[go: up one dir, main page]

CN111507626A - Uncertainty-considered economic evaluation method for photovoltaic roof-retired battery energy storage system - Google Patents

Uncertainty-considered economic evaluation method for photovoltaic roof-retired battery energy storage system Download PDF

Info

Publication number
CN111507626A
CN111507626A CN202010308156.1A CN202010308156A CN111507626A CN 111507626 A CN111507626 A CN 111507626A CN 202010308156 A CN202010308156 A CN 202010308156A CN 111507626 A CN111507626 A CN 111507626A
Authority
CN
China
Prior art keywords
photovoltaic
energy storage
storage system
user
battery energy
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202010308156.1A
Other languages
Chinese (zh)
Inventor
黄南天
王文婷
蔡国伟
包佳瑞琦
杨冬锋
孔令国
吴银银
杨学航
张祎祺
张良
王燕涛
黄大为
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Northeast Electric Power University
Original Assignee
Northeast Dianli University
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 Northeast Dianli University filed Critical Northeast Dianli University
Priority to CN202010308156.1A priority Critical patent/CN111507626A/en
Publication of CN111507626A publication Critical patent/CN111507626A/en
Pending legal-status Critical Current

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
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • 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
    • H02J3/008—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
    • H02J3/28—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
    • H02J3/381—Dispersed generators
    • 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
    • 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
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B10/00—Integration of renewable energy sources in buildings
    • Y02B10/10—Photovoltaic [PV]
    • 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
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • 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
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/10—Flexible AC transmission systems [FACTS]
    • 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
    • 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
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/80—Management or planning
    • Y02P90/82—Energy audits or management systems therefor
    • 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
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • Power Engineering (AREA)
  • Strategic Management (AREA)
  • Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Development Economics (AREA)
  • Educational Administration (AREA)
  • Marketing (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Theoretical Computer Science (AREA)
  • Tourism & Hospitality (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Game Theory and Decision Science (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Photovoltaic Devices (AREA)

Abstract

An uncertainty-considered economic assessment method for a photovoltaic roof-retired battery energy storage system is characterized by comprising the following steps of: the method comprises the following steps of analyzing the necessity of a photovoltaic roof user installation retired battery energy storage system, establishing an uncertain model of residential electricity load and roof photovoltaic output, establishing an economic analysis model of the roof photovoltaic and retired battery energy storage system, establishing a mixed integer linear programming model considering uncertainty of photovoltaic and user electricity consumption behaviors, and the like: the generated scene accurately captures the correlation and probability distribution of power utilization behaviors and photovoltaic output; meanwhile, the economic assessment model can correspond to the change of the price policy, provide reasonable economic assessment results for residents and avoid investment risks. Has the advantages of scientific and reasonable method, strong applicability, good effect and the like. The problem of photovoltaic power output and the uncertain and difficult economic appraisal of photovoltaic roof and retired battery energy storage combined system that cause of user's power consumption action that prior art exists is solved.

Description

计及不确定性的光伏屋顶-退役电池储能系统经济性评估 方法Economic evaluation of photovoltaic rooftop-decommissioned battery energy storage systems considering uncertainty method

技术领域technical field

本发明涉及住宅用户的屋顶光伏系统和退役电池储能系统，是一种计及不确定性的光伏 屋顶-退役电池储能系统经济性评估方法，应用于住宅用户屋顶光伏系统和退役电池储能联合 系统的经济性评估。The invention relates to a rooftop photovoltaic system and a retired battery energy storage system for residential users, and is a photovoltaic roof-retirement battery energy storage system economic evaluation method considering uncertainty, which is applied to the residential user rooftop photovoltaic system and retired battery energy storage system Economic evaluation of combined systems.

背景技术Background technique

随着屋顶光伏的大力推广与普及和大量的电动汽车动力电池进入可重复使用的电池市 场，可重复使用的电池可能是家用储能系统的实用替代品。但是，由于太阳能负载的不确定 性和政策的多样性，增加了屋顶光伏系统和退役电池储能联合系统经济性评估的难度。With the vigorous promotion and popularization of rooftop photovoltaics and the entry of a large number of electric vehicle power batteries into the reusable battery market, reusable batteries may be a practical alternative to home energy storage systems. However, due to the uncertainty of solar load and the diversity of policies, it is more difficult to evaluate the economics of the combined system of rooftop photovoltaic systems and decommissioned battery energy storage.

大多数研究没有深入分析住宅用户的用电行为和太阳辐照之间的相关性对光-储经济性 的影响，并且退役电池的梯次利用并未包括在内。并且，太阳辐照和住宅负荷具有高度不确 定性，直接影响屋顶光伏系统与退役电池储能系统的经济评估结果，且无法响应电力市场政 策的变化。采用去噪变分自编码器构建光伏与用电行为不确定性模型，并以太阳辐照与用电 行为的相关性刻画居民类型，结合实际电价政策，开展光伏屋顶和退役电池储能联合系统的 经济性评估。生成的用电行为和光伏场景降低了居民消费者的屋顶光伏与退役电池储能系统 投资风险。并且，相关性分析增强了推广光伏屋顶和退役电池储能联合系统的针对性，避免 了退役电池处理不当而造成的资源浪费和环境污染。Most studies did not deeply analyze the impact of the correlation between residential users' electricity consumption behavior and solar irradiation on the solar-storage economy, and the cascade utilization of retired batteries was not included. In addition, solar irradiation and residential loads are highly uncertain, which directly affect the economic evaluation results of rooftop photovoltaic systems and decommissioned battery energy storage systems, and cannot respond to changes in electricity market policies. Denoising variational autoencoder is used to build an uncertainty model of photovoltaic and electricity consumption behavior, and the type of residents is characterized by the correlation between solar irradiation and electricity consumption behavior. Combined with the actual electricity price policy, a combined photovoltaic roof and retired battery energy storage system is developed. economic assessment. The generated electricity consumption behavior and photovoltaic scenarios reduce the investment risk of residential consumers' rooftop photovoltaic and decommissioned battery energy storage systems. In addition, the correlation analysis enhances the pertinence of promoting the combined system of photovoltaic roof and decommissioned battery energy storage, and avoids resource waste and environmental pollution caused by improper disposal of decommissioned batteries.

发明内容SUMMARY OF THE INVENTION

本发明的目的是，克服现有住宅用户光伏屋顶和退役电池储能联合系统经济性评估的难 点，提供一种科学合理，适用性强，效果佳，能够降低用户投资风险，有利于可持续发展的 计及不确定性的光伏屋顶-退役电池储能系统经济性评估方法。The purpose of the present invention is to overcome the difficulty of economic evaluation of the existing combined photovoltaic roof and decommissioned battery energy storage system for residential users, and to provide a scientific and reasonable method with strong applicability and good effect, which can reduce the investment risk of users and is conducive to sustainable development. A method for evaluating the economics of photovoltaic rooftop-decommissioned battery energy storage systems accounting for uncertainty.

实现本发明目的采用的技术方案是，一种计及不确定性的光伏屋顶-退役电池储能系统经 济性评估方法，其特征是，它包括以下步骤：The technical scheme adopted to realize the object of the present invention is, a photovoltaic roof-retirement battery energy storage system economic evaluation method taking into account uncertainty, is characterized in that, it comprises the following steps:

1)光伏屋顶用户增装退役电池储能系统的必要性分析；1) The necessity of installing decommissioned battery energy storage systems for photovoltaic rooftop users;

根据式(1)～式(3)分别计算光伏出力、光伏出力与用户用电行为的相关性和各个用 户的光伏未消纳率，对居民用户进行编号；According to equations (1) to (3), the photovoltaic output, the correlation between photovoltaic output and users' electricity consumption behavior, and the photovoltaic non-consumption rate of each user are calculated respectively, and the residential users are numbered;

Figure BDA0002456506860000011
Figure BDA0002456506860000011

Figure BDA0002456506860000012
Figure BDA0002456506860000012

Figure BDA0002456506860000013
Figure BDA0002456506860000013

其中：G为照射到光伏面板上的太阳的辐射强度值；GSTC为标准辐射强度值；

Figure BDA0002456506860000021
为屋顶 光伏的容量值；xload为用电负荷值；xSR为太阳辐照值；
Figure BDA0002456506860000022
为用电负荷的标准差；
Figure BDA0002456506860000023
为太阳 辐照的标准差；
Figure BDA0002456506860000024
为光伏总出力值；
Figure BDA0002456506860000025
为居民用户没有消纳的光伏出力；PPV为光伏出力 值；ρ为光伏出力与用户用电行为的相关性；cov为协方差；Runabsorbed为用户的光伏未消纳率；Among them: G is the radiation intensity value of the sun shining on the photovoltaic panel; G STC is the standard radiation intensity value;
Figure BDA0002456506860000021
is the capacity value of rooftop photovoltaic; x load is the electricity load value; x SR is the solar irradiation value;
Figure BDA0002456506860000022
is the standard deviation of the electricity load;
Figure BDA0002456506860000023
is the standard deviation of solar irradiance;
Figure BDA0002456506860000024
is the total output value of photovoltaic;
Figure BDA0002456506860000025
is the photovoltaic output that is not absorbed by residential users; P PV is the photovoltaic output value; ρ is the correlation between photovoltaic output and users’ electricity consumption behavior; cov is the covariance; R unabsorbed is the photovoltaic unabsorbed rate of users;

2)建立居民用电负荷和屋顶光伏出力的不确定模型步骤：2) Steps to establish the uncertainty model of residential electricity load and rooftop photovoltaic output:

(a)根据式(4)～式(5)建立去噪变分自编码器模型；(a) Establish a denoising variational autoencoder model according to equations (4) to (5);

Figure BDA0002456506860000026
Figure BDA0002456506860000026

Figure BDA0002456506860000027
Figure BDA0002456506860000027

式中：DKL为Kullback–Leibler散度；u为观测数据值；a为潜变量值；p为概率分布；q为近似分布；E为期望；pμ(u|a)为生成网络；qε(a|u)为识别网络；log pμ(u)为对数似然 函数的下界；

Figure BDA0002456506860000028
为去噪变分下界；μ为生成神经网络权重值；ε为识别神经网络权重值；where D KL is the Kullback–Leibler divergence; u is the observed data value; a is the latent variable value; p is the probability distribution; q is the approximate distribution; E is the expectation; p μ (u|a) is the generative network; q ε (a|u) is the recognition network; log p μ (u) is the lower bound of the log-likelihood function;
Figure BDA0002456506860000028
is the denoising variational lower bound; μ is the weight value of the generating neural network; ε is the weight value of the recognition neural network;

(b)将居民用户的太阳辐照数据与用电负荷数据重塑为24×24元组；(b) Reshape the solar radiation data and electricity load data of residential users into 24×24 tuples;

(c)截取生成网络，输入n个服从标准正态分布的潜变量，生成n组与原始数据概率分 布相似的场景；(c) Intercept the generation network, input n latent variables that obey the standard normal distribution, and generate n groups of scenarios similar to the original data probability distribution;

(d)采用K-Means聚类得到潜在的居民用电负荷与光伏发电数据；(d) Using K-Means clustering to obtain potential residential electricity load and photovoltaic power generation data;

3)建立屋顶光伏与退役电池储能系统的经济性分析模型步骤：3) Steps to establish an economic analysis model for rooftop photovoltaic and decommissioned battery energy storage systems:

(a)建立退役电池储能系统模型(a) Build a model of a retired battery energy storage system

退役动力电池储能系统容量会随着电池使用次数的增加而衰减：The capacity of retired power battery energy storage system will decay with the increase of battery usage:

Figure BDA0002456506860000029
Figure BDA0002456506860000029

λRBESS＝a·NCRBESS+b (7)λ RBESS = a·NC RBESS +b (7)

Figure BDA00024565068600000210
Figure BDA00024565068600000210

式中，NCRBESS为退役动力电池储能系统的使用循环次数；a为容量系数；b为容量系数；

Figure BDA00024565068600000211
为退役动力电池储能系统的实际容量；
Figure BDA00024565068600000212
为退役动力电池储能系统的额定容量；NCday为退役电池储能系统的日运行等效完全充放电次数；NCb为放电深度为100％时的退役 电池循环寿命；NCi为第i个循环周期中退役电池在实际运行中的循环寿命，i＝1,2,…,N；λRBESS为容量保持率；In the formula, NC RBESS is the number of use cycles of the retired power battery energy storage system; a is the capacity coefficient; b is the capacity coefficient;
Figure BDA00024565068600000211
is the actual capacity of the retired power battery energy storage system;
Figure BDA00024565068600000212
is the rated capacity of the retired power battery energy storage system; NC day is the daily equivalent full charge and discharge times of the retired battery energy storage system; NC b is the cycle life of the retired battery when the depth of discharge is 100%; NC i is the i-th Cycle life of decommissioned batteries in actual operation during the cycle period, i=1,2,...,N; λ RBESS is the capacity retention rate;

(b)建立屋顶光伏系统模型(b) Build a rooftop photovoltaic system model

光伏系统的容量是根据用户的年耗电量与太阳辐照强度等因素确定：The capacity of the photovoltaic system is determined according to the user's annual power consumption and solar radiation intensity and other factors:

Figure BDA0002456506860000031
Figure BDA0002456506860000031

式中：

Figure BDA0002456506860000032
为光伏的容量；PAPC为居民用户的年耗电量；ηCR为太阳辐照覆盖率；H为太阳辐照年持续时间；where:
Figure BDA0002456506860000032
is the photovoltaic capacity; P APC is the annual power consumption of residential users; η CR is the solar irradiation coverage rate; H is the annual duration of solar irradiation;

(c)建立屋顶光伏与退役电池储能系统运行模型(c) Establish the operation model of rooftop photovoltaic and decommissioned battery energy storage system

Figure BDA0002456506860000033
Figure BDA0002456506860000033

Figure BDA0002456506860000034
Figure BDA0002456506860000034

Figure BDA0002456506860000035
Figure BDA0002456506860000035

式中：Copt为系统运行成本；πprice为电力价格；πRprice为居民用户向电网售电价格；πPprice为居民用户向电网购电价格；PRPV为屋顶光伏的功率；PRBESS为退役电池储能系统的功率；Pload为用户消耗的电功率；t为时间，t＝1,2,…,T；In the formula: C opt is the operating cost of the system; π price is the electricity price; π Rprice is the price of electricity sold by residential users to the grid; π Pprice is the price of electricity purchased by residential users from the grid; P RPV is the power of rooftop photovoltaics; P RBESS is the decommissioning The power of the battery energy storage system; P load is the electric power consumed by the user; t is the time, t=1,2,…,T;

(d)居民用户的净现值(d) Net present value of resident users

用户的净现值为正，从而表明它是有利润的；The user has a positive net present value, thus indicating that it is profitable;

Figure BDA0002456506860000036
Figure BDA0002456506860000036

Figure BDA0002456506860000037
Figure BDA0002456506860000037

式中：NCFRBESS为增装退役电池储能系统的净现金流量；NCFRPV为安装光伏屋顶的净现金流量； NPV为净现值；NCF为净现金流量；n为年份；φn(n)为未安装光伏与退役电池储能系统用户 的第n年账单；φRPV(n)为只安装光伏系统用户的第n年账单；φPVRB(n)为安装光伏与退役电池 储能联合系统用户的第n年账单；r为市场通货膨胀率；

Figure BDA0002456506860000041
为退役电池储能系统安装成本；
Figure BDA0002456506860000042
为光伏系统安装成本；OM为光伏维护成本；In the formula: NCF RBESS is the net cash flow of installing and decommissioning battery energy storage systems; NCF RPV is the net cash flow of installing photovoltaic roofs; NPV is the net present value; NCF is the net cash flow; n is the year; φ n (n) is the bill of the nth year for users who have not installed photovoltaic and decommissioned battery energy storage systems; φ RPV (n) is the nth year bill of users who only install photovoltaic systems; φ PVRB (n) is the user who installed photovoltaic and decommissioned battery energy storage systems The nth year bill; r is the market inflation rate;
Figure BDA0002456506860000041
Installation costs for decommissioning battery storage systems;
Figure BDA0002456506860000042
is the installation cost of photovoltaic system; OM is the cost of photovoltaic maintenance;

4)根据步骤1)至步骤3)，构建考虑光伏与用户用电行为不确定性的混合整数线性规 划模型，结合现行多电价政策，针对每种类型的居民用户，开展光伏屋顶-退役电池储能系统 的经济性评估。4) According to step 1) to step 3), construct a mixed integer linear programming model that considers the uncertainty of photovoltaic and user power consumption behavior, and combine the current multi-price policy to carry out photovoltaic rooftop-retirement battery storage for each type of residential users. Economic evaluation of energy systems.

本发明的一种计及不确定性的光伏屋顶-退役电池储能系统经济性评估方法，其特点是， 在建立光伏与用电行为不确定性模型时能够避免概率建模，特征提取和概率采样的繁琐步骤， 使生成的场景准确地捕获了用电行为和光伏出力的相关性和概率分布；同时，该经济性评估 模型能够相应电价政策的变化，为居民提供合理的经济性评估结果，规避投资风险。具有方 法科学合理，适用性强，效果佳等优点。解决了现有技术存在的光伏出力与用户用电行为不 确定而造成的光伏屋顶和退役电池储能联合系统的经济性评估困难的问题。The economic evaluation method of photovoltaic roof-retirement battery energy storage system considering uncertainty of the present invention is characterized in that probability modeling, feature extraction and probability modeling can be avoided when establishing photovoltaic and electricity consumption behavior uncertainty models. The tedious steps of sampling enable the generated scene to accurately capture the correlation and probability distribution of electricity consumption behavior and photovoltaic output; at the same time, the economic evaluation model can respond to changes in electricity price policies and provide residents with reasonable economic evaluation results. Avoid investment risks. It has the advantages of scientific and reasonable method, strong applicability and good effect. It solves the problem of difficulty in economic evaluation of the combined system of photovoltaic roof and retired battery energy storage caused by the uncertainty of photovoltaic output and user power consumption behavior in the existing technology.

附图说明Description of drawings

图1本发明的一种计及不确定性的光伏屋顶-退役电池储能系统经济性评估方法流程图；Fig. 1 is a flow chart of a method for economic evaluation of a photovoltaic roof-retirement battery energy storage system considering uncertainty according to the present invention;

图2为某日的屋顶光伏出力生成场景示例图；Figure 2 is an example diagram of a roof photovoltaic output generation scene on a certain day;

图3为某日的居民电负荷生成场景示例图。FIG. 3 is an example diagram of a residential electric load generation scenario on a certain day.

具体实施方式Detailed ways

本发明实施例的一种计及不确定性的光伏屋顶-退役电池储能系统经济性评估方法，针对 5个典型的居民用户进行经济性评估：(1)太阳辐照与用电行为的相关性为0.0004的用户， 即无相关；(2)太阳辐照与用电行为的相关性为0.2255的用户，即最大正相关；(3)太阳辐 照与用电行为的相关性为-0.2572的用户，即最大负相关；(4)太阳辐照与用电行为的相关性 为-0.1004的用户；(5)太阳辐照与用电行为的相关性为0.1003的用户。An economic evaluation method for a photovoltaic roof-retirement battery energy storage system considering uncertainty according to an embodiment of the present invention performs economic evaluation for five typical residential users: (1) Correlation between solar irradiation and electricity consumption behavior (2) The correlation between solar radiation and electricity consumption is 0.2255, that is, the maximum positive correlation; (3) The correlation between solar radiation and electricity consumption is -0.2572. Users, that is, the maximum negative correlation; (4) users whose correlation between solar radiation and electricity consumption is -0.1004; (5) users whose correlation between solar radiation and electricity consumption is 0.1003.

表1典型居民用户概况Table 1 Profiles of typical residential users

Figure BDA0002456506860000043
Figure BDA0002456506860000043

下面以光伏第3资源区中的居民屋顶光伏系统和退役电池储能联合系统为例来说明一种 计及不确定性的光伏屋顶-退役电池储能系统经济性评估方法。The following takes the residential rooftop photovoltaic system and the decommissioned battery energy storage combined system in the third photovoltaic resource area as an example to illustrate an economic evaluation method of photovoltaic rooftop-decommissioned battery energy storage system that takes into account uncertainty.

参考图1～图3，一种计及不确定性的光伏屋顶-退役电池储能系统经济性评估方法，其 特征是，它包括以下步骤：Referring to Figures 1 to 3, a method for evaluating the economics of photovoltaic rooftop-retirement battery energy storage systems considering uncertainty is characterized in that it includes the following steps:

1)光伏屋顶用户增装退役电池储能系统的必要性分析；1) The necessity of installing decommissioned battery energy storage systems for photovoltaic rooftop users;

根据式(1)～式(3)分别计算光伏出力、光伏出力与用户用电行为的相关性和各个用 户的光伏未消纳率，对居民用户进行编号；According to equations (1) to (3), the photovoltaic output, the correlation between photovoltaic output and users' electricity consumption behavior, and the photovoltaic non-consumption rate of each user are calculated respectively, and the residential users are numbered;

Figure BDA0002456506860000051
Figure BDA0002456506860000051

Figure BDA0002456506860000052
Figure BDA0002456506860000052

Figure BDA0002456506860000053
Figure BDA0002456506860000053

其中：G为照射到光伏面板上的太阳的辐射强度值；GSTC为标准辐射强度值；

Figure BDA0002456506860000054
为屋顶 光伏的容量值；xload为用电负荷值；xSR为太阳辐照值；
Figure BDA0002456506860000055
为用电负荷的标准差；
Figure BDA0002456506860000056
为太阳 辐照的标准差；
Figure BDA0002456506860000057
为光伏总出力值；
Figure BDA0002456506860000058
为居民用户没有消纳的光伏出力；PPV为光伏出力 值；ρ为光伏出力与用户用电行为的相关性；cov为协方差；Runabsorbed为用户的光伏未消纳率；Among them: G is the radiation intensity value of the sun shining on the photovoltaic panel; G STC is the standard radiation intensity value;
Figure BDA0002456506860000054
is the capacity value of rooftop photovoltaic; x load is the electricity load value; x SR is the solar irradiation value;
Figure BDA0002456506860000055
is the standard deviation of the electricity load;
Figure BDA0002456506860000056
is the standard deviation of solar irradiance;
Figure BDA0002456506860000057
is the total output value of photovoltaic;
Figure BDA0002456506860000058
is the photovoltaic output that is not absorbed by residential users; P PV is the photovoltaic output value; ρ is the correlation between photovoltaic output and users’ electricity consumption behavior; cov is the covariance; R unabsorbed is the photovoltaic unabsorbed rate of users;

2)建立居民用电负荷和屋顶光伏出力的不确定模型步骤：2) Steps to establish the uncertainty model of residential electricity load and rooftop photovoltaic output:

(a)根据式(4)～式(5)建立去噪变分自编码器模型；(a) Establish a denoising variational autoencoder model according to equations (4) to (5);

Figure BDA0002456506860000059
Figure BDA0002456506860000059

Figure BDA00024565068600000510
Figure BDA00024565068600000510

式中：DKL为Kullback–Leibler散度；u为观测数据值；a为潜变量值；p为概率分布；q为近似分布；E为期望；pμ(u|a)为生成网络；qε(a|u)为识别网络；log pμ(u)为对数似然 函数的下界；

Figure BDA00024565068600000511
为去噪变分下界；μ为生成神经网络权重值；ε为识别神经网络权重值；where D KL is the Kullback–Leibler divergence; u is the observed data value; a is the latent variable value; p is the probability distribution; q is the approximate distribution; E is the expectation; p μ (u|a) is the generative network; q ε (a|u) is the recognition network; log p μ (u) is the lower bound of the log-likelihood function;
Figure BDA00024565068600000511
is the denoising variational lower bound; μ is the weight value of the generating neural network; ε is the weight value of the recognition neural network;

(b)将居民用户的太阳辐照数据与用电负荷数据重塑为24×24元组；(b) Reshape the solar radiation data and electricity load data of residential users into 24×24 tuples;

(c)截取生成网络，输入n个服从标准正态分布的潜变量，生成n组与原始数据概率分 布相似的场景；(c) Intercept the generation network, input n latent variables that obey the standard normal distribution, and generate n groups of scenarios similar to the original data probability distribution;

(d)采用K-Means聚类得到潜在的居民用电负荷与光伏发电数据；(d) Using K-Means clustering to obtain potential residential electricity load and photovoltaic power generation data;

3)建立屋顶光伏与退役电池储能系统的经济性分析模型步骤：3) Steps to establish an economic analysis model for rooftop photovoltaic and decommissioned battery energy storage systems:

(a)建立退役电池储能系统模型(a) Build a model of a retired battery energy storage system

退役动力电池储能系统容量会随着电池使用次数的增加而衰减：The capacity of retired power battery energy storage system will decay with the increase of battery usage:

Figure BDA0002456506860000061
Figure BDA0002456506860000061

λRBESS＝a·NCRBESS+b (7)λ RBESS = a·NC RBESS +b (7)

Figure BDA0002456506860000062
Figure BDA0002456506860000062

式中，NCRBESS为退役动力电池储能系统的使用循环次数；a为容量系数；b为容量系数；

Figure BDA0002456506860000063
为退役动力电池储能系统的实际容量；
Figure BDA0002456506860000064
为退役动力电池储能系统的额定容量； NCday为退役电池储能系统的日运行等效完全充放电次数；NCb为放电深度为100％时的退役 电池循环寿命；NCi为第i个循环周期中退役电池在实际运行中的循环寿命，i＝1,2,…,N；λRBESS为容量保持率；In the formula, NC RBESS is the number of use cycles of the retired power battery energy storage system; a is the capacity coefficient; b is the capacity coefficient;
Figure BDA0002456506860000063
is the actual capacity of the retired power battery energy storage system;
Figure BDA0002456506860000064
is the rated capacity of the retired power battery energy storage system; NC day is the daily equivalent full charge and discharge times of the retired battery energy storage system; NC b is the cycle life of the retired battery when the depth of discharge is 100%; NC i is the i-th Cycle life of decommissioned batteries in actual operation during the cycle period, i=1,2,...,N; λ RBESS is the capacity retention rate;

(b)建立屋顶光伏系统模型(b) Build a rooftop photovoltaic system model

光伏系统的容量是根据用户的年耗电量与太阳辐照强度等因素确定：The capacity of the photovoltaic system is determined according to the user's annual power consumption and solar radiation intensity and other factors:

Figure BDA0002456506860000065
Figure BDA0002456506860000065

式中：

Figure BDA0002456506860000066
为光伏的容量；PAPC为居民用户的年耗电量；ηCR为太阳辐照覆盖率；H为太阳辐照年持续时间；where:
Figure BDA0002456506860000066
is the photovoltaic capacity; P APC is the annual power consumption of residential users; η CR is the solar irradiation coverage rate; H is the annual duration of solar irradiation;

(c)建立屋顶光伏与退役电池储能系统运行模型(c) Establish the operation model of rooftop photovoltaic and decommissioned battery energy storage system

Figure BDA0002456506860000067
Figure BDA0002456506860000067

Figure BDA0002456506860000068
Figure BDA0002456506860000068

Figure BDA0002456506860000069
Figure BDA0002456506860000069

式中：Copt为系统运行成本；πprice为电力价格；πRprice为居民用户向电网售电价格；πPprice为居民用户向电网购电价格；PRPV为屋顶光伏的功率；PRBESS为退役电池储能系统的功率；Pload为用户消耗的电功率；t为时间，t＝1,2,…,T；In the formula: C opt is the operating cost of the system; π price is the electricity price; π Rprice is the price of electricity sold by residential users to the grid; π Pprice is the price of electricity purchased by residential users from the grid; P RPV is the power of rooftop photovoltaics; P RBESS is the decommissioning The power of the battery energy storage system; P load is the electric power consumed by the user; t is the time, t=1,2,…,T;

(d)居民用户的净现值(d) Net present value of resident users

用户的净现值为正，从而表明它是有利润的；The user has a positive net present value, thus indicating that it is profitable;

Figure BDA0002456506860000071
Figure BDA0002456506860000071

Figure BDA0002456506860000072
Figure BDA0002456506860000072

式中：NCFRBESS为增装退役电池储能系统的净现金流量；NCFRPV为安装光伏屋顶的净现金流量； NPV为净现值；NCF为净现金流量；n为年份；φn(n)为未安装光伏与退役电池储能系统用户 的第n年账单；φRPV(n)为只安装光伏系统用户的第n年账单；φPVRB(n)为安装光伏与退役电池 储能联合系统用户的第n年账单；r为市场通货膨胀率；

Figure BDA0002456506860000073
为退役电池储能系统安装成本；
Figure BDA0002456506860000074
为光伏系统安装成本；OM为光伏维护成本；In the formula: NCF RBESS is the net cash flow of installing and decommissioning battery energy storage systems; NCF RPV is the net cash flow of installing photovoltaic roofs; NPV is the net present value; NCF is the net cash flow; n is the year; φ n (n) is the bill of the nth year for users who have not installed photovoltaic and decommissioned battery energy storage systems; φ RPV (n) is the nth year bill of users who only install photovoltaic systems; φ PVRB (n) is the user who installed photovoltaic and decommissioned battery energy storage systems The nth year bill; r is the market inflation rate;
Figure BDA0002456506860000073
Installation costs for decommissioning battery storage systems;
Figure BDA0002456506860000074
is the installation cost of photovoltaic system; OM is the cost of photovoltaic maintenance;

4)根据步骤1)至步骤3)，构建考虑光伏与用户用电行为不确定性的混合整数线性规 划模型，结合现行多电价政策，针对每种类型的居民用户，开展光伏屋顶-退役电池储能系统 的经济性评估。4) According to step 1) to step 3), construct a mixed integer linear programming model that considers the uncertainty of photovoltaic and user power consumption behavior, and combine the current multi-price policy to carry out photovoltaic rooftop-retirement battery storage for each type of residential users. Economic evaluation of energy systems.

上述实施例仅仅是为清楚地说明所作的举例，而并非对实施方式的限定，对于所属领域 的普通技术人员来说，在上述说明的基础上还可以做出其它不同形式的变化或变动，这里无 需也无法对所有的实施方式予以穷举，而由此所引伸出的显而易见的变化或变动仍处于本发 明创造的保护范围之中。The above-mentioned embodiments are merely examples for the purpose of clearly illustrating, rather than limiting the implementation manner. For those of ordinary skill in the art, changes or changes in other different forms can also be made on the basis of the above-mentioned descriptions. It is unnecessary and impossible to list all the embodiments, and the obvious changes or modifications derived therefrom still fall within the protection scope of the present invention.

Claims (1)

1. An uncertainty-considered economic assessment method for a photovoltaic roof-retired battery energy storage system is characterized by comprising the following steps of:
1) the necessity analysis of a photovoltaic roof user for additionally installing a decommissioned battery energy storage system is carried out;
respectively calculating the photovoltaic output, the correlation between the photovoltaic output and the user electricity consumption behavior and the photovoltaic non-consumption rate of each user according to the formulas (1) to (3), and numbering the resident users;
Figure FDA0002456506850000011
Figure FDA0002456506850000012
Figure FDA0002456506850000013
wherein: g is the radiation intensity value of the sun impinging on the photovoltaic panel; gSTCIs a standard radiation intensity value;
Figure FDA0002456506850000014
is the value of the capacity of the rooftop photovoltaic; x is the number ofloadIs the electric load value; x is the number ofSRIs the solar irradiation value;
Figure FDA0002456506850000015
the standard deviation of the electrical load is shown;
Figure FDA0002456506850000016
standard deviation of solar irradiation;
Figure FDA0002456506850000017
the photovoltaic total output value is obtained;
Figure FDA0002456506850000018
photovoltaic output which is not absorbed by resident users; pPVThe photovoltaic output value is obtained; rho is the correlation between photovoltaic output and user electricity consumption behavior; cov is covariance; runabsorbedThe photovoltaic non-absorption rate of the user is obtained;
2) establishing an uncertain model of residential electricity load and roof photovoltaic output:
(a) establishing a denoising variational self-encoder model according to the formula (4) to the formula (5);
Figure FDA0002456506850000019
Figure FDA00024565068500000110
in the formula: dKLIs Kullback-L eibler divergence, u is the observed data value, a is the latent variable value, p is the probability distribution, q is the approximate distribution, E is the expectation, p is the probability distributionμ(u | a) is a generation network; q. q.s(a | u) identifying a network; logp (Logp)μ(u) is the lower bound of the log-likelihood function;
Figure FDA00024565068500000111
a denoising variation lower bound; mu is a weight value of the generated neural network; to identify neural network weight values;
(b) reshaping the solar irradiation data and the electrical load data of the residential user into a 24 × 24 tuple;
(c) intercepting a generation network, inputting n latent variables which obey standard normal distribution, and generating n groups of scenes similar to the probability distribution of original data;
(d) potential residential electricity load and photovoltaic power generation data are obtained by adopting K-Means clustering;
3) establishing an economic analysis model of the roof photovoltaic and retired battery energy storage system:
(a) establishing a retired battery energy storage system model
The capacity of the energy storage system of the retired power battery is attenuated along with the increase of the using times of the battery:
Figure FDA0002456506850000021
λRBESS＝a·NCRBESS+b (7)
Figure FDA0002456506850000022
in the formula, NCRBESSThe number of use cycles of the energy storage system of the retired power battery; a is a capacity coefficient; b is a capacity coefficient;
Figure FDA0002456506850000023
the actual capacity of the energy storage system of the retired power battery;
Figure FDA0002456506850000024
the rated capacity of the energy storage system of the retired power battery; NC (numerical control)dayEquivalent complete charging and discharging times for the daily operation of the retired battery energy storage system; NC (numerical control)bThe cycle life of the retired battery is 100% of the discharge depth; NC (numerical control)iThe cycle life of the retired battery in the ith cycle in actual operation is 1,2, …, N; lambda [ alpha ]RBESSIs the capacity retention rate;
(b) building roof photovoltaic system model
The capacity of the photovoltaic system is determined according to factors such as annual power consumption and solar radiation intensity of a user:
Figure FDA0002456506850000025
in the formula:
Figure FDA0002456506850000026
is the capacity of the photovoltaic; pAPCAnnual power consumption for residential users ηCRSolar irradiation coverage; h is the annual duration of solar irradiation;
(c) building roof photovoltaic and retired battery energy storage system operation model
Figure FDA0002456506850000027
Figure FDA0002456506850000028
Figure FDA0002456506850000029
In the formula: coptThe system operating cost; pipriceIs the price of electricity; piRpriceSelling electricity to the power grid for the residential users; piPpricePurchasing electricity price for residential users to the power grid; pRPVPower for rooftop photovoltaics; pRBESSThe power of the energy storage system for the retired battery; ploadElectrical power consumed for a user; t is time, T ═ 1,2, …, T;
(d) net present value of residential user
The net present value of the user is positive, indicating that it is profitable;
Figure FDA0002456506850000031
Figure FDA0002456506850000032
in the formula: NCFRBESSThe net cash flow of the energy storage system of the retired battery is increased; NCFRPVNet cash flow for installation of photovoltaic roofs; NPV is the net present value; NCF is net cash flow; n is year; phi is an(n) the nth bill of the user without photovoltaic and retired battery energy storage system; phi is aRPV(n) is the nth year bill of the user who only installs the photovoltaic system; phi is aPVRB(n) the nth bill of the user who installs the photovoltaic and retired battery energy storage combined system; r is the market inflation rate;
Figure FDA0002456506850000033
the installation cost for the ex-service battery energy storage system;
Figure FDA0002456506850000034
installation costs for the photovoltaic system; OM is photovoltaic maintenance cost;
4) and 3) constructing a mixed integer linear programming model considering uncertainty of photovoltaic and user electricity utilization behaviors according to the steps 1) to 3), and carrying out economic evaluation on the photovoltaic roof-retired battery energy storage system aiming at each type of residential users by combining the current multi-electricity-price policy.
CN202010308156.1A 2020-04-18 2020-04-18 Uncertainty-considered economic evaluation method for photovoltaic roof-retired battery energy storage system Pending CN111507626A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010308156.1A CN111507626A (en) 2020-04-18 2020-04-18 Uncertainty-considered economic evaluation method for photovoltaic roof-retired battery energy storage system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010308156.1A CN111507626A (en) 2020-04-18 2020-04-18 Uncertainty-considered economic evaluation method for photovoltaic roof-retired battery energy storage system

Publications (1)

Publication Number Publication Date
CN111507626A true CN111507626A (en) 2020-08-07

Family

ID=71869523

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010308156.1A Pending CN111507626A (en) 2020-04-18 2020-04-18 Uncertainty-considered economic evaluation method for photovoltaic roof-retired battery energy storage system

Country Status (1)

Country Link
CN (1) CN111507626A (en)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114362225A (en) * 2021-12-30 2022-04-15 国网吉林省电力有限公司经济技术研究院 Energy storage system site selection and volume fixing method and device considering new energy consumption
CN114819450A (en) * 2021-09-22 2022-07-29 自然资源部国土卫星遥感应用中心 Regional construction distributed photovoltaic potential evaluation method based on satellite remote sensing
WO2022193794A1 (en) * 2021-03-19 2022-09-22 长沙理工大学 Multi-objective energy management method for smart community microgrid that takes into consideration decommissioned batteries
CN116305493A (en) * 2023-05-11 2023-06-23 中建五局第三建设有限公司 Construction evaluation method of photovoltaic energy-saving curtain wall

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102900207A (en) * 2012-10-17 2013-01-30 中海阳新能源电力股份有限公司 Device for two-way cooling of photovoltaic building by using natural low-temperature heat source
US8386197B1 (en) * 2009-01-08 2013-02-26 EchoFirst, Inc. Method and system for processing information from a combination of a solar thermal system and a photovoltaic apparatus
ES2526941A1 (en) * 2013-07-15 2015-01-16 Antonino Adriano Trimboli Longuetto Thermodynamic building (Machine-translation by Google Translate, not legally binding)
CN104933315A (en) * 2015-06-30 2015-09-23 中国电力科学研究院 Photovoltaic power generation output joint probability distributed modeling method
CN109450376A (en) * 2018-11-20 2019-03-08 中国铁路总公司 Photovoltaic generating system state on_line monitoring and fault location system and method
CN109586341A (en) * 2018-12-14 2019-04-05 国网山东省电力公司经济技术研究院 A kind of photovoltaic power generation receiving assessment models based on Radiation Characteristics meteorology scene
CN109659927A (en) * 2018-10-24 2019-04-19 国网天津市电力公司电力科学研究院 A kind of comprehensive energy microgrid energy accumulation capacity configuration considering energy storage participation
US20190197203A1 (en) * 2017-12-27 2019-06-27 Imec Vzw Simulation of Photovoltaic Systems

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8386197B1 (en) * 2009-01-08 2013-02-26 EchoFirst, Inc. Method and system for processing information from a combination of a solar thermal system and a photovoltaic apparatus
CN102900207A (en) * 2012-10-17 2013-01-30 中海阳新能源电力股份有限公司 Device for two-way cooling of photovoltaic building by using natural low-temperature heat source
ES2526941A1 (en) * 2013-07-15 2015-01-16 Antonino Adriano Trimboli Longuetto Thermodynamic building (Machine-translation by Google Translate, not legally binding)
CN104933315A (en) * 2015-06-30 2015-09-23 中国电力科学研究院 Photovoltaic power generation output joint probability distributed modeling method
US20190197203A1 (en) * 2017-12-27 2019-06-27 Imec Vzw Simulation of Photovoltaic Systems
CN109659927A (en) * 2018-10-24 2019-04-19 国网天津市电力公司电力科学研究院 A kind of comprehensive energy microgrid energy accumulation capacity configuration considering energy storage participation
CN109450376A (en) * 2018-11-20 2019-03-08 中国铁路总公司 Photovoltaic generating system state on_line monitoring and fault location system and method
CN109586341A (en) * 2018-12-14 2019-04-05 国网山东省电力公司经济技术研究院 A kind of photovoltaic power generation receiving assessment models based on Radiation Characteristics meteorology scene

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
潘宇: "计及损耗及电压水平的分布式光—储系统容量配置及经济性研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 *
赵志欣: "光伏微网系统优化调度机制研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2022193794A1 (en) * 2021-03-19 2022-09-22 长沙理工大学 Multi-objective energy management method for smart community microgrid that takes into consideration decommissioned batteries
CN114819450A (en) * 2021-09-22 2022-07-29 自然资源部国土卫星遥感应用中心 Regional construction distributed photovoltaic potential evaluation method based on satellite remote sensing
CN114362225A (en) * 2021-12-30 2022-04-15 国网吉林省电力有限公司经济技术研究院 Energy storage system site selection and volume fixing method and device considering new energy consumption
CN116305493A (en) * 2023-05-11 2023-06-23 中建五局第三建设有限公司 Construction evaluation method of photovoltaic energy-saving curtain wall
CN116305493B (en) * 2023-05-11 2023-08-01 中建五局第三建设有限公司 Construction evaluation method of photovoltaic energy-saving curtain wall

Similar Documents

Publication Publication Date Title
CN111507626A (en) Uncertainty-considered economic evaluation method for photovoltaic roof-retired battery energy storage system
Zhang et al. Battery sizing and rule-based operation of grid-connected photovoltaic-battery system: A case study in Sweden
Mei et al. Game approaches for hybrid power system planning
Magnor et al. Optimization of PV battery systems using genetic algorithms
CN105846423A (en) Method for photovoltaic microgrid energy storage multi-target capacity configuration by taking demand response into consideration
Aghamolaei et al. Feasibility analysis of community-based PV systems for residential districts: A comparison of on-site centralized and distributed PV installations
Pikas et al. Cost-benefit analysis of nZEB energy efficiency strategies with on-site photovoltaic generation
CN104915899A (en) Photovoltaic power station operation state classifying method based on characteristic cluster analysis
Huang et al. Economic analysis of household photovoltaic and reused-battery energy storage systems based on solar-load deep scenario generation under multi-tariff policies of China
Lu et al. Design of a reward-penalty cost for the promotion of net-zero energy buildings
Javed et al. Design, model & planning of prosumer microgrid for MNSUET multan campus
CN112819279A (en) Planning evaluation method and system for expansion adaptability of distributed energy and power distribution network
Bai et al. An online multi-level energy management system for commercial building microgrids with multiple generation and storage systems
Saurabh et al. Optimizing PV integration: Addressing energy fluctuations through BIPV and rooftop PV synergy
Sun et al. Multi-objective confidence gap decision based robust optimal dispatch of integrated energy system using entropy expectation maximization GMM
Fabrizio et al. Trade-off between environmental and economic objectives in the optimization of multi-energy systems
CN118504815A (en) Virtual power plant double optimization method considering source load uncertainty and electric carbon coupling
CN104598687A (en) Optimized construction method for photovoltaic storage battery power supply system of small buoy power source for water quality monitoring
Hilorme et al. DEVISING A CALCULATION METHOD FOR DETERMINING THE IMPACT OF DESIGN FEATURES OF SOLAR PANELS ON PERFORMANCE.
Akgayev et al. Assessing the Feasibility of Photovoltaic Systems in Türkiye: Technical and Economic Analysis of On-Grid, Off-Grid, and Utility-Scale PV Installations
Phap et al. Study on technical, economic, environmental efficiency of self-consumption rooftop solar power using lithium-ion battery for households in Vietnam
Zambri et al. Comparative study of net energy metering and feed-in tariff for the 496kWp UiTM segamat solar photovoltaic system
CN116029591A (en) Comprehensive energy service benefit quantitative evaluation method and device and terminal equipment
Casu et al. Designing nearly zero energy buildings: a recommendation tool for optimal sizing of renewable energy source systems
Schibuola et al. Parametric study on the financial performance of battery-supported photovoltaic systems connected to smart grids in current and future market scenarios

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20200807