CN112186801A - Method for improving rural distribution network distributed photovoltaic grid-connected capacity by adopting P2P energy trading mechanism - Google Patents
Method for improving rural distribution network distributed photovoltaic grid-connected capacity by adopting P2P energy trading mechanism Download PDFInfo
- Publication number
- CN112186801A CN112186801A CN202011021349.5A CN202011021349A CN112186801A CN 112186801 A CN112186801 A CN 112186801A CN 202011021349 A CN202011021349 A CN 202011021349A CN 112186801 A CN112186801 A CN 112186801A
- Authority
- CN
- China
- Prior art keywords
- power
- energy
- market
- photovoltaic
- trading
- 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.)
- Granted
Links
- 230000007246 mechanism Effects 0.000 title claims abstract description 31
- 238000000034 method Methods 0.000 title claims abstract description 25
- 230000005611 electricity Effects 0.000 claims abstract description 33
- 238000004146 energy storage Methods 0.000 claims abstract description 25
- 238000002347 injection Methods 0.000 claims description 13
- 239000007924 injection Substances 0.000 claims description 13
- 238000010248 power generation Methods 0.000 claims description 8
- 238000012937 correction Methods 0.000 claims description 6
- 238000005516 engineering process Methods 0.000 claims description 5
- 239000011159 matrix material Substances 0.000 claims description 5
- 238000005457 optimization Methods 0.000 claims description 5
- 238000012795 verification Methods 0.000 claims description 5
- 230000006735 deficit Effects 0.000 claims description 4
- 230000001737 promoting effect Effects 0.000 claims description 2
- 239000004576 sand Substances 0.000 claims description 2
- 238000011161 development Methods 0.000 abstract description 5
- 230000009466 transformation Effects 0.000 abstract description 3
- 230000035699 permeability Effects 0.000 abstract description 2
- 238000009434 installation Methods 0.000 description 7
- 230000008569 process Effects 0.000 description 4
- 230000006399 behavior Effects 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 230000001932 seasonal effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 238000003306 harvesting Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000003973 irrigation Methods 0.000 description 1
- 230000002262 irrigation Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
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 by adopting a P2P energy trading mechanism 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
Technical Field
The invention belongs to the field of renewable energy planning, relates to a method for improving the maximum capacity of a renewable energy access power distribution network, and particularly relates to a method for improving 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 is in 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 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 receptivity of the distributed power supply comprises (1) the tap adjustment of an on-load tap changer; (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 problem of overvoltage 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 network reconfiguration can substantially improve the photovoltaic access capacity, but the network reconfiguration faces high cost, especially for a rural power distribution network which is not reasonably planned; 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 lacking. 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 the market reformation of electric power is gradually promoted and the market trading of distributed generation is encouraged in China, the distributed photovoltaic energy is guided to participate in the electric power market, and the market effect is played and the new direction of improving the grid-connected capacity of the photovoltaic is realized. 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, a market main body invokes flexible resources 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 balancing the supply and demand power of the market, 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 by adopting a P2P energy trading mechanism 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 improve the access capacity of distributed photovoltaic, 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 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):
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 charge state of the energy storage device at the moment t of the nth user; enDenotes the n-thRated capacity of the user's energy storage device; 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) pricing mechanism should reflect the energy state of P2P network, guarantee economic balance in market all the time, most importantly guarantee that the internal price of P2P market should be between the price of buying and selling electricity of electric network. 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/benefit of the market main body under the trading mechanism is calculated according to the power utilization/generation power of the market main body.
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
Case 2: market for P2P with energy surplus
Case 3: the P2P market has energy shortages
In the formula: edAnd EsTotal energy deficit and surplus in the P2P market, respectively; en,dAnd En,sEnergy deficit and surplus of individual parity producers, respectively; 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:
the electricity purchase cost of the user n is equal to the load power multiplied by the internal electricity purchase price:
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
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) representsthe actual power utilization determined by the user n through a P2P transaction mechanism at the moment 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、0Wherein, U0For the purpose of the initial voltage amplitude,0is 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
Solving for the correction quantity Δ0、ΔU0(ii) a Wherein, Delta0For voltage phase angle unbalance, Δ U0J is a Jacobian matrix for voltage amplitude unbalance;
(5) based on the formula
The node voltage is modified by, among other things,1for the first correction of the voltage phase angle, U1The voltage amplitude after the first correction;
(6) verification1、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 kv single bus bar segment is wired and the tie 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
TABLE 2 user load data
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 consumption 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 country publishes 3 batches of 'photovoltaic poverty relief' projects, and the installation capacity of the accumulated distributed photovoltaic power station 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 income/electricity purchasing cost of the participants is calculated according to the MMR pricing method.
The revenue pair for photovoltaic plants before and after the P2P market was carried out is shown in table 5; the comparison of the electricity purchase cost of the electricity consumers is shown in table 6.
Table 6 carries out a revenue comparison of photovoltaic plants around the P2P market
Table 7 carries out a revenue comparison of photovoltaic plants around the P2P market
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
TABLE 4 comparison of node voltages before and after P2P energy market under heavy load conditions
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 considered to be 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
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 patent of the present invention, 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 by adopting a P2P energy trading mechanism 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):
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:
wherein P (t) represents the active injection power of the node i at the time t, Ppv(t) represents the photovoltaic power generation power at time t, Pl' (t, n) represents the actual power usage determined by user n through the P2P trading 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 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、0；
(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
Solving for the correction quantity Δ0、ΔU0；
(5) Correcting node voltage
(6) Verification1、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 by adopting the P2P energy trading mechanism according to claim 1, wherein in the step 1.2), the MMR pricing method is as follows:
case 1: P2P market equilibrium
Case 2: market for P2P with energy surplus
Case 3: the P2P market has energy shortages
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 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:
the electricity purchase cost of the user n is equal to the load power multiplied by the internal electricity purchase price:
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 true CN112186801A (en) | 2021-01-05 |
| CN112186801B CN112186801B (en) | 2022-05-17 |
Family
ID=73943490
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011021349.5A Expired - Fee Related 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) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112862175A (en) * | 2021-02-01 | 2021-05-28 | 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 (8)
| 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 pile structure with nested micro-grid and optimization planning method thereof |
| 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 |
| US20190356164A1 (en) * | 2018-05-18 | 2019-11-21 | General Electric Company | Distributed ledger based control of large-scale, power grid energy resources |
-
2020
- 2020-09-25 CN CN202011021349.5A patent/CN112186801B/en not_active Expired - Fee Related
Patent Citations (8)
| 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 |
| US20190356164A1 (en) * | 2018-05-18 | 2019-11-21 | General Electric Company | Distributed ledger based control of large-scale, power grid energy resources |
| CN108988336A (en) * | 2018-08-07 | 2018-12-11 | 深圳供电局有限公司 | Charging pile structure with nested micro-grid and optimization planning method thereof |
| 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)
| Title |
|---|
| S.WECKX: "Optimal Real-Time Pricing for Unbalanced Distribution Grids with Network Constraints", 《IEEE》 * |
| 何浩: "价格引导下多微网系统协调自治优化运行策略", 《电力系统保护与控制》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112862175A (en) * | 2021-02-01 | 2021-05-28 | 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 |
| 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 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112186801B (en) | 2022-05-17 |
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 | |
| CN113690877B (en) | Active power distribution network and centralized energy station interaction method considering energy consumption | |
| CN112186801B (en) | Method for improving distributed photovoltaic grid-connected capacity of rural distribution network | |
| Wu et al. | China's future in electric energy | |
| Zaferanlouei et al. | Integration of PEV and PV in Norway using multi-period ACOPF—Case study | |
| CN117578491A (en) | Micro-grid voltage control method considering price type demand response and inverter droop parameter optimization | |
| Sun et al. | Three-side coordinated dispatching method for intelligent distribution network considering dynamic capacity division of shared energy storage system | |
| NamKoong et al. | Voltage control of distribution networks to increase their hosting capacity in South Korea | |
| Wang et al. | Review and reflection on new energy participating in electricity spot market mechanism | |
| CN114629105A (en) | Power distribution network voltage reactive power optimization control method considering multi-party benefit balance | |
| Hernandez et al. | Energy Management System For University Photovoltaic Microgrid | |
| Yongzhen et al. | Grid reactive power optimization research under the background of source network load and storage | |
| Grundy et al. | Transmission constraint management on the National Grid system and the effect upon the commercial market place | |
| KE et al. | Scheme of east China power grid joint peak-regulation ancillary service market to promote trans-provincial regulation of negative reserve | |
| CN114611774B (en) | Optimal scheduling method for inhibiting generation of false transaction information of micro-grid group through master-slave game | |
| Zhuo et al. | Two-Stage Optimized Dispatch Strategy of AC/DC Hybrid Microgrid under Uncertainty | |
| Lan et al. | Research on Virtual Power Plants Participating in Ancillary Service Market | |
| Gao et al. | Study on Peer-to-Peer trading mechanism in local distribution network | |
| Li et al. | Analysis of Typical Application Scenarios of Electrochemical Energy Storage Participating in Auxiliary Service of Power System | |
| Wang et al. | Consider the Way of Gathering Network Tariff for High Frequency Distributed Electricity Trading in Distribution Networks | |
| Xiao et al. | Incremental Cost-driven Analysis Model of Power Grid Based on Multi-dimensional Penetration Mode of New Energy | |
| 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 | |
| Pan et al. | Coordinated Planning of Multi-Energy System with District Heating Network |
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 | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20220517 |