CN108695868B - Power distribution network energy storage location and volume fixing method based on power electronic transformer - Google Patents
Power distribution network energy storage location and volume fixing method based on power electronic transformer Download PDFInfo
- Publication number
- CN108695868B CN108695868B CN201810667774.8A CN201810667774A CN108695868B CN 108695868 B CN108695868 B CN 108695868B CN 201810667774 A CN201810667774 A CN 201810667774A CN 108695868 B CN108695868 B CN 108695868B
- Authority
- CN
- China
- Prior art keywords
- power
- load
- energy storage
- self
- grid
- 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
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
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
-
- 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]
Abstract
A power electronic transformer-based energy storage location and volume fixing method in a power distribution network comprises the steps of firstly establishing a double-layer optimization configuration model based on energy storage in the power distribution network of a power electronic transformer, obtaining the optimal capacity of an energy storage device and energy storage output and tie line power under the optimal capacity through inner layer capacity optimization, further obtaining a target function related to network loss in outer layer position optimization through load flow calculation, and finally obtaining the optimal position of the energy storage device, namely the port position of a PET (positron emission tomography) through a particle swarm optimization algorithm. The invention can obviously reduce the loss of the whole network and simultaneously improve the net income of the operation of the power grid.
Description
Technical Field
The invention relates to a technology in the field of power distribution network design, in particular to a power electronic transformer-based energy storage location and volume fixing method in a power distribution network.
Background
The existing micro-grid and the friendly connection of renewable energy sources thereof and a power distribution network both utilize the power regulation means of an energy storage device and the power distribution function of a power electronic transformer, the Power Electronic Transformer (PET) rectifies power frequency alternating current into direct current, then inverts the direct current into high-frequency alternating current, the high-frequency transformer is used for realizing the conversion of voltage and current, and finally the high-frequency alternating current is converted into power frequency alternating current and direct current; the energy storage system has the characteristics of rapid power regulation and energy storage capacity, and plays a great role in smooth intermittent energy power fluctuation, peak clipping and valley filling, voltage quality improvement and standby power supply.
When the storage battery is used as an energy storage system, the capacity configuration of the storage battery has great influence on photovoltaic power generation, and the capacity is selected too much, so that not only is the investment increased, but also the battery is in an insufficient charging state for a long time, the use effect and the service life of energy storage are influenced, and the economical efficiency of the storage battery cannot be better realized; when the capacity is selected too small, the photovoltaic system cannot fully realize economic benefits, and the power supply reliability of the power grid is reduced.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an energy storage site selection and volume fixing method in a power distribution network based on a power electronic transformer.
The invention is realized by the following technical scheme:
according to the method, firstly, a double-layer optimization configuration model of energy storage in a power distribution network based on a power electronic transformer is established, the optimal capacity of an energy storage device, the energy storage output under the optimal capacity and the tie line power are obtained through inner layer capacity optimization, then a target function related to network loss in outer layer position optimization is obtained through load flow calculation, and finally the optimal position of the energy storage device, namely the position of a port of a PET (positron emission tomography) is obtained through a particle swarm optimization algorithm.
The double-layer optimization configuration model comprises the following steps: the inner layer optimization part realizes energy storage constant volume and the outer layer optimization part realizes energy storage site selection, wherein: the inner layer optimization part takes the minimum electricity purchasing cost as an objective function, and the outer layer optimization part takes the network loss and the port loss of the power electronic transformer as objective functions.
The network loss comprises: line losses and power electronics transformer Port (PET) losses.
The line loss means: the distribution line loss calculation formula is
Wherein: plossIs the network loss, PiIs node iInjected active power of, QiIs the injected reactive power, V, of node iiIs the voltage amplitude of node i, RijIs the resistance of line ij.
② PET loss means: when P is presentgrid>0, 10kV port input power, 380V port output power, port loss is
Wherein: eta is the operating efficiency; beta is the load factor.
The optimal position of the energy storage device is obtained through the following modes:
1) initializing position and speed parameters of a particle swarm with the swarm size of N, wherein the value of N is preferably 20;
2) calculating an objective function value of each particle in the particle swarm, namely an objective function related to network loss in the outer layer optimization part;
3) preferentially updating the network loss of each particle at different positions;
4) comparing and preferentially updating the network loss of the best position of each particle with the network loss of the best position experienced by all the particles in the particle swarm;
5) updating the position and speed parameters of the particles according to the position corresponding to the self historical optimal network loss of the particles and the position corresponding to the overall optimal network loss of the particles, and obtaining an optimal optimization scheme when the maximum iterative algebra is reached; otherwise, returning to the step 2) to continue the optimization.
The inner layer optimization part is as follows: with the cost of electricity purchase as an objective function, i.e.
The constraint conditions comprise:
1) power balance constraint Pgrid＝Paggregate-Pbattery＝Pload-Ppv-PbatteryWherein: pgridInputting the power of the microgrid into a power grid; paggregateIs the net load power; ppvThe output is the photovoltaic power generation; ploadIs the load power; pbatteryThe output of the energy storage system;
2) energy storage power constraint Pbattery≤Pbatterymax；
3) Self-balancing rate constraint: the distribution network is connected with a large power grid, and can be provided with certain electric power support by the large power grid. With the distribution network in certain cycle, rely on the load demand proportion that self distributed power can satisfy to define as the self-balancing rate, specifically do:
4) self-sending self-rate constraint: the distributed power supply in the grid can supply power to loads and transmit power to a large power grid under the condition of excess power generation capacity. With the distribution network in certain cycle, be used for satisfying the distributed power generation capacity proportion definition of load demand for the spontaneous rate of utilization, specifically do:
wherein: rsuffIs the self-rate; eselfThe load power consumption which can be met by the power distribution network; eDGThe total power generation amount of the distributed power supply of the power distribution network;
5) self-smoothing rate constraint: self-smoothing rate also known as tie-line power fluctuation rate
Wherein: deltalineIs the self-smoothing rate, Pline,iIs the link power at the ith time,
is the average power of the links within a day.
The power relation among the photovoltaic, the storage battery and the main network in the power balance constraint is determined according to an operation strategy, and specifically comprises the following steps:
1) when the net load Paggregate(t)＝Pload(t)-Ppv(t)<At 0, the photovoltaic power generation charges the storage battery under the condition of meeting the load power supply, and at the moment, the load level is low, the electricity price is also low, so the storage battery stores low-price electricity.
1.1) if the battery pack is charged but not full, then there is Pbat(t)＝|Paggregate(t)|
Battery state of charge update, SOC (t +1) ═ SOC (t) (1- σ) + Pbat(t)/EbatWherein: ebatσ is the self-discharge rate of the battery per hour, which is the energy storage capacity of the battery.
1.2) when the storage battery pack is fully charged and still has residual power generation amount, power can be transmitted to the main network, namely:
Pgrid(t)＝-|Paggregate(t)|+Pchmaxwherein: pchmaxThe maximum charging power of the storage battery.
2) When the net load Paggregate(t)＝Pload(t)-PpvWhen (t) is 0, the battery state of charge is: SOC (t +1) ═ SOC (t) (1- σ);
3) when the net load Paggregate(t)＝Pload(t)-Ppv(t)>When 0, the low-price electricity stored in the storage battery is selected to supplement the power supply shortage.
3.1) when the low-price electricity stored by the storage battery can be supplemented, the charge state of the storage battery pack is as follows: pbat(t)＝-(Pload(t)-Ppv(t))；SOC(t+1)＝SOC(t)(1-σ)+Pbat(t)/Ebat
3.2) when the low-price electricity stored in the storage battery is not enough to meet the shortage of power supply, purchasing electricity from the main grid, wherein the electricity purchasing quantity is as follows:
Pgrid(t)＝Paggregate(t)-Pdhmaxwherein: pdhmaxIs the maximum discharge power of the battery.
Technical effects
Compared with the prior art, the method adopts double-layer optimization planning, the inner layer and the outer layer both adopt an improved particle swarm algorithm, the outer layer realizes site selection, the inner layer determines the optimal capacity, the inner layer and the outer layer are connected through the power of photovoltaic and energy storage, the energy flow mode and the port loss of the power electronic transformer are fully considered, and the whole network loss is reduced.
Drawings
FIG. 1 is a diagram of a power distribution network system based on a power electronic transformer in an embodiment;
in the figure: grid is a power Grid, PET is a power electronic transformer, PV is photovoltaic, ES is a storage battery, DC Load is a direct current Load, and AC Load is an alternating current Load;
FIG. 2 is a schematic diagram of an operation plan curve in an embodiment;
fig. 3 is a schematic diagram of an electricity purchasing cost optimization curve in the embodiment.
Detailed Description
As shown in fig. 1, the present embodiment is a power distribution network based on a power electronic transformer, the power electronic transformer has a three-port structure, one port of the power electronic transformer is connected to a 10kV ac main network, and the other two ports are respectively connected to a 380V ac bus and a ± 375V dc bus. The photovoltaic and energy storage devices are in an alternating current access mode, the position of energy storage can affect the trend of a power distribution network, and further line loss is affected, therefore, in the embodiment, the objective function of outer layer position optimization is line loss and port loss of a power electronic transformer, the loss calculation needs to make clear the real-time charging and discharging state of the energy storage device and the real-time running state of the whole power distribution network, the charging and discharging of energy storage and the running of the power distribution network are related to the energy storage capacity, therefore, inner layer energy storage capacity optimization is introduced, the objective function is electricity purchasing cost, and the real-time running state of the power distribution network based on the power electronic transformer is determined in the inner layer.
When the energy storage is connected to a 380V alternating current node,
wherein: ploss1For storing the total line loss, P, when connected at a 380V AC nodePVFor photovoltaic output, PESFor energy storage, during charging of the energy storage device, PESIs negative, P when the energy storage device is dischargedESIs positive, PACFor AC loading, R1Resistance of 380V AC line; pDCFor a DC load, R2Resistance of a +/-375V direct current line; pgridInputting the power of the power distribution network for the main network, and when the power is input into the main network from the power distribution network, PgridIs negative.
The loss of the power electronic transformer port refers to that: take 380V-10 kVAC port as an example (assuming the energy storage is connected at 380V AC node), when Pgrid>0, 10kV port input power, 380V port output power, port loss is
Wherein: eta is the operating efficiency; beta is the load factor.
TABLE 1 operating efficiency of PET
To measure the degree of line loss variation, this embodiment introduces a line loss sensitivity analysis method. The line Loss Sensitivity (LSF) refers to the amount of change in the loss of an electrical line caused by each unit of added power,
wherein: LSFiIs the line loss sensitivity, LSF, of node iiThe larger the output, the more obvious the network loss is reduced after the node i is added by one unit of output. Therefore, the objective function for the outer layer position optimization is defined as min (P)loss+1/LSF)。
Taking the industrial park shown in fig. 1 as an example, the industrial park comprises photovoltaic, industrial alternating current and direct current loads, and realizes the access and complementary coordination of the source and the load through a three-port power electronic transformer, thereby realizing the reliable access of the photovoltaic and the economic energy supply of the industrial loads. The self-discharge rate of the storage battery per hour is 0.01%, the initial state of charge SOC (0) is 0.4, the SOCmax is 0.9, the SOCmin is 0.2, and the maximum exchange power Pgridmax of the microgrid and the main grid is 500 kW. The electricity rates for the different periods are shown in table 2.
TABLE 2 time of use electricity price
The change curves of the purchased electric quantity and the stored energy charge state obtained by optimizing the inner layer capacity in the step 1) are shown in a figure 2. Fig. 3 is an electricity purchase cost variation optimization curve. Analysis of FIG. 2 yields:
1:00-12:00, wherein the net load is more than 0, namely the photovoltaic power generation does not meet the load power supply, and at the moment, because the electricity price is lower, the storage battery and the power purchasing to the power grid are used for supplementing the power supply shortage;
13:00-17:00, wherein the net load is less than 0, namely the photovoltaic power generation meets the load power supply, the rest is firstly charged to the storage battery, a certain standby is reserved, and then the rest is on the internet. The period with the highest electricity price is also corrected, so that the electricity purchasing cost is effectively reduced;
18:00-24:00, the net load is larger than 0 but not large, and the net load is mainly compensated by the power supply of the power grid, so that the stored energy is not discharged much, and enough reserve is reserved for compensating the power shortage on the next day.
The capacity allocation results are shown in table 3.
TABLE 3 Capacity allocation results
| Model number | Capacity of single machine | Number of allocated units | |
| Storage battery | Shengneng VRB-50 | 50kWh | 21 |
And 2) optimizing the outer layer position to obtain the optimal configuration position of the stored energy, wherein the optimal configuration position is a direct current port of +/-375V of PET.
The foregoing embodiments may be modified in many different ways by those skilled in the art without departing from the spirit and scope of the invention, which is defined by the appended claims and all changes that come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Claims (4)
1. An energy storage site selection and volume fixing method in a micro-grid based on a power electronic transformer is characterized by firstly establishing a double-layer optimization configuration model of energy storage in the micro-grid based on the power electronic transformer, obtaining the optimal capacity of an energy storage device and energy storage output and tie line power under the optimal capacity through inner layer capacity optimization, further obtaining a target function related to network loss in outer layer position optimization through load flow calculation, and finally obtaining the optimal position of the energy storage device, namely the position of a port of the power electronic transformer through a particle swarm optimization algorithm;
the double-layer optimization configuration model comprises the following steps: the inner layer optimization part realizes energy storage constant volume and the outer layer optimization part realizes energy storage site selection, wherein: the inner layer optimization part takes the minimum electricity purchasing cost as an objective function, and the outer layer optimization part takes the network loss and the port loss of the power electronic transformer as the objective function, wherein the network loss comprises the following components: line loss and power electronic transformer port loss specifically are:
said line losses, i.e. power distribution line losses
Wherein: piIs the injected active of node iPower, QiIs the injected reactive power, V, of node iiIs the voltage amplitude of node i, RijIs the resistance of line ij;
the port loss refers to: when the power grid inputs the power P of the microgridgridIf the voltage is more than 0, the voltage is specifically applied to the condition of 10kV port input power and 380V port output power
Wherein: eta is operating efficiency, beta is load factor, PDCIs a direct current load;
the optimal position of the energy storage device is obtained through the following modes:
1) initializing position and speed parameters of a particle swarm with the swarm size of N;
2) calculating an objective function value of each particle in the particle swarm, namely an objective function related to network loss in the outer layer optimization part;
3) preferentially updating the network loss of each particle at different positions;
4) comparing and preferentially updating the network loss of the best position of each particle with the network loss of the best position experienced by all the particles in the particle swarm;
5) updating the position and speed parameters of the particles according to the position corresponding to the self historical optimal network loss of the particles and the position corresponding to the overall optimal network loss of the particles, and obtaining an optimal optimization scheme when the maximum iterative algebra is reached; otherwise, returning to the step 2) to continue optimizing;
the inner layer optimization part is as follows: with the cost of electricity purchase as an objective function, i.e.
The constraints of the objective function include:
1) power balance constraint Pgrid＝Paggregate-Pbattery＝Pload-Ppv-PbatteryWherein: pgridInputting the power of the microgrid into a power grid; paggregateIs the net load power; ppvIs a photovoltaicThe output of power generation; ploadIs the load power; pbatteryThe output of the energy storage system;
2) energy storage power constraint
3) Self-balancing rate constraint: the microgrid is connected with a large power grid, the large power grid provides certain electric power support, the load demand proportion which can be met by the distributed power supply of the microgrid in a certain period is defined as a self-balancing rate, and the self-balancing rate is specifically as follows:
4) self-sending self-rate constraint: the distributed power supply in the network not only supplies power to the load, but also transmits power to the large power grid under the condition of surplus power generation capacity, and the power generation ratio of the distributed power supply for meeting the load demand is defined as the self-generating utilization ratio in a certain period, specifically as follows:
wherein: rsuffIs the self-rate; eselfThe load power consumption which can be met by the microgrid; eDGThe total power generation amount of the distributed power supply of the microgrid;
5) self-smoothing rate constraint: self-smoothing rate also known as tie-line power fluctuation rate
Wherein: deltalineIs the self-smoothing rate, Pline，iIs the link power at the ith time,
is the average power of the links within a day.
2. The method as claimed in claim 1, wherein the power relationship among the photovoltaic, the storage battery and the main network in the power balance constraint is determined according to an operation strategy, specifically:
step 1) as the net load Paggregate(t)＝Pload(t)-PpvWhen the (t) is less than 0, the photovoltaic power generation charges the storage battery under the condition of meeting the load power supply, and at the moment, the load level is lower, the electricity price is also low, so that the storage battery stores low-price electricity;
step 2) as the net load Paggregate(t)＝Pload(t)-PpvWhen (t) is 0, the battery state of charge SOC (t +1) is SOC (t) (1- σ), and σ is the self-discharge rate of the battery per hour;
step 3) when the net load Paggregate(t)＝Pload(t)-PpvWhen the value (t) > 0, the shortage of power supply is supplemented by the low-cost electricity stored in the storage battery.
3. The method as claimed in claim 2, wherein the step 1 specifically comprises:
1.1) if the battery pack is charged but not full, then there is Pbat(t)＝|Paggregate(t)|
The state of charge of the storage battery is updated as follows: SOC (t +1) ═ SOC (t) (1- σ) + Pbat(t)/EbatWherein: ebatThe energy storage capacity of the storage battery is shown, and sigma is the self-discharge rate of the storage battery per hour;
1.2) when the storage battery pack is fully charged and still has residual power generation amount, power can be transmitted to the main network, namely: pgrid(t)＝-|Paggregate(t)|+PchmaxWherein: pchmaxThe maximum charging power of the storage battery.
4. The method as claimed in claim 2, wherein the step 3 specifically comprises:
3.1) when the storage battery stores low-price electricity, i.e. Pbat(t)＝-(Pload(t)-Ppv(t)), the battery pack state of charge is updated to: SOC (t +1) ═ SOC (t) (1- σ) + Pbat(t)/Ebat；
3.2) when the low-price electricity stored in the storage battery is not enough to meet the shortage of power supply, purchasing electricity from the main grid, wherein the electricity purchasing quantity is as follows: pgrid(t)＝Paggregate(t)-PdhmaxWherein: pdhmaxIs the maximum discharge power of the battery.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810667774.8A CN108695868B (en) | 2018-06-26 | 2018-06-26 | Power distribution network energy storage location and volume fixing method based on power electronic transformer |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810667774.8A CN108695868B (en) | 2018-06-26 | 2018-06-26 | Power distribution network energy storage location and volume fixing method based on power electronic transformer |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108695868A CN108695868A (en) | 2018-10-23 |
| CN108695868B true CN108695868B (en) | 2021-10-01 |
Family
ID=63849136
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810667774.8A Active CN108695868B (en) | 2018-06-26 | 2018-06-26 | Power distribution network energy storage location and volume fixing method based on power electronic transformer |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108695868B (en) |
Families Citing this family (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109510224B (en) * | 2018-11-16 | 2021-11-09 | 上海交通大学 | Capacity allocation and operation optimization method combining photovoltaic energy storage and distributed energy |
| CN110034571A (en) * | 2019-03-21 | 2019-07-19 | 国网浙江省电力有限公司经济技术研究院 | A kind of distributed energy storage addressing constant volume method considering renewable energy power output |
| CN110601260B (en) * | 2019-09-11 | 2021-05-14 | 电子科技大学 | Light-storage system capacity optimization method for limiting power fluctuation on interconnection line |
| CN112039067A (en) * | 2020-09-01 | 2020-12-04 | 国网河北省电力有限公司邢台供电分公司 | Power distribution network new energy power generation utilization rate optimization method and terminal equipment |
| CN112069638A (en) * | 2020-09-08 | 2020-12-11 | 广东电网有限责任公司电力科学研究院 | Low-voltage distribution network energy storage equipment access point location method and related device |
| CN112134286A (en) * | 2020-09-22 | 2020-12-25 | 广东电网有限责任公司 | Alternating current-direct current hybrid micro-grid |
| CN112152219A (en) * | 2020-09-23 | 2020-12-29 | 广东电网有限责任公司东莞供电局 | Alternating current-direct current hybrid power distribution network system applied to office living park |
| CN114825451B (en) * | 2022-06-29 | 2022-10-11 | 西安热工研究院有限公司 | Light-storage micro-grid flexible networking system for thermal power plant |
| CN117559507A (en) * | 2024-01-04 | 2024-02-13 | 武汉理工大学 | Constant-volume and site-selection optimization configuration method and system for network-structured energy storage power station |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103903073A (en) * | 2014-04-23 | 2014-07-02 | 河海大学 | Planning method and system for optimizing micro-grid containing distributed power sources and stored energy |
| WO2015200931A1 (en) * | 2014-06-23 | 2015-12-30 | Gridbridge, Inc. | Versatile site energy router |
| CN105846423A (en) * | 2016-03-28 | 2016-08-10 | 华北电力大学 | Method for photovoltaic microgrid energy storage multi-target capacity configuration by taking demand response into consideration |
| CN106341044A (en) * | 2016-10-19 | 2017-01-18 | 华北电力大学（保定） | Comprehensive efficiency optimization control method for modularized power electronic transformer |
| CN106374515A (en) * | 2016-09-14 | 2017-02-01 | 国家电网公司 | Double-layer hierarchical optimization configuration method of energy storage system in active power distribution network |
| CN107301470A (en) * | 2017-05-24 | 2017-10-27 | 天津大学 | A kind of power distribution network Expansion Planning stores up the dual blank-holder of addressing constant volume with light |
| CN108170952A (en) * | 2017-12-27 | 2018-06-15 | 清华大学 | Micro-capacitance sensor Optimal Configuration Method and device based on electric power electric transformer |
-
2018
- 2018-06-26 CN CN201810667774.8A patent/CN108695868B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103903073A (en) * | 2014-04-23 | 2014-07-02 | 河海大学 | Planning method and system for optimizing micro-grid containing distributed power sources and stored energy |
| WO2015200931A1 (en) * | 2014-06-23 | 2015-12-30 | Gridbridge, Inc. | Versatile site energy router |
| CN105846423A (en) * | 2016-03-28 | 2016-08-10 | 华北电力大学 | Method for photovoltaic microgrid energy storage multi-target capacity configuration by taking demand response into consideration |
| CN106374515A (en) * | 2016-09-14 | 2017-02-01 | 国家电网公司 | Double-layer hierarchical optimization configuration method of energy storage system in active power distribution network |
| CN106341044A (en) * | 2016-10-19 | 2017-01-18 | 华北电力大学（保定） | Comprehensive efficiency optimization control method for modularized power electronic transformer |
| CN107301470A (en) * | 2017-05-24 | 2017-10-27 | 天津大学 | A kind of power distribution network Expansion Planning stores up the dual blank-holder of addressing constant volume with light |
| CN108170952A (en) * | 2017-12-27 | 2018-06-15 | 清华大学 | Micro-capacitance sensor Optimal Configuration Method and device based on electric power electric transformer |
Non-Patent Citations (2)
| Title |
|---|
| 含大规模风光电源的配电网储能电池选址定容优化方案;王彦虹，邰能灵，嵇康;《电力科学与技术学报》;20170630;第32卷(第2期);第23-30页 * |
| 基于多智能体一致性协同理论的智能配电网自动发电控制方法;张泽宇;《中国优秀硕士学位论文全文数据库工程科技II辑》;20170215;第18页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108695868A (en) | 2018-10-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108695868B (en) | Power distribution network energy storage location and volume fixing method based on power electronic transformer | |
| CN109301853A (en) | A kind of micro-capacitance sensor Multiple Time Scales energy management method for stabilizing power swing | |
| CN106099965B (en) | Exchange the control method for coordinating of COMPLEX MIXED energy-storage system under micro-grid connection state | |
| US8330293B2 (en) | Power factor correction system | |
| CN109066750B (en) | Photovoltaic-battery micro-grid hybrid energy scheduling management method based on demand side response | |
| CN111882105A (en) | Microgrid group with shared energy storage system and day-ahead economic optimization scheduling method thereof | |
| CN110311396A (en) | A kind of alternating current-direct current mixing micro-capacitance sensor hybrid energy-storing capacity configuration optimizing method | |
| CN103248064A (en) | Composite energy charging energy storage system and method thereof | |
| CN104767224A (en) | Energy management method of multi-energy-storage-type containing grid-connection type wind and light storage micro-grid | |
| CN109754112A (en) | A kind of light storage charging tower random optimization dispatching method considering power distribution network peak load shifting | |
| CN109102185B (en) | Method and system for evaluating whole life cycle economy of light storage and charging integrated power station | |
| CN110752629B (en) | Energy optimization management method for AC/DC hybrid household micro-grid | |
| CN106786692A (en) | One kind is based on the orderly charge control method of distributed electric automobile | |
| CN110880759A (en) | Energy management method and system of light storage micro-grid based on real-time electricity price mechanism | |
| CN111327078A (en) | Household energy scheduling method, energy management module and household energy system | |
| CN114889498B (en) | Power optimization distribution method of hydrogen-electricity hybrid power system | |
| CN107092975B (en) | AC-DC hybrid micro-grid economic optimization method based on energy storage loss integral | |
| CN109523079B (en) | Optimization method for combined auxiliary peak shaving by utilizing electric automobile and heat accumulating electric boiler | |
| CN111313444A (en) | Energy storage system optimal configuration method for high-density photovoltaic distribution network side | |
| CN206422586U (en) | A kind of intelligent scheduling equipment of household energy management system | |
| CN102931676A (en) | Structure and method of solar power grid-connected self-use multifunctional system | |
| CN105207240A (en) | Distributed energy storage scheduling and optimizing control method and system based on energy efficiency cloud terminal | |
| CN109119988B (en) | Photovoltaic-battery microgrid energy scheduling management method based on dynamic wholesale market price | |
| Hu et al. | Power grid peak shaving strategies based on electric vehicles and thermal storage electric boilers | |
| Liu et al. | The optimal sizing for AC/DC hybrid stand-alone microgrid based on energy dispatch strategy |
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 |