WO2017215599A1 - 馈线需求响应物理潜力的评估方法、装置及存储介质 - Google Patents

馈线需求响应物理潜力的评估方法、装置及存储介质 Download PDF

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WO2017215599A1
WO2017215599A1 PCT/CN2017/088135 CN2017088135W WO2017215599A1 WO 2017215599 A1 WO2017215599 A1 WO 2017215599A1 CN 2017088135 W CN2017088135 W CN 2017088135W WO 2017215599 A1 WO2017215599 A1 WO 2017215599A1
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load
type
identified
curve
feeder
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PCT/CN2017/088135
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English (en)
French (fr)
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王珂
雍太有
姚建国
杨胜春
於益军
李亚平
冯树海
刘建涛
曾丹
周竞
郭晓蕊
毛文博
王刚
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中国电力科学研究院
国家电网公司
国网山东省电力公司电力科学研究院
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Priority to US15/763,666 priority Critical patent/US10816950B2/en
Publication of WO2017215599A1 publication Critical patent/WO2017215599A1/zh

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
    • G05B19/048—Monitoring; Safety
    • 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
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • 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
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
    • G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/10—File systems; File servers
    • G06F16/11—File system administration, e.g. details of archiving or snapshots
    • 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
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/26—Pc applications
    • G05B2219/2639—Energy management, use maximum of cheap power, keep peak load low

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  • the invention relates to the technical field of power system automatic analysis, in particular to a method, a device and a storage medium for evaluating a physical demand of a feeder demand response.
  • DR Demand Response
  • the embodiment of the present invention provides a method for evaluating the demand response potential of the feeder, including:
  • the overall polymer potential of the load to be identified is obtained.
  • the obtaining a load curve of a portion to be identified in a load curve of the feeder to be evaluated includes:
  • EMS Energy Management System
  • the historical load curve based on the preset type load is used to construct a load database, including:
  • a load database of a preset type load is constructed based on a historical load curve corresponding to a pre-set climate type load to establish an association relationship between the preset type load and weather and time.
  • the load type and the load database of the to-be-identified portion are determined based on the load curve of the to-be-identified portion, and includes:
  • the load curve of the preset type load is generated by software (such as EnergyPlus software), and the preset is identified by the preset identification mode. Load curve for type load.
  • the physical potential of each type of load is obtained according to the load type of the load to be identified, including:
  • the demand response DR physical potential of each type of load is obtained through simulation.
  • the overall polymer potential of the load to be identified is obtained, including:
  • N b represents the number of types of loads.
  • Embodiments of the present invention also provide an apparatus for evaluating a physical potential of a feeder demand response, comprising: a processor and a memory for storing a computer program executable on the processor; wherein the processor is configured to run the computer program At the time, the evaluation method of the physical potential of the feeder demand response described above is performed.
  • the embodiment of the invention further provides a computer storage medium, wherein the computer storage medium stores computer executable instructions for performing the above-mentioned evaluation method of the physical potential of the feeder demand response.
  • the feeder demand response potential evaluation method proposed by the embodiment of the present invention uses the load data of the climate zone where the feeder is to be evaluated to identify the large feeder load composition and evaluate the physical potential of the overall DR response of the feeder, and solves the problem that the measurement cost and the inaccurate network exist.
  • the large feeder load of the power system caused by the model and the system ownership rights constitutes a problem that is difficult to identify effectively, and can quickly give the physical potential of the feeder demand response, in order to specify a reasonable Demand Side Management (DSM) policy and mechanism.
  • DSM Demand Side Management
  • FIG. 1 is a schematic flowchart of a feeder demand response potential evaluation process according to an embodiment of the present invention
  • FIG. 2 is a schematic flowchart of a load identification optimization algorithm according to an embodiment of the present invention.
  • FIG. 3 is a schematic diagram of a method for generating a simplified regression model database according to an embodiment of the present invention.
  • the inventors found that the DR schedulability potential of large feeders depends on the identification of the load of large feeder nodes.
  • the load composition identification mainly includes intrusive load monitoring (ILM) and non-intrusive load monitoring.
  • ILM intrusive load monitoring
  • Non-ILM Nonintrusive load monitoring
  • the embodiment of the present invention provides a large feeder demand response physical potential based on the load composition identification based on the Non-ILM idea. evaluation method.
  • the embodiment of the invention provides a method for evaluating the demand response potential of a feeder line.
  • the feeder line can be a large feeder line of a city power grid, and a load database of a preset type load is established by using changes and characteristics of different quarter, date and time load curves of a typical load in a climatic zone. Based on this, an optimization method is used to calculate the load composition of the feeder nodes and evaluate the overall physical response potential.
  • the evaluation method provided by the present invention includes:
  • Step a obtaining, by the grid EMS, a load curve of the feeder to be evaluated within a predetermined time (in one embodiment, the predetermined time may be one week, including working days and non-working days);
  • Step b directly acquiring the load curve of the identifiable load in the load curve by the EMS system installed by the user or at the measuring device interacting with the power grid;
  • Step c Subtracting the load curve obtained in step a from the identifiable load curve obtained in step b to obtain a load curve of the portion to be identified in the feeder load curve to be evaluated.
  • the historical load curve based on the preset type of load corresponding to the climate zone if not, the EnergyPlus software can be used to simulate the load curve of the preset type load to build the load of the preset type load
  • the relationship between the preset type load and the meteorology and time is established, and then the main characteristic parameters of the preset type load curve are obtained.
  • the load database of the preset type load can be expressed as:
  • N PB is the total number of load types in the climatic zone where the feeder to be evaluated is located.
  • the preset type load k load curve feature set includes the working day base value power set within the preset time period (eg, one year) of the load And non-working day base value power sets To describe the set of associated parameters for 24-hour power and external temperature on weekdays, A set of associated parameters for 24-hour power and external temperature for non-working days.
  • the above load database contains the main load types of the climate region where the feeder to be evaluated is located, which can effectively reflect the influence of season, date and external environmental factors on the power load curve; here the load type is explained: a total of 16 commercial types and 3 types are included.
  • Residential building types commercial including large office buildings, medium-sized office buildings, small office buildings, warehouses, retail stores, strip commercial streets, primary schools, junior high schools, supermarkets, fast restaurants, restaurants, hospitals, community hospitals, small hotels, large hotels and Multi-storey apartments, residential buildings including basic, low and high.
  • the preset load identification strategy may be a preset load identification optimization algorithm.
  • Step S1 determining whether the load database of the preset type load has a load curve of a preset type load corresponding to the climate zone in which the feeder to be evaluated is located within a preset time;
  • the load curve of the preset type load is directly identified by using a preset identification manner, and the load type and quantity of the load to be identified are obtained;
  • the preset identification manner may be a preset least squares method.
  • the method includes:
  • the load curve to be identified and the external environmental parameters of the specific climate zone, such as temperature, humidity, etc. the type of load to be identified and the number of each type of load can be expressed as:
  • ⁇ (t) is the load signal to be identified
  • ⁇ ( ⁇ ) is the load generated by the load estimated by the load separation algorithm
  • ⁇ [ ⁇ ] is the calculation error between the estimated value and the actual value.
  • the load database of the preset type load in the practical application contains all the possible building types except the uncertain impact load in the study climate zone.
  • Step S2 generating a load curve of a preset type load by using a load database and a time corresponding to a load curve of the load to be identified, and external weather information data, and identifying a load curve of the preset type load by using the preset identification manner ;
  • the load database and the time and external weather information data corresponding to the load curve of the load to be identified are used to generate a load curve of the preset type load, including:
  • ⁇ T p is the temperature deviation from the baseline temperature T p,0 ;
  • the base time power at time t, f k,1,t ,f k,2,t ,f k,3,t is the set of parameters related to the power and the external temperature. As shown in equation (2), the above parameters distinguish the working days. And non-working days.
  • Step S3 The least square method is used to obtain the load type of the load portion to be identified, so that the error between the load curve formed by the load separation separated by the load curve and the load curve to be identified is minimized.
  • Step S4 Output the result of identifying the type and quantity of the load this time, and jump to the next step.
  • the simulation can be simulated for EnergyPlus software, including:
  • the pre-generated simplified regression model database simulation is used to obtain the demand physical response DR physical potential of each type of load, as shown in FIG. 3, including the following:
  • DR k,h % is the percentage of the DR potential of the h-time load k as a percentage of the baseline load
  • OAT h is the ambient temperature of the time period h
  • They are segment points associated with time period h, respectively.
  • N b represents the number of types of loads.
  • Embodiments of the present invention also provide an apparatus for evaluating a physical potential of a feeder demand response, comprising: a processor and a memory for storing a computer program executable on the processor; wherein the processor is configured to run the computer program When executed, execute:
  • the overall polymer potential of the load to be identified is obtained.
  • a load database of a preset type load is constructed based on a historical load curve corresponding to a pre-set climate type load to establish an association relationship between the preset type load and weather and time.
  • the load curve of the preset type load is directly identified by using a preset identification manner, and the load type and quantity of the load to be identified are obtained;
  • the load database and the time and external weather information data corresponding to the load curve of the load to be identified are used to generate a load curve of the preset type load, and the load curve of the preset type load is identified by the preset identification manner. .
  • Class load demand responds to DR physical potential.
  • N b represents the number of types of loads.
  • the embodiment of the invention further provides a computer storage medium, wherein the computer storage medium stores computer executable instructions for performing the above-mentioned evaluation method of the physical potential of the feeder demand response.
  • the embodiment of the present invention obtains a load curve of a portion to be identified in a load curve of a feeder to be evaluated; constructs a load database based on a historical load curve of a preset type of load; and determines the load based on the load curve of the to-be-identified portion and the load database
  • the type and quantity of the load to be identified obtaining the physical potential of each type of load according to the load type of the load to be identified; and obtaining the overall load to be identified according to the physical potential of each type of load and the number of each type of load Polymer potential. In this way, the physical potential of the demand response of large feeder loads can be effectively evaluated.

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Abstract

一种馈线需求响应物理潜力的评估方法，其包括：获取待评估馈线的负荷曲线中待辨识部分的负荷曲线；基于预设类型负荷的历史负荷曲线构建负荷数据库；基于所述待辨识部分的负荷曲线及所述负荷数据库，确定所述待辨识负荷的负荷类型和数量；依据所述待辨识负荷的负荷类型获取每类负荷的物理潜力；依据所述每类负荷的物理潜力及每类负荷的数量，得到所述待辨识负荷整体的聚合物理潜力。及一种馈线需求响应物理潜力的评估装置及存储介质。如此，能够有效评估大型馈线负荷的需求响应物理潜力。

Description

馈线需求响应物理潜力的评估方法、装置及存储介质
相关申请的交叉引用
本申请基于申请号为201610424996.8、申请日为2016年6月15日的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此引入本申请作为参考。
技术领域
本发明涉及电力系统自动化分析技术领域，尤其涉及一种馈线需求响应物理潜力的评估方法、装置及存储介质。
背景技术
智能电网的快速发展使得需求响应资源成为提升电网调节能力的重要手段，且需求响应(Demand Response，DR)潜力评估是制定合理的需求响应政策或电价机制的基础。
目前，对有关单台设备、单个商业或居民建筑DR潜力的报道较多，然而，相较于负荷个体或单个建筑而言，电力公司更为关注大量负荷聚合后的母线节点和大型馈线的DR可调度潜力，而对于母线节点或大型馈线的DR物理潜力评估问题，目前尚不存在有效的解决方案。
发明内容
为有效解决母线节点或大型馈线的DR物理潜力评估问题，本发明实施例提出一种馈线需求响应潜力评估方法，包括：
获取待评估馈线的负荷曲线中待辨识部分的负荷曲线；
基于预设类型负荷的历史负荷曲线构建负荷数据库；
基于所述待辨识部分的负荷曲线及所述负荷数据库，确定所述待辨识 负荷的负荷类型和数量；
依据所述待辨识负荷的负荷类型获取每类负荷的物理潜力；
依据所述每类负荷的物理潜力及每类负荷的数量，得到所述待辨识负荷整体的聚合物理潜力。
上述方案中，所述获取待评估馈线的负荷曲线中待辨识部分的负荷曲线，包括：
通过电网能量管理系统(Energy Management System，EMS)获取待评估馈线预定时间内的负荷曲线；
提取所述负荷曲线中的可辨识负荷曲线；
基于所述预定时间内的馈线负荷曲线及所述可辨识负荷曲线得到待辨识部分的负荷曲线。
上述方案中，所述基于预设类型负荷的历史负荷曲线构建负荷数据库，包括：
基于对应气候区预设类型负荷的历史负荷曲线构建预设类型负荷的负荷数据库，以建立所述预设类型负荷与气象、时间的关联关系。
上述方案中，所述基于所述待辨识部分的负荷曲线及所述负荷数据库，确定所述待辨识负荷的负荷类型和数量，包括；
判断所述负荷数据库中是否具有预设时间内待评估馈线所在气候区对应的预设类型负荷的负荷曲线；
如果有，采用预设的辨识方式直接辨识待辨识负荷的负荷类型和数量；
如果没有，基于对应待辨识负荷的负荷曲线的时间和外部气象信息数据，通过软件(如EnergyPlus软件)仿真生成预设类型负荷的负荷曲线，并通过所述预设的辨识方式辨识所述预设类型负荷的负荷曲线。
上述方案中，所述依据所述待辨识负荷的负荷类型获取每类负荷的物理潜力，包括：
基于所述预设类型负荷与气象、时间的关联关系，通过仿真得到每一类负荷的需求响应DR物理潜力。
上述方案中，所述依据所述每类负荷的物理潜力及每类负荷的数量，得到所述待辨识负荷整体的聚合物理潜力，包括：
按下式计算馈线负荷整体在h时段内的聚合响应潜力DRBSP,h；
Figure PCTCN2017088135-appb-000001
其中，xk表示负荷k的数目，Nb表示负荷的类型数目。
本发明实施例还提供了一种馈线需求响应物理潜力的评估装置，包括：处理器和用于存储能够在处理器上运行的计算机程序的存储器；其中，所述处理器用于运行所述计算机程序时，执行上述馈线需求响应物理潜力的评估方法。
本发明实施例还提供了一种计算机存储介质，所述计算机存储介质中存储有计算机可执行指令，该计算机可执行指令用于执行上述馈线需求响应物理潜力的评估方法。
本发明实施例提供的技术方案至少具有以下优异效果：
本发明实施例提出的馈线需求响应潜力评估方法，利用待评估馈线所在气候区的负荷数据来辨识大型馈线负荷构成并评估该馈线整体DR响应物理潜力，解决了由于存在测量成本、不精确的网络模型以及系统归属权等问题造成的电力系统大型馈线负荷构成难以有效辨识的问题，并能快速给出馈线需求响应的物理潜力，为指定合理的需求侧管理(Demand Side Management，DSM)政策和机制奠定基础。
附图说明
图1为本发明实施例提供的馈线需求响应潜力评估流程示意图；
图2为本发明实施例提供的负荷辨识优化算法流程示意图；
图3为本发明实施例提供的简化回归模型数据库的生成方式示意图。
具体实施方式
发明人在研究过程中发现，大型馈线的DR可调度潜力依赖于对大型馈线节点的负荷构成辨识；其中，负荷构成辨识主要包括侵入式负荷监测(intrusive load monitoring，ILM)和非侵入式负荷监测(nonintrusive load monitoring，Non-ILM)，而为有效解决母线节点或大型馈线的DR物理潜力评估问题，本发明实施例基于Non-ILM思想，提供一种基于负荷构成辨识的大型馈线需求响应物理潜力评估方法。
为清楚的介绍本发明提供的技术方案，以下将结合附图说明具体描述技术方案。
本发明实施例提出一种馈线需求响应潜力评估方法，所述馈线可以为城市电网大型馈线，利用对应气候区典型负荷不同季度、日期、时间负荷曲线的变化及特点建立预设类型负荷的负荷数据库，在此基础上使用优化方法计算馈线节点的负荷构成并评估整体物理响应潜力。
如附图1所示的评估流程图，本发明提供的评估方法包括：
(1)获取待评估馈线负荷曲线，得到待辨识部分的负荷曲线，包括：
步骤a：通过电网EMS获取待评估馈线预定时间内(在一实施例中所述预定时间可以为一周，包括工作日和非工作日)的负荷曲线；
步骤b：通过用户自身安装的EMS系统或在与电网交互点的量测装置直接获取所述负荷曲线中的可辨识负荷的负荷曲线；
步骤c：将步骤a中得到的负荷曲线与步骤b中得到的可辨识负荷曲线相减得到待评估馈线负荷曲线中待辨识部分的负荷曲线。
(2)构建负荷数据库；
基于对应气候区预设类型负荷的历史负荷曲线(若无，可使用EnergyPlus软件仿真预设类型负荷的负荷曲线)构建预设类型负荷的负荷数 据库，建立预设类型负荷与气象、时间的关联关系，进而求取预设类型负荷曲线主要的特征参数。
预设类型负荷的负荷数据库可表示为：
Figure PCTCN2017088135-appb-000002
Figure PCTCN2017088135-appb-000003
上述公式中，NPB为待评估馈线所在气候区负荷类型的总数，
Figure PCTCN2017088135-appb-000004
为预设类型负荷k负荷曲线特征集合，包括该负荷预设时间段内(如一年)工作日基值功率集合
Figure PCTCN2017088135-appb-000005
和非工作日基值功率集合
Figure PCTCN2017088135-appb-000006
为描述工作日24小时功率与外部温度的关联参数集合，
Figure PCTCN2017088135-appb-000007
为非工作日24小时功率与外部温度的关联参数集合。
上述负荷数据库中包含待评估馈线所在气候区域的主要负荷类型，能够有效反应季节、日期及外部环境因素对用电负荷曲线的影响；这里对负荷类型进行说明：共包括16种商业类型和3种居民建筑类型，商业包括大型办公楼宇、中型办公楼宇、小型办公楼宇、仓库、零售商店、带状商业街、小学、初中、超市、快捷餐厅、饭店、医院、社区医院、小型宾馆、大型宾馆和多层公寓，居民建筑包括基本型、低型和高型。
(3)基于所述待辨识部分的负荷曲线及所述负荷数据库，调用预设的负荷辨识策略确定所述待辨识负荷的负荷类型和数量；
在一实施例中，所述预设的负荷辨识策略可以为预设的负荷辨识优化算法。
如图2所示的负荷辨识优化算法的流程图，该算法包括以下步骤：
步骤S1：判断预设类型负荷的负荷数据库中是否具有预设时间内待评估馈线所在气候区对应的预设类型负荷的负荷曲线；
如果有，采用预设的辨识方式直接辨识所述预设类型负荷的负荷曲线，得到所述待辨识负荷的负荷类型和数量；
这里，所述预设的辨识方式可以为预设的最小二乘法，在一实施例中，包括：
已知预设类型负荷的负荷数据库，待辨识负荷曲线以及特定气候区外部环境参数，如温度、湿度等，求解待辨识馈线的负荷类型和每类负荷的数量，可表示为：
Figure PCTCN2017088135-appb-000008
其中，φ(t)为待辨识负荷信号，Ω(·)为通过负荷分离算法估计得到的负荷构成叠加生成的用电负荷；ε[·]为估计值与实际值间的计算误差。
这里，在实际应用中预设类型负荷的负荷数据库包含了研究气候区除了不确定冲击负荷外所有可能存在的建筑类型。
如果没有，进行步骤S2。
步骤S2：采用负荷数据库和对应待辨识负荷的负荷曲线的时间和外部气象信息数据，生成预设类型负荷的负荷曲线，并通过所述预设的辨识方式辨识所述预设类型负荷的负荷曲线；
这里，采用负荷数据库和对应待辨识负荷的负荷曲线的时间和外部气象信息数据，生成预设类型负荷的负荷曲线，包括：
Figure PCTCN2017088135-appb-000009
Figure PCTCN2017088135-appb-000010
其中，△Tp是相对基线温度Tp,0的温度偏差；
Figure PCTCN2017088135-appb-000011
为t时刻基值功率、fk,1,t、fk,2,t、fk,3,t是功率与外部温度的关联参数集合，如公式(2)所示，上述参数区分工作日和非工作日。
步骤S3：利用最小二乘法求取待辨识负荷部分的负荷类型，使由负荷曲线分离出的负荷叠加形成的负荷曲线与待辨识负荷曲线的误差最小。
步骤S4：输出本次辨识负荷类型和数量的结果，并跳转至下一步骤。
(4)基于所述预设类型负荷与气象、时间的关联关系，通过仿真得到 每一类负荷的需求响应DR物理潜力。
这里，在实际应用中，所述仿真可以为EnergyPlus软件仿真，包括：
按下式计算建筑k在h时段内的需求响应潜力DRk,h：
Figure PCTCN2017088135-appb-000012
其中，
Figure PCTCN2017088135-appb-000013
为该负荷k在h时段的基线负荷；
Figure PCTCN2017088135-appb-000014
为该负荷响应DR事件后的实际负荷曲线。
在另一种实施方式中，利用预先生成的简化回归模型数据库仿真得到每一类负荷的需求响应DR物理潜力，如图3所示，包括如下：
根据大量历史数据或EnergyPlus仿真数据发现可采用分段线性回归模型建立环境温度和需求侧响应之间的关系模型：
Figure PCTCN2017088135-appb-000015
Figure PCTCN2017088135-appb-000016
Figure PCTCN2017088135-appb-000017
其中，DRk,h％为h时段负荷k的DR潜力占基线负荷的百分比，
Figure PCTCN2017088135-appb-000018
分别为与时段h相关的参数，OATh为时段h的环境温度，
Figure PCTCN2017088135-appb-000019
分别为与时段h相关的分段点。
(5)依据所述每类负荷的物理潜力及每类负荷的数量，得到所述待辨识负荷整体的聚合物理潜力。
按下式计算馈线负荷整体在h时段内的聚合响应潜力DRBSP,h；
Figure PCTCN2017088135-appb-000020
其中，xk表示负荷k的数目，Nb表示负荷的类型数目。
本发明实施例还提供了一种馈线需求响应物理潜力的评估装置，包括：处理器和用于存储能够在处理器上运行的计算机程序的存储器；其中，所述处理器用于运行所述计算机程序时，执行：
获取待评估馈线的负荷曲线中待辨识部分的负荷曲线；
基于预设类型负荷的历史负荷曲线构建负荷数据库；
基于所述待辨识部分的负荷曲线及所述负荷数据库，确定所述待辨识负荷的负荷类型和数量；
依据所述待辨识负荷的负荷类型获取每类负荷的物理潜力；
依据所述每类负荷的物理潜力及每类负荷的数量，得到所述待辨识负荷整体的聚合物理潜力。
在一实施例中，所述处理器用于运行所述计算机程序时，执行：
通过电网EMS获取待评估馈线预定时间内的负荷曲线；
提取所述负荷曲线中的可辨识负荷曲线；
基于所述预定时间内的负荷曲线及所述可辨识负荷曲线得到待辨识部分的负荷曲线。
在一实施例中，所述处理器用于运行所述计算机程序时，执行：
基于对应气候区预设类型负荷的历史负荷曲线构建预设类型负荷的负荷数据库，以建立所述预设类型负荷与气象、时间的关联关系。
在一实施例中，所述处理器用于运行所述计算机程序时，执行：
判断所述负荷数据库中是否具有预设时间内待评估馈线所在气候区对应的预设类型负荷的负荷曲线；
如果有，采用预设的辨识方式直接辨识所述预设类型负荷的负荷曲线，得到所述待辨识负荷的负荷类型和数量；
如果没有，采用负荷数据库和对应待辨识负荷的负荷曲线的时间和外部气象信息数据，生成预设类型负荷的负荷曲线，并通过所述预设的辨识方式辨识所述预设类型负荷的负荷曲线。
在一实施例中，所述处理器用于运行所述计算机程序时，执行：
基于所述预设类型负荷与气象、时间的关联关系，通过仿真得到每一 类负荷的需求响应DR物理潜力。
在一实施例中，所述处理器用于运行所述计算机程序时，执行：
按下式计算馈线负荷整体在h时段内的聚合响应潜力DRBSP,h；
Figure PCTCN2017088135-appb-000021
其中，xk表示负荷k的数目，Nb表示负荷的类型数目。
本发明实施例还提供了一种计算机存储介质，所述计算机存储介质中存储有计算机可执行指令，该计算机可执行指令用于执行上述馈线需求响应物理潜力的评估方法。
以上实施例仅用以说明本发明的技术方案而非对其限制，尽管参照上述实施例对本发明进行了详细的说明，所属领域的普通技术人员依然可以对本发明的具体实施方式进行修改或者等同替换，这些未脱离本发明精神和范围的任何修改或者等同替换，均在申请待批的本发明的权利要求保护范围之内。
工业实用性
本发明实施例获取待评估馈线的负荷曲线中待辨识部分的负荷曲线；基于预设类型负荷的历史负荷曲线构建负荷数据库；基于所述待辨识部分的负荷曲线及所述负荷数据库，确定所述待辨识负荷的负荷类型和数量；依据所述待辨识负荷的负荷类型获取每类负荷的物理潜力；依据所述每类负荷的物理潜力及每类负荷的数量，得到所述待辨识负荷整体的聚合物理潜力。如此，能够有效评估大型馈线负荷的需求响应物理潜力。

Claims (8)

  1. 一种馈线需求响应物理潜力的评估方法，所述评估方法包括：
    获取待评估馈线的负荷曲线中待辨识部分的负荷曲线；
    基于预设类型负荷的历史负荷曲线构建负荷数据库；
    基于所述待辨识部分的负荷曲线及所述负荷数据库，确定所述待辨识负荷的负荷类型和数量；
    依据所述待辨识负荷的负荷类型获取每类负荷的物理潜力；
    依据所述每类负荷的物理潜力及每类负荷的数量，得到所述待辨识负荷整体的聚合物理潜力。
  2. 如权利要求1所述的评估方法，其中，所述获取待评估馈线的负荷曲线中待辨识部分的负荷曲线，包括：
    通过电网能量管理系统EMS获取待评估馈线预定时间内的负荷曲线；
    提取所述负荷曲线中的可辨识负荷曲线；
    基于所述预定时间内的馈线负荷曲线及所述可辨识负荷曲线得到待辨识部分的负荷曲线。
  3. 如权利要求1或2所述的评估方法，其中，所述基于预设类型负荷的历史负荷曲线构建负荷数据库，包括：
    基于对应气候区预设类型负荷的历史负荷曲线构建预设类型负荷的负荷数据库，以建立所述预设类型负荷与气象、时间的关联关系。
  4. 如权利要求1或2所述的评估方法，其中，所述基于所述待辨识部分的负荷曲线及所述负荷数据库，确定所述待辨识负荷的负荷类型和数量，包括；
    判断所述负荷数据库中是否具有预设时间内待评估馈线所在气候区对应的预设类型负荷的负荷曲线；
    如果有，采用预设的辨识方式直接辨识待辨识负荷的负荷类型和数量；
    如果没有，基于对应待辨识负荷的负荷曲线的时间和外部气象信息数据，通过软件仿真生成预设类型负荷的负荷曲线，并通过所述预设的辨识方式辨识所述预设类型负荷的负荷曲线。
  5. 如权利要求4所述的评估方法，其中，所述依据所述待辨识负荷的负荷类型获取每类负荷的物理潜力，包括：
    基于所述预设类型负荷与气象、时间的关联关系，通过仿真得到每一类负荷的需求响应DR物理潜力。
  6. 如权利要求1所述的评估方法，其中，所述依据所述每类负荷的物理潜力及每类负荷的数量，得到所述待辨识负荷整体的聚合物理潜力，包括：
    按下式计算馈线负荷整体在h时段内的聚合响应潜力DRBSP,h；
    Figure PCTCN2017088135-appb-100001
    其中，xk表示负荷k的数目，Nb表示负荷的类型数目。
  7. 一种馈线需求响应物理潜力的评估装置，包括：处理器和用于存储能够在处理器上运行的计算机程序的存储器；其中，所述处理器用于运行所述计算机程序时，执行权利要求1至6任一项所述的馈线需求响应物理潜力的评估方法。
  8. 一种计算机存储介质，所述计算机存储介质中存储有计算机可执行指令，该计算机可执行指令用于执行权利要求1至6任一项所述的馈线需求响应物理潜力的评估方法。
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