WO2019184344A1 - 一种微能源网多目标运行控制方法 - Google Patents

一种微能源网多目标运行控制方法 Download PDF

Info

Publication number
WO2019184344A1
WO2019184344A1 PCT/CN2018/113243 CN2018113243W WO2019184344A1 WO 2019184344 A1 WO2019184344 A1 WO 2019184344A1 CN 2018113243 W CN2018113243 W CN 2018113243W WO 2019184344 A1 WO2019184344 A1 WO 2019184344A1
Authority
WO
WIPO (PCT)
Prior art keywords
energy
optimization
micro
solution
pareto
Prior art date
Application number
PCT/CN2018/113243
Other languages
English (en)
French (fr)
Inventor
林晓明
张勇军
Original Assignee
华南理工大学
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 华南理工大学 filed Critical 华南理工大学
Priority to US17/042,948 priority Critical patent/US11443252B2/en
Publication of WO2019184344A1 publication Critical patent/WO2019184344A1/zh

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • 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
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected 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
    • H02J3/38—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
    • 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
    • H02J2300/28—The renewable source being wind energy
    • 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
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation

Definitions

  • the invention relates to a micro energy network optimization operation technology, in particular to a micro energy network multi-objective operation control method.
  • the micro-energy network operation is based on the premise of meeting the safety constraints, and uses different targets to reasonably arrange the operation of various energy devices inside the micro-energy network.
  • the optimization operation of the micro energy network is mainly aimed at minimizing the running cost.
  • the single target optimization scheduling scheme is difficult to adapt to the complex integrated energy supply environment and the energy structure that is continuously transformed and upgraded.
  • the micro-energy network provides a platform for comprehensive utilization of multiple energy sources. Energy utilization rate is an effective indicator for measuring the efficiency and energy-saving of micro-energy networks. The energy utilization rate is included in the optimization goal of micro-energy networks, and the pursuit of efficient use of energy is to achieve energy. Important initiatives for sustainable development.
  • the present invention provides a multi-objective operation control method for a micro energy network, which can effectively balance the economics and efficiency of the operation of the micro energy network.
  • the object of the invention is the operation control problem of the micro energy network, which can improve the economy of the operation of the micro energy network and improve the efficiency of the operation.
  • the present invention provides a multi-objective operation control method for a micro energy network, comprising the following steps:
  • the standard satisfaction degree of the Pareto optimal solution is obtained, and the Pareto optimal solution with the largest standard satisfaction is selected as the optimal compromise solution;
  • the comprehensive income f 1 includes energy service income C Ser , energy transaction income C Trade , operation and maintenance cost C OM and carbon tax expense C CO2 :
  • ⁇ t is the optimization time interval
  • T is the optimization total time period
  • L e,t , L h,t and L g,t are respectively the electricity, heat and natural gas user power in the t period, and the value of t ranges from 1 to T
  • c e,t , c h,t and c g,t are the prices of the micro energy network to provide electricity, heat and natural gas services to users during the t period
  • the t-time period is the trading power of the micro-energy network and the grid and the purchase of natural gas power from the gas network
  • the price of the micro-energy network and the grid is the purchase price of natural gas and the price of natural gas purchased from the gas network
  • N is the total number of energy conversion equipment
  • K is the total number of energy storage equipment
  • the operation and maintenance coefficient of the kth energy storage device the value of n is 1 to N, and the value of k
  • the comprehensive energy utilization rate f 2 is:
  • W e,t is the output of wind power generation in t period.
  • the step (3) described includes:
  • w is the weight of the optimization target f 1 , and the value range is [0, 1]; (1-w) is the weight of the optimization target f 2 ;
  • step (3-3) For each weight coefficient value, the Pareto optimal solution of the optimization objective function described in step (3-1) is obtained by GAMS software, and the J Pareto optimal solutions form the Pareto front .
  • the fuzzy membership degree is:
  • f i,j and ⁇ i,j are the objective function value and the fuzzy membership degree of the i-th optimization target of the j-th Pareto optimal solution , respectively, and the value range of i is 1 or 2, The value ranges from 1 to J;
  • the multi-objective operation control method of the micro energy network provided by the invention can optimize multi-objectives for the comprehensive income and comprehensive energy utilization of the micro-energy network, thereby ensuring the economical operation of the micro-energy network and improving micro-energy.
  • the comprehensive energy efficiency of the energy network can optimize multi-objectives for the comprehensive income and comprehensive energy utilization of the micro-energy network, thereby ensuring the economical operation of the micro-energy network and improving micro-energy.
  • FIG. 1 is a schematic diagram of steps of a multi-objective operation control method for a micro energy network
  • Figure 2 is a structural diagram of a typical micro energy network
  • Figure 3 is a graph of output curves of electricity, heat, natural gas and wind power
  • Figure 4 shows the Pareto front of a multi-objective optimization run.
  • FIG. 1 is a schematic diagram of a multi-target operation control method for a micro energy network according to an embodiment of the present invention, including the following steps:
  • the standard satisfaction degree of the Pareto optimal solution is obtained, and the Pareto optimal solution with the largest standard satisfaction is selected as the optimal compromise solution;
  • the comprehensive income f 1 includes energy service income C Ser , energy transaction income C Trade , operation and maintenance cost C OM and carbon tax expense C CO2 :
  • ⁇ t is the optimization time interval
  • T is the optimization total time period
  • L e,t , L h,t and L g,t are respectively the electricity, heat and natural gas user power in the t period, and the value of t ranges from 1 to T
  • c e,t , c h,t and c g,t are the prices of the micro energy network to provide electricity, heat and natural gas services to users during the t period; with The electricity consumption of the micro-energy network and the grid in the t-time period and the purchase of natural gas power from the gas network; with The price of the micro-energy network and the grid is the purchase price of natural gas and the price of natural gas purchased from the gas network
  • N is the total number of energy conversion equipment
  • K is the total number of energy storage equipment
  • the operation and maintenance coefficient of the kth energy storage device the value of n is 1 to N, and the value of k
  • the comprehensive energy utilization rate f 2 is:
  • W e,t is the output of wind power generation in t period.
  • the step (3) described includes:
  • w is the weight of the optimization target f 1 , and the value range is [0, 1]; (1-w) is the weight of the optimization target f 2 ;
  • step (3-3) For each weight coefficient value, the Pareto optimal solution of the optimization objective function described in step (3-1) is obtained by GAMS software, and the J Pareto optimal solutions form the Pareto front .
  • the fuzzy membership degree is:
  • f i,j and ⁇ i,j are the objective function value and the fuzzy membership degree of the i-th optimization target of the j-th Pareto optimal solution , respectively, and the value range of i is 1 or 2, The value ranges from 1 to J;
  • FIG. 2 Taking a typical micro-energy network as an example, its structure is shown in Figure 2. It includes three energy conversion equipments for cogeneration, electric boilers and gas boilers. There are two energy storage devices for electricity storage and heat storage. Energy is wind power.
  • the optimized running time interval is 1 hour, with 24 optimized time periods.
  • the electric, thermal, natural gas load and wind power output curves are shown in Figure 3.
  • the optimal solution and the worst solution for solving the goal of maximizing comprehensive income and maximizing comprehensive energy utilization are shown in Table 1.
  • the optimal solution for comprehensive income is 42,900 yuan
  • the worst solution is 25,900 yuan
  • the optimal solution for comprehensive energy utilization is 90.0%
  • the worst solution is 82.4%.
  • the weight coefficient w is uniformly taken as 0, 0.05, 0.10, ..., 0.095, 1 in the range of [0, 1], and 21 values are obtained.
  • the Pareto front obtained by GAMS is shown in Fig. 4.
  • the standard satisfaction of the Pareto optimal solution is calculated, and the Pareto optimal solution with the largest standard satisfaction is selected as the optimal compromise solution.
  • the optimal compromise solution is the point marked by the circle in Figure 4.
  • the optimization results are shown in Table 1.
  • the comprehensive benefit of the optimal compromise solution is 3.86, and the energy utilization efficiency is 86.7%.

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Human Resources & Organizations (AREA)
  • Health & Medical Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Tourism & Hospitality (AREA)
  • Power Engineering (AREA)
  • Water Supply & Treatment (AREA)
  • Primary Health Care (AREA)
  • General Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Development Economics (AREA)
  • Game Theory and Decision Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Feedback Control In General (AREA)
  • Supply And Distribution Of Alternating Current (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

一种微能源网多目标运行控制方法，具体步骤为：建立微能源网的优化目标，包括综合收益最大化和综合能源利用率最大化(1)；通过GAMS软件求解每个优化目标的最优解和最劣解(2)；采用加权法处理优化目标，均匀改变权重系数，通过GAMS软件求得帕累托前沿(3)；根据模糊隶属度求取帕累托最优解的标准满意度，选取具有最大标准满意度的帕累托最优解作为最优折衷解(4)；根据最优折衷解进行微能源网调度(5)。该方法提供的调度方案可对微能源网综合收益和综合能源利用率进行多目标优化，提高微能源网运行的经济性和高效性。

Description

一种微能源网多目标运行控制方法 技术领域
本发明涉及微能源网优化运行技术，特别涉及一种微能源网多目标运行控制方法。
背景技术
当前，化石能源日渐枯竭和环境污染日益严峻，能源结构面临转型升级的挑战。微能源网集成可再生能源、利用多种能源优势互补和提高能源利用效率，是实现能源可持续发展的有效途径。
微能源网运行是在满足安全约束的前提下，采用不同目标，合理安排微能源网内部各能源设备的运行。目前研究中，微能源网的优化运行主要以运行成本最小化等为目标，然而单目标的优化调度方案，难以适应复杂的综合能源供用环境和不断转型升级的能源结构。微能源网为实现多元能源综合利用提供平台，能源利用率是衡量微能源网高效性和节能性的有效指标，将能源利用率纳入微能源网优化目标，追求能源的高效利用，是实现能源可持续发展的重要举措。
针对以上问题，本发明提供一种微能源网多目标运行控制方法，能够有效权衡微能源网运行的经济性和高效性。
发明内容
本发明的目的在于微能源网运行控制问题，既能提高微能源网运行的经济性，又能提高运行的高效性。
为实现上述目的，本发明提出一种微能源网多目标运行控制方法，包括以下步骤：
(1)建立微能源网优化目标，包括综合收益最大化和综合能源利用率最大化；
(2)通过GAMS软件求解每个优化目标的最优解和最劣解；
(3)采用加权法处理优化目标，均匀改变权重系数，通过GAMS软件求得帕累托前沿；
(4)根据模糊隶属度求取帕累托最优解的标准满意度，选取具有最大标准满意度的帕累托最优解作为最优折衷解；
(5)根据最优折衷解进行微能源网调度。
所述的综合收益f 1包括能源服务收益C Ser、能源交易收益C Trade、运行维护费用C OM和碳税费用C CO2：
f 1＝C Ser+C Trade-C OM-C CO2
Figure PCTCN2018113243-appb-000001
其中，Δt为优化时间间隔，T为优化总时段；L e,t、L h,t和L g,t分别为t时段电、热和天然气用户功率，t的取值范围为1～T；c e,t、c h,t和c g,t分别为t时段微能源网向用户提供电、热和天然气服务的价格；
Figure PCTCN2018113243-appb-000002
和
Figure PCTCN2018113243-appb-000003
分别t时段为微能源网与电网的买卖电功率和从气网购买天然气功率；
Figure PCTCN2018113243-appb-000004
和
Figure PCTCN2018113243-appb-000005
分别为t时段微能源网与电网的买卖电价和从气网购买天然气价格；N为能源转换设备总数，K为能源储存设备总数，c d,n和c s,k分别第n个能源转换设备和第k个能源储存设备的运行维护系数，n的取值为1～N，k的取值为1～K；
Figure PCTCN2018113243-appb-000006
和
Figure PCTCN2018113243-appb-000007
分别为t时段第n个能源转换设备的输入功率和第k个能源储存设备充放能功率；a e和a g分别为电能和天然气CO 2排放系数；c c为单位碳排放成本。
所述的综合能源利用率f 2为：
Figure PCTCN2018113243-appb-000008
其中，W e,t为t时段风电发电出力。
所述的步骤(3)包括：
(3-1)对优化目标f 1和f 2进行标准化，并做加权处理，得到优化目标函数：
Figure PCTCN2018113243-appb-000009
其中，
Figure PCTCN2018113243-appb-000010
和
Figure PCTCN2018113243-appb-000011
和
Figure PCTCN2018113243-appb-000012
分别为优化目标f 1和f 2的最优解和最劣解；w为优化目标f 1的权重，取值范围为[0,1]；(1-w)为优化目标f 2的权重；
(3-2)将权重系数w在[0,1]范围内均匀地取J个值，J为设定值；
(3-3)对于每个权重系数值，通过GAMS软件求得步骤(3-1)所述的优化目标函数的帕累托最优解，J个帕累托最优解形成帕累托前沿。
所述的模糊隶属度为：
Figure PCTCN2018113243-appb-000013
其中，f i,j和γ i,j分别为第j个帕累托最优解的第i个优化目标的目标函数值和模糊隶属度，i的取值范围为1或2，j的取值范围为1～J；
所述的帕累托最优解的标准满意度为：
Figure PCTCN2018113243-appb-000014
其中，ζ j为第j个帕累托最优解的标准满意度。
与现有技术相比，本发明提供的微能源网多目标运行控制方法可以对微能源网综合收益和综合能源利用率进行多目标优化，既保证微能源网运行的经济性，又能提高微能源网的综合能源利用率。
附图说明
图1为一种微能源网多目标运行控制方法的步骤示意图；
图2为典型微能源网的结构图；
图3为电、热、天然气和风电出力曲线图；
图4为多目标优化运行的帕累托前沿。
具体实施方式
以下结合附图和实例对本发明的具体实施做进一步说明，但本法发明的实施不限于此，需指出的是，以下若有未特别详细说明之处，均是本领域技术人员可参照现有技术实现的。
如图1为本发明实施例提供的一种微能源网多目标运行控制方法，包括以下步骤：
(1)建立微能源网优化目标，包括综合收益最大化和综合能源利用率最大化；
(2)通过GAMS软件求解每个优化目标的最优解和最劣解；
(3)采用加权法处理优化目标，均匀改变权重系数，通过GAMS软件求得帕累托前沿；
(4)根据模糊隶属度求取帕累托最优解的标准满意度，选取具有最大标准满意度的帕累托最优解作为最优折衷解；
(5)根据最优折衷解进行微能源网调度。
所述的综合收益f 1包括能源服务收益C Ser、能源交易收益C Trade、运行维护费用C OM和碳税费用C CO2：
f 1＝C Ser+C Trade-C OM-C CO2
Figure PCTCN2018113243-appb-000015
其中，Δt为优化时间间隔，T为优化总时段；L e,t、L h,t和L g,t分别为t时段电、热和天然气用户功率，t的取值范围为1～T；c e,t、c h,t和c g,t分别为t时段微能源网向用户提供电、热和天然气服务的价格；
Figure PCTCN2018113243-appb-000016
和
Figure PCTCN2018113243-appb-000017
分别为t时段微能源网与电网的买卖电功率和从气网购买天然气功率；
Figure PCTCN2018113243-appb-000018
和
Figure PCTCN2018113243-appb-000019
分别为t时段微能源网与电网的买卖电价和从气网购买天然气价格；N为能源转换设备总数，K为能源储存设备总数，c d,n和c s,k分别第n个能源转换设备和第k个能源储存设备的运行维护系数，n的取值为1～N，k的取值为1～K；
Figure PCTCN2018113243-appb-000020
和
Figure PCTCN2018113243-appb-000021
分别为t时段第n个能源转换设备的输入功率和第k个能源储存设备充放能功率；a e和a g分别为电能和天然气CO 2排放系数；c c为单位碳排放成本。
所述的综合能源利用率f 2为：
Figure PCTCN2018113243-appb-000022
其中，W e,t为t时段风电发电出力。
所述的步骤(3)包括：
(3-1)对优化目标f 1和f 2进行标准化，并做加权处理，得到优化目标函数：
Figure PCTCN2018113243-appb-000023
其中，
Figure PCTCN2018113243-appb-000024
和
Figure PCTCN2018113243-appb-000025
和
Figure PCTCN2018113243-appb-000026
分别为优化目标f 1和f 2的最优解和最劣解；w为优化目标f 1的权重，取值范围为[0,1]；(1-w)为优化目标f 2的权重；
(3-2)将权重系数w在[0,1]范围内均匀地取J个值，J为设定值；
(3-3)对于每个权重系数值，通过GAMS软件求得步骤(3-1)所述的优化目标函数的帕累托最优解，J个帕累托最优解形成帕累托前沿。
所述的模糊隶属度为：
Figure PCTCN2018113243-appb-000027
其中，f i,j和γ i,j分别为第j个帕累托最优解的第i个优化目标的目标函数值和模糊隶属度，i的取值范围为1或2，j的取值范围为1～J；
所述的帕累托最优解的标准满意度为：
Figure PCTCN2018113243-appb-000028
其中，ζ j为第j个帕累托最优解的标准满意度。
以一个典型微能源网为例进行说明，其结构如图2所示，包括热电联产、电锅炉和燃气锅炉共3个能源转换设备，储电和储热共2个能源储存设备，可再生能源为风力发电。
优化运行时间间隔为1小时，共有24个优化时间段，电、热、天然气负荷和风电出力曲线如图3所示。
分别求解以综合收益最大化和综合能源利用率最大化为目标的最优解和最劣解如表1所示。综合收益的最优解为4.29万元，最劣解为2.59万元，综合能源利用率的最优解为90.0％，最劣解为82.4％。
将权重系数w在[0,1]范围内均匀地取为0，0.05，0.10，…，0.095，1，共21个值，通过GAMS求解得到的帕累托前沿如图4所示。
计算帕累托最优解的标准满意度，选取具有最大标准满意度的帕累托最优解作为的最优折衷解，最优折衷解为图4圆圈标记的点。优化结果如表1所示，最优折衷解的综合收益为3.86，能源利用效率为86.7％。
表1不同优化目标下的优化结果
Figure PCTCN2018113243-appb-000029
由表1可知，对比综合收益最大化的优化结果，多目标运行的最优折衷解的优化结果，微能源网的综合收益降低了，但是综合能源利用率提高了，微能源网运行更加高效环保；对比综合能源利用率最大化的优化结果，多目标运行的最优折衷解的优化结果，微能源网的综合能源利用率降低了，但是综合收益提高了，微能源网运行经济性更好。由此可见，多目标运行的最优折衷解的两个优化目标值都相对较优，可作为微能源网多目标优化调度方案，能够有效提高微能源网运行的经济性和高效性。
上述实施例为本发明较佳的实施方式，但本发明的实施方式并不受上述实施例的限制，其他任何未背离本发明的精神实质和原理下所作的修改、修饰、替代、组合、简化，均应为等效的置换方式，都应包含在本发明的保护范围之内。

Claims (5)

  1. 一种微能源网多目标运行控制方法，其特征在于，包括：
    (1)建立微能源网优化目标，包括综合收益最大化和综合能源利用率最大化；
    (2)通过GAMS软件求解每个优化目标的最优解和最劣解；
    (3)采用加权法处理优化目标，均匀改变权重系数，通过GAMS软件求得帕累托前沿；
    (4)根据模糊隶属度求取帕累托最优解的标准满意度，选取具有最大标准满意度的帕累托最优解作为最优折衷解；
    (5)根据最优折衷解进行微能源网调度。
  2. 根据权利要求1所述的微能源网多目标运行控制方法，其特征在于，所述的综合收益f 1包括能源服务收益C Ser、能源交易收益C Trade、运行维护费用C OM和碳税费用C CO2：
    f 1＝C Ser+C Trade-C OM-C CO2
    Figure PCTCN2018113243-appb-100001
    其中，Δt为优化时间间隔，T为优化总时段；L e,t、L h,t和L g,t分别为t时段电、热和天然气用户功率，t的取值范围为1～T；c e,t、c h,t和c g,t分别为t时段微能源网向用户提供电、热和天然气服务的价格；
    Figure PCTCN2018113243-appb-100002
    和
    Figure PCTCN2018113243-appb-100003
    分别为t时段微能源网与电网的买卖电功率和从气网购买天然气功率；
    Figure PCTCN2018113243-appb-100004
    和
    Figure PCTCN2018113243-appb-100005
    分别为t时段微能源网与电网的买卖电价和从气网购买天然气价格；N为能源转换设备总数，K为能源储存设备总数，c d,n和c s,k分别第n个能源转换设备和第k个能源储存设备的运行维护系数，n的取值为1～N，k的取值为1～K；
    Figure PCTCN2018113243-appb-100006
    和
    Figure PCTCN2018113243-appb-100007
    分别为t时段第n个能源转换设备的输入功率和第k个能源储存设备充放能功率；a e和a g分别为电能和天然气CO 2排放系数；c c为单位碳排放成本。
  3. 根据权利要求2所述的微能源网多目标运行控制方法，其特征在于，所述的综合能源利用率f 2为：
    Figure PCTCN2018113243-appb-100008
    其中，W e,t为t时段风电发电出力。
  4. 根据权利要求1所述的微能源网多目标运行控制方法，其特征在于，所述的步骤(3)包括：
    (3-1)对优化目标f 1和f 2进行标准化，并做加权处理，得到优化目标函数：
    Figure PCTCN2018113243-appb-100009
    其中，
    Figure PCTCN2018113243-appb-100010
    和
    Figure PCTCN2018113243-appb-100011
    和
    Figure PCTCN2018113243-appb-100012
    分别为优化目标f 1和f 2的最优解和最劣解；w为优化目标f 1的权重，取值范围为[0,1]；(1-w)为优化目标f 2的权重；
    (3-2)将权重系数w在[0,1]范围内均匀地取J个值，J为设定值；
    (3-3)对于每个权重系数值，通过GAMS软件求得步骤(3-1)所述的优化目标函数的帕累托最优解，J个帕累托最优解形成帕累托前沿。
  5. 根据权利要求1所述的微能源网多目标运行控制方法，其特征在于，所述的模糊隶属度为：
    Figure PCTCN2018113243-appb-100013
    其中，f i,j和γ i,j分别为第j个帕累托最优解的第i个优化目标的目标函数值和模糊隶属度，i的取值范围为1或2，j的取值范围为1～J；
    所述的帕累托最优解的标准满意度为：
    Figure PCTCN2018113243-appb-100014
    其中，ζ j为第j个帕累托最优解的标准满意度。
PCT/CN2018/113243 2018-03-29 2018-10-31 一种微能源网多目标运行控制方法 WO2019184344A1 (zh)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US17/042,948 US11443252B2 (en) 2018-03-29 2018-10-31 Multi-objective operation control method for micro energy grid

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201810268635.8A CN108491976A (zh) 2018-03-29 2018-03-29 一种微能源网多目标运行控制方法
CN201810268635.8 2018-03-29

Publications (1)

Publication Number Publication Date
WO2019184344A1 true WO2019184344A1 (zh) 2019-10-03

Family

ID=63317218

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2018/113243 WO2019184344A1 (zh) 2018-03-29 2018-10-31 一种微能源网多目标运行控制方法

Country Status (3)

Country Link
US (1) US11443252B2 (zh)
CN (1) CN108491976A (zh)
WO (1) WO2019184344A1 (zh)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113344249A (zh) * 2021-05-14 2021-09-03 合肥工业大学 基于区块链的冷热电联供多微网优化调度方法和系统
CN113852097A (zh) * 2021-09-22 2021-12-28 云南电网有限责任公司电力科学研究院 一种基于maso的光伏阵列重构参与电网调频方法
CN115983508A (zh) * 2023-03-21 2023-04-18 国网信息通信产业集团有限公司 一种融合碳排放流的综合能源系统调度方法及终端机
CN116663823A (zh) * 2023-05-25 2023-08-29 国网江苏省电力有限公司连云港供电分公司 融合精准碳数据的配电能源网格碳排放最优规划方法及系统
CN116974241A (zh) * 2023-07-10 2023-10-31 清华大学 面向绿色低碳制造的数控机床几何优化方法及装置

Families Citing this family (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108491976A (zh) * 2018-03-29 2018-09-04 华南理工大学 一种微能源网多目标运行控制方法
CN110086184A (zh) * 2019-04-11 2019-08-02 华北电力大学 一种基于投资约束的园区级综合能源系统容量优化方法
CN111431212A (zh) * 2020-02-19 2020-07-17 国电新能源技术研究院有限公司 一种风力发电系统多控制器-多目标协调优化方法
CN111611712B (zh) * 2020-05-21 2023-05-02 武汉轻工大学 基于粒子群算法的稻谷碳足迹计量优化方法、装置及设备
CN112103946B (zh) * 2020-08-20 2022-04-22 西安理工大学 一种基于粒子群算法的微电网储能优化配置方法
WO2023089640A1 (en) * 2021-11-17 2023-05-25 Hitachi Energy Switzerland Ag Method and system for operating an energy management system
CN116169682B (zh) * 2023-03-15 2023-10-24 国网湖北省电力有限公司十堰供电公司 一种考虑碳排放流及风光消纳的综合能源系统优化调度策略

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130024014A1 (en) * 2011-07-20 2013-01-24 Nec Laboratories America, Inc. Optimal energy management of a rural microgrid system using multi-objective optimization
CN104463374A (zh) * 2014-12-23 2015-03-25 国家电网公司 一种分布式电源优化配置的方法及系统
CN104808489A (zh) * 2015-03-09 2015-07-29 山东大学 冷热电联供系统的三级协同整体优化方法
CN106712111A (zh) * 2017-01-23 2017-05-24 南京邮电大学 有源配电网环境下多目标模糊优化的多能源经济调度方法
CN106920177A (zh) * 2017-01-17 2017-07-04 无锡协鑫分布式能源开发有限公司 一种多能互补微能源的经济运行策略
CN108491976A (zh) * 2018-03-29 2018-09-04 华南理工大学 一种微能源网多目标运行控制方法

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103326353B (zh) * 2013-05-21 2015-02-18 武汉大学 基于改进多目标粒子群算法的环境经济发电调度求解方法
US20150311713A1 (en) * 2014-04-28 2015-10-29 Nec Laboratories America, Inc. Service-based Approach Toward Management of Grid-Tied Microgrids
CN106549392B (zh) * 2016-10-12 2019-06-28 中国南方电网有限责任公司电网技术研究中心 一种配电网协调控制方法
CN106709611A (zh) * 2017-02-28 2017-05-24 南京国电南自电网自动化有限公司 全寿命周期框架下的微电网优化配置方法
CN107844055A (zh) * 2017-11-03 2018-03-27 南京国电南自电网自动化有限公司 一种基于博弈论的冷热电三联供微网系统优化运行方法

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130024014A1 (en) * 2011-07-20 2013-01-24 Nec Laboratories America, Inc. Optimal energy management of a rural microgrid system using multi-objective optimization
CN104463374A (zh) * 2014-12-23 2015-03-25 国家电网公司 一种分布式电源优化配置的方法及系统
CN104808489A (zh) * 2015-03-09 2015-07-29 山东大学 冷热电联供系统的三级协同整体优化方法
CN106920177A (zh) * 2017-01-17 2017-07-04 无锡协鑫分布式能源开发有限公司 一种多能互补微能源的经济运行策略
CN106712111A (zh) * 2017-01-23 2017-05-24 南京邮电大学 有源配电网环境下多目标模糊优化的多能源经济调度方法
CN108491976A (zh) * 2018-03-29 2018-09-04 华南理工大学 一种微能源网多目标运行控制方法

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113344249A (zh) * 2021-05-14 2021-09-03 合肥工业大学 基于区块链的冷热电联供多微网优化调度方法和系统
CN113344249B (zh) * 2021-05-14 2022-09-30 合肥工业大学 基于区块链的冷热电联供多微网优化调度方法和系统
CN113852097A (zh) * 2021-09-22 2021-12-28 云南电网有限责任公司电力科学研究院 一种基于maso的光伏阵列重构参与电网调频方法
CN113852097B (zh) * 2021-09-22 2024-03-19 云南电网有限责任公司电力科学研究院 一种基于maso的光伏阵列重构参与电网调频方法
CN115983508A (zh) * 2023-03-21 2023-04-18 国网信息通信产业集团有限公司 一种融合碳排放流的综合能源系统调度方法及终端机
CN115983508B (zh) * 2023-03-21 2023-11-03 国网信息通信产业集团有限公司 一种融合碳排放流的综合能源系统调度方法及终端机
CN116663823A (zh) * 2023-05-25 2023-08-29 国网江苏省电力有限公司连云港供电分公司 融合精准碳数据的配电能源网格碳排放最优规划方法及系统
CN116974241A (zh) * 2023-07-10 2023-10-31 清华大学 面向绿色低碳制造的数控机床几何优化方法及装置
CN116974241B (zh) * 2023-07-10 2024-02-06 清华大学 面向绿色低碳制造的数控机床几何优化方法及装置

Also Published As

Publication number Publication date
US20210125114A1 (en) 2021-04-29
US11443252B2 (en) 2022-09-13
CN108491976A (zh) 2018-09-04

Similar Documents

Publication Publication Date Title
WO2019184344A1 (zh) 一种微能源网多目标运行控制方法
CN108173282B (zh) 一种考虑电转气运行成本综合能源系统优化调度方法
CN111950807B (zh) 计及不确定性与需求响应的综合能源系统优化运行方法
CN108665188B (zh) 一种基于优化模型的园区多能源主体综合配比方法
CN110826815B (zh) 一种考虑综合需求响应的区域综合能源系统运行优化方法
CN109345012B (zh) 基于综合评价指标的园区能源互联网运行优化方法
CN103151797A (zh) 基于多目标调度模型的并网运行方式下微网能量控制方法
CN114330827B (zh) 多能流虚拟电厂分布式鲁棒自调度优化方法及其应用
WO2019223279A1 (zh) 计及环境成本与实时电价的可平移负荷模型构建方法
CN107221965A (zh) 一种基于分布式设计的日前计划计算方法
CN113642802A (zh) 一种基于碳计量模型的综合能源站能源优化调度方法和系统
CN111047097B (zh) 一种综合能源系统日中滚动优化方法
CN110991764B (zh) 一种综合能源系统日前滚动优化方法
CN115170343A (zh) 一种区域综合能源系统分布式资源和储能协同规划方法
CN115759610A (zh) 一种电力系统源网荷储协同的多目标规划方法及其应用
Qu et al. Synergetic power-gas flow with space-time diffusion control of air pollutants using a convex multi-objective optimization
Li et al. Planning model of integrated energy system considering P2G and energy storage
CN110826778B (zh) 一种主动适应新能源发展的负荷特性优化计算方法
CN116187648A (zh) 基于热电解耦的虚拟电厂热电联合优化调度方法
CN112713590B (zh) 计及idr的冷热电联供微网与主动配电网联合优化调度方法
CN111900714A (zh) 多能源协同系统优化调度模型构建方法、装置和计算设备
CN107086579B (zh) 一种基于回滞效应的空调用户对实时电价的响应方法
Wang et al. A two-layer coordinated operation optimization model for multi-energy complementary systems considering demand response
CN111523792B (zh) 综合能源系统调度参数计算、设备控制方法及相关装置
CN112084463B (zh) 一种碳排放下的电力系统分布式光伏配置方法

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 18912254

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 19/01/2021)

122 Ep: pct application non-entry in european phase

Ref document number: 18912254

Country of ref document: EP

Kind code of ref document: A1