CN102570609B - Resident intelligent energy management system suitable for intelligent power grid - Google Patents
Resident intelligent energy management system suitable for intelligent power grid Download PDFInfo
- Publication number
- CN102570609B CN102570609B CN201210015860.3A CN201210015860A CN102570609B CN 102570609 B CN102570609 B CN 102570609B CN 201210015860 A CN201210015860 A CN 201210015860A CN 102570609 B CN102570609 B CN 102570609B
- Authority
- CN
- China
- Prior art keywords
- household electrical
- distributed power
- electrical appliances
- power source
- electric
- 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.)
- Expired - Fee Related
Links
Images
Classifications
-
- 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
- Y02B90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02B90/20—Smart grids as enabling technology in buildings sector
-
- 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
- Y04S40/00—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
- Y04S40/12—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them characterised by data transport means between the monitoring, controlling or managing units and monitored, controlled or operated electrical equipment
- Y04S40/124—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them characterised by data transport means between the monitoring, controlling or managing units and monitored, controlled or operated electrical equipment using wired telecommunication networks or data transmission busses
-
- 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
- Y04S40/00—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
- Y04S40/12—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them characterised by data transport means between the monitoring, controlling or managing units and monitored, controlled or operated electrical equipment
- Y04S40/126—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them characterised by data transport means between the monitoring, controlling or managing units and monitored, controlled or operated electrical equipment using wireless data transmission
-
- 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
- Y04S40/00—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
- Y04S40/12—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them characterised by data transport means between the monitoring, controlling or managing units and monitored, controlled or operated electrical equipment
- Y04S40/128—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them characterised by data transport means between the monitoring, controlling or managing units and monitored, controlled or operated electrical equipment involving the use of Internet protocol
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a resident intelligent energy management system suitable for an intelligent power grid. Due to the adoption of the resident intelligent energy management system, peak and off peak load regulation of a power load is realized, and the power quality and power reliability are improved. An intelligent energy management controller and an intelligent electric meter are arranged in the home of each resident; a control terminal is arranged on each controllable domestic appliance and a user switch respectively; a distributed power supply accessing device is arranged on each distributed power supply; the controller is connected with the control terminal and the distributed power accessing device through a Zigbee wireless communication network; the intelligent electric meter is connected with the distributed power supply accessing device and the controllable domestic appliance; the controller is communicated with the intelligent electric meter through a bus; the intelligent electric meter is further connected with the secondary sides of a voltage mutual inductor and a current mutual inductor; the controller undergoes information interaction with a high-level measuring system AMI (Advanced Metering Infrastructure) of a main station through Ethernet communication; the controller is used for controlling switching of the control terminal and the distributed power supply accessing device; the control terminal is used for submitting the state of the switch to the controller; the controller is used for monitoring the state of the switch.
Description
Technical field
The present invention relates to Smart Home and intelligent grid field, relate in particular to the resident's intelligence EMS that is applicable to intelligent grid.
Background technology
Due to the driving of the many factors such as the energy, environment, economy, politics, intelligent grid has become the developing direction of Future Power System.At present, no matter be developed country or developing country, all actively carrying out traditional electrical network to the transformation of intelligent grid.And energy savings, the long-term great policy of protection of the environment ，Shi China, therefore, China just builds at Efforts To Develop intelligent grid.Intelligent grid is interactive electrical network, and it requires to realize information interaction between power supply department and resident, allows power consumer initiatively participate in electricity market and electrical power services, realizes the peak load shifting of electric load, to improve power supply quality and power supply reliability.For this reason, power supply department certainly will be carried out Spot Price, encourages power consumer valley power consumption, reduces peak electricity consumption; Meanwhile, power supply department encourages power consumer access distributed power generation and energy storage device to participate in peak load shifting.Resident's One's name is legion, electricity consumption selection of time surplus is large; And along with the development of distributed power source, distributed power source will go deep into huge numbers of families.Therefore, design consideration Spot Price realizes resident's EMS that resident's intelligent appliance and inhabitant distribution formula power supply smart control and has great importance.Correlation technique in the past only limits to two-way charging or the automatic control to intelligent appliance of intelligent electric meter, does not realize according to Spot Price situation resident's household electrical appliance are carried out to Based Intelligent Control, makes resident's electric cost expenditure minimum.The present invention can control resident's household electrical appliances at valley power consumption, on peak by distributed power source to electrical network feedback energy, user is cut down expenses, make power supply department improve power supplying efficiency, meanwhile, can improve the load factor of the generating set of generating department, improve the efficiency of unit, reduce cost of electricity-generating.
Summary of the invention
The object of the invention is cannot realize the Based Intelligent Control to household electrical appliances according to Spot Price situation in order to overcome traditional resident family Energy Management System, make electric cost expenditure minimum, simultaneously, also cannot realize the Based Intelligent Control to distributed power source according to Spot Price situation, make the problem that its income is the highest, a kind of resident's intelligence EMS that is applicable to intelligent grid is provided, realizes the peak load shifting of electric load, improve power supply quality and power supply reliability.
For achieving the above object, the present invention adopts following technical scheme:
A kind of resident's intelligence EMS that is applicable to intelligent grid, in each household resident's family, install an intelligent energy management controller and intelligent electric meter, at every controlled household electrical appliances and subscriber switch place, all install a control terminal, at every distributed power source place, all install a distributed power source access device; Between controller and control terminal and distributed power source access device, by Zigbee wireless communication networks, interconnect, intelligent electric meter is electrically connected to distributed power source access device and controlled, between controller and intelligent electric meter, also by bus, intercoms mutually; Intelligent electric meter is also connected with the secondary side of voltage transformer summation current transformer, and the advanced measuring system AMI of controller and main website is by the interconnected information interaction of carrying out of Ethernet; Controller is controlled respectively the switching of control terminal, distributed power source access device, and control terminal reports controller by the state of switch by Zigbee wireless communication mode, makes controller monitor the state of switch.
Described control comprises main control module, data processing module, electric parameters input module, touch screen module, memory module, real-time clock module, ethernet module, Zigbee communication module and RS232/485 module; Main control module is mainly responsible for communication and man-machine interface, realizes the input and output of touch-screen, the storage of historical data, ethernet communication, Zigbee communication, RS232/485 communication and real-time clock input; Real-time clock module is realized the input of perpetual calendar real-time clock; Data processing module is responsible for the data acquisition of electric current and voltage electric parameters, and image data is carried out to digital filtering, calculating voltage effective value, current effective value, power factor, active power and reactive power; According to the Spot Price in historical Spot Price prediction each time interval on the same day, and according to Spot Price predicted value, controlled household electrical appliances and distributed power source situation, obtain the optimum operating time section decision-making of controlled household electrical appliances and distributed power source; According to real-time electric parameter, realize the protection decision-making to household electrical appliance; The result of decision is sent to main control module, by main control module, realize the optimal control to household electrical appliance.
Described data processing module comprises dsp chip and extensive field programmable logic array FPGA, between dsp chip and main control module, adopt dma mode to communicate by letter, voltage and current signal is through analog input transformer or fly electric capacity conversion, then filtering, through one 8, select 1CMOS multiplexer to select again, the output of multiplexer is driven by voltage follow-up amplifier, send into 16 A/D converters at a high speed and be converted to digital quantity, the output of A/D converter is sent into DSP with the form of serial data stream and is processed, and adopts 128 points of every cycle sampling; Described main control module adopts MCF5272 chip; Described memory module adopts static read/write memory SRAM, the 16M byte SDRAM of 256K byte, the flash memory FLASH RAM of 4M byte electric erasable, and wherein, SDRAM is the work internal memory of master controller, and SRAM is used for storing important historical data; Flash memory is for save set operation bootstrap routine, operating system, application program, DSP program, configuration file.
Described control terminal adopts the SOC (system on a chip) MC13213 with Zigbee communication function, this terminal receives after the break-make power command of controller by Zigbee communication modes, by output driving circuit MC1413, control electromagnetic relay, and then control the break-make power supply of controlled household electrical appliances; The state of electromagnetic relay is delivered to MC13213 after by photoisolator, realizes the break-make power supply status monitoring to controlled household electrical appliances; The temperature survey of household electrical appliances adopts digital temperature sensor DS18B20, sends into MC13213 process by serial data stream.
Described controller is according to the historical Spot Price information of obtaining from main website, utilize neural net to realize the prediction to Spot Price on the same day, being input as of neural net: the historical Spot Price of D-14 day, D-7 day and D-1 day, neural net is output as the Spot Price predicted value of D day, the structure of neural net is: 3-8-3-1, utilize historical data to train this neural net, the neural net after having trained is the neural network prediction model of Spot Price.
Described controller according to electricity price, the household electrical appliances type of prediction, household electrical appliances running times, resident to conditions such as the desired value of household electrical appliances running status, ambient temperatures, calculate the prediction electric cost expenditure of each time period household electrical appliances operation, get time period that electric cost expenditure is minimum as the time period of household electrical appliances operation, controller sends to the control terminal at household electrical appliances place the order that puts into operation by Zigbee communication modes, realizes household electrical appliances the lowest coursing cost is controlled.
The profit and loss that described controller puts into operation according to the subsidy of degree electricity, the operating cost of the every generating a kilowatt of distributed power source and the energy output Computation distribution formula power supply of distributed power source of Spot Price predicted value, every degree electricity; If profit and loss value is greater than 0, and distributed power source meets the maximum of access point and allows capacity limit, by resident family's Energy Management System, by Zigbee communication modes, to the access device of corresponding distributed power source, send the order that puts into operation, corresponding distributed power source is put into operation; If profit and loss value is less than 0, make distributed power source out of service.
The Spot Price that described controller obtains according to the user's real consumption electric weight He Cong main website obtaining from intelligent electric meter, calculate user's actual electric cost expenditure, and user's real consumption electric weight and actual electric cost expenditure monthly reported to main website by Ethernet, realize remote meter-reading function.
Described prediction electric cost expenditure is
Z
a＝Z
th+Z
ad (1)
Z
th＝(C
1+C
2+...+C
n)W
15+C
n+1W
t-15n (2)
Wherein, Z
aprediction electric cost expenditure for household electrical appliances; Z
thfor household electrical appliances are in the time interval 1,2 ..., n, the theoretical expenditure of electricity charge during n+1 energising operation; Z
adfor household electrical appliances are owing to having moved in advance the needed extra electric cost expenditure of the energy loss producing; C
1, C
2..., C
n, C
n+1be respectively the time interval 1,2 ..., n, the Spot Price of n+1; W
15for household electrical appliances move the electric energy consuming for 15 minutes; W
t-15nfor household electrical appliances operation, t-15n is less than the electric energy consuming for 15 minutes; T is total running times of household electrical appliances; t
efor household electrical appliances are expected the moment of having moved; t
cthe moment of having moved for family's electric theory; t
ifor family's electro-temperature reduces or raises 1 ℃ of needed time; W
tfor household electrical appliances raise or reduce by 1 ℃ of electric energy consuming; For the household electrical appliances relevant with ambient temperature running time, prediction electric cost expenditure calculates by formula (1); For temperature independent household electrical appliances running time, predict that electric cost expenditure calculates by formula (2); Get household electrical appliances operation and predict the final control time that the minimum time period of electric cost expenditure puts into operation as these household electrical appliances; Control mode adopts Zigbee wireless communication mode, by realizing the input of household electrical appliances or exit control controlling the control of the control terminal of household electrical appliances operation; Control to charging electric vehicle, according to Spot Price predicted value, batteries of electric automobile state and the desired value of user to rechargeable battery charged state, determine the time period of final charging, by controlling distributed power source access device, realize the input of electric automobile or exit control.
The profit and loss that described distributed power source puts into operation are
Y＝W
DG(C+S-R) (4)
Wherein, W
dGthe energy output that represents distributed power source, is transferred to resident's intelligence energy management controller by distributed power source access device by Zigbee wireless communication networks; C represents Spot Price predicted value, and S represents the degree electricity subsidy that every degree is electric, and the AMI of these two parameter Shi You main websites is transferred to resident's intelligence energy management controller by Ethernet; R is the operating cost of the every generating a kilowatt of distributed power source, comprises the depreciation cost of equipment and maintenance expense etc.When the operation profit and loss value Y calculating is greater than 0, represent that the operation of input distributed power source is rear profitable, can drop into distributed power source, otherwise, excision distributed power source;
The input capacity of distributed power source is retrained by following formula
P
DG≤P
max (5)
Wherein, P
dGthe gross power of all distributed power sources for access; P
maxthe maximum access capacity of allowing for distributed power source access point, the parameter value that the AMI of Shi You main website transmits by Ethernet, resident's intelligence energy management controller is according to the operation profit and loss of the real-time Computation distribution formula power supply of formula (4), if profit and loss value Y is greater than 0, and meet formula (5), input capacity is P
dGdistributed power source operation; If do not meet formula (5), input capacity is P
maxdistributed power source operation.
Resident's household electrical appliances energy management controller of native system is according to historical Spot Price data, utilizing neural net to realize predicts the Spot Price of each time period on the same day, according to the Spot Price, household electrical appliances type, running time, resident of prediction to conditions such as the desired value of household electrical appliances running status, ambient temperatures, calculate the prediction electric cost expenditure of each household electrical appliances operation time period, get time period that electric cost expenditure is minimum as the time period of household electrical appliances operation.In the corresponding moment, resident's household electrical appliances energy management controller sends to the control terminal of household electrical appliances the order that puts into operation by Zigbee communication modes, and household electrical appliances are put into operation, when running time then, control terminal to household electrical appliances sends order out of service again, makes household electrical appliances out of service.Meanwhile, the profit and loss that resident's household electrical appliances energy management controller puts into operation according to the subsidy of degree electricity, the operating cost of the every generating a kilowatt of distributed power source and the energy output Computation distribution formula power supply of distributed power source of Spot Price predicted value, every degree electricity.If profit and loss value is greater than 0, and distributed power source meets the maximum of access point and allows capacity limit, by resident's household electrical appliances energy management controller, by Zigbee communication modes, to the access device of corresponding distributed power source, send the order that puts into operation, corresponding distributed power source is put into operation; If profit and loss value is less than 0, make distributed power source out of service.The Spot Price that the controller of native system obtains according to the user's real consumption electric weight He Cong main website obtaining from intelligent electric meter, calculate user's actual electric cost expenditure, and user's real consumption electric weight and actual electric cost expenditure monthly reported to main website by Ethernet, realize remote meter-reading function.
Beneficial effect of the present invention: the present invention is applicable to the intelligent grid resident who carries out Spot Price and intelligent electric meter is housed, the demand charge of take expenditure is minimum is target, resident's household electrical appliances and distributed power source are carried out to Based Intelligent Control, realize the peak load shifting of electric load, improve power supply quality and power supply reliability.
Accompanying drawing explanation
Fig. 1 is resident's intelligence Ems Architecture block diagram of the present invention;
Fig. 2 is resident's intelligence energy management controller function block diagram of the present invention;
Fig. 3 is the Spot Price forecast model that the present invention is based on neural net;
Fig. 4 is the hardware structure diagram of resident's intelligence energy management controller of the present invention;
Fig. 5 is the hardware structure diagram of resident's intelligence energy management control terminal of the present invention;
Fig. 6 is the software flow pattern of resident's intelligence energy management controller of the present invention.
Embodiment
Below in conjunction with the drawings and specific embodiments, invention is described further.
The resident's intelligence EMS that is applicable to intelligent grid that the present invention proposes, realizes by the following technical solutions, and its step comprises:
Be illustrated in figure 1 resident's intelligence Ems Architecture block diagram of the present invention.It is core that this system be take resident intelligence energy management controller, this controller carries out information interaction by the advanced measuring system (AMI) of Ethernet and main website, from AMI, obtain Spot Price information, from intelligent electric meter, obtain real-time consumption information about power, calculate actual electric cost expenditure, and the electric energy monthly consuming to AMI report of user and electric cost expenditure, and the operational factor such as voltage, electric current and power factor, for AMI, according to customer charge situation, determine Spot Price, realize remote meter-reading function.This controller is according to the historical Spot Price (Spot Price of D-14 day, D-7 day and D-1 day) of obtaining from AMI, utilize the neural network prediction D Spot Price in day in each time interval, according to the Spot Price of prediction and controlled household electrical appliances situation optimized operation time period of determining controlled household electrical appliances, to corresponding time period during initial time, controller assigns to the control terminal of controlling controlled household electrical appliances the order that puts into operation by Zigbee wireless communication mode, control terminal makes to control the electromagnetic relay closure of household electrical appliances, and corresponding household electrical appliances are put into operation.Simultaneously, controller is according to the Spot Price of prediction, distributed power source operating cost and electricity price subsidy, the time period that the profit and loss value that Computation distribution formula power supply puts into operation is greater than 0, while arriving the initial time of corresponding time period, controller is assigned distributed power source by Zigbee communication modes to distributed power source access device and is dropped into order, and corresponding distributed power source is put into operation.Controller obtains the information about power of user's actual consumption from intelligent electric meter by RS485 communication modes, and according to Spot Price, calculate user's actual electric cost expenditure, to realize the remote meter-reading function of AMI.Controller obtains user's voltage and current state from the secondary side of voltage transformer (PT), current transformer (CT), and rated output factor, so that the electric parameter ，Shi main website to the electricity consumption of AMI report of user adjusts electric parameter in time, improve power supply quality.Meanwhile, controller, according to the voltage and current detecting, is realized overvoltage, overcurrent, under-voltage and earth leakage protective; during if there is corresponding fault; controller passes through Zigbee wireless communication mode to the control terminal transmission trip signal at subscriber switch place, subscriber switch is disconnected, protection household electrical appliance.Each control terminal reports controller by the state of each switch by Zigbee wireless communication mode, makes controller can monitor the state of each switch.
Be illustrated in figure 2 resident's intelligence energy management controller function block diagram of the present invention.This controller by: main control module, data processing module, electric parameters input module, touch screen module, memory module, clock module, ethernet module, Zigbee communication module and RS232/485 module etc. form.Main control module is mainly responsible for communication and man-machine interface, realizes the input and output of touch-screen, the storage of historical data, ethernet communication, Zigbee communication, RS232/485 communication and real-time clock input.Real-time clock module is realized the input of perpetual calendar real-time clock.Data processing module is responsible for the data acquisition of electric current and voltage electric parameters, and image data is carried out to digital filtering, calculating voltage effective value, current effective value, power factor, active power and reactive power etc.; According to the Spot Price in historical Spot Price prediction each time interval on the same day, and according to Spot Price predicted value, controlled household electrical appliances and distributed power source situation, obtain the optimum operating time section decision-making of controlled household electrical appliances and distributed power source; According to real-time electric parameter, realize the protection decision-making to household electrical appliance; The result of decision is sent to main control module, by main control module, realize the optimal control to household electrical appliance.
Be illustrated in figure 3 the Spot Price forecast model that the present invention is based on neural net.The structure of this model is 3-8-3-1, and input layer has 3 nodes, respectively corresponding 3 inputs: the historical Spot Price (C of D-14 day, D-7 day and D-1 day
1(t), C
2and C (t)
3(t)); Comprise 2 hidden layers, first hidden layer has 8 nodes, and second hidden layer has 3 nodes; Output layer has 1 node, corresponding to the Spot Price prediction output of D day (
t)).Utilize historical Spot Price data to train neural net, obtain neural net Spot Price forecast model.The historical Spot Price in D-14 day, D-7 day and D-1 day in each time interval is input to neural network prediction model, can obtains the Spot Price predicted value of D day.
Be illustrated in figure 4 the hardware structure diagram of resident's intelligence energy management controller of the present invention.Master controller adopts the 32-bit embedded processor MCF5272 CVF66 of Freescale company; Data processor adopts the high integration single-chip digital signal processor ADSP-2185 of AD company, between it and master controller, by dma mode, communicates; Adopt the extensive field programmable logic array FPGA of a slice to realize the logic control of system; The flash memory (FLASH RAM) of the static read/write memory (SRAM) of employing 256K byte, 16M byte SDRAM, 4M byte electric erasable, wherein, SDRAM is the work internal memory of master controller, SRAM is used for storing important historical data; Flash memory is for save set operation bootstrap routine, operating system, application program, DSP program, configuration file etc.In analog input channel, voltage and current signal is through analog input transformer or fly electric capacity conversion, then filtering, through one 8, select 1CMOS multiplexer to select again, the output of multiplexer is driven by voltage follow-up amplifier, send into 16 A/D converters at a high speed and be converted to digital quantity, the output of A/D is sent into DSP with the form of serial data stream and is processed.In order to improve sampling precision, adopt 128 points of every cycle sampling.Zigbee interface adopts the CC2420 chip of TI company; Real-time clock adopts the DS1302 of U.S. DALLAS company; Ethernet interface adopts the Ethernet interface modules A X11001 of Taiwan Asix company; Touch-screen adopts the touch screen controller ADS7846 of TI company.
Be illustrated in figure 5 the hardware structure diagram of resident's intelligence energy management control terminal of the present invention.This control terminal adopts the SOC (system on a chip) MC13213 with Zigbee communication function of Freescale company, this terminal receives after the break-make power command of controller, by output driving circuit MC1413, control electromagnetic relay, control the break-make power supply of controlled household electrical appliances; The state of electromagnetic relay is delivered to MCU after by photoisolator, realizes the break-make power supply status monitoring to controlled household electrical appliances; The temperature survey of household electrical appliances adopts digital temperature sensor DS18B20, sends into MC13213 process by serial data stream.
Be illustrated in figure 6 the software flow pattern of resident's intelligence energy management controller of the present invention.First main program is predicted the Spot Price in each time interval on the same day according to historical Spot Price data, and according to the situation of Spot Price predicted value and controlled household electrical appliances and distributed power source, is determined the optimized operation time period of controlled household electrical appliances and distributed power source.The control terminal of corresponding household electrical appliances or distributed power source access device are sent to the corresponding command according to the optimized operation time period.Send order and adopt interrupt mode, interrupt being divided into two classes, a class is the initial interruption of section running time, and another kind of is section end interrupt running time.When the corresponding time then, enter corresponding interrupt service subroutine, control corresponding home appliance control terminal or distributed power source access device, corresponding household electrical appliances or distributed power source are dropped into or out of service.
In order to specifically describe implementer's case of resident intelligence EMS, the domestic, electric water heater of take is below described in detail as example.The power of supposing electric heater is 2000W, and capacity is 60L, and the initial temperature of the water of electric heater is 20 ℃, and the final temperature after electric water heater heating is 45 ℃.Having heated the energy needing is:
Q＝cmΔT＝4.2×10
3×60×(45-20)＝6.3×1
6J＝1.75kWh (6)
The time that heating needs is:
Suppose that user expects that the electric water heater heating deadline is 21:45, after having heated, every 4 hours, the water temperature of electric heater reduced by 1 ℃, and the energy of corresponding loss is:
ΔQ＝cmΔT＝4.2×10
3×60×1＝2.52×10
5J＝0.07kWh (8)
If the time interval of Spot Price is 15 minutes, the Spot Price predicted value of neural network prediction output is as shown in table 1.Because the electric water heater heating time needs 52.5min, relate to C
t, C
t+15, C
t+30and C
t+45the Spot Price predicted value of four time periods, wherein, C
tfor the electric heater constantly Spot Price predicted value C of t that puts into operation
t+15, C
t+30and C
t+45be respectively t+15, t+30 and t+45 Spot Price predicted value constantly.The electric energy that the every 15min of this electric heater consumes is: 0.5kWh, and therefore theoretical electric cost expenditure is:
Z
th＝C
t×0.5+C
t+15×0.5+C
t+30×0.5+C
t+45×0.25 (9)
Consider the energy loss heated in advance, the final temperature of heating should be than 45 ℃ that require high, so there is extra electric cost expenditure:
While putting into operation in each time period, extra electric cost expenditure is as shown in table 1.The prediction electric cost expenditure calculating according to formula (1) is as shown in table 1.As shown in Table 1, minimum prediction electric cost expenditure is 0.879 yuan, and the corresponding electric heater time of putting into operation is while being 2:00.Therefore, resident's intelligence energy management controller control terminal to electric heater when 2:00 sends the order that puts into operation, and electric heater energising starts heating.Because complete and constantly expected to user to have energy loss in a period of time constantly in heating, so should extend heating time, time expand, is:
Therefore, the control terminal transmission power failure order at 3:02 moment controller to electric heater, makes electric heater deenergization.
The electric cost expenditure of table 1 Spot Price predicted value and electric heater
| Time | 0:00 | 0:15 | 0:30 | 0:45 | 1:00 | 1:15 | 1:30 | 1:45 | 2:00 | 2:15 | 2:30 | 2:45 | 3:00 | 3:15 | 3:30 | 3:45 |
| Spot Price prediction (unit) | 0.52 | 0.51 | 0.5 | 0.48 | 0.46 | 0.45 | 0.44 | 0.43 | 0.42 | 0.42 | 0.42 | 0.43 | 0.44 | 0.45 | 0.46 | 0.47 |
| Extra electric cost expenditure (unit) | 0.174 | 0.165 | 0.159 | 0.154 | 0.149 | 0.143 | 0.141 | 0.14 | 0.141 | 0.142 | 0.144 | 0.145 | 0.146 | 0.147 | 0.151 | 0.152 |
| Prediction electric cost expenditure (unit) | 1.059 | 1.025 | 0.992 | 0.959 | 0.931 | 0.908 | 0.891 | 0.88 | 0.879 | 0.887 | 0.901 | 0.92 | 0.938 | 0.957 | 0.981 | 1.004 |
| Time | 4:00 | 4:15 | 4:30 | 4:45 | 5:00 | 5:15 | 5:30 | 5:45 | 6:00 | 6:15 | 6:30 | 6:45 | 7:00 | 7:15 | 7:30 | 7:45 |
| Spot Price prediction (unit) | 0.48 | 0.5 | 0.51 | 0.51 | 0.52 | 0.53 | 0.54 | 0.55 | 0.56 | 0.56 | 0.57 | 0.57 | 0.57 | 0.58 | 0.58 | 0.59 |
| Extra electric cost expenditure (unit) | 0.15 | 0.15 | 0.151 | 0.151 | 0.152 | 0.152 | 0.149 | 0.15 | 0.147 | 0.145 | 0.145 | 0.142 | 0.142 | 0.144 | 0.141 | 0.143 |
| Prediction electric cost expenditure (unit) | 1.022 | 1.04 | 1.053 | 1.066 | 1.084 | 1.102 | 1.114 | 1.127 | 1.135 | 1.137 | 1.145 | 1.147 | 1.154 | 1.172 | 1.184 | 1.209 |
| Time | 8:00 | 8:15 | 8:30 | 8:45 | 9:00 | 9:15 | 9:30 | 9:45 | 10:00 | 10:15 | 10:30 | 10:45 | 11:00 | 11:15 | 11:30 | 11:45 |
| Spot Price prediction (unit) | 0.61 | 0.61 | 0.63 | 0.64 | 0.66 | 0.65 | 0.66 | 0.65 | 0.65 | 0.67 | 0.67 | 0.68 | 0.68 | 0.7 | 0.7 | 0.72 |
| Extra electric cost expenditure (unit) | 0.143 | 0.144 | 0.139 | 0.139 | 0.134 | 0.131 | 0.132 | 0.129 | 0.128 | 0.125 | 0.126 | 0.123 | 0.123 | 0.12 | 0.117 | 0.11 |
| Prediction electric cost expenditure (unit) | 1.228 | 1.249 | 1.267 | 1.279 | 1.281 | 1.273 | 1.279 | 1.281 | 1.293 | 1.305 | 1.316 | 1.328 | 1.343 | 1.36 | 1.367 | 1.365 |
| Time | 12:00 | 12:15 | 12:30 | 12:45 | 13:00 | 13:15 | 13:30 | 13:45 | 14:00 | 14:15 | 14:30 | 14:45 | 15:00 | 15:15 | 15:30 | 15:45 |
| Spot Price prediction (unit) | 0.72 | 0.72 | 0.7 | 0.71 | 0.7 | 0.67 | 0.68 | 0.68 | 0.66 | 0.61 | 0.59 | 0.58 | 0.57 | 0.56 | 0.56 | 0.55 |
| Extra electric cost expenditure (unit) | 0.109 | 0.104 | 0.097 | 0.095 | 0.092 | 0.087 | 0.077 | 0.072 | 0.069 | 0.065 | 0.061 | 0.059 | 0.055 | 0.053 | 0.052 | 0.047 |
| Prediction electric cost expenditure (unit) | 1.356 | 1.344 | 1.319 | 1.305 | 1.287 | 1.267 | 1.24 | 1.195 | 1.144 | 1.097 | 1.071 | 1.054 | 1.038 | 1.025 | 1.022 | 1.012 |
| Time | 16:00 | 16:15 | 16:30 | 16:45 | 17:00 | 17:15 | 17:30 | 17:45 | 18:00 | 18:15 | 18:30 | 18:45 | 19:00 | 19:15 | 19:30 | 19:45 |
| Spot Price prediction (unit) | 0.55 | 0.56 | 0.54 | 0.55 | 0.57 | 0.58 | 0.61 | 0.63 | 0.65 | 0.67 | 0.69 | 0.71 | 0.72 | 0.72 | 0.72 | 0.71 |
| Extra electric cost expenditure (unit) | 0.046 | 0.045 | 0.043 | 0.043 | 0.041 | 0.04 | 0.038 | 0.036 | 0.034 | 0.032 | 0.028 | 0.025 | 0.022 | 0.019 | 0.016 | 0.012 |
| Prediction electric cost expenditure (unit) | 1.008 | 1.012 | 1.018 | 1.045 | 1.079 | 1.112 | 1.151 | 1.184 | 1.217 | 1.247 | 1.268 | 1.28 | 1.279 | 1.274 | 1.268 | 1.255 |
| Time | 20:00 | 20:15 | 20:30 | 20:45 | 21:00 | 21:15 | 21:30 | 21:45 | 22:00 | 22:15 | 22:30 | 22:45 | 23:00 | 23:15 | 23:30 | 23:45 |
| Spot Price prediction (unit) | 0.72 | 0.71 | 0.7 | 0.68 | 0.66 | 0.59 | 0.58 | 0.59 | 0.57 | 0.56 | 0.55 | 0.56 | 0.54 | 0.54 | 0.53 | 0.52 |
| Extra electric cost expenditure (unit) | 0.009 | 0.006 | 0.003 | 0 | 0.245 | 0.234 | 0.228 | 0.221 | 0.223 | 0.213 | 0.21 | 0.204 | 0.198 | 0.196 | 0.19 | 0.184 |
| Prediction electric cost expenditure (unit) | 1.244 | 1.216 | 1.17 | 1.11 | 1.308 | 1.257 | 1.238 | 1.219 | 1.203 | 1.183 | 1.17 | 1.157 | 1.133 | 1.121 | 1.102 | 1.084 |
Claims (8)
1. the resident's intelligence EMS that is applicable to intelligent grid, it is characterized in that, in each household resident's family, install an intelligent energy management controller and intelligent electric meter, at every controlled household electrical appliances and subscriber switch place, all install a control terminal, at every distributed power source place, all install a distributed power source access device; Between controller and control terminal and distributed power source access device, by Zigbee wireless communication networks, interconnect, intelligent electric meter is electrically connected to distributed power source access device and controlled, between controller and intelligent electric meter, also by bus, intercoms mutually; Intelligent electric meter is also connected with the secondary side of voltage transformer summation current transformer, and the advanced measuring system AMI of controller and main website is by the interconnected information interaction of carrying out of Ethernet; Controller is controlled respectively the switching of control terminal, distributed power source access device, and control terminal reports controller by the state of switch by Zigbee wireless communication mode, makes controller monitor the state of switch; Described controller according to electricity price, the household electrical appliances type of prediction, household electrical appliances running times, resident to the desired value of household electrical appliances running status, ambient temperature conditions, calculate the prediction electric cost expenditure of each time period household electrical appliances operation, get time period that electric cost expenditure is minimum as the time period of household electrical appliances operation, controller sends to the control terminal at household electrical appliances place the order that puts into operation by Zigbee communication modes, realizes household electrical appliances the lowest coursing cost is controlled;
Described household electrical appliances are paid at the operation power charge of each time period:
Z
a＝Z
th+Z
ad （1）
Z
th＝(C
1+C
2+...+C
n)W
15+C
n+1W
t-15n （2）
Wherein, Z
aprediction electric cost expenditure for household electrical appliances; Z
thfor household electrical appliances are in the time interval 1,2 ..., n, the theoretical expenditure of electricity charge during n+1 energising operation; Z
adfor household electrical appliances are owing to having moved in advance the needed extra electric cost expenditure of the energy loss producing; C
1, C
2..., C
n, C
n+1be respectively household electrical appliances interval running time 1,2 ..., n, the Spot Price of n+1; W
15for household electrical appliances move the electric energy consuming for 15 minutes; W
t-15nfor household electrical appliances operation, t-15n is less than the electric energy consuming for 15 minutes; T is total running times of household electrical appliances; t
efor household electrical appliances are expected the moment of having moved; t
cthe moment of having moved for family's electric theory; t
ifor family's electro-temperature reduces or raises 1 ℃ of needed time; W
tfor household electrical appliances raise or reduce by 1 ℃ of electric energy consuming; For the household electrical appliances relevant with ambient temperature running time, prediction electric cost expenditure calculates by formula (1); For temperature independent household electrical appliances running time, predict that electric cost expenditure calculates by formula (2); Get household electrical appliances operation and predict the final control time that the minimum time period of electric cost expenditure puts into operation as these household electrical appliances; Control mode adopts Zigbee wireless communication mode, by realizing the input of household electrical appliances or exit control controlling the control of the control terminal of household electrical appliances operation; Control to charging electric vehicle, according to Spot Price predicted value, batteries of electric automobile state and the desired value of user to rechargeable battery charged state, determine the time period of final charging, by controlling distributed power source access device, realize the input of electric automobile or exit control.
2. the resident's intelligence EMS that is applicable to intelligent grid as claimed in claim 1, it is characterized in that, described intelligent energy management controller comprises main control module, data processing module, electric parameters input module, touch screen module, memory module, real-time clock module, ethernet module, Zigbee communication module and RS232/485 module; Main control module is mainly responsible for communication and man-machine interface, realizes the input and output of touch-screen, the storage of historical data, ethernet communication, Zigbee communication, RS232/485 communication and real-time clock input; Real-time clock module is realized the input of perpetual calendar real-time clock; Data processing module is responsible for the data acquisition of electric current and voltage electric parameters, and image data is carried out to digital filtering, calculating voltage effective value, current effective value, power factor, active power and reactive power; According to the Spot Price in historical Spot Price prediction each time interval on the same day, and according to Spot Price predicted value, controlled household electrical appliances and distributed power source situation, obtain the optimum operating time section decision-making of controlled household electrical appliances and distributed power source; According to real-time electric parameter, realize the protection decision-making to household electrical appliance; The result of decision is sent to main control module, by main control module, realize the optimal control to household electrical appliance.
3. the resident's intelligence EMS that is applicable to intelligent grid as claimed in claim 2, it is characterized in that, described data processing module comprises dsp chip and extensive field programmable logic array FPGA, between dsp chip and main control module, adopt dma mode to communicate by letter, voltage and current signal is through analog input transformer or fly electric capacity conversion, then filtering, through one 8, select 1CMOS multiplexer to select again, the output of multiplexer is driven by voltage follow-up amplifier, send into 16 A/D converters at a high speed and be converted to digital quantity, the output of A/D converter is sent into DSP with the form of serial data stream and is processed, adopt 128 points of every cycle sampling, described main control module adopts MCF5272 chip, described memory module adopts static read/write memory SRAM, the 16M byte SDRAM of 256K byte, the flash memory FLASH RAM of 4M byte electric erasable, and wherein, SDRAM is the work internal memory of master controller, and SRAM is used for storing important historical data, flash memory is for save set operation bootstrap routine, operating system, application program, DSP program, configuration file.
4. the resident's intelligence EMS that is applicable to intelligent grid as claimed in claim 1, it is characterized in that, described control terminal adopts the SOC (system on a chip) MC13213 with Zigbee communication function, this terminal receives after the break-make power command of controller by Zigbee communication modes, by output driving circuit MC1413, control electromagnetic relay, and then control the break-make power supply of controlled household electrical appliances; The state of electromagnetic relay is delivered to MC13213 after by photoisolator, realizes the break-make power supply status monitoring to controlled household electrical appliances; The temperature survey of household electrical appliances adopts digital temperature sensor DS18B20, sends into MC13213 process by serial data stream.
5. the resident's intelligence EMS that is applicable to intelligent grid as claimed in claim 1, it is characterized in that, described controller is according to the historical Spot Price information of obtaining from main website, utilize neural net to realize the prediction to Spot Price on the same day, being input as of neural net: D-14 day, the historical Spot Price of D-7 day and D-1 day, neural net is output as the Spot Price predicted value of D day, the structure of neural net is: 3-8-3-1, utilize historical data to train this neural net, neural net after having trained is the neural network prediction model of Spot Price.
6. the resident's intelligence EMS that is applicable to intelligent grid as claimed in claim 1, it is characterized in that the profit and loss that described controller puts into operation according to the subsidy of degree electricity, the operating cost of the every generating a kilowatt of distributed power source and the energy output Computation distribution formula power supply of distributed power source of Spot Price predicted value, every degree electricity; If profit and loss value is greater than 0, and distributed power source meets the maximum of access point and allows capacity limit, by resident family's Energy Management System, by Zigbee communication modes, to the access device of corresponding distributed power source, send the order that puts into operation, corresponding distributed power source is put into operation; If profit and loss value is less than 0, make distributed power source out of service.
7. the resident's intelligence EMS that is applicable to intelligent grid as claimed in claim 1, it is characterized in that, the Spot Price that described controller obtains according to the user's real consumption electric weight He Cong main website obtaining from intelligent electric meter, calculate user's actual electric cost expenditure, and user's real consumption electric weight and actual electric cost expenditure monthly reported to main website by Ethernet, realize remote meter-reading function.
8. the resident's intelligence EMS that is applicable to intelligent grid as claimed in claim 7, is characterized in that the profit and loss that described distributed power source puts into operation:
Y＝W
DG(C+S-R) （4）
Wherein, W
dGthe energy output that represents distributed power source, is transferred to resident's intelligence energy management controller by distributed power source access device by Zigbee wireless communication networks; C represents Spot Price predicted value, and S represents the degree electricity subsidy that every degree is electric, and the AMI of these two parameter Shi You main websites is transferred to resident's intelligence energy management controller by Ethernet; R is the operating cost of the every generating a kilowatt of distributed power source, comprises depreciation cost and the maintenance expense of equipment; When the operation profit and loss value Y calculating is greater than 0, represent that the operation of input distributed power source is rear profitable, can drop into distributed power source, otherwise, excision distributed power source;
The access capacity of distributed power source can not surpass maximum and allow capacity, and the input capacity of distributed power source is retrained by following formula
P
DG≤P
max （5）
Wherein, P
dGthe gross power of all distributed power sources for access; P
maxthe parameter value that the AMI of the maximum access capacity ，Shi You main website allowing for distributed power source access point transmits by Ethernet; Resident's intelligence energy management controller, according to the operation profit and loss value of the real-time Computation distribution formula power supply of formula (4), if profit and loss value Y is greater than 0, and meets formula (5), and input capacity is P
dGdistributed power source operation; If do not meet formula (5), input capacity is P
maxdistributed power source operation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210015860.3A CN102570609B (en) | 2012-01-18 | 2012-01-18 | Resident intelligent energy management system suitable for intelligent power grid |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210015860.3A CN102570609B (en) | 2012-01-18 | 2012-01-18 | Resident intelligent energy management system suitable for intelligent power grid |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102570609A CN102570609A (en) | 2012-07-11 |
| CN102570609B true CN102570609B (en) | 2014-03-05 |
Family
ID=46415315
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201210015860.3A Expired - Fee Related CN102570609B (en) | 2012-01-18 | 2012-01-18 | Resident intelligent energy management system suitable for intelligent power grid |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102570609B (en) |
Families Citing this family (26)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103676679A (en) * | 2012-09-07 | 2014-03-26 | 冯晓东 | Electrical switch system |
| CN102903185B (en) * | 2012-10-25 | 2014-11-26 | 国网能源研究院 | Power consumer response system and method |
| CN102969791B (en) * | 2012-11-23 | 2015-07-08 | 国电南瑞科技股份有限公司 | Flexible load management system and method of power system based on bidirectional intelligent electric meter |
| CN104300664B (en) * | 2013-07-18 | 2017-05-24 | 李孝忠 | Energy saving and carbon reduction cheap electric power system and method thereof |
| CN104600837B (en) * | 2013-10-31 | 2017-03-15 | 南通富士特电力自动化有限公司 | A kind of idle quick compensating controller based on ZigBee and RS485 communication modes |
| CN103700028B (en) * | 2013-12-13 | 2017-02-01 | 广东电网公司电力科学研究院 | Power consumption control method and device |
| CN103645655B (en) * | 2013-12-24 | 2016-03-09 | 上海贝岭股份有限公司 | A kind of control method of relay for one-phase smart electric meter |
| CN103954814A (en) * | 2014-01-09 | 2014-07-30 | 何亚雄 | Guiderail-type intelligent electric energy meter with protection function to electric appliances |
| CN104122819B (en) * | 2014-07-18 | 2016-08-17 | 东北电力大学 | Home intelligent power method based on user habit |
| CN104124692A (en) * | 2014-08-19 | 2014-10-29 | 国家电网公司 | System and method for controlling off-peak parameter adjustment of electric heating |
| TW201614930A (en) * | 2014-10-03 | 2016-04-16 | Electric Energy Express Corp | Intelligent electric grid management system and method |
| CN104935075B (en) * | 2015-04-01 | 2017-05-17 | 南方电网科学研究院有限责任公司 | Distributed power supply access user bidirectional metering, monitoring and energy efficiency management system and distributed power supply access user bidirectional metering, monitoring and energy efficiency management method |
| CN104834297A (en) * | 2015-05-08 | 2015-08-12 | 上海电机学院 | Household electrical appliance remote control system based on Internet-of-things smart meter and method thereof |
| CN105741433A (en) * | 2016-01-29 | 2016-07-06 | 国网山东省电力公司荣成市供电公司 | Intelligent electricity charge management system |
| CN107131650A (en) * | 2016-02-29 | 2017-09-05 | 青岛海尔新能源电器有限公司 | One kind is according to the electrically heated water heater of peak valley |
| CN107131649A (en) * | 2016-02-29 | 2017-09-05 | 青岛海尔新能源电器有限公司 | A kind of multi-functional water heater |
| CN106779306A (en) * | 2016-11-22 | 2017-05-31 | 南方电网科学研究院有限责任公司 | The structure of user's disaggregated model, electric energy efficiency analysis user classification method and device |
| CN108171957B (en) * | 2016-12-07 | 2021-11-30 | 中兴通讯股份有限公司 | Meter reading method, device and system and communication gateway |
| CN106786555A (en) * | 2017-01-20 | 2017-05-31 | 南京南瑞继保电气有限公司 | A kind of intelligent dispatching method of household energy management system |
| CN107769270B (en) * | 2017-10-26 | 2020-12-01 | 广西师范大学 | Intelligent ammeter and charging method thereof |
| CN109344967B (en) * | 2018-08-31 | 2021-09-14 | 武汉大学 | Intelligent electric meter life cycle prediction method based on artificial neural network |
| CN109474005A (en) * | 2018-10-27 | 2019-03-15 | 嘉兴市恒创电力设备有限公司 | It is a kind of can two-way detection the grid-connected switch of novel intelligent distributed generation resource |
| CN110727202A (en) * | 2019-09-25 | 2020-01-24 | 中国电力科学研究院有限公司 | Power grid interaction-oriented random measurement equipment and load control method |
| CN111917184B (en) * | 2020-05-19 | 2022-02-01 | 国网山东省电力公司济宁供电公司 | Power consumption monitoring method and system based on BP neural network |
| CN111864748A (en) * | 2020-08-14 | 2020-10-30 | 国网山东省电力公司威海供电公司 | Monitoring and scheduling integrated system and method suitable for distributed photovoltaic grid connection |
| CN112389241B (en) * | 2020-11-06 | 2022-09-09 | 国网山东省电力公司淄博供电公司 | Electric vehicle charging pile load interactive control system and application method thereof |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101873009A (en) * | 2010-07-12 | 2010-10-27 | 浙江昱能光伏科技集成有限公司 | Home intelligent power monitoring system |
| CN102130507A (en) * | 2011-03-26 | 2011-07-20 | 河南省电力公司 | Intelligent home system accessing to intelligent power gird and new energy and energy efficiency managing method |
| CN102280935A (en) * | 2011-06-24 | 2011-12-14 | 中国科学院电工研究所 | Intelligent power grid management system |
-
2012
- 2012-01-18 CN CN201210015860.3A patent/CN102570609B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101873009A (en) * | 2010-07-12 | 2010-10-27 | 浙江昱能光伏科技集成有限公司 | Home intelligent power monitoring system |
| CN102130507A (en) * | 2011-03-26 | 2011-07-20 | 河南省电力公司 | Intelligent home system accessing to intelligent power gird and new energy and energy efficiency managing method |
| CN102280935A (en) * | 2011-06-24 | 2011-12-14 | 中国科学院电工研究所 | Intelligent power grid management system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102570609A (en) | 2012-07-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102570609B (en) | Resident intelligent energy management system suitable for intelligent power grid | |
| Hou et al. | Smart home energy management optimization method considering energy storage and electric vehicle | |
| CN102591276B (en) | Intelligent electric meter system for residents for intelligent smart grid | |
| CN114498639B (en) | Day-ahead scheduling method of multi-microgrid combined mutual aid considering demand response | |
| CN105471109A (en) | Intelligent power consumption management system facing household wind and solar energy mutual-complementing power station and management method thereof | |
| Wang et al. | Load curve smoothing strategy based on unified state model of different demand side resources | |
| Wang et al. | The application of electric vehicles as mobile distributed energy storage units in smart grid | |
| CN103578047A (en) | Source-grid-load interactive control method of power system | |
| CN115170343A (en) | Distributed resource and energy storage collaborative planning method for regional comprehensive energy system | |
| Dang et al. | EV Fleet as Virtual Battery Resource for Community Microgrid Energy Storage Planning | |
| Rashid et al. | Simulation based energy and cost optimization for home users in a community smart grid | |
| CN113964860B (en) | Optical storage offline coordination system and method based on demand management | |
| CN112736950B (en) | Public energy storage power station configuration method and system for micro-grid group | |
| Luo et al. | Coordinated scheduling of generalized energy storage in multi-voltage level AC/DC hybrid distribution network | |
| Zhu et al. | Demand response strategy for promoting distributed power consumption based on resident load participation | |
| Jiarui et al. | Research on Demand Response Strategy of Electricity Market Based on Intelligent Power Consumption | |
| Cheng et al. | Research on the mode of energy distribution network for smart low-carbon city | |
| Li et al. | Research on energy management scheme of commercial buildings cluster considering demand response | |
| Bolurian et al. | Energy management in a smart grid including demand response programs considering internet of things | |
| Wang et al. | Research on coordination dispatching strategy of active distribution network and intermittent energy | |
| Wu et al. | A Differentiated Time-of-use Pricing Method for Park Users Based on Source-charge Interaction | |
| CN113690878B (en) | Three-phase switching control method for micro-grid | |
| Danylchenko et al. | Research on Demographically Based Electricity Demand Schedules for the Potential of Demand Management | |
| Ming et al. | Research and application of interactive control technology of source and load in photovoltaic village | |
| Zhichun et al. | Research on Orderly Charging Strategy of Electric Vehicles Based on Intelligent Fusion Terminal |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20180726 Address after: 264005 191 Binhai Middle Road, Laishan District, Yantai, Shandong Co-patentee after: Yantai Cloud Control Automation Technology Co., Ltd. Patentee after: Shandong Institute of Business And Technology Address before: 264000 191 Binhai Middle Road, Laishan District, Yantai, Shandong Patentee before: Shandong Institute of Business And Technology |
|
| 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: 20140305 Termination date: 20190118 |