CN112186801B - Method for improving distributed photovoltaic grid-connected capacity of rural distribution network - Google Patents

Method for improving distributed photovoltaic grid-connected capacity of rural distribution network Download PDF

Info

Publication number
CN112186801B
CN112186801B CN202011021349.5A CN202011021349A CN112186801B CN 112186801 B CN112186801 B CN 112186801B CN 202011021349 A CN202011021349 A CN 202011021349A CN 112186801 B CN112186801 B CN 112186801B
Authority
CN
China
Prior art keywords
power
market
energy
photovoltaic
electricity
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.)
Active
Application number
CN202011021349.5A
Other languages
Chinese (zh)
Other versions
CN112186801A (en
Inventor
周玮
高垚
彭飞翔
孙辉
王誉颖
胡姝博
高正男
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.)
Dalian University of Technology
Original Assignee
Dalian University of Technology
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 Dalian University of Technology filed Critical Dalian University of Technology
Priority to CN202011021349.5A priority Critical patent/CN112186801B/en
Publication of CN112186801A publication Critical patent/CN112186801A/en
Application granted granted Critical
Publication of CN112186801B publication Critical patent/CN112186801B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • 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
    • 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
    • H02J2300/24—The renewable source being solar energy of photovoltaic 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
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

A method for improving the distributed photovoltaic grid-connected capacity of a rural distribution network belongs to the field of renewable energy planning. Establishing a P2P energy trading model for improving the photovoltaic grid-connected capacity, wherein the established P2P trading model is divided into two parts of trading and pricing, and a trading mechanism adopts nonlinear programming to calculate the energy storage power of a user so as to minimize the exchange power between a market and a main network; the MMR method serves as a pricing mechanism. And verifying whether the photovoltaic access capacity meets the technical constraint of the network structure or not based on the established energy transaction model. The invention realizes the win-win among photovoltaic power, users and the power grid. For photovoltaic, grid-connected capacity is improved without bearing high cost of power grid transformation, and electricity selling income is increased; for users, the electricity purchasing cost is reduced; for the power grid, the power supply quality is improved, and the operation pressure is reduced. From the perspective of social development, the permeability of renewable energy sources is improved, and the policy of establishing a distributed power market in China is responded.

Description

Method for improving distributed photovoltaic grid-connected capacity of rural distribution network
Technical Field
The invention belongs to the field of renewable energy planning, relates to a method for increasing the maximum capacity of a renewable energy accessed to a power distribution network, and particularly relates to a method for increasing the grid-connected capacity of distributed photovoltaic of a medium-low voltage area power distribution network by using a Peer-to-Peer (P2P) energy trading mechanism.
Background
The current increasingly serious environmental pollution and energy crisis continuously promote the vigorous development and utilization of photovoltaics. Photovoltaic power generation presents the situation of omnibearing propulsion in cities and rural areas. Due to the problems of low daytime load density, small transformer capacity, lack of on-load voltage regulation capability, long power supply radius, unreasonable reactive compensation and the like of a rural power grid, the safe and stable operation of a power distribution network is seriously influenced by photovoltaic grid connection. Under the background, the photovoltaic receiving capacity problem of a power distribution network in rural areas needs to be improved urgently. Factors that limit photovoltaic access generally include: node voltage, line current, power quality, relay protection and the like. Among the biggest impacts on power distribution networks are voltage out-of-limit and line overload problems. The existing technology for improving the receiving capacity of the distributed power supply comprises (1) on-load tap changing of a voltage regulating transformer; (2) inverter power factor control; (3) reactive compensation; (4) equipment transformation; (5) network reconstruction; (6) introduction of energy storage technology, etc. The first three ways solve the overvoltage problem by adjusting the voltage level to improve the photovoltaic access capacity, but the photovoltaic access capacity is still restricted by the current carrying of the line; the photovoltaic access capacity can be substantially improved by network reconfiguration, but the network reconfiguration faces high cost, especially for planning unreasonable rural power distribution networks; for the application of energy storage technology, time-of-use electricity price is not yet implemented in most areas in China, and signals for guiding energy storage work are lacked. Therefore, a new method for improving the photovoltaic access capacity, which meets the current power development situation of China and has practical significance, is urgently needed.
Under the background that our country gradually promotes electric power marketization innovation and encourages distributed generation marketization trade, the distributed photovoltaic energy is guided to participate in the electric power market, and the market effect is played and is a new direction for improving the photovoltaic grid-connected capacity. Therefore, a P2P energy market is established in a local distribution network area where the photovoltaic power station is located, the photovoltaic power station and users in the area directly conduct electric energy transaction, and unbalanced power of the market is balanced through a superior power grid. And the market participants adjust the electricity utilization behavior according to the market mechanism, and the running state of the system is improved, so that the photovoltaic access capacity is improved. The market-internal price is set between the grid purchase price and the retail price, so each producer or consumer (whether photovoltaic plant or electricity consumer) will benefit from the P2P energy market. The method can improve the access capacity of the photovoltaic power station, reduce the electricity purchasing cost of users, improve the low-voltage problem and reduce the peak load regulation pressure of the power grid, so that the photovoltaic power station, the power users and the power grid realize win-win.
Disclosure of Invention
The invention aims to provide a novel method which accords with the development policy of China and reasonably and effectively improves the photovoltaic grid-connected capacity. Under the policy of encouraging distributed marketization in China, a P2P energy market is established in a local network area of a photovoltaic power station, energy interaction between the photovoltaic power station and a nearby user is encouraged, flexible resources are mobilized by a market main body through a market mechanism, self-balancing of energy in the area is promoted, and photovoltaic access capacity is improved. The P2P trading model in the invention is divided into two parts of a trading mechanism and a pricing mechanism. The trading mechanism optimizes the electricity generation/utilization behaviors of market members to realize the process of market supply and demand power balance, and the pricing mechanism realizes the function of market settlement. The method achieves the aim of promoting photovoltaic resource consumption by applying the concept of P2P energy trading to the capacity planning research of distributed photovoltaic.
A method for improving the distributed photovoltaic grid-connected capacity of a rural distribution network comprises the following steps:
the first step is as follows: P2P energy transaction model for improving photovoltaic grid-connected capacity
The established P2P trading model is divided into two parts, trading and pricing.
1.1) since the aim of the invention is to increase the access capacity of distributed photovoltaics, the trading mechanism aims to promote the self-balancing of local energy sources, and the energy storage power of a user is calculated by adopting a nonlinear programming to minimize the exchange power between the market and the main network. Namely, the trading mechanism guides the bidding behavior of market members to realize the balance of the supply and demand power of the market.
The trading mechanism adopts a nonlinear programming method to establish a P2P energy trading optimization model containing a distributed energy technology. The switching power of the P2P market and the main network is minimized by finding the best operating decision related to energy storage. The objective function is as in formula (1):
Figure GDA0003458393310000021
wherein T is a total scheduling period; n is the number of users; plAnd PbThe T multiplied by N matrix respectively represents the load power and the storage of the userThe charging and discharging power is positive, and the discharging is negative; ppvIs the output power of the photovoltaic power station; t represents a time; n represents a user.
The constraints associated with energy storage are as follows:
Pb,n,min≤Pb(t,n)≤Pb,n,max (2)
soc(t+1,n)＝soc(t,n)+Pb(t,n)/En (3)
socn，min≤soc(t,n)≤socn,max (4)
soc(T,n)＝soc(1,n) (5)
wherein, Pb(t, n) represents the energy storage power of the nth user at the moment t; pb,n,minAnd Pb,n,maxThe current power values of the nth user energy storage equipment are respectively obtained; soc (t, n) represents the state of charge of the energy storage device at the moment t of the nth user; enIndicating the rated capacity of the energy storage device of the nth user; socn,minAnd socn,maxIs a state of charge constraint limit. Equation (5) indicates that the energy storage state of charge is the same at the end of each day and at the initial time.
1.2) the pricing mechanism can reflect the energy state of the P2P network, guarantee the economic balance in the market at any moment, and most importantly, guarantee that the internal price of the P2P market is between the price of electricity purchased and sold by the power grid. The MMR (Mid-Market rate) method is selected as the pricing mechanism based on the above requirements. Namely, the pricing mechanism is used for realizing the function of market settlement, and the cost/income of the market main body under the trading mechanism is calculated according to the power consumption/generation power.
The sending/using power of the market participant at each moment determined by the trading link is used as input data of a pricing mechanism, and the output is the electricity purchasing price and the electricity selling price in the market at each moment P2P.
The MMR pricing method is described as follows:
case 1: P2P market equilibrium
Figure GDA0003458393310000031
Figure GDA0003458393310000032
Case 2: P2P market has energy surplus
Figure GDA0003458393310000033
Figure GDA0003458393310000041
Case 3: the P2P market has energy shortages
Figure GDA0003458393310000042
Figure GDA0003458393310000043
In the formula: edAnd EsTotal energy deficit and surplus in the P2P market, respectively; en,dAnd En,sEnergy deficit and surplus, respectively, for a single parity; n is a radical ofbAnd NsA set of consumers and producers, respectively; p is a radical ofg,sAnd pg,bThe price of selling and purchasing electricity of the power grid are known data respectively. p is a radical ofin_sellAnd pin_buyIndicating the selling and purchasing prices of electricity within the P2P market.
The electricity selling income of the photovoltaic power station is equal to the power generation power multiplied by the internal electricity selling price:
Figure GDA0003458393310000044
the electricity purchase cost of the user n is equal to the load power multiplied by the internal electricity purchase price:
Figure GDA0003458393310000045
the second step is that: verifying whether photovoltaic access capacity satisfies technical constraints of a network architecture
2.1) calculating the injection power of the photovoltaic access node i according to the generation/utilization power of the market participants obtained by the P2P energy trading optimization model
Figure GDA0003458393310000046
Figure GDA0003458393310000047
In the formula: p (t) represents the active injection power of the node i at the time t, Ppv(t) represents photovoltaic power generation power at time t, P'l(t, n) represents the actual power usage determined by user n through the P2P transaction mechanism at time t; q (t) represents the reactive injection power of the node i at the time t, Q'l(t, n) represents the reactive power of user n at time t.
2.2) according to the actual network data, carrying out power flow verification on the node injection power, and judging whether the voltage and the current of the node are out of limit or not
(1) Giving an initial value U of each node voltage0、δ0Wherein, U0Is the initial voltage amplitude, δ0Is the initial voltage phase angle.
(2) Solving the unbalance amount delta P and delta Q of the injection power of each node by using the initial value of the node voltage; wherein, Δ P is the active power unbalance amount, and Δ Q is the reactive power unbalance amount;
(3) solving a Jacobian matrix J by using the initial value of the node voltage;
(4) according to a correction equation
Figure GDA0003458393310000051
Solving correction quantity delta0、ΔU0(ii) a Wherein, Delta delta0For voltage phase angle unbalance, Δ U0Is the voltage amplitude unbalance amount, J is JackA ratio matrix;
(5) based on the formula
Figure GDA0003458393310000052
Correcting the node voltage, wherein1For correcting the phase angle of the voltage after the first time, U1The voltage amplitude after the first correction;
(6) checking delta1、U1Whether the convergence is achieved or not, and if the convergence is achieved, outputting a result; otherwise, turning back to the second step;
(7) and solving the line current carrying according to the node voltage and the injection power.
The invention has the beneficial effects that: the win-win situation among the photovoltaic system, the user and the power grid is realized. For photovoltaic, grid-connected capacity is improved without bearing high cost of power grid transformation, and electricity selling income is increased; for users, the electricity purchasing cost is reduced; for the power grid, the power supply quality is improved, and the operation pressure is reduced. From the perspective of social development, the permeability of renewable energy sources is improved, and the policy of establishing a distributed power market in China is responded.
Drawings
Fig. 1 is a P2P energy trading structure under a local network;
FIG. 2 is a P2P energy trading framework;
fig. 3 is a photovoltaic capacity calculation process based on the P2P market.
Fig. 4 is a local network under a 66kV substation.
Detailed Description
The specific implementation mode of the invention is described in detail by taking a local network under a 66kV transformer substation in a certain rural area in northeast as an example and combining a technical scheme and a drawing.
As shown in fig. 4, two 20MVA main transformers, each model of SZ11-20000/66, are installed in the substation, and the normal operation mode is as follows: the No. 2 transformer is operated with all loads, and the No. 1 transformer is in hot standby. The 10 kilovolt single bus is sectionally connected, and the interconnection switch is in a closed state. And 3 lines are connected in, the line models are all LGJ-240, the line models are respectively 10 kilovolts of an A line, a B line and a C line, and the corresponding line lengths are respectively 20km, 18km and 18 km. Photovoltaic power plant access A line end (node 3). In the simulation process, a 66kV bus is used as a balance node, and measures such as adjustment of a transformer tap or reactive compensation are not considered. A total of 4 users participate in the P2P energy market, and the energy storage installation capacity of each user is the same.
The node load data is shown in table 1; the user load data for participation in the P2P energy market is shown in table 2;
TABLE 1 node load data
Figure GDA0003458393310000061
TABLE 2 user load data
Figure GDA0003458393310000062
The problems of rural distribution networks and the factors limiting photovoltaic access are determined: due to unreasonable rural network structure and seasonal characteristics of electricity utilization, access of a large number of distributed power sources brings a significant challenge to safe operation of a power grid.
(1) Rural power grid loads have typical seasonal power usage characteristics. The load is light in the slack season, and a large number of electric facilities are in a light load state; 6. the irrigation period of 7 months and the mechanical power consumption in the autumn harvesting period of 10 months are load power peak periods.
(2) The rural power grid planning is unscientific and structurally weak. The problems of unreasonable model and layout of the transformer, lack of reactive compensation, small wire section, long power supply radius and the like exist.
(3) Countryside areas have wide personnel and contain abundant solar energy resources. The country advocates building photovoltaic power stations by taking local conditions such as barren mountains, barren slopes or agricultural greenhouses; the accumulated installation capacity of the distributed photovoltaic power station in China reaches 17.04 GW.
The method comprises the following specific steps:
step 1: when the grid-connected capacity of the photovoltaic is 3.8MW and the energy storage installation capacity of a user is 4MWh, a P2P energy transaction optimization model is established, the power generation/power utilization power of the participants is determined, and the electricity selling benefit/electricity purchasing cost of the participants is calculated according to an MMR pricing method.
The revenue pair for photovoltaic plants before and after the P2P market was carried out is shown in table 5; the electricity purchase cost pair ratios of the electricity consumers are shown in table 6.
Table 5 carries out a revenue comparison of photovoltaic plants around the P2P market
Figure GDA0003458393310000071
TABLE 6 comparison of electricity purchase costs of electricity consumers
Figure GDA0003458393310000072
Step 2: firstly, calculating the injection power of a node according to the power generation/utilization data of each main body determined in a transaction link; and secondly, calculating the node voltage according to the power flow program, and comparing whether the node voltage is improved before and after the P2P market is implemented.
Before and after the P2P energy market is implemented, the node voltage ratio in a light load state is shown in a table 3; the node voltage pairs under heavy load are shown in Table 4.
Table 3 comparison of node voltages before and after light load of P2P energy market
Figure GDA0003458393310000081
TABLE 4 comparison of node voltages before and after P2P energy market under heavy load conditions
Figure GDA0003458393310000082
Figure GDA0003458393310000091
And step 3: and calculating the maximum access capacity of the photovoltaic on the premise of giving the energy storage installation capacity. The calculation process is as shown in fig. 3, on the premise that the energy storage installation capacity is known, an initial value of the photovoltaic access capacity is given, the power generation/utilization power of the participants is calculated according to a trading mechanism, and the power flow verification is carried out, and when the node voltage or the line power flow meets the calculation accuracy, the photovoltaic installation capacity at the moment is regarded as the maximum grid-connected capacity under the energy storage installation capacity.
Table 7 shows the impact of P2P energy trading on grid-connected PV capacity
TABLE 7 influence of P2P energy trading on grid-connected PV Capacity
Figure GDA0003458393310000092
Figure GDA0003458393310000101
The above-mentioned embodiments only express the embodiments of the present invention, but not should be understood as the limitation of the scope of the invention patent, it should be noted that, for those skilled in the art, many variations and modifications can be made without departing from the concept of the present invention, and these all fall into the protection scope of the present invention.

Claims (2)

1. A method for improving the distributed photovoltaic grid-connected capacity of a rural distribution network is characterized by comprising the following steps:
the first step is as follows: P2P energy transaction model for improving photovoltaic grid-connected capacity
The established P2P trading model is divided into two parts of trading and pricing, the trading mechanism aims at promoting the self-balance of local energy sources, and the energy storage power of a user is calculated by adopting a nonlinear programming mode so that the exchange power between a market and a main network is minimum;
1.1) a trading mechanism adopts a nonlinear programming method to establish a P2P energy trading optimization model containing a distributed energy technology, and the exchange power of a P2P market and a main network is reduced to the minimum by finding out the optimal operation decision related to energy storage; the objective function is as in formula (1):
Figure FDA0003458393300000011
wherein T is a total scheduling period; n is the number of users; plAnd PbThe T multiplied by N matrix respectively represents the load power of a user and the charge and discharge power of stored energy, the charge is a positive value, and the discharge is a negative value; ppvIs the output power of the photovoltaic power station; t represents a time; n represents a user;
the constraints associated with energy storage are as follows:
Pb,n,min≤Pb(t,n)≤Pb,n,max (2)
soc(t+1,n)＝soc(t,n)+Pb(t,n)/En (3)
socn，min≤soc(t,n)≤socn,max (4)
soc(T,n)＝soc(1,n) (5)
wherein, Pb(t, n) represents the energy storage power of the nth user at the moment t; pb,n,minAnd Pb,n,maxThe current power values of the nth user energy storage equipment are respectively obtained; soc (t, n) represents the state of charge of the energy storage device at the moment t of the nth user; enIndicating the rated capacity of the energy storage device of the nth user; socn,minAnd socn,maxIs a state of charge constraint limit; formula (5) indicates that the energy storage state of charge is the same at the end of each day and the initial moment;
1.2) the pricing mechanism is used for reflecting the energy state of the P2P network, ensuring the economic balance in the market at any time and ensuring that the internal price of the P2P market is between the price of electricity purchased by the power grid; selecting the MMR method as a pricing mechanism based on the above requirements;
the sending/using power of the market participant at each moment determined in the transaction link is used as input data of a pricing mechanism, and the output is the electricity purchasing price and the electricity selling price in the P2P market at each moment;
the second step is that: verifying whether photovoltaic access capacity satisfies constraints of a network architecture
2.1) calculating the injection power of the photovoltaic access node i according to the generation/utilization power of the market participants obtained by the P2P energy trading optimization model:
Figure FDA0003458393300000021
Figure FDA0003458393300000022
wherein, Pi(t) represents the active injection power of the node i at the time t, Ppv(t) represents photovoltaic power generation power at time t, P'l(t, n) represents the actual power usage determined by user n through the P2P transaction mechanism at time t; qi(t) denotes reactive injection power of node i at time t, Q'l(t, n) represents the reactive power of user n at time t;
2.2) according to actual network data, carrying out power flow verification on the node injection power, and judging whether the voltage and the current of the node are out of limit:
(1) giving an initial value U of each node voltage0、δ0；
(2) Solving the unbalance amount delta P and delta Q of the injection power of each node by using the initial value of the node voltage;
(3) solving a Jacobian matrix J by using the initial value of the node voltage;
(4) according to a correction equation
Figure FDA0003458393300000023
Solving correction quantity delta0、ΔU0；
(5) Correcting node voltage
Figure FDA0003458393300000024
(6) Checking delta1、U1Whether the convergence is achieved or not, and if the convergence is achieved, outputting a result; otherwise, returning to the step 2;
(7) and solving the line current carrying according to the node voltage and the injection power.
2. The method for improving the distributed photovoltaic grid-connected capacity of the rural distribution network according to claim 1, wherein in the step 1.2), the MMR pricing method is as follows:
case 1: P2P market equilibrium
Figure FDA0003458393300000031
Figure FDA0003458393300000032
Case 2: market for P2P with energy surplus
Figure FDA0003458393300000033
Figure FDA0003458393300000034
Case 3: the P2P market has energy shortages
Figure FDA0003458393300000035
Figure FDA0003458393300000036
In the formula: edAnd EsTotal energy deficit and surplus in the P2P market, respectively; en,dAnd En,sEnergy deficit and surplus, respectively, of a single parity; n is a radical ofbAnd NsA set of consumers and producers, respectively; p is a radical ofg,sAnd pg,bThe price of selling electricity and purchasing electricity of the power grid are known data respectively; p is a radical ofin_sellAnd pin_buyRepresents the selling price and the purchasing price of the electricity in the P2P market;
the electricity selling income of the photovoltaic power station is equal to the power generation power multiplied by the internal electricity selling price:
Figure FDA0003458393300000037
the electricity purchase cost of the user n is equal to the load power multiplied by the internal electricity purchase price:
Figure FDA0003458393300000038
CN202011021349.5A 2020-09-25 2020-09-25 Method for improving distributed photovoltaic grid-connected capacity of rural distribution network Active CN112186801B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011021349.5A CN112186801B (en) 2020-09-25 2020-09-25 Method for improving distributed photovoltaic grid-connected capacity of rural distribution network

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011021349.5A CN112186801B (en) 2020-09-25 2020-09-25 Method for improving distributed photovoltaic grid-connected capacity of rural distribution network

Publications (2)

Publication Number Publication Date
CN112186801A CN112186801A (en) 2021-01-05
CN112186801B true CN112186801B (en) 2022-05-17

Family

ID=73943490

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011021349.5A Active CN112186801B (en) 2020-09-25 2020-09-25 Method for improving distributed photovoltaic grid-connected capacity of rural distribution network

Country Status (1)

Country Link
CN (1) CN112186801B (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112862175B (en) * 2021-02-01 2023-04-07 天津天大求实电力新技术股份有限公司 Local optimization control method and device based on P2P power transaction
CN117559520A (en) * 2023-11-02 2024-02-13 中国能源建设集团广东火电工程有限公司 Distributed photovoltaic and energy storage combined planning system and method

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2007108933A (en) * 2005-10-12 2007-04-26 Tokyo Electric Power Co Inc:The Intermediary server bidding for transaction of electric power
CN105811397A (en) * 2016-03-11 2016-07-27 国网天津市电力公司 Multi-energy complementation microgrid scheduling method based on multi-time scales
CN107834574A (en) * 2017-07-31 2018-03-23 上海电气分布式能源科技有限公司 A kind of distributed energy resource system exchanges the control method of power with power network
CN108988336A (en) * 2018-08-07 2018-12-11 深圳供电局有限公司 Charging pilework and its Method for optimized planning with nested type micro-capacitance sensor
CN109286186A (en) * 2018-10-11 2019-01-29 南京南瑞继保电气有限公司 A kind of active distribution network optimal reconfiguration method
CN109586299A (en) * 2018-12-18 2019-04-05 中国电力科学研究院有限公司 A kind of power distribution network active power partition zone optimizing control method and system
CN110061524A (en) * 2019-05-06 2019-07-26 中国科学院电工研究所 A kind of distributed generation resource virtual plant active power dispatch equivalence polymerization and system based on deep neural network

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10673273B2 (en) * 2018-05-18 2020-06-02 General Electric Company Distributed ledger based control of large-scale, power grid energy resources

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2007108933A (en) * 2005-10-12 2007-04-26 Tokyo Electric Power Co Inc:The Intermediary server bidding for transaction of electric power
CN105811397A (en) * 2016-03-11 2016-07-27 国网天津市电力公司 Multi-energy complementation microgrid scheduling method based on multi-time scales
CN107834574A (en) * 2017-07-31 2018-03-23 上海电气分布式能源科技有限公司 A kind of distributed energy resource system exchanges the control method of power with power network
CN108988336A (en) * 2018-08-07 2018-12-11 深圳供电局有限公司 Charging pilework and its Method for optimized planning with nested type micro-capacitance sensor
CN109286186A (en) * 2018-10-11 2019-01-29 南京南瑞继保电气有限公司 A kind of active distribution network optimal reconfiguration method
CN109586299A (en) * 2018-12-18 2019-04-05 中国电力科学研究院有限公司 A kind of power distribution network active power partition zone optimizing control method and system
CN110061524A (en) * 2019-05-06 2019-07-26 中国科学院电工研究所 A kind of distributed generation resource virtual plant active power dispatch equivalence polymerization and system based on deep neural network

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Optimal Real-Time Pricing for Unbalanced Distribution Grids with Network Constraints;S.Weckx;《IEEE》;20131225;1-5 *
价格引导下多微网系统协调自治优化运行策略;何浩;《电力系统保护与控制》;20190816;第47卷(第16期);17-26 *

Also Published As

Publication number Publication date
CN112186801A (en) 2021-01-05

Similar Documents

Publication Publication Date Title
Bayod-Rújula Future development of the electricity systems with distributed generation
CN111882111B (en) Power spot market clearing method based on source network load storage cooperative interaction
CN109492824B (en) Distributed wind storage system optimization method considering source-network-load multi-party benefits
CN112186801B (en) Method for improving distributed photovoltaic grid-connected capacity of rural distribution network
Yesilbudak et al. Integration challenges and solutions for renewable energy sources, electric vehicles and demand-side initiatives in smart grids
CN113690877B (en) Active power distribution network and centralized energy station interaction method considering energy consumption
CN108599235A (en) A kind of constant volume method that distributed photovoltaic networks
CN114977320A (en) Power distribution network source-network charge-storage multi-target collaborative planning method
Tasnim et al. A critical review of the effect of light duty electric vehicle charging on the power grid
Wu et al. China's future in electric energy
CN114629105A (en) Power distribution network voltage reactive power optimization control method considering multi-party benefit balance
Wang et al. Review and reflection on new energy participating in electricity spot market mechanism
Hernandez et al. Energy Management System For University Photovoltaic Microgrid
Zhang et al. Research on the Market Trading Model of Ancillary Services of Diversified Flexible Ramping
Zhu et al. Study on the Clearing Mechanism of Provincial Power Spot Market Considering Regional Standby Market Trading
Li et al. Analysis of Typical Application Scenarios of Electrochemical Energy Storage Participating in Auxiliary Service of Power System
Murray Energy wheeling viability of distributed renewable energy for industry
Han et al. Multi-time Scale Energy Management Strategy for Smart Community Considering Demand Response
Xu et al. Research on the Construction of China’s Electricity Spot Market (II): Analysis of Problems and Challenges
Gao et al. Study on Peer-to-Peer trading mechanism in local distribution network
Sun et al. The Status quo and Issue of the Guangdong Electricity Market
Xu The Improvement Method of Distributed Renewable Energy Consumption Capability and the Operation Control Strategy in 10kV Distribution Network
Ding et al. Multi-Objective Congestion Dispatch in Active Distribution Network with Flexible Charging/Discharging of Electric Vehicle
Yang et al. Rural Energy Flexibility Resource Capacity Allocation Optimization Based on Virtual Power Plant Operation Optimization Model
Jin et al. The Enlightenment of Foreign Energy Storage Market Development Experience to China

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
GR01 Patent grant
GR01 Patent grant