CN117077932B - Port self-consistent energy configuration method considering transportation demand and resource endowment constraint - Google Patents

Port self-consistent energy configuration method considering transportation demand and resource endowment constraint Download PDF

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CN117077932B
CN117077932B CN202310921439.7A CN202310921439A CN117077932B CN 117077932 B CN117077932 B CN 117077932B CN 202310921439 A CN202310921439 A CN 202310921439A CN 117077932 B CN117077932 B CN 117077932B
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钟鸣
李林锋
赵浩威
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Wuhan University of Technology WUT
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Abstract

The invention relates to a port self-consistent energy configuration method considering transportation demand and resource endowment constraint, which comprises the following steps: s1, calculating harbour resource endowment available abundance; s2, acquiring transportation demand data such as port throughput, daily operation plans and the like, and power data of energy equipment for a port office area and a storage yard warehouse, and predicting port peak load; s3, constructing a mixed integer programming model of port self-consistent energy equipment configuration; the mixed integer programming model comprises an objective function and constraint conditions; and S4, solving the mixed integer programming model by using a meta heuristic algorithm to obtain a configuration scheme of the port self-consistent energy equipment. According to the invention, after the self-consistent energy equipment of the port is reasonably configured, the economical efficiency and the environmental protection of the self-consistent energy application of the port can be ensured, and the clean energy can be better applied and popularized in the port.

Description

Port self-consistent energy configuration method considering transportation demand and resource endowment constraint
Technical Field
The invention relates to the field of port self-consistent energy equipment configuration, in particular to a port self-consistent energy configuration method considering transportation requirements and resource endowment constraints.
Background
Along with the proposal of the 'double-carbon strategy', the traffic energy structure is optimized, new energy application is greatly promoted, and the method is a necessary trend for realizing carbon emission reduction in the traffic field. The port is used as an important node in the transportation process, a great amount of energy is consumed in the port shore power and loading and unloading operation process, clean energy is used for meeting the energy demand of the port, the consumption of traditional energy is reduced, the port energy source is achieved to a certain extent, and the power-assisted double-carbon target is achieved.
Most of the existing researches focus on optimizing, dispatching and managing port logistics and energy sources to improve the utilization efficiency of clean energy after the clean energy is accessed to a port micro-grid, and related researches on how to reasonably configure self-consistent energy equipment in a port are few. The port self-consistent energy source refers to clean energy power generation equipment such as fans, photovoltaic panels and the like configured at the port, and the port self-consistent energy source is partially self-sufficient in realizing the port electric energy demand. When the self-consistent energy equipment configuration is carried out in the port, the natural condition of resources such as solar energy, hydroenergy and wind energy in the area where the port is located needs to be considered, the utilization rate of the clean energy power generation equipment installed in the place where the natural condition of the resources is insufficient is low, and the clean energy power generation equipment has larger economic benefit and environmental benefit only when the self-consistent energy equipment is installed in the area where the natural condition of the resources is abundant. In the prior art, only the optimal scheduling of energy sources and logistics after clean energy is accessed into a port power grid is considered, and the transportation requirements and the resource endowment constraints of the port are not considered, so that clean energy equipment of the port is reasonably configured.
Disclosure of Invention
The technical problem to be solved by the invention is to provide the port self-consistent energy configuration method considering the transportation demand and the resource endowment constraint, which is used for reasonably configuring port clean energy equipment, improving the duty ratio of clean energy in a port energy system and realizing the self-consistent clean energy part.
The technical scheme adopted for solving the technical problems is as follows: a port self-consistent energy configuration method considering transportation demand and resource endowment constraint is constructed, which comprises the following steps:
s1, calculating harbour resource endowment available abundance;
s2, acquiring transportation demand data such as port throughput, daily operation plans and the like, and power data of energy equipment for a port office area and a storage yard warehouse, and predicting port peak load;
s3, constructing a mixed integer programming model of port self-consistent energy equipment configuration; the mixed integer programming model comprises an objective function and constraint conditions;
and S4, solving the mixed integer programming model by using a meta heuristic algorithm to obtain a configuration scheme of the port self-consistent energy equipment.
According to the scheme, in the step S1, the harbour resource endowment available abundance is divided into solar energy available abundance, water energy available abundance and wind energy available abundance, and is determined by natural geographic conditions of the area where the harbour is located;
according to the above scheme, in the step S2, the port load includes a dynamic power load of shore power, a shore bridge, an electric collector card, a field bridge logistics equipment, a static power load of an office area and a storage yard warehouse area, and the change of the port load is mainly affected by the dynamic load; the transportation demand determines the simultaneous coefficient of the dynamic load of the port shore power, the shore bridge and the portal crane loading and unloading operation machinery, and the port peak load is obtained according to the simultaneous coefficient when the transportation demand is maximum.
According to the scheme, the port energy consumption peak load is expressed as:
P max ＝CF max ·P T +P′ wh ·S wh +P′ co ·S co
wherein P is max The energy peak load power is used for ports; CF (compact flash) max The system is a simultaneous coefficient of logistics equipment operation at the peak time of port freight; p (P) T The total power of all logistics equipment in the port is calculated; p'. wh And P' co The unit area power of the port yard warehouse and the office area is respectively represented; s is S wh And S is co Respectively representBuilding area of port yard warehouse and office area.
According to the above scheme, in the step S3, the mixed integer programming model includes an objective function and a constraint condition; the objective function is to maximize annual return on investment; the constraint conditions include peak load clean energy source consistency rate constraint, system reliability constraint, availability constraint, maintainability constraint, power rejection rate constraint, investment budget constraint and equipment installation space constraint.
According to the scheme, the maximum annual return on investment rate is as follows:
wherein, the ROI represents annual return on investment; price ele Representing the unit electricity price of industrial electricity; x is x i Representing the configuration quantity of the clean energy output devices i, M represents all device types of the selectable clean energy output device set, and x i ∈{0，1，2，...，N}；Ep id Representing the power generated by the clean energy output equipment i in d days; eh (Eh) id The operation time of the clean energy output equipment i in d days is represented; pc (Pc) i Representing the purchase cost of the single clean energy output equipment i; ic (Ic) i Representing the installation cost of the single clean energy output device i; mc i Representing the i-year average maintenance cost of single clean energy output equipment; y represents the total service life of the port self-consistent energy equipment;
the peak load cleaning energy is derived from the constraint of the rate of consistency:
wherein SR is as follows u The cleaning energy source consistent rate at the time of expressing port peak load; SR (SR) 0 Representing the peak load self-consistent rate minimum value of the port self-consistent energy equipment configuration;
the system reliability constraint is:
wherein R represents the integral reliability of the port self-consistent energy equipment; lambda (lambda) i Representing the fault rate of the clean energy output equipment i of the self-consistent energy equipment in the port; r is R 0 Representing the minimum reliability of the port self-consistent energy equipment;
the availability constraint is:
wherein A represents the whole availability of port self-consistent energy equipment; MTBF (methyl tert-butyl function) i Representing average fault interval time of clean energy output equipment i of the self-consistent energy equipment in the port; MTTR (methyl thiazolyl tetrazolium) i Representing the average maintenance time of the clean energy output equipment i of the self-consistent energy equipment in the port; a is that 0 Representing the minimum availability of the self-consistent energy equipment in the port;
the maintainability constraint is:
wherein M represents the average maintenance time required by the self-consistent energy equipment of the port after the failure occurs; m is M 0 Representing the maximum value of the planning average maintenance time of the port self-consistent energy equipment;
the rejection rate constraint is as follows:
wherein ABR represents average daily power rejection rate of the port self-consistent energy equipment; ep (Ep) i Representing the average power generation power of the clean energy output equipment i; eh (Eh) i The average operation hours of the clean energy output equipment i per day is represented; ABR (ABR) 0 The maximum power rejection rate of the self-consistent energy source equipment for the port;
the investment budget constraints are:
PI represents the total construction cost of the port self-consistent energy equipment; b (B) 0 Representing the planning budget maximum value of the self-consistent energy equipment of the port;
the equipment installation space constraint is as follows:
wherein SP represents the occupied area required by the construction of the port self-consistent energy equipment; s is(s) i Representing the installation space required by a single clean energy output device i, AS 0 And the maximum available area for planning and installing the self-consistent energy equipment in the port is represented.
According to the above scheme, in the step S4, the equipment is clean energy output equipment, including photovoltaic equipment, hydroelectric power generation equipment and wind power generation equipment; the configuration scheme comprises the types and the numbers of photovoltaic equipment, hydroelectric power generation equipment and wind power equipment installed in the port area.
The implementation of the port self-consistent energy configuration method considering the transportation demand and the resource endowment constraint has the following beneficial effects:
according to the method, whether the harbour has abundant wind energy, water energy and solar energy resources can be utilized is judged according to the endowment condition of the resources of the area where the harbour is located; under the condition that the harbour resource endowment available abundance reaches a certain threshold value, acquiring transportation demand data such as harbour throughput, daily operation plans and the like, and predicting peak load of harbour electric equipment; then, a mixed integer programming model for port self-consistent energy equipment configuration is established, wherein the mixed integer programming model comprises an objective function and constraint conditions, the objective function is that the return on investment of the port self-consistent energy equipment is maximized, and the constraint conditions comprise peak load clean energy source consistency rate constraint, system reliability constraint, availability constraint, maintainability constraint, power rejection rate constraint, investment budget constraint, equipment installation space constraint and natural endowment availability abundance constraint; and finally, solving the mixed integer programming model by using a meta heuristic algorithm to obtain a configuration scheme of the port self-consistent energy equipment, and reasonably planning the port self-consistent energy equipment to realize the self-consistent port energy.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
fig. 1 is a flow chart of a port self-consistent energy allocation method taking transportation demand and resource endowment constraints into consideration.
Detailed Description
For a clearer understanding of technical features, objects and effects of the present invention, a detailed description of embodiments of the present invention will be made with reference to the accompanying drawings.
As shown in fig. 1, the port self-consistent energy configuration method taking transportation demand and resource endowment constraint into consideration of the invention comprises the following steps:
s1, calculating harbour resource endowment available abundance; the harbour resource endowment available abundance is divided into solar energy available abundance, water energy available abundance and wind energy available abundance, and is determined by the natural geographic conditions of the area where the harbour is located; the total radiation quantity of the annual horizontal plane is less than 1050 kW.h/m according to the total quantity and the abundance level of solar resources in the national standard solar resource assessment method (GB/T37526-2019) 2 When the solar energy power grid is used, the solar energy utilization abundance is general and is not suitable for grid-connected power generation; wind energy availability abundance refers to wind power density class table in national standard wind farm wind energy resource assessment method (GB/T18710-2002), wind power density is less than 300W/m at a height of 50m from the ground 2 When the wind energy is not suitable for grid-connected power generation; when the harbour resource endowment available abundance is larger than a specific threshold value, the installation of clean energy output equipment such as fans, water turbines, photovoltaic panels and the like is considered.
S2, acquiring transportation demand data such as port throughput, daily operation plans and the like, and power data of energy equipment for a port office area and a storage yard warehouse, and predicting port peak load; the port load comprises dynamic power loads of logistics equipment such as shore power, shore bridges, electric collection cards, field bridges and the like, static power loads of areas such as office areas, storage yard warehouses and the like, and the change of the port load is mainly influenced by the dynamic loads; the transportation demand determines the simultaneous coefficient of dynamic loads of loading and unloading operation machines such as a port shore power, a shore bridge, a portal crane and the like, and the port peak load is obtained according to the simultaneous coefficient when the transportation demand is maximum; the peak load of port energy can be expressed as:
P max ＝CF max ·P T +P′ wh ·S wh +P′ co ·S co
wherein P is max The energy peak load power is used for ports; CF (compact flash) max The system is a simultaneous coefficient of logistics equipment operation at the peak time of port freight; p (P) T The total power of all logistics equipment in the port is calculated; p'. wh And P' co The unit area power of the port yard warehouse and the office area is respectively represented; s is S wh And S is co Respectively representing the building areas of the port yard warehouse and the office area.
S3, constructing a mixed integer programming model of port self-consistent energy equipment configuration; the mixed integer programming model comprises an objective function and constraint conditions; the objective function is to maximize annual return on investment; constraint conditions include peak load clean energy source consistent rate constraints, system reliability constraints, availability constraints, maintainability constraints, power rejection rate constraints, investment budget constraints, and equipment installation space constraints;
the maximum annual return on investment rate is as follows:
wherein, the ROI represents annual return on investment; price ele Representing the unit electricity price of industrial electricity; x is x i Representing the configuration quantity of the clean energy output devices i, M represents all device types of the selectable clean energy output device set, and x i ∈{0，1，2，...，N}；Ep id Representing the power generated by the clean energy output equipment i in d days; eh (Eh) id The operation time of the clean energy output equipment i in d days is represented; pc (Pc) i Representing a single pieceThe purchase cost of the clean energy output equipment i; ic (Ic) i Representing the installation cost of the single clean energy output device i; mc i Representing the i-year average maintenance cost of single clean energy output equipment; y represents the total service life of the port self-consistent energy equipment.
The peak load cleaning energy is derived from the rate constraint:
wherein SR is as follows u The cleaning energy source consistent rate at the time of expressing port peak load; SR (SR) 0 And the peak load self-consistent rate minimum value of the port self-consistent energy equipment configuration is represented.
The system reliability constraints are:
wherein R represents the integral reliability of the port self-consistent energy equipment; lambda (lambda) i Representing the fault rate of the clean energy output equipment i of the self-consistent energy equipment in the port; r is R 0 Representing the minimum reliability of the port self-consistent energy equipment.
The availability constraints are:
wherein A represents the whole availability of port self-consistent energy equipment; MTBF (methyl tert-butyl function) i Representing average fault interval time of clean energy output equipment i of the self-consistent energy equipment in the port; MTTR (methyl thiazolyl tetrazolium) i Representing the average maintenance time of the clean energy output equipment i of the self-consistent energy equipment in the port; a is that 0 Representing the minimum availability of the self-consistent energy equipment in the port.
The maintainability constraint is:
wherein M represents the average maintenance time required by the self-consistent energy equipment of the port after the failure occurs; m is M 0 And (5) representing the maximum value of the planning average maintenance time of the port self-consistent energy equipment.
The rejection rate constraint is:
wherein ABR represents average daily power rejection rate of the port self-consistent energy equipment; ep (Ep) i Representing the average power generation power of the clean energy output equipment i; eh (Eh) i The average operation hours of the clean energy output equipment i per day is represented; ABR (ABR) 0 The maximum power rejection rate of the self-consistent energy source equipment for the port.
The investment budget constraints are:
PI represents the total construction cost of the port self-consistent energy equipment; b (B) 0 Representing the planning budget maximum value of the self-consistent energy equipment of the port.
The equipment installation space constraints are:
wherein SP represents the occupied area required by the construction of the port self-consistent energy equipment; s is(s) i Representing the installation space required by a single clean energy output device i, AS 0 And the maximum available area for planning and installing the self-consistent energy equipment in the port is represented.
S4, solving the mixed integer programming model by using a meta heuristic algorithm to obtain a configuration scheme of the port self-consistent energy equipment; the equipment is clean energy output equipment, including photovoltaic equipment, hydroelectric power equipment and wind power equipment; the configuration scheme comprises the types and the numbers of photovoltaic equipment, hydroelectric power generation equipment and wind power equipment installed in the port area.
The embodiments of the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those having ordinary skill in the art without departing from the spirit of the present invention and the scope of the claims, which are to be protected by the present invention.

Claims (3)

1. A port self-consistent energy configuration method considering transportation demand and resource endowment constraint is characterized by comprising the following steps:
s1, calculating harbour resource endowment available abundance;
s2, acquiring port throughput, daily operation plan transportation demand data and port office area and yard warehouse energy equipment power data, and predicting port peak load;
the port load comprises a dynamic power load of shore power, a shore bridge, an electric collector card and field bridge logistics equipment, and a static power load of an office area and a storage yard warehouse area, and the change of the port load is influenced by the dynamic load; the transportation demand determines the simultaneous coefficient of the dynamic load of the port shore power, the shore bridge and the portal crane loading and unloading operation machinery, and the port peak load is obtained according to the simultaneous coefficient when the transportation demand is maximum;
the harbour peak load is expressed as:
wherein,the energy peak load power is used for ports; />The system is a simultaneous coefficient of logistics equipment operation at the peak time of port freight; />The total power of all logistics equipment in the port is calculated; />And->The unit area power of the port yard warehouse and the office area is respectively represented; />And->Respectively representing the building areas of a port yard warehouse and an office area;
s3, constructing a mixed integer programming model of port self-consistent energy equipment configuration; the mixed integer programming model comprises an objective function and constraint conditions;
the mixed integer programming model comprises an objective function and constraint conditions; the objective function is to maximize annual return on investment; the constraint conditions comprise a peak load clean energy source consistency constraint, a system reliability constraint, an availability constraint, a maintainability constraint, a power rejection rate constraint, an investment budget constraint and an equipment installation space constraint;
the maximum annual return on investment rate is as follows:
wherein,ROIrepresenting annual return on investment;representing the unit electricity price of industrial electricity; />Clean energy source output indicating equipmentConfiguration quantity of->All device types representing a selectable set of cleaning energy output devices, < >>；/>Clean energy source output indicating equipmentiAt the position ofdPower generated by the day; />Clean energy source output indicating equipmentiAt the position ofdThe running time of the day; />Representing a single clean energy output deviceiPurchasing cost; />Representing a single clean energy output deviceiInstallation cost;representing a single clean energy output deviceiAnnual average maintenance costs;Yrepresenting the total service life of the self-consistent energy equipment in the port;
the peak load cleaning energy is derived from the constraint of the rate of consistency:
wherein,the cleaning energy source consistent rate at the time of expressing port peak load; />Representing the peak load self-consistent rate minimum value of the port self-consistent energy equipment configuration;
the system reliability constraint is:
wherein,the whole reliability of the port self-consistent energy equipment is represented; />Clean energy output equipment for expressing self-consistent energy equipment in portiIs a failure rate of (1); />Representing the minimum reliability of the port self-consistent energy equipment;
the availability constraint is:
wherein,representing the overall availability of the port self-consistent energy equipment; />Clean energy output equipment for expressing self-consistent energy equipment in portiAverage fault interval time; />Clean energy output equipment for expressing self-consistent energy equipment in portiAverage maintenance time; />Representing the minimum availability of the self-consistent energy equipment in the port;
the maintainability constraint is:
wherein,Mrepresenting the average maintenance time required by the self-consistent energy equipment of the port after the failure occurs;representing the maximum value of the planning average maintenance time of the port self-consistent energy equipment;
the rejection rate constraint is as follows:
wherein,representing the average daily power rejection rate of the self-consistent energy equipment in the port; />Clean energy source output indicating equipmentiAverage power generated; />Clean energy source output indicating equipmentiAverage number of operating hours per day; />The maximum power rejection rate of the self-consistent energy source equipment for the port;
the investment budget constraints are:
wherein,representing the total construction cost of the self-consistent energy equipment of the port; />Representing the planning budget maximum value of the self-consistent energy equipment of the port;
the equipment installation space constraint is as follows:
wherein,representing the occupied area required by the construction of the self-consistent energy equipment of the port; />Representing a single clean energy output deviceiThe required installation space is->Representing the maximum available area for planning and installing the self-consistent energy equipment in the port;
and S4, solving the mixed integer programming model by using a meta heuristic algorithm to obtain a configuration scheme of the port self-consistent energy equipment.
2. The method for configuring the self-consistent energy source of the port taking transportation demand and resource endowment constraint into consideration according to claim 1, wherein in the step S1, the harbour resource endowment available abundance is divided into solar energy available abundance, water energy available abundance and wind energy available abundance, and is determined by natural geographical conditions of the area where the port is located.
3. The method for configuring self-consistent energy in port taking transportation demand and resource endowment constraint into consideration according to claim 1, wherein in the step S4, the equipment is clean energy output equipment, including photovoltaic equipment, hydroelectric power generation equipment and wind power generation equipment; the configuration scheme comprises the types and the numbers of photovoltaic equipment, hydroelectric power generation equipment and wind power equipment installed in the port area.
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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20180044561A (en) * 2016-10-24 2018-05-03 한국전기연구원 Apparatus for determining optimal capacity of energy storage system in stand alone micro-grid and method thereof
CN108122068A (en) * 2017-12-05 2018-06-05 中国电力科学研究院有限公司 A kind of power distribution network risk-averse retailer method and system
CN110322051A (en) * 2019-06-06 2019-10-11 国网浙江省电力有限公司经济技术研究院 Consider the integrated energy system Optimal Configuration Method of N-1 security constraint
US10719636B1 (en) * 2014-02-03 2020-07-21 Clean Power Research, L.L.C. Computer-implemented system and method for estimating gross energy load of a building
CN115062930A (en) * 2022-06-01 2022-09-16 深圳供电局有限公司 Power distribution network source and network load and storage coordination planning method
CN115239032A (en) * 2022-09-26 2022-10-25 华北电力大学 Highway service area microgrid planning method and system considering energy self-consistency rate
CN115293697A (en) * 2022-08-05 2022-11-04 河海大学 Port comprehensive energy system and container logistics system cooperative scheduling method

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110071952A1 (en) * 2009-09-18 2011-03-24 Gaffney Michael P System and method of optimizing resource consumption

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10719636B1 (en) * 2014-02-03 2020-07-21 Clean Power Research, L.L.C. Computer-implemented system and method for estimating gross energy load of a building
KR20180044561A (en) * 2016-10-24 2018-05-03 한국전기연구원 Apparatus for determining optimal capacity of energy storage system in stand alone micro-grid and method thereof
CN108122068A (en) * 2017-12-05 2018-06-05 中国电力科学研究院有限公司 A kind of power distribution network risk-averse retailer method and system
CN110322051A (en) * 2019-06-06 2019-10-11 国网浙江省电力有限公司经济技术研究院 Consider the integrated energy system Optimal Configuration Method of N-1 security constraint
CN115062930A (en) * 2022-06-01 2022-09-16 深圳供电局有限公司 Power distribution network source and network load and storage coordination planning method
CN115293697A (en) * 2022-08-05 2022-11-04 河海大学 Port comprehensive energy system and container logistics system cooperative scheduling method
CN115239032A (en) * 2022-09-26 2022-10-25 华北电力大学 Highway service area microgrid planning method and system considering energy self-consistency rate

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
Optimal sustainable life cycle maintenance strategies for port infrastructures;Yi Zhang等;《Journal of Cleaner Production》;全文 *
含分布式可再生能源集群的配电系统规划研究;胡迪;《中国优秀博士论文 工程科技Ⅱ辑》;全文 *
基于机会约束规划的DG与配电网架多目标协调规划;白牧可;唐巍;张璐;所丽;;电工技术学报(10);全文 *
考虑分布式可再生电源和储能的智能配电网可靠性评估综述;刘凯诚等;《 电测与仪表》;全文 *
考虑时序特性的主动配电网无功电源规划;符杨;缪润利;李振坤;崔勇;张开华;张智伟;;电网与清洁能源(09);全文 *

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