydrogen production device at the moment t H Conversion efficiency for hydrogen production by water electrolysis;
the hydrogen storage level of the hydrogen storage tank is
Figure BDA0003664606140000106
The calculation formula is as follows:
Figure BDA0003664606140000107
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003664606140000111
is t 0 The storage capacity of hydrogen at any moment;
Figure BDA0003664606140000112
the amount of hydrogen produced at time t;
Figure BDA0003664606140000113
the consumption of hydrogen at time t.
Specifically, according to the requirement characteristics of the typical daily load of the port, an economic objective function comprehensively considering the power grid electricity purchasing cost, the new energy equipment maintenance cost, the wind and light abandoning cost, the energy storage cost and the carbon emission cost is constructed, so that the optimal configuration of the multi-energy system is realized, and the economy of the port comprehensive energy system is improved;
specifically, the objective function in the optimized configuration model adopts:
Figure BDA0003664606140000114
wherein the content of the first and second substances,t is the total scheduling time of the scheme; cost min Cost to a minimum grid The electricity purchasing cost for the power grid; p t GRID Trading the electric quantity for the power grid at the moment t; price t The corresponding electricity purchase price at the time t; cost e Cost for new energy equipment maintenance;
Figure BDA0003664606140000115
Figure BDA0003664606140000116
respectively output at t moment of the electrolytic hydrogen production device, the wind power generation device and the photovoltaic power generation device, and gamma is H 、γ WT 、γ PV The cost coefficients of the operation and maintenance costs of the electrolytic hydrogen production device, the wind power generation device and the photovoltaic power generation device are respectively; cost cut The cost of abandoning light for abandoning wind; p is WT,f,t 、P WT,r,t Respectively predicting wind power output and actual output, lambda, at t moment of the fan WT The cost coefficient of the waste wind is obtained; p PV,f,t 、P PV,r,t Predicted output and actual output lambda of photovoltaic power at t moment PV Cost coefficient of waste light; cost ehc For the cost of the energy storage device, r and n are depreciation rate and depreciation age limit;
Figure BDA0003664606140000117
charging and discharging power and loss electric quantity for the energy storage device at the moment t; alpha (alpha) ("alpha") ehc Is a cost per capacity coefficient; mu.s ehc A cost factor for operation and maintenance; theta ehc Cost factor for lost energy; cost C C is the carbon emission cost processing coefficient per unit of electricity;
specifically, the constraint conditions in the optimized configuration model adopt:
Figure BDA0003664606140000121
wherein, P L,t Is the electrical load at time t;
Figure BDA0003664606140000122
the maximum output value of the power grid is obtained; p t H Represents the output power, eta, of the water electrolysis hydrogen production device at the time t H Represents the conversion efficiency, P, of hydrogen production by electrolysis of water H,max The maximum working power of the hydrogen storage tank;
Figure BDA0003664606140000123
the maximum value of the charging and discharging power of the energy storage device is obtained; s min 、S max Respectively planning the upper limit boundary value and the lower limit boundary value of the capacity for energy storage; s is the rated capacity of energy storage;
Figure BDA0003664606140000124
the energy storage capacity is respectively the minimum value and the maximum value of the energy storage capacity which are met during the energy storage charging and discharging power under the rated energy storage capacity;
Figure BDA0003664606140000125
respectively representing the energy storage capacity at the time t-1 and the time t-24;
Figure BDA0003664606140000126
the energy storage system is complementary constraint of energy storage, the unification of energy storage states is limited, and the energy storage system can only be in a charging or discharging state at the same time;
Figure BDA0003664606140000127
representing the discharge power of the energy storage system at time t,
Figure BDA0003664606140000128
represents the charging power of the energy storage system,
Figure BDA0003664606140000129
indicating that the energy storage capacity is restored to the initial value after the energy storage operation is carried out for 24 hours.
Specifically, the Particle Swarm Optimization (PSO) is a stochastic search algorithm based on the bionics theory, which simulates the predation behavior of a bird Swarm. In the algorithm, the particles in each space are potential optimal solutions, correspond to a fitness, can be evaluated by a fitness function, namely an objective function, and have a current position value and a current speed value. In the overall process of the PSO algorithm, a group of particles with proper size is randomly generated, and then iterative search is carried out according to a certain iteration number. Each particle has two aspects of guidance, namely the historical optimal solution of each particle and the global optimal solution of all the particles. Under the direction of the two optimal solutions, each particle continuously updates its own velocity and position until the optimal solution is found. Wherein, the speed and position updating is determined by the inertia weight, the individual current optimal solution and the global current optimal solution, and the expression is as follows:
Figure BDA00036646061400001210
wherein, c 1 、c 2 A factor representing the particle learning process, namely the acceleration constant; r is 1 、r 2 Represented is a random number, which ranges from 0,1]The goal is to increase the stochastic nature of particle flight; w denotes the inertial weight, v ij (t+1)、v ij (t) denotes the velocity of the particle at time t +1 and time t, x ij (t+1)、x ij (t) indicates the position of the particle at time t +1 and time t; p is a radical of ij (t) represents the optimal position of the individual particle from the search, p gi (t) represents the overall optimum position found by the particle group from the time of search.
Therefore, the algorithm can integrate the guidance of global optimization particles and individual optimization particles, not only refers to the self experience of the particle individuals, but also combines the historical information of particle groups, balances the capability relationship between local search and global optimization, and can effectively find the optimal solution. In the scheme, a solution is provided for the optimal configuration of the green port multi-energy system based on a particle swarm optimization algorithm, and the optimization flow is shown in fig. 2.
Specifically, the step S3 employs:
step S3.1: initializing particle swarm optimization variables, including time-sharing variables such as power grid output power, energy storage charge-discharge power, new energy output power, shore power exchange power and the like, and determining the capacity of a power storage device and a hydrogen storage device, the upper and lower boundary values of charge-discharge power, the upper and lower boundary values of shore power system power exchange and the maximum value of power grid output;
step S3.2: randomly giving the position and the speed of each particle, and setting a target function, namely the target function of the economic total cost of the port multi-energy system;
step S3.3: obtaining an individual optimal finger based on an objective function, and obtaining a global optimal value from all the individual optimal values; updating the position and the speed of the particle swarm under a given constraint condition, comparing the updated economic fitness value with the historical economic fitness value, determining a better fitness value, comparing the individual optimal values and the global optimal values of all economic indexes, and updating the current economic global optimal value;
step S3.4: and judging whether the target function value is optimal or not, if not, repeatedly triggering the step S3.2 to the step S3.3 until the target function value is optimal, outputting an optimal target function, and determining the optimal configuration capacity of the multi-energy system.
According to the hydrogen energy storage optimal configuration system for the green port shore power system, as shown in fig. 1, the hydrogen energy storage optimal configuration system comprises: the electricity load demand can be supplied by a power grid, wind power and photovoltaic, and the electricity-hydrogen hybrid energy storage system can be used as a power generation source and also can be used as an electricity load under the real-time influence of the load demand and the power price of the power grid;
specifically, the method comprises the following steps:
module M1: hydrogen energy is configured in the port comprehensive energy system to form a multi-energy hybrid energy storage system;
module M2: an optimized configuration model is established for a green port multi-energy system, the influence of factors such as port energy supply, demand characteristics and time-of-use electricity price is comprehensively considered, and the effective utilization of hydrogen energy storage in the port energy system is realized;
module M3: performing optimization solution on the optimization configuration model by using a particle swarm optimization algorithm to obtain an optimal configuration scheme;
the optimization configuration model is established on the basis of the green port hydrogen-containing energy storage multi-energy system and comprises an economic cost objective function and various constraint conditions, wherein the economic cost objective function comprises power grid electricity purchasing cost, new energy equipment maintenance cost, wind and light abandoning cost, energy storage cost and carbon emission cost, and the constraint conditions comprise power balance constraint, energy storage constraint, public power grid constraint and new energy system constraint. The method has the function of finding the optimal configuration capacity for the hydrogen-containing energy storage green port multi-energy system. And finally, the economic cost of each part of the model can be effectively evaluated by using economic indexes.
Specifically, the multi-energy hybrid energy storage system comprises: the system comprises a wind power generation system, a photovoltaic power generation system, an electricity storage device and a hydrogen storage device;
the wind power generation system adopts: the wind power generation power specifically refers to wind energy flowing through the cross section of the swept wind in the vertical direction in unit time. The wind power generation power is related to relevant parameters such as air density, wind energy utilization degree, the cross section area of power generation equipment and the like, and is in a nonlinear relation with the ambient wind speed;
Figure BDA0003664606140000141
wherein, W PT,t The wind power generation power is the time t; ρ is the air density; c p The wind energy conversion coefficient; v. of t The wind speed at the moment t; s is the cross section of the wind sweeping area of the wind power generation equipment;
the photovoltaic power generation system adopts:
Figure BDA0003664606140000142
wherein, P PV Representing the real-time power output by the photovoltaic cell panel; v N And I PV Rated voltage and rated current of the photovoltaic cell panel are respectively; p VN Rated power for the photovoltaic panel, K T Is a temperature coefficient, T n Is a standard temperature, G n Is the standard irradiance, T e 、G e The working environment temperature and the radiation illumination of the photovoltaic cell panel at the moment are obtained;
the electricity storage device adopts:
Figure BDA0003664606140000143
wherein: delta is the self leakage rate of the power storage system; e ehc (t)、E ehc (t-1) the residual electric quantity of the power storage system at the time t and the time t-1 respectively; eta char 、η dis The charging and discharging efficiencies of the battery, respectively; p char (t)、P dis (t) total power of charging and discharging of the power storage system in the tth time period respectively; Δ t is the metering period;
the hydrogen storage device adopts:
firstly, the water electrolysis hydrogen production device converts electric energy into hydrogen energy to be stored in a hydrogen storage tank, and the hydrogen energy is expressed as follows:
Figure BDA0003664606140000144
wherein, P t H And
Figure BDA0003664606140000151
output power and input power, eta, of the water electrolysis hydrogen production device at the moment t H Conversion efficiency for hydrogen production by water electrolysis;
the hydrogen storage level of the hydrogen storage tank is
Figure BDA0003664606140000152
The calculation formula is as follows:
Figure BDA0003664606140000153
wherein the content of the first and second substances,
Figure BDA0003664606140000154
is t 0 Instantaneous storage of hydrogenAn amount;
Figure BDA0003664606140000155
the amount of hydrogen produced at time t;
Figure BDA0003664606140000156
the consumption of hydrogen at time t.
Specifically, according to the requirement characteristics of the typical daily load of the port, an economic objective function comprehensively considering the power grid electricity purchasing cost, the new energy equipment maintenance cost, the wind and light abandoning cost, the energy storage cost and the carbon emission cost is constructed, so that the optimal configuration of the multi-energy system is realized, and the economy of the port comprehensive energy system is improved;
specifically, the objective function in the optimized configuration model adopts:
Figure BDA0003664606140000157
wherein T is the total scheduling time of the scheme; cost min Cost to a minimum grid The electricity purchasing cost for the power grid; p t GRID Trading the electric quantity for the power grid at the moment t; price t The corresponding electricity purchase price at the time t; cost e Cost for new energy equipment maintenance;
Figure BDA0003664606140000158
Figure BDA0003664606140000159
respectively output at t moment of the electrolytic hydrogen production device, the wind power generation device and the photovoltaic power generation device, and gamma is H 、γ WT 、γ PV The cost coefficients of the operation and maintenance costs of the electrolytic hydrogen production device, the wind power generation device and the photovoltaic power generation device are respectively; cost cut The cost of abandoning light for abandoning wind; p WT,f,t 、P WT,r,t Respectively predicting wind power output and actual output, lambda, at t moment of the fan WT The cost coefficient of the waste wind is obtained; p PV,f,t 、P PV,r,t Respectively predicting output and actual output for the photovoltaic power at the moment t,λ PV cost coefficient for discarding light; cost ehc For the cost of the energy storage device, r and n are depreciation rate and depreciation age limit;
Figure BDA00036646061400001510
charging and discharging power and loss electric quantity for the energy storage device at the moment t; alpha is alpha ehc Is a unit capacity cost coefficient; mu.s ehc A cost factor for operation and maintenance; theta ehc Is a loss energy cost factor; cost C C is the carbon emission cost processing coefficient per unit of electricity;
specifically, the constraint conditions in the optimized configuration model adopt:
Figure BDA0003664606140000161
wherein, P L,t Is the electrical load at time t;
Figure BDA0003664606140000162
the maximum output value of the power grid is obtained; p t H Represents the output power, eta, of the water electrolysis hydrogen production device at the time t H Represents the conversion efficiency, P, of hydrogen production by electrolysis of water H,max The maximum working power of the hydrogen storage tank;
Figure BDA0003664606140000163
the maximum value of the charging and discharging power of the energy storage device is obtained; s min 、S max Respectively planning the upper limit boundary value and the lower limit boundary value of the capacity for energy storage; s is the rated capacity of energy storage;
Figure BDA0003664606140000164
the energy storage capacity is respectively the minimum value and the maximum value of the energy storage capacity which are met during the energy storage charging and discharging power under the rated energy storage capacity;
Figure BDA0003664606140000165
respectively representing the energy storage capacity at the moment t-1 and the moment t-24;
Figure BDA0003664606140000166
the energy storage system is complementary constraint of energy storage, the unification of energy storage states is limited, and the energy storage system can only be in a charging or discharging state at the same time;
Figure BDA0003664606140000167
representing the discharge power of the energy storage system at time t,
Figure BDA0003664606140000168
the charging power of the energy storage system is indicated,
Figure BDA0003664606140000169
indicating that the energy storage capacity is restored to the initial value after the energy storage operation is carried out for 24 hours.
Specifically, the Particle Swarm Optimization (PSO) is a stochastic search algorithm based on the bionics theory, which simulates the predation behavior of a bird Swarm. In the algorithm, the particles in each space are potential optimal solutions, correspond to a fitness, can be evaluated by a fitness function, namely an objective function, and have a current position value and a current speed value. In the overall process of the PSO algorithm, a group of particles with proper size is randomly generated, and then iterative search is carried out according to a certain iteration number. Each particle has two aspects of guidance, namely the historical optimal solution of each particle and the global optimal solution of all the particles. Under the direction of the two optimal solutions, each particle continuously updates its own velocity and position until the optimal solution is found. The speed and position updating is determined by inertia weight, an individual current optimal solution and a global current optimal solution, and the expression is as follows:
Figure BDA0003664606140000171
wherein, c 1 、c 2 A factor representing the particle learning process, namely the acceleration constant; r is a radical of hydrogen 1 、r 2 Represented is a random number, which ranges from 0,1]The aim being to increase the flight of the particlesThe random feature of (a); w denotes the inertial weight, v ij (t+1)、v ij (t) denotes the velocity of the particle at time t +1 and time t, x ij (t+1)、x ij (t) indicates the position of the particle at time t +1 and time t; p is a radical of ij (t) represents the optimal position of the individual particle from the search, p gi (t) represents the overall optimum position found by the particle group from the time of search.
Therefore, the algorithm can integrate the guidance of global optimization particles and individual optimization particles, not only refers to the self experience of the particle individuals, but also combines the historical information of particle groups, balances the capability relationship between local search and global optimization, and can effectively find the optimal solution. In the scheme, a solution is provided for the optimal configuration of the green port multi-energy system based on a particle swarm optimization algorithm, and the optimization flow is shown in fig. 2.
Specifically, the module M3 employs:
module M3.1: initializing particle swarm optimization variables, including time-sharing variables such as power grid output power, energy storage charge-discharge power, new energy output power, shore power exchange power and the like, and determining the capacity of a power storage device and a hydrogen storage device, the upper and lower boundary values of charge-discharge power, the upper and lower boundary values of shore power system power exchange and the maximum value of power grid output;
module M3.2: randomly endowing the positions and the speeds of all particles, and setting a target function, namely the target function of the economic total cost of the port multi-energy system;
module M3.3: obtaining an individual optimal finger based on an objective function, and obtaining a global optimal value from all the individual optimal values; updating the position and the speed of the particle swarm under a given constraint condition, comparing the updated economic fitness value with the historical economic fitness value to determine a better fitness value, comparing the individual optimal values and the global optimal values of all economic indexes, and updating the current economic global optimal value;
module M3.4: and judging whether the target function value reaches the optimum, if not, repeatedly triggering the module M3.2 to the module M3.3 until the target function value reaches the optimum, outputting the optimum target function, and determining the optimum configuration capacity of the multi-energy system.
Example 2
Example 2 is a preferred example of example 1
Taking the typical daily wind power output, photovoltaic output and power load demand data of a certain harbor as an example. An energy storage battery and a hydrogen storage tank are taken as energy storage planning objects, the rated power of a fan is 17.5MW, the rated power of photovoltaic is 15MW, the rated power of electrolytic hydrogen production is 7MW, the maximum charge-discharge power of an energy storage system is 5MW, and the maximum capacity of the energy storage system is 200 MW. Electricity price A time-of-use electricity price scheme is implemented, and the 'Peak-Flat-Valley' time-of-use electricity prices are 1.08 yuan/kWh, 0.73 yuan/kWh and 0.36 yuan/kWh respectively.
Aiming at peak-valley electricity prices, the hybrid energy storage device can participate in power grid dispatching in a low-storage high-generation mode so as to reduce electricity purchasing cost. During the load peak period, the stored hydrogen energy and electric energy can relieve the power supply pressure of the power grid. In the time period of abandoning the wind and abandoning the great light quantity, energy memory can preferentially absorb the scene resource, saves unnecessary energy with the form of electric energy or hydrogen energy to reduce and abandon the wind and abandon light cost, guarantee the electric energy quality.
In order to illustrate the effectiveness of the scheme, the economy of the two modes of energy storage participation configuration in the port comprehensive energy system is analyzed and compared.
Strategy 1: the port comprehensive energy system is not provided with an energy storage device;
strategy 2: and the port comprehensive energy system is provided with an electricity-hydrogen hybrid energy storage device for system optimization.
The optimization analysis result is shown in table 1, and the output of the port comprehensive energy system configured with electricity-hydrogen hybrid energy storage is shown in fig. 3, fig. 4 and fig. 5.
TABLE 1 typical day economic optimization analysis of Port Integrated energy System
Figure BDA0003664606140000181
The data in the table can show that the economic cost of hydrogen storage is better than that of electricity storage under the same condition, the economy of the electricity-hydrogen hybrid energy storage configuration scheme is optimal, and the total cost economic efficiency is improved by 19.1%. Therefore, the economic benefit and the environmental benefit of the comprehensive energy system of the green port are further improved based on the optimal configuration scheme of the electricity-hydrogen hybrid energy storage.
It is known to those skilled in the art that, in addition to implementing the system, apparatus and its various modules provided by the present invention in pure computer readable program code, the system, apparatus and its various modules provided by the present invention can be implemented in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like by completely programming the method steps. Therefore, the system, the apparatus, and the modules thereof provided by the present invention may be considered as a hardware component, and the modules included in the system, the apparatus, and the modules for implementing various programs may also be considered as structures in the hardware component; modules for performing various functions may also be considered to be both software programs for performing the methods and structures within hardware components.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.

Claims (10)

1. A hydrogen energy storage optimal configuration method for a green port shore power system is characterized by comprising the following steps:
step S1: hydrogen energy is configured in the port comprehensive energy system to form a multi-energy hybrid energy storage system;
step S2: establishing an optimized configuration model for a green port multi-energy system;
step S3: performing optimization solution on the optimization configuration model by using a particle swarm optimization algorithm to obtain an optimal configuration scheme;
the optimal configuration model is based on the hydrogen-containing energy storage multi-energy system of the green port, and the optimal configuration capacity is found for the hydrogen-containing energy storage multi-energy system of the green port according to a preset objective function and a preset constraint condition.
2. The optimal configuration method for hydrogen energy storage facing a green port shore power system as claimed in claim 1, wherein the multi-energy hybrid energy storage system comprises: the system comprises a wind power generation system, a photovoltaic power generation system, an electricity storage device and a hydrogen storage device;
the wind power generation system adopts:
Figure FDA0003664606130000011
wherein, W PT,t The wind power generation power is the time t; ρ is the air density; c p The wind energy conversion coefficient; v. of t The wind speed at the moment t; s is the cross section of the wind sweeping area of the wind power generation equipment;
the photovoltaic power generation system adopts:
Figure FDA0003664606130000012
wherein, P PV Representing the real-time power output by the photovoltaic cell panel; v N And I PV Rated voltage and rated current of the photovoltaic cell panel respectively; p VN Rated power for the photovoltaic panel, K T Is a temperature coefficient, T n Is a standard temperature, G n Is the standard irradiance, T e 、G e The working environment temperature and the radiation illumination of the photovoltaic cell panel at the moment are obtained;
the electricity storage device adopts:
Figure FDA0003664606130000013
wherein: delta is the self-leakage rate of the power storage system; e ehc (t)、E ehc (t-1) the residual electric quantity of the power storage system at the time t and the time t-1 respectively; eta char 、η dis The charging and discharging efficiencies of the battery, respectively; p char (t)、P dis (t) total power of charging and discharging of the power storage system in the tth time period respectively; Δ t is the metering period;
the hydrogen storage device adopts:
firstly, the water electrolysis hydrogen production device converts electric energy into hydrogen energy to be stored in a hydrogen storage tank, and the hydrogen energy is expressed as follows:
Figure FDA0003664606130000021
wherein, P t H And
Figure FDA0003664606130000022
output power and input power, eta, of the water electrolysis hydrogen production device at the moment t H Conversion efficiency for hydrogen production by water electrolysis;
the hydrogen storage level of the hydrogen storage tank is
Figure FDA0003664606130000023
The calculation formula is as follows:
Figure FDA0003664606130000024
wherein the content of the first and second substances,
Figure FDA0003664606130000025
is t 0 The storage capacity of hydrogen at any moment;
Figure FDA0003664606130000026
the amount of hydrogen produced at time t;
Figure FDA0003664606130000027
the consumption of hydrogen at time t.
3. The green port shore power system-oriented hydrogen energy storage optimal configuration method according to claim 1, wherein an objective function in the optimal configuration model is as follows:
Figure FDA0003664606130000028
wherein T is the total scheduling time of the scheme; cost min Cost to a minimum grid The electricity purchasing cost for the power grid; p t GRID Trading the electric quantity for the power grid at the moment t; price t The corresponding electricity purchase price at the time t is obtained; cost e Maintenance cost for new energy equipment;
Figure FDA0003664606130000029
Figure FDA00036646061300000210
respectively output at t moment of the electrolytic hydrogen production device, the wind power generation device and the photovoltaic power generation device, and gamma is H 、γ WT 、γ PV The cost coefficients of the operation and maintenance costs of the electrolytic hydrogen production device, the wind power generation device and the photovoltaic power generation device are respectively; cost cut The cost of abandoning light for abandoning wind; p WT,f,t 、P WT,r,t Respectively predicting wind power output and actual output, lambda, at t moment of the fan WT The cost coefficient of the waste wind is obtained; p PV,f,t 、P PV,r,t Predicted output and actual output lambda of photovoltaic power at t moment PV Cost coefficient of waste light; cost ehc For the cost of the energy storage device, r and n are depreciation rate and depreciation age limit; p t ehc 、
Figure FDA00036646061300000211
Charging and discharging power and loss electric quantity for the energy storage device at the moment t; alpha is alpha ehc Is a cost per capacity coefficient; mu.s ehc A cost factor for operation and maintenance; theta ehc Cost factor for lost energy; cost C And c is a carbon emission cost processing coefficient per unit electricity.
4. The green port shore power system-oriented hydrogen energy storage optimal configuration method according to claim 1, wherein the constraint conditions in the optimal configuration model are as follows:
Figure FDA0003664606130000031
wherein, P L,t Is the electrical load at time t;
Figure FDA0003664606130000032
the maximum output value of the power grid is obtained; p t H Represents the output power, eta, of the water electrolysis hydrogen production device at the time t H Represents the conversion efficiency, P, of hydrogen production by electrolysis of water H,max The maximum working power of the hydrogen storage tank;
Figure FDA0003664606130000033
the maximum value of the charging and discharging power of the energy storage device is obtained; s. the min 、S max Respectively planning the upper limit boundary value and the lower limit boundary value of the capacity for energy storage; s is the rated capacity of energy storage;
Figure FDA0003664606130000034
the energy storage capacity is respectively the minimum value and the maximum value of the energy storage capacity which are met during the energy storage charging and discharging power under the rated energy storage capacity;
Figure FDA0003664606130000035
respectively representing the energy storage capacity at the moment t-1 and the moment t-24;
Figure FDA0003664606130000036
the energy storage system is complementary constraint of energy storage, the unification of energy storage states is limited, and the energy storage system can only be in a charging or discharging state at the same time;
Figure FDA0003664606130000037
represents tThe discharging power of the energy storage system at the moment,
Figure FDA0003664606130000038
represents the charging power of the energy storage system,
Figure FDA0003664606130000039
indicating that the energy storage capacity is restored to the initial value after the energy storage operation is carried out for 24 hours.
5. The optimal configuration method for hydrogen energy storage for the green port shore power system according to claim 1, wherein the step S3 comprises:
step S3.1: initializing particle swarm optimization variables including time-sharing variables such as power grid output power, energy storage charge-discharge power, new energy output power, shore power exchange power and the like, and determining the capacity of a power storage device and a hydrogen storage device, the upper and lower boundary values of charge-discharge power, the upper and lower boundary values of shore power system power exchange and the maximum value of power grid output;
step S3.2: randomly giving the position and the speed of each particle, and setting a target function;
step S3.3: obtaining an individual optimal finger based on an objective function, and obtaining a global optimal value from all the individual optimal values;
step S3.4: and judging whether the objective function value reaches the optimum, if not, repeatedly triggering the step S3.2 to the step S3.3 until the objective function value reaches the optimum, outputting the optimum objective function, and determining the optimum configuration capacity of the multi-energy system.
6. A hydrogen energy storage optimal configuration system for a green port shore power system is characterized by comprising:
module M1: hydrogen energy is configured in the port comprehensive energy system to form a multi-energy hybrid energy storage system;
module M2: establishing an optimized configuration model for a green port multi-energy system;
module M3: performing optimization solution on the optimization configuration model by using a particle swarm optimization algorithm to obtain an optimal configuration scheme;
the optimal configuration model is based on the hydrogen-containing energy storage multi-energy system of the green port, and the optimal configuration capacity is found for the hydrogen-containing energy storage multi-energy system of the green port according to a preset objective function and a preset constraint condition.
7. The hydrogen energy storage optimal configuration system facing a green port shore power system as claimed in claim 6, wherein the multi-energy hybrid energy storage system comprises: the system comprises a wind power generation system, a photovoltaic power generation system, an electricity storage device and a hydrogen storage device;
the wind power generation system adopts:
Figure FDA0003664606130000041
wherein, W PT,t The wind power generation power is the time t; ρ is the air density; c p The wind energy conversion coefficient; v. of t The wind speed at the moment t; s is the cross section of the wind sweeping area of the wind power generation equipment;
the photovoltaic power generation system adopts:
Figure FDA0003664606130000042
wherein, P PV Representing the real-time power output by the photovoltaic cell panel; v N And I PV Rated voltage and rated current of the photovoltaic cell panel are respectively; p VN Rated power for the photovoltaic panel, K T Is a temperature coefficient, T n Is a standard temperature, G n Is the standard irradiance, T e 、G e The working environment temperature and the radiation illumination of the photovoltaic cell panel at the moment are obtained;
the electricity storage device adopts:
Figure FDA0003664606130000043
wherein: delta is the self-leakage rate of the power storage system; e ehc (t)、E ehc (t-1) the residual electric quantity of the power storage system at the time t and the time t-1 respectively; eta char 、η dis The charging and discharging efficiencies of the battery, respectively; p char (t)、P dis (t) total power of charging and discharging of the power storage system in the tth time period respectively; Δ t is the metering period;
the hydrogen storage device adopts:
firstly, the water electrolysis hydrogen production device converts electric energy into hydrogen energy to be stored in a hydrogen storage tank, and the hydrogen energy is expressed as follows:
Figure FDA0003664606130000051
wherein, P t H And
Figure FDA0003664606130000052
output power and input power, eta, of the water electrolysis hydrogen production device at the moment t H Conversion efficiency for hydrogen production by water electrolysis;
the hydrogen storage level of the hydrogen storage tank is
Figure FDA0003664606130000053
The calculation formula is as follows:
Figure FDA0003664606130000054
wherein the content of the first and second substances,
Figure FDA0003664606130000055
is t 0 The storage capacity of hydrogen at any moment;
Figure FDA0003664606130000056
the amount of hydrogen produced at time t;
Figure FDA0003664606130000057
the consumption of hydrogen at time t.
8. The green-harbor shore power system-oriented hydrogen energy storage optimization configuration system as claimed in claim 6, wherein the objective function in the optimization configuration model is as follows:
Figure FDA0003664606130000058
wherein T is the total scheduling time of the scheme; cost min Cost to a minimum grid The electricity purchasing cost for the power grid; p t GRID Trading the electric quantity for the power grid at the moment t; price t The corresponding electricity purchase price at the time t; cost e Cost for new energy equipment maintenance;
Figure FDA0003664606130000059
Figure FDA00036646061300000510
respectively output at t moment of the electrolytic hydrogen production device, the wind power generation device and the photovoltaic power generation device, and gamma is H 、γ WT 、γ PV The cost coefficients of the operation and maintenance costs of the electrolytic hydrogen production device, the wind power generation device and the photovoltaic power generation device are respectively; cost cut The cost of abandoning light for abandoning wind; p WT,f,t 、P WT,r,t Respectively predicting wind power output and actual output, lambda, at t moment of the fan WT The cost coefficient of the waste wind is obtained; p PV,f,t 、P PV,r,t Predicted output and actual output, lambda, of the photovoltaic power at time t PV Cost coefficient of waste light; cost ehc For the cost of the energy storage device, r and n are depreciation rate and depreciation age limit; p t ehc 、
Figure FDA00036646061300000511
Charging and discharging power and loss electric quantity for the energy storage device at the moment t; alpha is alpha ehc Is a unit capacity cost coefficient; mu.s ehc A cost factor for operation and maintenance; theta ehc Is a loss energy cost factor; cost C And c is a carbon emission cost processing coefficient per unit electricity.
9. The green port shore power system-oriented hydrogen energy storage optimal configuration system as claimed in claim 6, wherein the constraint conditions in the optimal configuration model are as follows:
Figure FDA0003664606130000061
wherein, P L,t Is the electrical load at time t;
Figure FDA0003664606130000062
the maximum output value of the power grid is obtained; p t H Represents the output power, eta, of the water electrolysis hydrogen production device at the time t H Represents the conversion efficiency, P, of hydrogen production by electrolysis of water H,max The maximum working power of the hydrogen storage tank;
Figure FDA0003664606130000063
the maximum value of the charging and discharging power of the energy storage device is obtained; s min 、S max Respectively planning the upper limit boundary value and the lower limit boundary value of the capacity for energy storage; s is the rated capacity of energy storage;
Figure FDA0003664606130000064
the energy storage capacity is respectively the minimum value and the maximum value of the energy storage capacity which are met during the energy storage charging and discharging power under the rated energy storage capacity;
Figure FDA0003664606130000065
respectively representing the energy storage capacity at the moment t-1 and the moment t-24;
Figure FDA0003664606130000066
the energy storage system limits the uniformity of the energy storage state for the complementary constraint of the energy storageCan only be in a charging or discharging state at the same time;
Figure FDA0003664606130000067
representing the discharge power of the energy storage system at time t,
Figure FDA0003664606130000068
represents the charging power of the energy storage system,
Figure FDA0003664606130000069
indicating that the energy storage capacity is restored to the initial value after the energy storage operation is carried out for 24 hours.
10. The hydrogen energy storage optimal configuration system facing a green port shore power system is characterized in that the module M3 adopts:
module M3.1: initializing particle swarm optimization variables, including time-sharing variables such as power grid output power, energy storage charge-discharge power, new energy output power, shore power exchange power and the like, and determining the capacity of a power storage device and a hydrogen storage device, the upper and lower boundary values of charge-discharge power, the upper and lower boundary values of shore power system power exchange and the maximum value of power grid output;
module M3.2: randomly giving the position and the speed of each particle, and setting a target function;
module M3.3: obtaining an individual optimal finger based on an objective function, and obtaining a global optimal value from all the individual optimal values;
module M3.4: and judging whether the target function value reaches the optimum, if not, repeatedly triggering the module M3.2 to the module M3.3 until the target function value reaches the optimum, outputting the optimum target function, and determining the optimum configuration capacity of the multi-energy system.
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Publication number Priority date Publication date Assignee Title
CN116632945A (en) * 2023-07-21 2023-08-22 长江三峡集团实业发展(北京)有限公司 Multi-energy supply operation control method, system and device and electronic equipment

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116632945A (en) * 2023-07-21 2023-08-22 长江三峡集团实业发展(北京)有限公司 Multi-energy supply operation control method, system and device and electronic equipment
CN116632945B (en) * 2023-07-21 2024-01-05 长江三峡集团实业发展(北京)有限公司 Multi-energy supply operation control method, system and device and electronic equipment

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