CN108695876A - Power grid flexible resource emergency response policy development method based on dynamic programming - Google Patents
Power grid flexible resource emergency response policy development method based on dynamic programming Download PDFInfo
- Publication number
- CN108695876A CN108695876A CN201810736258.6A CN201810736258A CN108695876A CN 108695876 A CN108695876 A CN 108695876A CN 201810736258 A CN201810736258 A CN 201810736258A CN 108695876 A CN108695876 A CN 108695876A
- Authority
- CN
- China
- Prior art keywords
- temperature control
- flexible resource
- control load
- energy storage
- storage device
- 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
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
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
-
- 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]
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
- Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
- Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
- Y04S20/20—End-user application control systems
- Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The power grid flexible resource emergency response policy development method based on dynamic programming that the invention discloses a kind of, includes the following steps:(1) mains frequency difference is obtained to differentiate operation of power networks state;(2) when differentiating that result instruction power grid is in a state of emergency, power supply vacancy is obtained with schedulable flexible resource capacity to determine whether by only dispatching flexible resource reply frequency to fall, the flexible resource includes energy storage device and temperature control load;(3) when that can fall by only dispatching flexible resource reply frequency, the response model of the energy storage device and the temperature control load is established;(4) response model based on the power supply vacancy, node electricity price predictive information, the energy storage device and the temperature control load solves the minimum flexible resource schedule of electricity price using dynamic programming.The present invention alleviates falling for mains frequency by optimizing scheduling social resources to the maximum extent, and based on Dynamic Programming solve and improve calculating speed.
Description
Technical field
The present invention relates to a kind of power grid flexible resource emergency response policy development methods, especially a kind of to be based on Dynamic Programming
The power grid flexible resource emergency response policy development method of method.
Background technology
As national economy is continuously improved, the contradiction of economic structure is also further prominent, and network load increases year by year, power grid
The constantly soaring peak-valley difference simultaneously of peak value is also in that gradually widened trend, the power supply and demand imbalance problem of some areas are very tight
Weight, seriously threatens the safe and stable operation of power grid.When power grid is in emergency, generally use means such as power cuts to limit consumption by force
Power supply and demand balance is maintained, to alleviate the state of emergency of power grid, however user is sacrificed using these load management means
Interests, influence the electricity consumption experience of user, the interactive of intelligent grid will be unfavorable for and carried out and transition.It is more serious in order to solve
Unbalanced supply-demand contradiction, meet ever-increasing customer charge demand, country every year will input substantial contribution for peak regulation electricity
Factory builds, but simple rely on increases installed capacity to meet of short duration peak load, will make a large amount of power plant's resources idle,
It is unfavorable for the reasonably optimizing of social resources, eventually leads to the rising of hair power supply cost.
Invention content
Goal of the invention:In order to overcome the deficiencies of the prior art, the present invention is intended to provide a kind of power grid based on dynamic programming
Flexible resource emergency response policy development method, to optimize what social resources fell suddenly to solve mains frequency to the maximum extent
Problem ensures power network safety operation.
Technical solution:The power grid flexible resource emergency response policy development method of the present invention includes the following steps:(1) it obtains
For the difference of power grid current frequency and rated frequency to differentiate operation of power networks state, wherein rated frequency is 50HZ;(2) when differentiation is tied
Fruit instruction power grid is when being in a state of emergency, and obtains power supply vacancy and schedulable flexible resource capacity so that determine whether can be by only adjusting
Degree flexible resource reply frequency is fallen, and the flexible resource includes energy storage device and temperature control load;(3) when can be soft by only dispatching
Property resource reply frequency when falling, establish the response model of the energy storage device and the temperature control load;(4) it is based on the power supply
Vacancy, node electricity price predictive information, the energy storage device and the temperature control load response model utilize dynamic programming to solve
The minimum flexible resource schedule of electricity price.
Further, the above method is further comprising the steps of:(5) after judging that response policy is implemented, whether mains frequency returns
Return normal operation range, if not having, evaluates flexible resource schedulable capacity again, response plan is again carried out since step (2)
Slightly.
Further, step (1) further includes:If it is normal condition that difference, which is less than 0.2, otherwise is the state of emergency.
Further, step (2) further includes:Power supply vacancy and schedulable flexible resource capacity are denoted as P respectivelyrAnd PdIf
Pr<Pd, it indicates that frequency only can be coped with by scheduling flexible resource and fallen;If Pr>Pd, it indicates that it needs to cut off all controllable
Flexible resource, and system spinning reserve is combined, power shortage is made up, frequency is retracted into normal operation range.
Further, in step (3), the response model for the energy storage device being set up is:
Wherein, SCo(t) the energy storage device charged state of t-th of period, P are indicatedo(t) t-th of period energy storage device is indicated
Charge-discharge electric power, SmaxIndicate the specified maximum reserve of electricity of energy storage device, Δ t is the length of each period;
The response model for the temperature control load being set up is:
Wherein, To(t) indicate that t-th of period temperature control load energy-accumulating medium temperature, M indicate the matter of temperature control load heat-storage medium
Amount, c indicate that the specific heat capacity of temperature control load heat-storage medium, HL indicate the heat loss factor of temperature control load, ToutIndicate ambient temperature;
Qo(t) t-th of period temperature control load thermal power is indicated.
Further, in step (4), when being solved using dynamic programming, object function is:
USC=argmin[Costc(N)+Coste(N)],
Wherein, USC indicates the integrated scheduling scheme of energy storage device and temperature control load, Costc(t) it indicates to open from initial time period
Begin to the power cost of t-th of period energy storage device;Coste(t) it indicates since initial time period to t-th of period temperature control load
Power cost, N is the total number of period, and is had:
Costc(t)=Po(t)price(t)+Costc(t-1),
Coste(t)=Qo(t)price(t)+Coste(t-1),
Wherein, price (t) indicates the node electricity price of t-th predicted of period;
Constraints is:
pr=po(t)W+[Qo(0)-Q0(t)]N,
Wherein, PeIndicate the rated power of energy storage device, prFor vacancy of powering, Qo(0) initial time period temperature control load is indicated
Thermal power;
It solves to obtain based on object function, constraints and the response model of the energy storage device being set up and temperature control load
Po(t) and Qo(t) as optimize the minimum flexible resource schedule of obtained electricity price.
Advantageous effect:Compared with the existing technology, the present invention has the advantage that:
1, temperature control load and energy storage device are combined, falling situation for different frequency is responded, by greatest extent
Ground optimizing scheduling social resources alleviate falling for mains frequency, ensure electric power netting safe running.
2, it is based on dynamic programming method, can simplify problem complexity in conjunction with reasonable programming, improve calculating speed.
Description of the drawings
Fig. 1 is the flow of the power grid flexible resource emergency response policy development method based on dynamic programming of the present invention
Figure;
Fig. 2 is that interior joint Research on electricity price prediction information of the embodiment of the present invention changes over time relational graph;
Fig. 3 is that the energy storage device charge-discharge electric power that simulation optimization obtains in the embodiment of the present invention changes over time relational graph;
Fig. 4 is that the electric heater hot water service condition that simulation optimization obtains in the embodiment of the present invention changes over time relationship
Figure.
Specific implementation mode
Below in conjunction with attached drawing, further the present invention is described in detail.
Such as Fig. 1, flexible resource emergency response policy development method of the invention includes the following steps:
Step S1:The difference of power grid current frequency and rated frequency is obtained to differentiate operation of power networks state.
Since power grid rated frequency is 50HZ, so if difference is denoted as ε, ε=s |f-50|, f is the current of power grid
Frequency.If difference illustrates that current electric grid is normal condition less than 0.2HZ, otherwise is the state of emergency.
Step S2 obtains power supply vacancy P when power grid is in a state of emergencyrWith schedulable flexible resource capacity Pd, choose
Corresponding emergency response scheme;
Specifically, when choosing corresponding emergency response scheme, if Pr<Pd, it indicates that only by dispatching flexible resource
Reply frequency is fallen;If Pr>Pd, it indicates that it needs to cut off all controllable flexible resources, and combines system spinning reserve, make up work(
Frequency is retracted normal operation range by rate vacancy.
Step S3 establishes flexible resource response mould when instruction only can cope with frequency by scheduling flexible resource to be fallen
Type.
Specifically, in the present embodiment, selection is widely distributed, using more energy storage device with temperature control load as soft
Property resource response establishes its response model.Particularly, the discrete response of energy storage and temperature control load is established using Δ t as time interval
Model.
(1) energy storage device:
Wherein, SCo(t) it indicates since initial time period to the energy storage device charged state of t-th of period;Po(t) indicate from
Initial time period starts to t-th of period energy storage device charge-discharge electric power.
(2) temperature control load:
Wherein, To(t) it indicates since initial time period to the temperature control load energy-accumulating medium temperature of t-th of period;M indicates temperature
Control the quality of load heat-storage medium;C indicates the specific heat capacity of temperature control load heat-storage medium;HL indicates the heat loss system of temperature control load
Number;ToutIndicate ambient temperature (in order to improve the operability of the method for the present invention, it is assumed that ambient temperature is in policy enforcement procedure
Steady state value);Qo(t) the temperature control load thermal power of t-th of period is indicated.
Step S4 obtains node electricity price predictive information LMP, by LMP and power supply difference PrAs input, in conjunction with flexible resource
Response model solves flexible resource schedule under the state of emergency using dynamic programming.
The present invention is using energy storage device and temperature control load integrated scheduling cost minimization as optimization aim, therefore, is utilizing dynamic
When law of planning solves, object function is:
USC=argmin[Costc(N)+Coste(N)] (3)
Wherein, USC indicates the integrated scheduling side for making the total activation cost minimization of energy storage device and temperature control load in N number of period
Case, Costc(t) show since initial time period to the power cost of t-th of period energy storage device;Coste(t) indicate from it is initial when
Section starts to the power cost of the temperature control load of t-th of period;Costc(t) and Coste(t) meet following relational expression:
Costc(t)=Po(t)price(t)+Costc(t-1) (4)
Coste(t)=Qo(t)price(t)+Coste(t-1) (5)
Wherein, price (t) indicates the electricity price of t-th predicted of period, is provided by Utilities Electric Co..
Constraints:
According to the operation principle of energy storage device, in normal operation, the P of energy storage deviceo(t) should meet as follows about
Beam:
Wherein, PeIndicate the rated power of energy storage device, SmaxIndicate the specified maximum reserve of electricity of energy storage device, this is differed
The charge or discharge power of formula constraint representation, arbitrary period energy storage device must not exceed rated power, in order to avoid damage energy storage device.
In addition, for entire model, need to meet following constraint:
Pr=Po(t)N+[Qo(0)-Q0(t)]N (8)
Wherein, Qo(0) thermal power of initial time period temperature control load is indicated.Above-mentioned formula shows to power vacancy equal to by cutting
Disconnected temperature control load or the sum of the power for reducing the power and energy storage device electric discharge under its Save power consumption.
After object function and constraints is determined, it can be solved by recursive algorithm, memorandum scheduling algorithm, with
Node electricity price LMP and power supply vacancy P i.e. predicted price (t)r, optimization aim is minimised as with response policy implementation cost,
Export the schedule of flexible resource, i.e. Po(t) (the energy storage device charge-discharge electric power of i.e. t-th period) and Qo(t) (i.e. t-th
The temperature control load thermal power of period).In the model, input quantity is power supply vacancy PrWith node electricity price price (t), output quantity
For the integrated scheduling scheme USC of energy storage device and temperature control load, it is embodied as the energy storage device charge and discharge electric work of the t period
Rate Po(t) and the temperature control load thermal power Q of t-th of periodo(t), or the obtained slave initial time period of optimization is to t-th period
Energy storage device charged state SCo(t) and being situated between to the temperature control load energy storage of t-th period since initial time period of obtaining of optimization
Matter temperature To(t), remaining is all known quantity or process intermediate quantity.
Step S5, after judging that response policy is implemented, whether mains frequency returns normal operation range, if not having, comments again
Determine flexible resource schedulable capacity Pd, response policy is again carried out since step S2.
In order to verify the effect of the method for the present invention, simulating, verifying has been carried out.
Such as Fig. 2, predicted LMP node electricity prices presentation of information afternoon 6 for acquiring:30 or so have an electricity price peak,
This illustrates that mains frequency falls generation in the afternoon 6:30 or so.Such as Fig. 3, in the afternoon 6:Energy storage after 30 power grid occurrence frequencies fall
Equipment does not receive any electric energy from power grid, but substantially electric discharge supports mains frequency.In case study on implementation, temperature control load selects electricity
Water heater load carries out simulation analysis, obtains Fig. 4 electric heater hot water service condition schematic diagrames.As can be seen from the figure in the afternoon
6:After 30 power grid occurrence frequencies fall event, hot water usage amount is greatly decreased.Cause it can be seen that falling for mains frequency
Electricity price peak energy storage device and temperature control load are scheduled, the problem of mains frequency falls has been effectively relieved.
In conclusion embodiment demonstrates the practicability and validity of the present invention.
Claims (6)
1. a kind of power grid flexible resource emergency response policy development method, which is characterized in that include the following steps:
(1) difference of power grid current frequency and rated frequency is obtained to differentiate operation of power networks state, and wherein rated frequency is 50HZ;
(2) when differentiating that result instruction power grid is in a state of emergency, power supply vacancy is obtained with schedulable flexible resource capacity with true
Whether fixed to be fallen by only dispatching flexible resource reply frequency, the flexible resource includes energy storage device and temperature control load;
(3) when that can cope with frequency by only dispatching flexible resource and fall, the energy storage device and the temperature control load are established
Response model;
(4) response model based on the power supply vacancy, node electricity price predictive information, the energy storage device and the temperature control load
The minimum flexible resource schedule of electricity price is solved using dynamic programming.
2. power grid flexible resource emergency response policy development method according to claim 1, which is characterized in that further include with
Lower step:
(5) after judging that response policy is implemented, whether mains frequency returns normal operation range, if not having, evaluation is flexible again
Resource schedulable capacity is again carried out response policy since step (2).
3. power grid flexible resource emergency response policy development method according to claim 1, which is characterized in that step (1)
Further include:If it is normal condition that difference, which is less than 0.2HZ, otherwise it is the state of emergency.
4. power grid flexible resource emergency response policy development method according to claim 1, which is characterized in that step (2)
Further include:Power supply vacancy and schedulable flexible resource capacity are denoted as P respectivelyrAnd PdIf Pr<Pd, it indicates that it is only soft by dispatching
Property resource can cope with frequency and fall;If Pr>Pd, it indicates that it needs to cut off all controllable flexible resources, and it is standby to combine system to rotate
With making up power shortage, frequency retracted normal operation range.
5. power grid flexible resource emergency response policy development method according to claim 1, which is characterized in that in step
(3) in, the response model for the energy storage device being set up is:
Wherein, SCo(t) the energy storage device charged state of t-th of period, P are indicatedo(t) t-th of period energy storage device charge and discharge is indicated
Electrical power, SmaxIndicate the specified maximum reserve of electricity of energy storage device, Δ t is the length of each period;
The response model for the temperature control load being set up is:
Wherein, To(t) indicate that t-th of period temperature control load energy-accumulating medium temperature, M indicate the quality of temperature control load heat-storage medium, c
Indicate that the specific heat capacity of temperature control load heat-storage medium, HL indicate the heat loss factor of temperature control load, ToutIndicate ambient temperature;Qo(t)
Indicate t-th of period temperature control load thermal power.
6. power grid flexible resource emergency response policy development method according to claim 5, which is characterized in that in step
(4) in,
When being solved using dynamic programming, object function is:
USC=argmin[Costc(N)+Coste(N)],
Wherein, USC indicates the integrated scheduling scheme of energy storage device and temperature control load, Costc(t) indicate since initial time period to
The power cost of t-th of period energy storage device;Coste(t) it indicates since initial time period to the electricity of t-th of period temperature control load
Power cost, N are the total number of period, and have:
Costc(t)=Po(t)price(t)+Costc(t-1),
Coste(t)=Qo(t)price(t)+Coste(t-1),
Wherein, price (t) indicates the node electricity price of t-th predicted of period;
Constraints is:
Pr=Po(t)N+[Qo(0)-Q0(t)]N,
Wherein, PeIndicate the rated power of energy storage device, PrFor vacancy of powering, and Qo(0) heat of initial time period temperature control load is indicated
Power;
The P solved based on object function, constraints and the response model of the energy storage device being set up and temperature control loado
(t) and Qo(t) as optimize the minimum flexible resource schedule of obtained electricity price.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810736258.6A CN108695876B (en) | 2018-07-06 | 2018-07-06 | Power grid flexible resource emergency response strategy making method based on dynamic planning method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810736258.6A CN108695876B (en) | 2018-07-06 | 2018-07-06 | Power grid flexible resource emergency response strategy making method based on dynamic planning method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108695876A true CN108695876A (en) | 2018-10-23 |
| CN108695876B CN108695876B (en) | 2020-06-26 |
Family
ID=63850565
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810736258.6A Active CN108695876B (en) | 2018-07-06 | 2018-07-06 | Power grid flexible resource emergency response strategy making method based on dynamic planning method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108695876B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117277310A (en) * | 2023-10-18 | 2023-12-22 | 南京国电南自电网自动化有限公司 | Aggregate resource adjustable capability calculating method and system based on valley filling demand response |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2010002825A1 (en) * | 2008-07-01 | 2010-01-07 | Carina Technology, Inc. | Water heater demand side management system |
| CN105762835A (en) * | 2016-03-31 | 2016-07-13 | 北方工业大学 | Temperature control load-based coordination control method and system for isolated microgrid frequency |
-
2018
- 2018-07-06 CN CN201810736258.6A patent/CN108695876B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2010002825A1 (en) * | 2008-07-01 | 2010-01-07 | Carina Technology, Inc. | Water heater demand side management system |
| CN105762835A (en) * | 2016-03-31 | 2016-07-13 | 北方工业大学 | Temperature control load-based coordination control method and system for isolated microgrid frequency |
Non-Patent Citations (1)
| Title |
|---|
| 边媛: "基于可控负荷优化利用的微电网频率调剂策略研究", 《中国优秀硕士学位论文全文数据库》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117277310A (en) * | 2023-10-18 | 2023-12-22 | 南京国电南自电网自动化有限公司 | Aggregate resource adjustable capability calculating method and system based on valley filling demand response |
| CN117277310B (en) * | 2023-10-18 | 2024-08-02 | 南京国电南自电网自动化有限公司 | Aggregate resource adjustable capability calculating method and system based on valley filling demand response |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108695876B (en) | 2020-06-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Chua et al. | A novel fuzzy control algorithm for reducing the peak demands using energy storage system | |
| Arnold et al. | Model predictive control of energy storage including uncertain forecasts | |
| CN110188950A (en) | Virtual plant supply side and Demand-side Optimized Operation modeling method based on multi-agent technology | |
| CN106786547A (en) | A kind of new micro-grid system and the networking scheduling method based on the system | |
| US20120215368A1 (en) | Storage integrated management systems for energy microgrids | |
| JPWO2017150376A1 (en) | Power system | |
| Wang et al. | Load curve smoothing strategy based on unified state model of different demand side resources | |
| JP2018143046A (en) | Virtual Power Plant | |
| Kouache et al. | A new approach to demand response in a microgrid based on coordination control between smart meter and distributed superconducting magnetic energy storage unit | |
| Masuta et al. | Modeling of a number of heat pump water heaters as control equipment for load frequency control in power systems | |
| JP2013017284A (en) | Power control system, electric apparatus and charge/discharge control section | |
| Chauhan et al. | Optimization of grid energy using demand and source side management for DC microgrid | |
| CN115663791A (en) | Intelligent power distribution network multi-target adaptive scheduling method based on time-varying property of operating environment | |
| CN111193293A (en) | Power distribution network coordinated multi-main-body scheduling processing method and device | |
| Saleh et al. | Distributed energy storage unit-based active demand response for residential loads | |
| CN117081177B (en) | Micro-grid operation power control method for hydroelectric main dispatching unit in island mode | |
| JP2018148627A (en) | Power system | |
| CN113746105A (en) | Optimal control method, device, equipment and storage medium for power demand response | |
| CN116760025B (en) | Risk scheduling optimization method and system for electric power system | |
| CN108695876A (en) | Power grid flexible resource emergency response policy development method based on dynamic programming | |
| CN112350435A (en) | Virtual power plant management and control device based on micro-grid group and electric power controllable load | |
| CN116961008A (en) | Micro-grid capacity double-layer optimization method considering power spring and load demand response | |
| Yang et al. | Inverse proportion technique based scheduling strategy for energy storage system considering load demand differences | |
| CN115021406B (en) | Microgrid controller integrating economic model predictive control | |
| CN115081707A (en) | Micro-grid multi-time scale optimization scheduling method based on source and load flexibility |
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 |