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CN111899122A - User decentralized clearing method based on energy storage control - Google Patents

User decentralized clearing method based on energy storage control Download PDF

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CN111899122A
CN111899122A CN202010630124.3A CN202010630124A CN111899122A CN 111899122 A CN111899122 A CN 111899122A CN 202010630124 A CN202010630124 A CN 202010630124A CN 111899122 A CN111899122 A CN 111899122A
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CN111899122B (en
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王勇
陈嵘
万灿
张占龙
汤茜
张韬
马天睿
蒋嗣凡
沈开程
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Zhenjiang Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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Zhenjiang Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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Abstract

The invention discloses a user decentralized clearing method based on energy storage control, which comprises the steps of firstly modeling an electricity utilization scene of a user, carrying out scene modeling according to an electricity generation and utilization plan and an electricity utilization behavior habit of the user, and carrying out short-term electricity utilization behavior prediction based on the electricity utilization scene and the user plan; secondly, performing energy storage control by analyzing and predicting market behaviors of the scattered markets in the day ahead; through the charging and discharging of the stored energy, the power demand is translated along the time axis direction, and the optimized energy storage control decision is carried out through the energy storage value model, so that the power based on the energy storage control is realized. The invention optimizes the load fluctuation condition of the power grid by utilizing the stored energy and improves the benefit of users.

Description

User decentralized clearing method based on energy storage control
Technical Field
The invention relates to an energy technology of an electric power market, in particular to a user decentralized clearing method based on energy storage control.
Background
With the continuous promotion of electric power marketization, users are more and more willing to actively participate in electric power market trading. On one hand, market trading can enable users to further master price initiative, and electric power trading experience and enthusiasm of the users are improved; on the other hand, the user can promote the trading income of the user by means of market through judgment and technical forms of the market of the user in the process of participating in trading.
However, with diversified user conditions, the existing trading method has poor trading flexibility, poor user response capability and relative passivity, and meanwhile, unpredictable energy storage participation is not beneficial to stable operation of a power grid.
Disclosure of Invention
In order to overcome the disadvantages of the prior art, it is an object of the present invention to provide a user decentralization method based on energy storage control, which solves the above problems.
The purpose of the invention is realized by adopting the following technical scheme:
a user scatter-clear transaction method based on energy storage control comprises the following steps:
step 1: modeling the value of the energy storage device: and establishing an energy storage value model according to the energy storage characteristics of the energy storage equipment and the heterogeneity of the energy storage value of the energy storage equipment in different periods of the market, and using the energy storage value model for clearing calculation.
Step 2: and (3) predicting the quotation of the user: and for the quotation characteristics and behavior habits of different electricity vendors, forecasting the quotation curve at a future moment by using a BP network model and a historical clearing curve.
And step 3: calculating a dispersion clearing strategy: and performing double-layer planning operation according to the previously established value model and related constraints of the energy storage equipment, and solving a quotation decision for maximizing the benefits of the energy storage equipment.
Preferably, in the step 1, the relevant energy storage characteristics include a maximum capacity of energy storage, a charge-discharge efficiency, a charge-discharge power, and the like, and the value of the energy storage in the market is heterologously expressed in the value of the energy storage variation electric quantity in the current time period of the market.
Preferably, in step 2, each time period of the user is calculated separately considering 96 time periods of one point every 15 minutes of the day. The quotation matching strategy adopted in the market is high-low matching, and the following steps are dispersed: the user reports the related quotation and the report quantity on the electricity purchasing side and the electricity selling side respectively, all the reported quotation information is collected on the platform side, two quotation curves of electricity purchasing and selling are formed according to the price sequence, the user at the front end in the curves is matched, the matched price is the arithmetic average value of the quotation of the two sides, and the clearing is finished until the two curves are crossed. In the user quotation prediction, a quotation curve of a user in the past 7 days and a quotation curve of the user in the past two years in the present day are used as training set input, and a data-driven analysis mode is used for calculating the quotation curve of the user in the present day.
Preferably, in step 3, a quotation mode and an energy storage control mode under the condition of maximizing user profits are calculated by adding a double-layer plan of an energy storage device value model, the upper layer is an energy storage decision and quotation calculation for maximizing user profits, and the lower layer is a market clearing model. Under this calculation, several basic assumptions are considered:
(1) the power generation amount of the user in each time interval participates in the control and market process, namely the power only comprises two parts, namely the power participating in market trading and the power participating in energy storage.
(2) The market consideration links are the single-stage day-ahead market mentioned above, and the market clearing form is high-low matching and dispersed clearing.
(3) Market users all participate independently in the market trading process without forming a federation or aggregator.
(4) The user participates in the decision making in an intelligent mode, namely, the price quoted is determined by the marginal electric quantity of the user.
(5) The effect of the output resistor plug is not considered.
Compared with the prior art, the invention has the beneficial effects that: compared with the traditional electric power trading form in the market, the electric power trading method based on the energy storage control considers diversified user conditions, enables users to have certain demand side response capability, and realizes active regulation of the electric network load involved in energy storage through the marketization form, so that on one hand, flexible trading requirements of the users are met, trading benefits and trading enthusiasm of the users are improved, on the other hand, the electric power trading method is beneficial to relieving the energy flow pressure of the electric network, improving the operation stability of the electric network, and helping the electric network to realize peak clipping and valley filling to a certain degree.
Drawings
FIG. 1 is a flow chart of market matching liquidation
Fig. 2 is a flow chart of user energy storage control and market quotation.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
A user scatter-clear transaction method based on energy storage control comprises the following steps:
step 1: firstly, a user models a value model of energy storage equipment, for the user, the value of energy storage of the user mainly comes from the value of the stored electric quantity of the user at the moment of energy storage, in brief, when the user i participates in market trading, if the market trading price in a k time period is p, when the electric energy with v capacity is stored to the energy storage, the energy storage value of the part of the stored energy should be p
Figure RE-GDA0002685377520000031
Wherein,
Figure RE-GDA0002685377520000032
and the energy storage value of the user i in the k time period, p is the market trading price in the k time period, and v is the stored electric energy capacity.
When the electric quantity is stored in the form of energy storage, the value of the electric quantity is directly hooked with the storage quantity, and the electric quantity does not change along with the change of time and market electricity price and is only influenced by the attenuation of the energy storage.
Therefore, for the user group N {1,2, …, i, … N } the energy storage value model is defined as:
Figure RE-GDA0002685377520000041
in the formula, the energy storage variation of the user group in the time period k is integrated into
Figure RE-GDA0002685377520000042
When the stored energy is discharged, the value is positive; when the stored energy is charged, the value is negative; dBThe attenuation coefficient of the stored energy is the attenuation residual proportion of the stored energy in each time interval, and the attenuation coefficient mainly depends on the energy storage material; v. ofi,k-1The reserve capacity of the previous time interval is stored, and the reserve capacity change and the transaction electric quantity value of the current time interval jointly form the electricity generation and utilization quantity of the user in the time interval;
Figure RE-GDA0002685377520000043
for the user's transaction benefits over the period of time,
Figure RE-GDA0002685377520000044
the transaction electric quantity of the user in the time period is represented, and the absolute number of the quotient of the transaction electric quantity and the transaction electric quantity represents the electric energy value of the user in the time period; eta represents the charge-discharge efficiency of the stored energy, and the charge efficiency and the discharge efficiency are different, and eta is defined as follows:
Figure RE-GDA0002685377520000045
wherein etacRepresents the charging efficiency, ηdRepresents the discharge efficiency;
the efficiency depends on the energy storage material and the energy storage using environment, and the charge-discharge efficiency is 0.85-0.97 in a normal state;
according to the formula, when energy storage is discharged, firstly, the energy storage value when the energy storage is not discharged in the period is obtained by considering the attenuation of electric energy, then, the discharging is carried out, because the discharging efficiency exists, the actual discharging amount is larger than the obtained energy storage variable amount, and the residual energy storage value after the discharging is obtained through the proportional change of the energy storage electric quantity before and after the discharging;
when the energy storage is charged, the stored energy is multiplied by the average price of the user transaction in the current time period after the original energy storage attenuation is calculated, and the energy storage value of the stored electric quantity in the current time period can be obtained.
Step 2: in this market, the matching clearance mode is a high-low matching, and the dispersion clearance: the specific flow is shown in fig. 1, a user reports the quoted price and the report quantity based on the power utilization plan and the electric quantity prediction at the power purchasing or power selling side, all the quoted price information is collected on a trading platform, two quoted price curves of purchasing and selling power are formed according to the price sequence, the user at the front end in the curves is matched, the matched price is the arithmetic mean value of the quoted prices of both sides, and the clearing is finished until the two curves are crossed.
In the user quotation prediction, a BP neural network is used, a quotation curve of a user in the past 7 days and a quotation curve of the user in the past two years in the present day are used as training set input, and a data-driven analysis mode is used for calculating the quotation curve of the user in the present day.
After the completion, the user utilizes the quotation prediction information to plan the energy storage control behavior and the quotation behavior through the optimal quotation strategy, and accordingly, the optimization benefit is obtained.
And step 3: and performing energy storage control and quotation strategy calculation considering the user income maximization, wherein the user income maximization is planned to be a double-layer model, and the income calculation model of the user i under the time period k is calculated as follows:
Figure RE-GDA0002685377520000051
wherein,
Figure RE-GDA0002685377520000052
Figure RE-GDA0002685377520000053
Figure RE-GDA0002685377520000054
Figure RE-GDA0002685377520000055
Figure RE-GDA0002685377520000056
vmin≤vi,k≤vmaxformula 10
Figure RE-GDA0002685377520000061
Figure RE-GDA0002685377520000062
subject to：
Figure RE-GDA0002685377520000063
pmin≤pi,k≤pmaxFormula 14
pmin≤pj,k≤pmaxFormula 15
The upper model is a profit maximization model of the user, in which ui,kIndicating that the user is at time kThe yield of (a) to (b) is,
Figure RE-GDA0002685377520000064
for the benefit of the transaction at that moment,
Figure RE-GDA0002685377520000065
and adding the energy storage value change of the moment compared with the previous moment to obtain the total income of the user at the moment. p is a radical ofi,kThe transaction offer at time k on behalf of user i.
Figure RE-GDA0002685377520000066
And
Figure RE-GDA0002685377520000067
representing the energy storage minimum and maximum power constraints.
The formula is a calculation formula of the trading income of the user i, and the trading price is the average value of the quoted prices of the matched users due to the adoption of a scattered clearing trading mode,
Figure RE-GDA0002685377520000068
a bottom-of-pocket sales revenue representing an unmatched portion of the user, having a value of:
Figure RE-GDA0002685377520000069
i.e. the product of the electric quantity of the bottom part of the bag and the price of the bottom part of the bag.
The formula is the energy storage value, and the formula is the electric quantity constraint, which represents that the electric quantity of the user must trade in the market or perform energy storage conversion in a time period. The formula is the energy storage reserve of the user in the current time period. The formula is energy storage capacity constraint, and the formula is energy storage power constraint.
The lower model is a market clearing model and is used for calculating trading users and trading prices matched with the decision users. The objective function is the maximization of social welfare, the user group I is divided into two groups according to the identity of the user group I in the transaction, and the electricity purchasing group IbAnd electricity selling group Is。
Figure RE-GDA00026853775200000610
Match the electricity purchased and sold on behalf of the user with the transaction amount of electricity, ci,kThe power generation cost corresponding to the electricity selling user.
The formula is trade electric quantity balance constraint, and the formula is trade price constraint.
From this, the user is through comparing energy storage value and trade income, and the point will deposit the electric quantity in the energy storage at the price low point, takes out at the price high point and sells, can learn user's income maximize decision-making.
Taking a certain city in Jiangsu as an example, a daily power generation and power utilization curve and a quotation curve of 24 photovoltaic users under a 38KV transformer are simulated, the average daily power generation peak value of the users is 4KW, the daily regional power generation total amount and the daily regional power utilization total amount are 651MWh and 613MWh respectively, and through calculation, regional users participate in transactions through energy storage control, and the income is improved by 8% -15%.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (7)

1.一种基于储能控制的用户分散出清方法，其特征在于，通过储能参与分散出清控制，方法包含以下步骤：1. a user dispersed clearing method based on energy storage control, is characterized in that, participates in dispersed clearing control by energy storage, and the method comprises the following steps: 步骤1，储能设备的价值建模：根据储能设备的储能特性和储能在市场不同时段储能价值的异质性，用户i在k时段的储能价值为：Step 1. Value modeling of energy storage devices: According to the energy storage characteristics of energy storage devices and the heterogeneity of energy storage value in different periods of the market, the energy storage value of user i in period k is:
Figure RE-FDA0002685377510000011
Figure RE-FDA0002685377510000011
 其中，
Figure RE-FDA0002685377510000012
为用户i在k时段的储能价值，p为k时段市场交易价格，v为储存的电能容量；in,
Figure RE-FDA0002685377510000012
is the energy storage value of user i in the k period, p is the market transaction price in the k period, and v is the stored electric energy capacity; 故对于用户组
Figure RE-FDA0002685377510000013
储能价值模型定义为：So for the user group
Figure RE-FDA0002685377510000013
The energy storage value model is defined as:
Figure RE-FDA0002685377510000014
Figure RE-FDA0002685377510000014
 式中，用户组在时段k的储能变化量集合为
Figure RE-FDA0002685377510000015
当储能放电时，其值为正；当储能充电时，其值为负；dB为储能的衰减系数，储能在每一时段的电能衰减剩余比例，该衰减系数由主要取决于储能材料；vi,k-1为储能在上一时段的储量，当前时段的储量变化和交易电量值共同构成该时段用户的发用电量；
Figure RE-FDA0002685377510000016
为用户在该时段的交易收益，
Figure RE-FDA0002685377510000017
为用户在该时段的交易电量，两者商的绝对数代表用户在该时段的电能价值；η代表储能的充放电效率，由于其充电和放电效率并不相同，定义η如下：In the formula, the set of energy storage changes of the user group in period k is:
Figure RE-FDA0002685377510000015
When the energy storage is discharged, its value is positive; when the energy storage is charged, its value is negative; d B is the attenuation coefficient of the energy storage, and the remaining proportion of the energy attenuation of the energy storage in each period, the attenuation coefficient is mainly determined by Energy storage material; v i,k-1 is the storage of energy storage in the previous period, and the change of storage in the current period and the value of transaction electricity together constitute the power generation and consumption of users in this period;
Figure RE-FDA0002685377510000016
is the trading income of the user during this period,
Figure RE-FDA0002685377510000017
is the user's transaction electricity in this period, the absolute number of the quotient of the two represents the user's electric energy value in this period; η represents the charging and discharging efficiency of the energy storage. Since the charging and discharging efficiencies are not the same, the definition of η is as follows:
Figure RE-FDA0002685377510000018
Figure RE-FDA0002685377510000018
 其中ηc代表充电效率，ηd代表放电效率；where η c represents the charging efficiency, and η d represents the discharging efficiency; 效率取决于储能材料和储能使用环境，常态下充放电效率为0.85-0.97；The efficiency depends on the energy storage material and the environment in which the energy storage is used, and the charge-discharge efficiency is 0.85-0.97 under normal conditions; 由式可知，当储能放电时，其首先考虑电能衰减得到该时段未放电时的储能价值，然后进行放电，由于放电效率的存在，实际放电量要大于得到的储能变化量，通过放电前后储能电量的比例变化，得到放电后的剩余储能价值；It can be seen from the formula that when the energy storage is discharged, it first considers the energy attenuation to obtain the energy storage value when it is not discharged during this period, and then discharges. Due to the existence of the discharge efficiency, the actual discharge amount is greater than the obtained energy storage change. The proportion of energy storage before and after the change to obtain the remaining value of energy storage after discharge; 但储能充电时，计算原储能衰减后，将存入的储能量乘上当时段用户交易的平均价格，即可得到当时段存入电量的储能价值；However, when the energy storage is charged, after calculating the attenuation of the original energy storage, multiply the stored energy by the average price of user transactions at that time, and then the energy storage value of the stored electricity at that time can be obtained; 步骤2，用户报价预测：对于不同售电商的报价特点和行为习惯，利用BP网络模型和数据库记录的历史出清曲线进行未来时刻的报价曲线预测；Step 2, user quotation prediction: for the quotation characteristics and behavior habits of different e-commerce retailers, use the BP network model and the historical clearing curve recorded in the database to predict the quotation curve in the future time; 步骤3：分散出清策略计算：根据先前建立的自身储能设备价值模型和相关约束，进行双层规划运算，求解出自身利益最大化的报价决策。Step 3: Decentralized clearing strategy calculation: According to the previously established value model of its own energy storage equipment and related constraints, a two-level planning operation is performed to solve the quotation decision that maximizes its own interests. 2.根据权利要求1所述的基于储能控制的用户分散出清方法，其特征在于：在步骤1中，储能特性包括储能的最大容量、充放电效率、和充放电功率；储能在市场中的价值异质性体现在储能变化电量在市场当前时段的价值。2. The user dispersed clearing method based on energy storage control according to claim 1, characterized in that: in step 1, energy storage characteristics include the maximum capacity, charge-discharge efficiency, and charge-discharge power of energy storage; The value heterogeneity in the market is reflected in the value of energy storage changes in the current period of the market. 3.根据权利要求1所述的基于储能控制的用户分散出清方法，其特征在于：在步骤2中，设置等间隔时段，对用户的每一个时段进行单独计算。3. The distributed clearing method for users based on energy storage control according to claim 1, characterized in that: in step 2, time periods at equal intervals are set, and each time period of the user is independently calculated. 4.根据权利要求3所述的基于储能控制的用户分散出清方法，其特征在于：等间隔设置每间隔15分钟为一点的96个时段。4 . The distributed clearing method for users based on energy storage control according to claim 3 , characterized in that: 96 time periods with every 15 minutes as one point are set at equal intervals. 5 . 5.根据权利要求3所述的基于储能控制的用户分散出清方法，其特征在于：在步骤3中，市场中采取高低匹配的报价匹配策略，分散出清：用户在购电和售电两侧分别上报报价和报量，平台侧汇总上报的所有报价信息，按照价格次序形成购售和售电两条报价曲线，曲线中处于前端的用户进行匹配，匹配价格为双方报价的算数平均值，直到两条曲线交叉为止，完成出清。5. The distributed clearing method based on energy storage control according to claim 3, characterized in that: in step 3, a price matching strategy of high-low matching is adopted in the market, and distributed clearing: users are purchasing and selling electricity Both sides report quotations and quotations respectively. The platform side summarizes all the reported quotation information, and forms two quotation curves for purchase and sale and electricity sales according to the price order. The users at the front end of the curve are matched, and the matching price is the arithmetic average of the quotations of both parties. , until the two curves cross, complete the clearing. 6.根据权利要求5所述的基于储能控制的用户分散出清方法，其特征在于：在用户报价预测中，将用户过去7天的报价曲线和过去两年当天的报价曲线作为训练集输入，利用数据驱动的分析模式计算当天用户的报价曲线。6. The method for user decentralized clearing based on energy storage control according to claim 5, characterized in that: in the user quotation prediction, the quotation curve of the user in the past seven days and the quotation curve of the current day in the past two years are input as a training set , using the data-driven analysis mode to calculate the user's quotation curve for the current day. 7.根据权利要求1所述的基于储能控制的用户分散出清方法，其特征在于：在步骤3中，通过加入储能设备价值模型的双层规划，计算用户收益最大化情况下的报价方式和储能控制形式，上层为用户利益最大化的储能决策和报价计算，下层为市场出清模型。7. The distributed clearing method for users based on energy storage control according to claim 1, characterized in that: in step 3, by adding a two-tier planning of the value model of energy storage equipment, calculate the quotation under the condition of maximizing user income mode and energy storage control form, the upper layer is the energy storage decision-making and quotation calculation to maximize the interests of users, and the lower layer is the market clearing model.
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Citations (28)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130245847A1 (en) * 2009-10-23 2013-09-19 Alain P. Steven Facilitating revenue generation from wholesale electricity markets using an enineering-based energy asset model
US20170045902A1 (en) * 2015-08-14 2017-02-16 Nec Laboratories America, Inc. Control strategies for grid scale storage operation in frequency regulation markets considering battery health factors
CN206005259U (en) * 2016-07-19 2017-03-15 任惠保 It is convenient to clean combination type storage and dissipates wind system
WO2017143272A2 (en) * 2016-02-19 2017-08-24 New World Medical, Inc. Apparatus and method for cataract extraction
CN107239847A (en) * 2017-04-12 2017-10-10 广州供电局有限公司 A kind of active distribution network energy-storage system dynamic programming method
CN107644370A (en) * 2017-09-29 2018-01-30 中国电力科学研究院 Price competing method and system are brought in a kind of self-reinforcing study together
US20180083482A1 (en) * 2016-09-19 2018-03-22 Nestfield Co., Ltd. Supply-demand balancing method and system for power management in smart grid
CN108564230A (en) * 2018-04-28 2018-09-21 湖南红太阳新能源科技有限公司 A kind of family distributed energy management method and system
CN109087213A (en) * 2018-07-04 2018-12-25 国家电网有限公司 A kind of distributing power spot market transaction simulation method
CN109103914A (en) * 2018-10-17 2018-12-28 上海电力设计院有限公司 The micro-capacitance sensor energy storage Optimal Configuration Method of consideration source lotus storage synthetic operation
CN109118121A (en) * 2018-09-13 2019-01-01 中国电力科学研究院有限公司 Go out clear mode evaluation method based on the electricity market for improving PageRank
CN109190882A (en) * 2018-07-25 2019-01-11 南京邮电大学 Microgrid economic optimization method of commerce under Power Market based on cloud energy storage
CN109245091A (en) * 2018-09-27 2019-01-18 东南大学 A kind of two_layer strategies dispatching method towards sale of electricity company
CN109299829A (en) * 2018-10-11 2019-02-01 国网甘肃省电力公司电力科学研究院 Multi-form energy storage configuration method and system for photovoltaic greenhouse micro-energy grid system
CN109462252A (en) * 2018-10-19 2019-03-12 国网湖南省电力有限公司 The coordinating power control method and system of directly driven wind-powered superconducting energy storage multi-computer system
US20190172159A1 (en) * 2017-12-06 2019-06-06 NAD Grid Corp Method and system for facilitating electricity services
CN109858712A (en) * 2019-03-11 2019-06-07 北京天润新能投资有限公司西北分公司 The transregional automated transaction of the stock a few days ago strategy of one kind and economic evaluation method
CN110009160A (en) * 2019-04-11 2019-07-12 东北大学 A kind of power price prediction technique based on improved deepness belief network
CN110163677A (en) * 2019-05-21 2019-08-23 西安交通大学 A kind of long-term peak Pinggu electricity deals match method in the distributed power generation containing energy storage
CN110264105A (en) * 2019-06-28 2019-09-20 新奥数能科技有限公司 A kind of electric power spot exchange method and system based on Demand Side Response
CN110321379A (en) * 2019-07-01 2019-10-11 中国南方电网有限责任公司 Frequency modulation assisted hatching goes out analytic method, device, computer equipment and the storage medium of clear file
CN110334756A (en) * 2019-06-26 2019-10-15 国网江苏省电力有限公司 Power system monitor alarm event knows method for distinguishing, terminal installation, equipment and medium
CN110362845A (en) * 2019-03-18 2019-10-22 广东电力交易中心有限责任公司 A kind of Regional Electric Market development plan mode determining method and device
CN110489915A (en) * 2019-08-27 2019-11-22 南方电网科学研究院有限责任公司 Combined dispatching method and system of electricity and heat considering comprehensive demand response
CN110502814A (en) * 2019-08-09 2019-11-26 国家电网有限公司 Consider the active distribution network multi-objective planning method of energy storage and load management technology
US20200082422A1 (en) * 2018-09-12 2020-03-12 Mitsubishi Electric Research Laboratories, Inc. Day-Ahead Joint Generation Scheduling and Bidding Optimization for Power Plants
US20200143460A1 (en) * 2018-11-02 2020-05-07 University-Industry Cooperation Group Of Kyung-Hee University System of management of energy trading and method of the same
US20200143489A1 (en) * 2018-11-01 2020-05-07 Battelle Memorial Institute Flexible allocation of energy storage in power grids

Patent Citations (28)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130245847A1 (en) * 2009-10-23 2013-09-19 Alain P. Steven Facilitating revenue generation from wholesale electricity markets using an enineering-based energy asset model
US20170045902A1 (en) * 2015-08-14 2017-02-16 Nec Laboratories America, Inc. Control strategies for grid scale storage operation in frequency regulation markets considering battery health factors
WO2017143272A2 (en) * 2016-02-19 2017-08-24 New World Medical, Inc. Apparatus and method for cataract extraction
CN206005259U (en) * 2016-07-19 2017-03-15 任惠保 It is convenient to clean combination type storage and dissipates wind system
US20180083482A1 (en) * 2016-09-19 2018-03-22 Nestfield Co., Ltd. Supply-demand balancing method and system for power management in smart grid
CN107239847A (en) * 2017-04-12 2017-10-10 广州供电局有限公司 A kind of active distribution network energy-storage system dynamic programming method
CN107644370A (en) * 2017-09-29 2018-01-30 中国电力科学研究院 Price competing method and system are brought in a kind of self-reinforcing study together
US20190172159A1 (en) * 2017-12-06 2019-06-06 NAD Grid Corp Method and system for facilitating electricity services
CN108564230A (en) * 2018-04-28 2018-09-21 湖南红太阳新能源科技有限公司 A kind of family distributed energy management method and system
CN109087213A (en) * 2018-07-04 2018-12-25 国家电网有限公司 A kind of distributing power spot market transaction simulation method
CN109190882A (en) * 2018-07-25 2019-01-11 南京邮电大学 Microgrid economic optimization method of commerce under Power Market based on cloud energy storage
US20200082422A1 (en) * 2018-09-12 2020-03-12 Mitsubishi Electric Research Laboratories, Inc. Day-Ahead Joint Generation Scheduling and Bidding Optimization for Power Plants
CN109118121A (en) * 2018-09-13 2019-01-01 中国电力科学研究院有限公司 Go out clear mode evaluation method based on the electricity market for improving PageRank
CN109245091A (en) * 2018-09-27 2019-01-18 东南大学 A kind of two_layer strategies dispatching method towards sale of electricity company
CN109299829A (en) * 2018-10-11 2019-02-01 国网甘肃省电力公司电力科学研究院 Multi-form energy storage configuration method and system for photovoltaic greenhouse micro-energy grid system
CN109103914A (en) * 2018-10-17 2018-12-28 上海电力设计院有限公司 The micro-capacitance sensor energy storage Optimal Configuration Method of consideration source lotus storage synthetic operation
CN109462252A (en) * 2018-10-19 2019-03-12 国网湖南省电力有限公司 The coordinating power control method and system of directly driven wind-powered superconducting energy storage multi-computer system
US20200143489A1 (en) * 2018-11-01 2020-05-07 Battelle Memorial Institute Flexible allocation of energy storage in power grids
US20200143460A1 (en) * 2018-11-02 2020-05-07 University-Industry Cooperation Group Of Kyung-Hee University System of management of energy trading and method of the same
CN109858712A (en) * 2019-03-11 2019-06-07 北京天润新能投资有限公司西北分公司 The transregional automated transaction of the stock a few days ago strategy of one kind and economic evaluation method
CN110362845A (en) * 2019-03-18 2019-10-22 广东电力交易中心有限责任公司 A kind of Regional Electric Market development plan mode determining method and device
CN110009160A (en) * 2019-04-11 2019-07-12 东北大学 A kind of power price prediction technique based on improved deepness belief network
CN110163677A (en) * 2019-05-21 2019-08-23 西安交通大学 A kind of long-term peak Pinggu electricity deals match method in the distributed power generation containing energy storage
CN110334756A (en) * 2019-06-26 2019-10-15 国网江苏省电力有限公司 Power system monitor alarm event knows method for distinguishing, terminal installation, equipment and medium
CN110264105A (en) * 2019-06-28 2019-09-20 新奥数能科技有限公司 A kind of electric power spot exchange method and system based on Demand Side Response
CN110321379A (en) * 2019-07-01 2019-10-11 中国南方电网有限责任公司 Frequency modulation assisted hatching goes out analytic method, device, computer equipment and the storage medium of clear file
CN110502814A (en) * 2019-08-09 2019-11-26 国家电网有限公司 Consider the active distribution network multi-objective planning method of energy storage and load management technology
CN110489915A (en) * 2019-08-27 2019-11-22 南方电网科学研究院有限责任公司 Combined dispatching method and system of electricity and heat considering comprehensive demand response

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